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Sample records for algorithms artificial neural

  1. Quantum-based algorithm for optimizing artificial neural networks.

    PubMed

    Tzyy-Chyang Lu; Gwo-Ruey Yu; Jyh-Ching Juang

    2013-08-01

    This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms.

  2. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.

    PubMed

    Garro, Beatriz A; Vázquez, Roberto A

    2015-01-01

    Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.

  3. A new training algorithm using artificial neural networks to classify gender-specific dynamic gait patterns.

    PubMed

    Andrade, Andre; Costa, Marcelo; Paolucci, Leopoldo; Braga, Antônio; Pires, Flavio; Ugrinowitsch, Herbert; Menzel, Hans-Joachim

    2015-01-01

    The aim of this study was to present a new training algorithm using artificial neural networks called multi-objective least absolute shrinkage and selection operator (MOBJ-LASSO) applied to the classification of dynamic gait patterns. The movement pattern is identified by 20 characteristics from the three components of the ground reaction force which are used as input information for the neural networks in gender-specific gait classification. The classification performance between MOBJ-LASSO (97.4%) and multi-objective algorithm (MOBJ) (97.1%) is similar, but the MOBJ-LASSO algorithm achieved more improved results than the MOBJ because it is able to eliminate the inputs and automatically select the parameters of the neural network. Thus, it is an effective tool for data mining using neural networks. From 20 inputs used for training, MOBJ-LASSO selected the first and second peaks of the vertical force and the force peak in the antero-posterior direction as the variables that classify the gait patterns of the different genders.

  4. Optimal groundwater remediation using artificial neural networks and the genetic algorithm

    SciTech Connect

    Rogers, Leah L.

    1992-08-01

    An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ``recycle`` or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models.

  5. Prediction of CHF in concentric-tube open thermosiphon using artificial neural network and genetic algorithm

    NASA Astrophysics Data System (ADS)

    Chen, R. H.; Su, G. H.; Qiu, S. Z.; Fukuda, Kenji

    2010-03-01

    In this paper, an artificial neural network (ANN) for predicting critical heat flux (CHF) of concentric-tube open thermosiphon has been trained successfully based on the experimental data from the literature. The dimensionless input parameters of the ANN are density ratio, ρ l/ ρ v; the ratio of the heated tube length to the inner diameter of the outer tube, L/ D i; the ratio of frictional area, d i/( D i + d o); and the ratio of equivalent heated diameter to characteristic bubble size, D he/[ σ/ g( ρ l- ρ v)]0.5, the output is Kutateladze number, Ku. The predicted values of ANN are found to be in reasonable agreement with the actual values from the experiments with a mean relative error (MRE) of 8.46%. New correlations for predicting CHF were also proposed by using genetic algorithm (GA) and succeeded to correlate the existing CHF data with better accuracy than the existing empirical correlations.

  6. Fuzzy Logic, Neural Networks, Genetic Algorithms: Views of Three Artificial Intelligence Concepts Used in Modeling Scientific Systems

    ERIC Educational Resources Information Center

    Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.

    2003-01-01

    Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…

  7. Surface Roughness Optimization of Polyamide-6/Nanoclay Nanocomposites Using Artificial Neural Network: Genetic Algorithm Approach

    PubMed Central

    Moghri, Mehdi; Omidi, Mostafa; Farahnakian, Masoud

    2014-01-01

    During the past decade, polymer nanocomposites attracted considerable investment in research and development worldwide. One of the key factors that affect the quality of polymer nanocomposite products in machining is surface roughness. To obtain high quality products and reduce machining costs it is very important to determine the optimal machining conditions so as to achieve enhanced machining performance. The objective of this paper is to develop a predictive model using a combined design of experiments and artificial intelligence approach for optimization of surface roughness in milling of polyamide-6 (PA-6) nanocomposites. A surface roughness predictive model was developed in terms of milling parameters (spindle speed and feed rate) and nanoclay (NC) content using artificial neural network (ANN). As the present study deals with relatively small number of data obtained from full factorial design, application of genetic algorithm (GA) for ANN training is thought to be an appropriate approach for the purpose of developing accurate and robust ANN model. In the optimization phase, a GA is considered in conjunction with the explicit nonlinear function derived from the ANN to determine the optimal milling parameters for minimization of surface roughness for each PA-6 nanocomposite. PMID:24578636

  8. Thermomechanical processing optimization for 304 austenitic stainless steel using artificial neural network and genetic algorithm

    NASA Astrophysics Data System (ADS)

    Feng, Wen; Yang, Sen

    2016-12-01

    Thermomechanical processing has an important effect on the grain boundary character distribution. To obtain the optimal thermomechanical processing parameters is the key of grain boundary engineering. In this study, genetic algorithm (GA) based on artificial neural network model was proposed to optimize the thermomechanical processing parameters. In this model, a back-propagation neural network (BPNN) was established to map the relationship between thermomechanical processing parameters and the fraction of low-Σ CSL boundaries, and GA integrated with BPNN (BPNN/GA) was applied to optimize the thermomechanical processing parameters. The validation of the optimal thermomechanical processing parameters was verified by an experiment. Moreover, the microstructures and the intergranular corrosion resistance of the base material (BM) and the materials produced by the optimal thermomechanical processing parameters (termed as the GBEM) were studied. Compared to the BM specimen, the fraction of low-Σ CSL boundaries was increased from 56.8 to 77.9% and the random boundary network was interrupted by the low-Σ CSL boundaries, and the intergranular corrosion resistance was improved in the GBEM specimen. The results indicated that the BPNN/GA model was an effective and reliable means for the thermomechanical processing parameters optimization, which resulted in improving the intergranular corrosion resistance in 304 austenitic stainless steel.

  9. Artificial-neural-network-based atmospheric correction algorithm: application to MERIS data

    NASA Astrophysics Data System (ADS)

    Schroeder, Thomas; Fischer, Juergen; Schaale, Michael; Fell, Frank

    2003-05-01

    After the successful launch of the Medium Resolution Imaging Spectrometer (MERIS) on board of the European Space Agency (ESA) Environmental Satellite (ENVISAT) on March 1st 2002, first MERIS data are available for validation purposes. The primary goal of the MERIS mission is to measure the color of the sea with respect to oceanic biology and marine water quality. We present an atmospheric correction algorithm for case-I waters based on the inverse modeling of radiative transfer calculations by artificial neural networks. The proposed correction scheme accounts for multiple scattering and high concentrations of absorbing aerosols (e.g. desert dust). Above case-I waters, the measured near infrared path radiance at Top-Of-Atmosphere (TOA) is assumed to originate from atmospheric processes only and is used to determine the aerosol properties with the help of an additional classification test in the visible spectral region. A synthetic data set is generated from radiative transfer simulations and is subsequently used to train different Multi-Layer-Perceptrons (MLP). The atmospheric correction scheme consists of two steps. First a set of MLPs is used to derive the aerosol optical thickness (AOT) and the aerosol type for each pixel. Second these quantities are fed into a further MLP trained with simulated data for various chlorophyll concentrations to perform the radiative transfer inversion and to obtain the water-leaving radiance. In this work we apply the inversion algorithm to a MERIS Level 1b data track covering the Indian Ocean along the west coast of Madagascar.

  10. Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

    PubMed Central

    Boonjing, Veera; Intakosum, Sarun

    2016-01-01

    This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span. PMID:27974883

  11. Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend.

    PubMed

    Inthachot, Montri; Boonjing, Veera; Intakosum, Sarun

    2016-01-01

    This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

  12. Pile-up correction by Genetic Algorithm and Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Kafaee, M.; Saramad, S.

    2009-08-01

    Pile-up distortion is a common problem for high counting rates radiation spectroscopy in many fields such as industrial, nuclear and medical applications. It is possible to reduce pulse pile-up using hardware-based pile-up rejections. However, this phenomenon may not be eliminated completely by this approach and the spectrum distortion caused by pile-up rejection can be increased as well. In addition, inaccurate correction or rejection of pile-up artifacts in applications such as energy dispersive X-ray (EDX) spectrometers can lead to losses of counts, will give poor quantitative results and even false element identification. Therefore, it is highly desirable to use software-based models to predict and correct any recognized pile-up signals in data acquisition systems. The present paper describes two new intelligent approaches for pile-up correction; the Genetic Algorithm (GA) and Artificial Neural Networks (ANNs). The validation and testing results of these new methods have been compared, which shows excellent agreement with the measured data with 60Co source and NaI detector. The Monte Carlo simulation of these new intelligent algorithms also shows their advantages over hardware-based pulse pile-up rejection methods.

  13. Production of Engineered Fabrics Using Artificial Neural Network-Genetic Algorithm Hybrid Model

    NASA Astrophysics Data System (ADS)

    Mitra, Ashis; Majumdar, Prabal Kumar; Banerjee, Debamalya

    2015-10-01

    The process of fabric engineering which is generally practised in most of the textile mills is very complicated, repetitive, tedious and time consuming. To eliminate this trial and error approach, a new approach of fabric engineering has been attempted in this work. Data sets of construction parameters [comprising of ends per inch, picks per inch, warp count and weft count] and three fabric properties (namely drape coefficient, air permeability and thermal resistance) of 25 handloom cotton fabrics have been used. The weights and biases of three artificial neural network (ANN) models developed for the prediction of drape coefficient, air permeability and thermal resistance were used to formulate the fitness or objective function and constraints of the optimization problem. The optimization problem was solved using genetic algorithm (GA). In both the fabrics which were attempted for engineering, the target and simulated fabric properties were very close. The GA was able to search the optimum set of fabric construction parameters with reasonably good accuracy except in case of EPI. However, the overall result is encouraging and can be improved further by using larger data sets of handloom fabrics by hybrid ANN-GA model.

  14. [Medium optimization for mycelia production of Antrodia camphorata based on artificial neural network-genetic algorithm].

    PubMed

    Lu, Zhenming; He, Zhe; Xu, Hongyu; Shi, Jinsong; Xu, Zhenghong

    2011-12-01

    To illustrate the complex fermentation process of submerged culture of Antrodia camphorata ATCC 200183, we observed the morphology change of this filamentous fungus. Then we used two optimization models namely response surface methodology (RSM) and artificial neural network (ANN) to model the fermentation process of Antrodia camphorata. By genetic algorithm (GA), we optimized the inoculum size and medium components for Antrodia camphorata production. The results show that fitness and prediction accuracy of ANN model was higher when compared to those of RSM model. Using GA, we optimized the input space of ANN model, and obtained maximum biomass of 6.2 g/L at the GA-optimized concentrations of spore (1.76x 10(5) /mL) and medium components (glucose, 29.1 g/L; peptone, 9.3 g/L; and soybean flour, 2.8 g/L). The biomass obtained using the ANN-GA designed medium was (6.1+/-0.2) g/L which was in good agreement with the predicted value. The same optimization process may be used to improve the production of mycelia and bioactive metabolites from potent medicinal fungi by changing the fermentation parameters.

  15. A new optimization framework using genetic algorithm and artificial neural network to reduce uncertainties in petroleum reservoir models

    NASA Astrophysics Data System (ADS)

    Maschio, Célio; José Schiozer, Denis

    2015-01-01

    In this article, a new optimization framework to reduce uncertainties in petroleum reservoir attributes using artificial intelligence techniques (neural network and genetic algorithm) is proposed. Instead of using the deterministic values of the reservoir properties, as in a conventional process, the parameters of the probability density function of each uncertain attribute are set as design variables in an optimization process using a genetic algorithm. The objective function (OF) is based on the misfit of a set of models, sampled from the probability density function, and a symmetry factor (which represents the distribution of curves around the history) is used as weight in the OF. Artificial neural networks are trained to represent the production curves of each well and the proxy models generated are used to evaluate the OF in the optimization process. The proposed method was applied to a reservoir with 16 uncertain attributes and promising results were obtained.

  16. A Novel User Classification Method for Femtocell Network by Using Affinity Propagation Algorithm and Artificial Neural Network

    PubMed Central

    Ahmed, Afaz Uddin; Tariqul Islam, Mohammad; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

    2014-01-01

    An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation. PMID:25133214

  17. A novel user classification method for femtocell network by using affinity propagation algorithm and artificial neural network.

    PubMed

    Ahmed, Afaz Uddin; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

    2014-01-01

    An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation.

  18. Soy sauce classification by geographic region and fermentation based on artificial neural network and genetic algorithm.

    PubMed

    Xu, Libin; Li, Yang; Xu, Ning; Hu, Yong; Wang, Chao; He, Jianjun; Cao, Yueze; Chen, Shigui; Li, Dongsheng

    2014-12-24

    This work demonstrated the possibility of using artificial neural networks to classify soy sauce from China. The aroma profiles of different soy sauce samples were differentiated using headspace solid-phase microextraction. The soy sauce samples were analyzed by gas chromatography-mass spectrometry, and 22 and 15 volatile aroma compounds were selected for sensitivity analysis to classify the samples by fermentation and geographic region, respectively. The 15 selected samples can be classified by fermentation and geographic region with a prediction success rate of 100%. Furans and phenols represented the variables with the greatest contribution in classifying soy sauce samples by fermentation and geographic region, respectively.

  19. Hybrid artificial neural network genetic algorithm technique for modeling chemical oxygen demand removal in anoxic/oxic process.

    PubMed

    Ma, Yongwen; Huang, Mingzhi; Wan, Jinquan; Hu, Kang; Wang, Yan; Zhang, Huiping

    2011-01-01

    In this paper, a hybrid artificial neural network (ANN) - genetic algorithm (GA) numerical technique was successfully developed to deal with complicated problems that cannot be solved by conventional solutions. ANNs and Gas were used to model and simulate the process of removing chemical oxygen demand (COD) in an anoxic/oxic system. The minimization of the error function with respect to the network parameters (weights and biases) has been considered as training of the network. Real-coded genetic algorithm was used to train the network in an unsupervised manner. Meanwhile the important process parameters, such as the influent COD (COD(in)), reflux ratio (R(r)), carbon-nitrogen ratio (C/N) and the effluent COD (COD(out)) were considered. The result shows that compared with the performance of ANN model, the performance of the GA-ANN (genetic algorithm - artificial neural network) network was found to be more impressive. Using ANN, the mean absolute percentage error (MAPE), mean squared error (MSE) and correlation coefficient (R) were 9.33×10(-4), 2.82 and 0.98596, respectively; while for the GA-ANN, they were converged to be 4.18×10(-4), 1.12 and 0.99476, respectively.

  20. Artificial Neural Networks, and Evolutionary Algorithms as a systems biology approach to a data-base on fetal growth restriction.

    PubMed

    Street, Maria E; Buscema, Massimo; Smerieri, Arianna; Montanini, Luisa; Grossi, Enzo

    2013-12-01

    One of the specific aims of systems biology is to model and discover properties of cells, tissues and organisms functioning. A systems biology approach was undertaken to investigate possibly the entire system of intra-uterine growth we had available, to assess the variables of interest, discriminate those which were effectively related with appropriate or restricted intrauterine growth, and achieve an understanding of the systems in these two conditions. The Artificial Adaptive Systems, which include Artificial Neural Networks and Evolutionary Algorithms lead us to the first analyses. These analyses identified the importance of the biochemical variables IL-6, IGF-II and IGFBP-2 protein concentrations in placental lysates, and offered a new insight into placental markers of fetal growth within the IGF and cytokine systems, confirmed they had relationships and offered a critical assessment of studies previously performed.

  1. Predicting Tooth Surface Loss Using Genetic Algorithms-Optimized Artificial Neural Networks

    PubMed Central

    Al Haidan, Ali; Abu-Hammad, Osama

    2014-01-01

    Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy. PMID:25114713

  2. Forecasting Jet Fuel Prices Using Artificial Neural Networks.

    DTIC Science & Technology

    1995-03-01

    Artificial neural networks provide a new approach to commodity forecasting that does not require algorithm or rule development. Neural networks have...NeuralWare, more people can take advantage of the power of artificial neural networks . This thesis provides an introduction to neural networks, and reviews

  3. A Real-Time and Closed-Loop Control Algorithm for Cascaded Multilevel Inverter Based on Artificial Neural Network

    PubMed Central

    Wang, Libing; Mao, Chengxiong; Wang, Dan; Lu, Jiming; Zhang, Junfeng; Chen, Xun

    2014-01-01

    In order to control the cascaded H-bridges (CHB) converter with staircase modulation strategy in a real-time manner, a real-time and closed-loop control algorithm based on artificial neural network (ANN) for three-phase CHB converter is proposed in this paper. It costs little computation time and memory. It has two steps. In the first step, hierarchical particle swarm optimizer with time-varying acceleration coefficient (HPSO-TVAC) algorithm is employed to minimize the total harmonic distortion (THD) and generate the optimal switching angles offline. In the second step, part of optimal switching angles are used to train an ANN and the well-designed ANN can generate optimal switching angles in a real-time manner. Compared with previous real-time algorithm, the proposed algorithm is suitable for a wider range of modulation index and results in a smaller THD and a lower calculation time. Furthermore, the well-designed ANN is embedded into a closed-loop control algorithm for CHB converter with variable direct voltage (DC) sources. Simulation results demonstrate that the proposed closed-loop control algorithm is able to quickly stabilize load voltage and minimize the line current's THD (<5%) when subjecting the DC sources disturbance or load disturbance. In real design stage, a switching angle pulse generation scheme is proposed and experiment results verify its correctness. PMID:24772025

  4. A real-time and closed-loop control algorithm for cascaded multilevel inverter based on artificial neural network.

    PubMed

    Wang, Libing; Mao, Chengxiong; Wang, Dan; Lu, Jiming; Zhang, Junfeng; Chen, Xun

    2014-01-01

    In order to control the cascaded H-bridges (CHB) converter with staircase modulation strategy in a real-time manner, a real-time and closed-loop control algorithm based on artificial neural network (ANN) for three-phase CHB converter is proposed in this paper. It costs little computation time and memory. It has two steps. In the first step, hierarchical particle swarm optimizer with time-varying acceleration coefficient (HPSO-TVAC) algorithm is employed to minimize the total harmonic distortion (THD) and generate the optimal switching angles offline. In the second step, part of optimal switching angles are used to train an ANN and the well-designed ANN can generate optimal switching angles in a real-time manner. Compared with previous real-time algorithm, the proposed algorithm is suitable for a wider range of modulation index and results in a smaller THD and a lower calculation time. Furthermore, the well-designed ANN is embedded into a closed-loop control algorithm for CHB converter with variable direct voltage (DC) sources. Simulation results demonstrate that the proposed closed-loop control algorithm is able to quickly stabilize load voltage and minimize the line current's THD (<5%) when subjecting the DC sources disturbance or load disturbance. In real design stage, a switching angle pulse generation scheme is proposed and experiment results verify its correctness.

  5. Simulation and optimization of a pulsating heat pipe using artificial neural network and genetic algorithm

    NASA Astrophysics Data System (ADS)

    Jokar, Ali; Godarzi, Ali Abbasi; Saber, Mohammad; Shafii, Mohammad Behshad

    2016-11-01

    In this paper, a novel approach has been presented to simulate and optimize the pulsating heat pipes (PHPs). The used pulsating heat pipe setup was designed and constructed for this study. Due to the lack of a general mathematical model for exact analysis of the PHPs, a method has been applied for simulation and optimization using the natural algorithms. In this way, the simulator consists of a kind of multilayer perceptron neural network, which is trained by experimental results obtained from our PHP setup. The results show that the complex behavior of PHPs can be successfully described by the non-linear structure of this simulator. The input variables of the neural network are input heat flux to evaporator (q″), filling ratio (FR) and inclined angle (IA) and its output is thermal resistance of PHP. Finally, based upon the simulation results and considering the heat pipe's operating constraints, the optimum operating point of the system is obtained by using genetic algorithm (GA). The experimental results show that the optimum FR (38.25 %), input heat flux to evaporator (39.93 W) and IA (55°) that obtained from GA are acceptable.

  6. The superior fault tolerance of artificial neural network training with a fault/noise injection-based genetic algorithm.

    PubMed

    Su, Feng; Yuan, Peijiang; Wang, Yangzhen; Zhang, Chen

    2016-10-01

    Artificial neural networks (ANNs) are powerful computational tools that are designed to replicate the human brain and adopted to solve a variety of problems in many different fields. Fault tolerance (FT), an important property of ANNs, ensures their reliability when significant portions of a network are lost. In this paper, a fault/noise injection-based (FIB) genetic algorithm (GA) is proposed to construct fault-tolerant ANNs. The FT performance of an FIB-GA was compared with that of a common genetic algorithm, the back-propagation algorithm, and the modification of weights algorithm. The FIB-GA showed a slower fitting speed when solving the exclusive OR (XOR) problem and the overlapping classification problem, but it significantly reduced the errors in cases of single or multiple faults in ANN weights or nodes. Further analysis revealed that the fit weights showed no correlation with the fitting errors in the ANNs constructed with the FIB-GA, suggesting a relatively even distribution of the various fitting parameters. In contrast, the output weights in the training of ANNs implemented with the use the other three algorithms demonstrated a positive correlation with the errors. Our findings therefore indicate that a combination of the fault/noise injection-based method and a GA is capable of introducing FT to ANNs and imply that the distributed ANNs demonstrate superior FT performance.

  7. Network Intrusion Detection Based on a General Regression Neural Network Optimized by an Improved Artificial Immune Algorithm

    PubMed Central

    Wu, Jianfa; Peng, Dahao; Li, Zhuping; Zhao, Li; Ling, Huanzhang

    2015-01-01

    To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accuracy. In this paper, the mean square errors (MSEs) were considered the affinity function. The AIAE was used to optimize the smooth factors of the GRNN; then, the optimal smooth factor was solved and substituted into the trained GRNN. Thus, the intrusive data were classified. The paper selected a GRNN that was separately optimized using a genetic algorithm (GA), particle swarm optimization (PSO), and fuzzy C-mean clustering (FCM) to enable a comparison of these approaches. As shown in the results, the AIAE-GRNN achieves a higher classification accuracy than PSO-GRNN, but the running time of AIAE-GRNN is long, which was proved first. FCM and GA-GRNN were eliminated because of their deficiencies in terms of accuracy and convergence. To improve the running speed, the paper adopted principal component analysis (PCA) to reduce the dimensions of the intrusive data. With the reduction in dimensionality, the PCA-AIAE-GRNN decreases in accuracy less and has better convergence than the PCA-PSO-GRNN, and the running speed of the PCA-AIAE-GRNN was relatively improved. The experimental results show that the AIAE-GRNN has a higher robustness and accuracy than the other algorithms considered and can thus be used to classify the intrusive data. PMID:25807466

  8. Network intrusion detection based on a general regression neural network optimized by an improved artificial immune algorithm.

    PubMed

    Wu, Jianfa; Peng, Dahao; Li, Zhuping; Zhao, Li; Ling, Huanzhang

    2015-01-01

    To effectively and accurately detect and classify network intrusion data, this paper introduces a general regression neural network (GRNN) based on the artificial immune algorithm with elitist strategies (AIAE). The elitist archive and elitist crossover were combined with the artificial immune algorithm (AIA) to produce the AIAE-GRNN algorithm, with the aim of improving its adaptivity and accuracy. In this paper, the mean square errors (MSEs) were considered the affinity function. The AIAE was used to optimize the smooth factors of the GRNN; then, the optimal smooth factor was solved and substituted into the trained GRNN. Thus, the intrusive data were classified. The paper selected a GRNN that was separately optimized using a genetic algorithm (GA), particle swarm optimization (PSO), and fuzzy C-mean clustering (FCM) to enable a comparison of these approaches. As shown in the results, the AIAE-GRNN achieves a higher classification accuracy than PSO-GRNN, but the running time of AIAE-GRNN is long, which was proved first. FCM and GA-GRNN were eliminated because of their deficiencies in terms of accuracy and convergence. To improve the running speed, the paper adopted principal component analysis (PCA) to reduce the dimensions of the intrusive data. With the reduction in dimensionality, the PCA-AIAE-GRNN decreases in accuracy less and has better convergence than the PCA-PSO-GRNN, and the running speed of the PCA-AIAE-GRNN was relatively improved. The experimental results show that the AIAE-GRNN has a higher robustness and accuracy than the other algorithms considered and can thus be used to classify the intrusive data.

  9. Biomarker Discovery Based on Hybrid Optimization Algorithm and Artificial Neural Networks on Microarray Data for Cancer Classification.

    PubMed

    Moteghaed, Niloofar Yousefi; Maghooli, Keivan; Pirhadi, Shiva; Garshasbi, Masoud

    2015-01-01

    The improvement of high-through-put gene profiling based microarrays technology has provided monitoring the expression value of thousands of genes simultaneously. Detailed examination of changes in expression levels of genes can help physicians to have efficient diagnosing, classification of tumors and cancer's types as well as effective treatments. Finding genes that can classify the group of cancers correctly based on hybrid optimization algorithms is the main purpose of this paper. In this paper, a hybrid particle swarm optimization and genetic algorithm method are used for gene selection and also artificial neural network (ANN) is adopted as the classifier. In this work, we have improved the ability of the algorithm for the classification problem by finding small group of biomarkers and also best parameters of the classifier. The proposed approach is tested on three benchmark gene expression data sets: Blood (acute myeloid leukemia, acute lymphoblastic leukemia), colon and breast datasets. We used 10-fold cross-validation to achieve accuracy and also decision tree algorithm to find the relation between the biomarkers for biological point of view. To test the ability of the trained ANN models to categorize the cancers, we analyzed additional blinded samples that were not previously used for the training procedure. Experimental results show that the proposed method can reduce the dimension of the data set and confirm the most informative gene subset and improve classification accuracy with best parameters based on datasets.

  10. Performing target specific band reduction using artificial neural networks and assessment of its efficacy using various target detection algorithms

    NASA Astrophysics Data System (ADS)

    Yadav, Deepti; Arora, M. K.; Tiwari, K. C.; Ghosh, J. K.

    2016-04-01

    Hyperspectral imaging is a powerful tool in the field of remote sensing and has been used for many applications like mineral detection, detection of landmines, target detection etc. Major issues in target detection using HSI are spectral variability, noise, small size of the target, huge data dimensions, high computation cost, complex backgrounds etc. Many of the popular detection algorithms do not work for difficult targets like small, camouflaged etc. and may result in high false alarms. Thus, target/background discrimination is a key issue and therefore analyzing target's behaviour in realistic environments is crucial for the accurate interpretation of hyperspectral imagery. Use of standard libraries for studying target's spectral behaviour has limitation that targets are measured in different environmental conditions than application. This study uses the spectral data of the same target which is used during collection of the HSI image. This paper analyze spectrums of targets in a way that each target can be spectrally distinguished from a mixture of spectral data. Artificial neural network (ANN) has been used to identify the spectral range for reducing data and further its efficacy for improving target detection is verified. The results of ANN proposes discriminating band range for targets; these ranges were further used to perform target detection using four popular spectral matching target detection algorithm. Further, the results of algorithms were analyzed using ROC curves to evaluate the effectiveness of the ranges suggested by ANN over full spectrum for detection of desired targets. In addition, comparative assessment of algorithms is also performed using ROC.

  11. Preparation of agar nanospheres: comparison of response surface and artificial neural network modeling by a genetic algorithm approach.

    PubMed

    Zaki, Mohammad Reza; Varshosaz, Jaleh; Fathi, Milad

    2015-05-20

    Multivariate nature of drug loaded nanospheres manufacturing in term of multiplicity of involved factors makes it a time consuming and expensive process. In this study genetic algorithm (GA) and artificial neural network (ANN), two tools inspired by natural process, were employed to optimize and simulate the manufacturing process of agar nanospheres. The efficiency of GA was evaluated against the response surface methodology (RSM). The studied responses included particle size, poly dispersity index, zeta potential, drug loading and release efficiency. GA predicted greater extremum values for response factors compared to RSM. However, real values showed some deviations from predicted data. Appropriate agreement was found between ANN model predicted and real values for all five response factors with high correlation coefficients. GA was more successful than RSM in optimization and along with ANN were efficient tools in optimizing and modeling the fabrication process of drug loaded in agar nanospheres.

  12. Geometrical features assessment of liver's tumor with application of artificial neural network evolved by imperialist competitive algorithm.

    PubMed

    Keshavarz, M; Mojra, A

    2015-05-01

    Geometrical features of a cancerous tumor embedded in biological soft tissue, including tumor size and depth, are a necessity in the follow-up procedure and making suitable therapeutic decisions. In this paper, a new socio-politically motivated global search strategy which is called imperialist competitive algorithm (ICA) is implemented to train a feed forward neural network (FFNN) to estimate the tumor's geometrical characteristics (FFNNICA). First, a viscoelastic model of liver tissue is constructed by using a series of in vitro uniaxial and relaxation test data. Then, 163 samples of the tissue including a tumor with different depths and diameters are generated by making use of PYTHON programming to link the ABAQUS and MATLAB together. Next, the samples are divided into 123 samples as training dataset and 40 samples as testing dataset. Training inputs of the network are mechanical parameters extracted from palpation of the tissue through a developing noninvasive technology called artificial tactile sensing (ATS). Last, to evaluate the FFNNICA performance, outputs of the network including tumor's depth and diameter are compared with desired values for both training and testing datasets. Deviations of the outputs from desired values are calculated by a regression analysis. Statistical analysis is also performed by measuring Root Mean Square Error (RMSE) and Efficiency (E). RMSE in diameter and depth estimations are 0.50 mm and 1.49, respectively, for the testing dataset. Results affirm that the proposed optimization algorithm for training neural network can be useful to characterize soft tissue tumors accurately by employing an artificial palpation approach.

  13. Generalized Adaptive Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul

    1993-01-01

    Mathematical model of supervised learning by artificial neural network provides for simultaneous adjustments of both temperatures of neurons and synaptic weights, and includes feedback as well as feedforward synaptic connections. Extension of mathematical model described in "Adaptive Neurons For Artificial Neural Networks" (NPO-17803). Dynamics of neural network represented in new model by less-restrictive continuous formalism.

  14. Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach.

    PubMed

    Erguzel, Turker Tekin; Ozekes, Serhat; Tan, Oguz; Gultekin, Selahattin

    2015-10-01

    Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set.

  15. An Improved Cloud Classification Algorithm for China’s FY-2C Multi-Channel Images Using Artificial Neural Network

    PubMed Central

    Liu, Yu; Xia, Jun; Shi, Chun-Xiang; Hong, Yang

    2009-01-01

    The crowning objective of this research was to identify a better cloud classification method to upgrade the current window-based clustering algorithm used operationally for China’s first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. First, the capabilities of six widely-used Artificial Neural Network (ANN) methods are analyzed, together with the comparison of two other methods: Principal Component Analysis (PCA) and a Support Vector Machine (SVM), using 2864 cloud samples manually collected by meteorologists in June, July, and August in 2007 from three FY-2C channel (IR1, 10.3–11.3 μm; IR2, 11.5–12.5 μm and WV 6.3–7.6 μm) imagery. The result shows that: (1) ANN approaches, in general, outperformed the PCA and the SVM given sufficient training samples and (2) among the six ANN networks, higher cloud classification accuracy was obtained with the Self-Organizing Map (SOM) and Probabilistic Neural Network (PNN). Second, to compare the ANN methods to the present FY-2C operational algorithm, this study implemented SOM, one of the best ANN network identified from this study, as an automated cloud classification system for the FY-2C multi-channel data. It shows that SOM method has improved the results greatly not only in pixel-level accuracy but also in cloud patch-level classification by more accurately identifying cloud types such as cumulonimbus, cirrus and clouds in high latitude. Findings of this study suggest that the ANN-based classifiers, in particular the SOM, can be potentially used as an improved Automated Cloud Classification Algorithm to upgrade the current window-based clustering method for the FY-2C operational products. PMID:22346714

  16. An Improved Cloud Classification Algorithm for China's FY-2C Multi-Channel Images Using Artificial Neural Network.

    PubMed

    Liu, Yu; Xia, Jun; Shi, Chun-Xiang; Hong, Yang

    2009-01-01

    The crowning objective of this research was to identify a better cloud classification method to upgrade the current window-based clustering algorithm used operationally for China's first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. First, the capabilities of six widely-used Artificial Neural Network (ANN) methods are analyzed, together with the comparison of two other methods: Principal Component Analysis (PCA) and a Support Vector Machine (SVM), using 2864 cloud samples manually collected by meteorologists in June, July, and August in 2007 from three FY-2C channel (IR1, 10.3-11.3 μm; IR2, 11.5-12.5 μm and WV 6.3-7.6 μm) imagery. The result shows that: (1) ANN approaches, in general, outperformed the PCA and the SVM given sufficient training samples and (2) among the six ANN networks, higher cloud classification accuracy was obtained with the Self-Organizing Map (SOM) and Probabilistic Neural Network (PNN). Second, to compare the ANN methods to the present FY-2C operational algorithm, this study implemented SOM, one of the best ANN network identified from this study, as an automated cloud classification system for the FY-2C multi-channel data. It shows that SOM method has improved the results greatly not only in pixel-level accuracy but also in cloud patch-level classification by more accurately identifying cloud types such as cumulonimbus, cirrus and clouds in high latitude. Findings of this study suggest that the ANN-based classifiers, in particular the SOM, can be potentially used as an improved Automated Cloud Classification Algorithm to upgrade the current window-based clustering method for the FY-2C operational products.

  17. Trimaran Resistance Artificial Neural Network

    DTIC Science & Technology

    2011-01-01

    11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to

  18. [Artificial neural networks in Neurosciences].

    PubMed

    Porras Chavarino, Carmen; Salinas Martínez de Lecea, José María

    2011-11-01

    This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.

  19. New algorithm for centroiding in elongated spots for Shack-Hartmann wavefront sensors using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Mello, A. T.; Kanaan, A.; Guzmán, D.

    2014-10-01

    To recover the resolution lost in a ground-based telescopes due to the atmospheric turbulence, it is necessary to use a technique known as Adaptive Optics (AO). The next generation of telescopes will have primary mirrors of more than 30 meter in diameter and will require AO systems from the ground up (Nelson et al. 2006). There are a number of challenges to implement an AO system at these scales. One of these challenges is the accurate measurement of the aberrated wavefronts using a laser guide star and a Shack-Hartmann wavefront sensor. Due to the diameter of the telescope and the use of the sodium layer in the upper atmosphere as photon return for the laser guide stars, the image of the guide star will appear elongated in the wavefront sensor. Typical centroiding algorithms such as Center of Gravity do not perform well under these conditions (Thomas et al. 2008). We present a new technique based on artificial neural networks for measuring the spot position with better accuracy than existing methods. Simulation results confirms that the new algorithm incurs in smaller errors with respect to other centroiding techniques in use.

  20. A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network

    PubMed Central

    Marto, Aminaton; Jahed Armaghani, Danial; Tonnizam Mohamad, Edy; Makhtar, Ahmad Mahir

    2014-01-01

    Flyrock is one of the major disturbances induced by blasting which may cause severe damage to nearby structures. This phenomenon has to be precisely predicted and subsequently controlled through the changing in the blast design to minimize potential risk of blasting. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of imperialist competitive algorithm (ICA) and artificial neural network (ANN). For this purpose, the parameters of 113 blasting operations were accurately recorded and flyrock distances were measured for each operation. By applying the sensitivity analysis, maximum charge per delay and powder factor were determined as the most influential parameters on flyrock. In the light of this analysis, two new empirical predictors were developed to predict flyrock distance. For a comparison purpose, a predeveloped backpropagation (BP) ANN was developed and the results were compared with those of the proposed ICA-ANN model and empirical predictors. The results clearly showed the superiority of the proposed ICA-ANN model in comparison with the proposed BP-ANN model and empirical approaches. PMID:25147856

  1. Enhanced antibiotic production by Streptomyces sindenensis using artificial neural networks coupled with genetic algorithm and Nelder-Mead downhill simplex.

    PubMed

    Tripathi, C K M; Khan, Mahvish; Praveen, Vandana; Khan, Saif; Srivastava, Akanksha

    2012-07-01

    Antibiotic production with Streptomyces sindenensis MTCC 8122 was optimized under submerged fermentation conditions by artificial neural network (ANN) coupled with genetic algorithm (GA) and Nelder-Mead downhill simplex (NMDS). Feed forward back-propagation ANN was trained to establish the mathematical relationship among the medium components and length of incubation period for achieving maximum antibiotic yield. The optimization strategy involved growing the culture with varying concentrations of various medium components for different incubation periods. Under non-optimized condition, antibiotic production was found to be 95 microgram/ml, which nearly doubled (176 microgram/ml) with the ANN-GA optimization. ANN-NMDS optimization was found to be more efficacious, and maximum antibiotic production (197 microgram/ml) was obtained by cultivating the cells with (g/l) fructose 2.7602, MgSO4 1.2369, (NH4)2PO4 0.2742, DL-threonine 3.069%, and soyabean meal 1.952%, for 9.8531 days of incubation, which was roughly 12% higher than the yield obtained by ANN coupled with GA under the same conditions.

  2. Optimization of controlled release nanoparticle formulation of verapamil hydrochloride using artificial neural networks with genetic algorithm and response surface methodology.

    PubMed

    Li, Yongqiang; Abbaspour, Mohammadreza R; Grootendorst, Paul V; Rauth, Andrew M; Wu, Xiao Yu

    2015-08-01

    This study was performed to optimize the formulation of polymer-lipid hybrid nanoparticles (PLN) for the delivery of an ionic water-soluble drug, verapamil hydrochloride (VRP) and to investigate the roles of formulation factors. Modeling and optimization were conducted based on a spherical central composite design. Three formulation factors, i.e., weight ratio of drug to lipid (X1), and concentrations of Tween 80 (X2) and Pluronic F68 (X3), were chosen as independent variables. Drug loading efficiency (Y1) and mean particle size (Y2) of PLN were selected as dependent variables. The predictive performance of artificial neural networks (ANN) and the response surface methodology (RSM) were compared. As ANN was found to exhibit better recognition and generalization capability over RSM, multi-objective optimization of PLN was then conducted based upon the validated ANN models and continuous genetic algorithms (GA). The optimal PLN possess a high drug loading efficiency (92.4%, w/w) and a small mean particle size (∼100nm). The predicted response variables matched well with the observed results. The three formulation factors exhibited different effects on the properties of PLN. ANN in coordination with continuous GA represent an effective and efficient approach to optimize the PLN formulation of VRP with desired properties.

  3. Prediction of Pharmacokinetic Parameters Using a Genetic Algorithm Combined with an Artificial Neural Network for a Series of Alkaloid Drugs

    PubMed Central

    Zandkarimi, Majid; Shafiei, Mohammad; Hadizadeh, Farzin; Darbandi, Mohammad Ali; Tabrizian, Kaveh

    2014-01-01

    An important goal for drug development within the pharmaceutical industry is the application of simple methods to determine human pharmacokinetic parameters. Effective computing tools are able to increase scientists’ ability to make precise selections of chemical compounds in accordance with desired pharmacokinetic and safety profiles. This work presents a method for making predictions of the clearance, plasma protein binding, and volume of distribution for alkaloid drugs. The tools used in this method were genetic algorithms (GAs) combined with artificial neural networks (ANNs) and these were applied to select the most relevant molecular descriptors and to develop quantitative structure-pharmacokinetic relationship (QSPkR) models. Results showed that three-dimensional structural descriptors had more influence on QSPkR models. The models developed in this study were able to predict systemic clearance, volume of distribution, and plasma protein binding with normalized root mean square error (NRMSE) values of 0.151, 0.263, and 0.423, respectively. These results demonstrate an acceptable level of efficiency of the developed models for the prediction of pharmacokinetic parameters. PMID:24634842

  4. Artificial Astrocytes Improve Neural Network Performance

    PubMed Central

    Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-01-01

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

  5. Artificial astrocytes improve neural network performance.

    PubMed

    Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-04-19

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  6. Knowledge Discovery in Medical Mining by using Genetic Algorithms and Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Srivathsa, P. K.

    2011-12-01

    Medical Data mining could be thought of as the search for relationships and patterns within the medical data, which facilitates the acquisition of useful knowledge for effective medical diagnosis. Consequently, the predictability of disease will become more effective and the early detection of disease certainly facilitates an increased exposure to required patient care with focused treatment, economic feasibility and improved cure rates. So, the present investigation is carried on medical data(PIMA) using DM and GA based Neural Network technique and the results predict that the methodology is not only reliable but also helps in furthering the scope of the subject.

  7. Artificial intelligence: Deep neural reasoning

    NASA Astrophysics Data System (ADS)

    Jaeger, Herbert

    2016-10-01

    The human brain can solve highly abstract reasoning problems using a neural network that is entirely physical. The underlying mechanisms are only partially understood, but an artificial network provides valuable insight. See Article p.471

  8. A self-adaptive genetic algorithm-artificial neural network algorithm with leave-one-out cross validation for descriptor selection in QSAR study.

    PubMed

    Wu, Jingheng; Mei, Juan; Wen, Sixiang; Liao, Siyan; Chen, Jincan; Shen, Yong

    2010-07-30

    Based on the quantitative structure-activity relationships (QSARs) models developed by artificial neural networks (ANNs), genetic algorithm (GA) was used in the variable-selection approach with molecule descriptors and helped to improve the back-propagation training algorithm as well. The cross validation techniques of leave-one-out investigated the validity of the generated ANN model and preferable variable combinations derived in the GAs. A self-adaptive GA-ANN model was successfully established by using a new estimate function for avoiding over-fitting phenomenon in ANN training. Compared with the variables selected in two recent QSAR studies that were based on stepwise multiple linear regression (MLR) models, the variables selected in self-adaptive GA-ANN model are superior in constructing ANN model, as they revealed a higher cross validation (CV) coefficient (Q(2)) and a lower root mean square deviation both in the established model and biological activity prediction. The introduced methods for validation, including leave-multiple-out, Y-randomization, and external validation, proved the superiority of the established GA-ANN models over MLR models in both stability and predictive power. Self-adaptive GA-ANN showed us a prospect of improving QSAR model.

  9. Finding Risk Groups by Optimizing Artificial Neural Networks on the Area under the Survival Curve Using Genetic Algorithms

    PubMed Central

    Kalderstam, Jonas; Edén, Patrik; Ohlsson, Mattias

    2015-01-01

    We investigate a new method to place patients into risk groups in censored survival data. Properties such as median survival time, and end survival rate, are implicitly improved by optimizing the area under the survival curve. Artificial neural networks (ANN) are trained to either maximize or minimize this area using a genetic algorithm, and combined into an ensemble to predict one of low, intermediate, or high risk groups. Estimated patient risk can influence treatment choices, and is important for study stratification. A common approach is to sort the patients according to a prognostic index and then group them along the quartile limits. The Cox proportional hazards model (Cox) is one example of this approach. Another method of doing risk grouping is recursive partitioning (Rpart), which constructs a decision tree where each branch point maximizes the statistical separation between the groups. ANN, Cox, and Rpart are compared on five publicly available data sets with varying properties. Cross-validation, as well as separate test sets, are used to validate the models. Results on the test sets show comparable performance, except for the smallest data set where Rpart’s predicted risk groups turn out to be inverted, an example of crossing survival curves. Cross-validation shows that all three models exhibit crossing of some survival curves on this small data set but that the ANN model manages the best separation of groups in terms of median survival time before such crossings. The conclusion is that optimizing the area under the survival curve is a viable approach to identify risk groups. Training ANNs to optimize this area combines two key strengths from both prognostic indices and Rpart. First, a desired minimum group size can be specified, as for a prognostic index. Second, the ability to utilize non-linear effects among the covariates, which Rpart is also able to do. PMID:26352405

  10. Optimization of auto-induction medium for G-CSF production by Escherichia coli using artificial neural networks coupled with genetic algorithm.

    PubMed

    Tian, H; Liu, C; Gao, X D; Yao, W B

    2013-03-01

    Granulocyte colony-stimulating factor (G-CSF) is a cytokine widely used in cancer patients receiving high doses of chemotherapeutic drugs to prevent the chemotherapy-induced suppression of white blood cells. The production of recombinant G-CSF should be increased to meet the increasing market demand. This study aims to model and optimize the carbon source of auto-induction medium to enhance G-CSF production using artificial neural networks coupled with genetic algorithm. In this approach, artificial neural networks served as bioprocess modeling tools, and genetic algorithm (GA) was applied to optimize the established artificial neural network models. Two artificial neural network models were constructed: the back-propagation (BP) network and the radial basis function (RBF) network. The root mean square error, coefficient of determination, and standard error of prediction of the BP model were 0.0375, 0.959, and 8.49 %, respectively, whereas those of the RBF model were 0.0257, 0.980, and 5.82 %, respectively. These values indicated that the RBF model possessed higher fitness and prediction accuracy than the BP model. Under the optimized auto-induction medium, the predicted maximum G-CSF yield by the BP-GA approach was 71.66 %, whereas that by the RBF-GA approach was 75.17 %. These predicted values are in agreement with the experimental results, with 72.4 and 76.014 % for the BP-GA and RBF-GA models, respectively. These results suggest that RBF-GA is superior to BP-GA. The developed approach in this study may be helpful in modeling and optimizing other multivariable, non-linear, and time-variant bioprocesses.

  11. Learning in Artificial Neural Systems

    NASA Technical Reports Server (NTRS)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

    This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

  12. Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection.

    PubMed

    Franco, Vanina G; Perín, Juan C; Mantovani, Víctor E; Goicoechea, Héctor C

    2006-01-15

    An experiment was developed as a simple alternative to existing analytical methods for the simultaneous quantitation of glucose (substrate) and glucuronic acid (main product) in the bioprocesses Kombucha by using FTIR spectroscopy coupled to multivariate calibration (partial least-squares, PLS-1 and artificial neural networks, ANNs). Wavelength selection through a novel ranked regions genetic algorithm (RRGA) was used to enhance the predictive ability of the chemometric models. Acceptable results were obtained by using the ANNs models considering the complexity of the sample and the speediness and simplicity of the method. The accuracy on the glucuronic acid determination was calculated by analysing spiked real fermentation samples (recoveries ca. 115%).

  13. Advances in Artificial Neural Networks - Methodological Development and Application

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other ne...

  14. Neural Network Algorithm for Particle Loading

    SciTech Connect

    J. L. V. Lewandowski

    2003-04-25

    An artificial neural network algorithm for continuous minimization is developed and applied to the case of numerical particle loading. It is shown that higher-order moments of the probability distribution function can be efficiently renormalized using this technique. A general neural network for the renormalization of an arbitrary number of moments is given.

  15. Livermore Big Artificial Neural Network Toolkit

    SciTech Connect

    Essen, Brian Van; Jacobs, Sam; Kim, Hyojin; Dryden, Nikoli; Moon, Tim

    2016-07-01

    LBANN is a toolkit that is designed to train artificial neural networks efficiently on high performance computing architectures. It is optimized to take advantages of key High Performance Computing features to accelerate neural network training. Specifically it is optimized for low-latency, high bandwidth interconnects, node-local NVRAM, node-local GPU accelerators, and high bandwidth parallel file systems. It is built on top of the open source Elemental distributed-memory dense and spars-direct linear algebra and optimization library that is released under the BSD license. The algorithms contained within LBANN are drawn from the academic literature and implemented to work within a distributed-memory framework.

  16. Artificial neural networks in medicine

    SciTech Connect

    Keller, P.E.

    1994-07-01

    This Technology Brief provides an overview of artificial neural networks (ANN). A definition and explanation of an ANN is given and situations in which an ANN is used are described. ANN applications to medicine specifically are then explored and the areas in which it is currently being used are discussed. Included are medical diagnostic aides, biochemical analysis, medical image analysis and drug development.

  17. Synthesis of zinc oxide nanoparticles-chitosan for extraction of methyl orange from water samples: Cuckoo optimization algorithm-artificial neural network

    NASA Astrophysics Data System (ADS)

    Khajeh, Mostafa; Golzary, Ali Reza

    2014-10-01

    In this work, zinc nanoparticles-chitosan based solid phase extraction has been developed for separation and preconcentration of trace amount of methyl orange from water samples. Artificial neural network-cuckoo optimization algorithm has been employed to develop the model for simulation and optimization of this method. The pH, volume of elution solvent, mass of zinc oxide nanoparticles-chitosan, flow rate of sample and elution solvent were the input variables, while recovery of methyl orange was the output. The optimum conditions were obtained by cuckoo optimization algorithm. At the optimum conditions, the limit of detections of 0.7 μg L-1was obtained for the methyl orange. The developed procedure was then applied to the separation and preconcentration of methyl orange from water samples.

  18. Synthesis of zinc oxide nanoparticles-chitosan for extraction of methyl orange from water samples: cuckoo optimization algorithm-artificial neural network.

    PubMed

    Khajeh, Mostafa; Golzary, Ali Reza

    2014-10-15

    In this work, zinc nanoparticles-chitosan based solid phase extraction has been developed for separation and preconcentration of trace amount of methyl orange from water samples. Artificial neural network-cuckoo optimization algorithm has been employed to develop the model for simulation and optimization of this method. The pH, volume of elution solvent, mass of zinc oxide nanoparticles-chitosan, flow rate of sample and elution solvent were the input variables, while recovery of methyl orange was the output. The optimum conditions were obtained by cuckoo optimization algorithm. At the optimum conditions, the limit of detections of 0.7μgL(-1)was obtained for the methyl orange. The developed procedure was then applied to the separation and preconcentration of methyl orange from water samples.

  19. Porosity Log Prediction Using Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Dwi Saputro, Oki; Lazuardi Maulana, Zulfikar; Dzar Eljabbar Latief, Fourier

    2016-08-01

    Well logging is important in oil and gas exploration. Many physical parameters of reservoir is derived from well logging measurement. Geophysicists often use well logging to obtain reservoir properties such as porosity, water saturation and permeability. Most of the time, the measurement of the reservoir properties are considered expensive. One of method to substitute the measurement is by conducting a prediction using artificial neural network. In this paper, artificial neural network is performed to predict porosity log data from other log data. Three well from ‘yy’ field are used to conduct the prediction experiment. The log data are sonic, gamma ray, and porosity log. One of three well is used as training data for the artificial neural network which employ the Levenberg-Marquardt Backpropagation algorithm. Through several trials, we devise that the most optimal input training is sonic log data and gamma ray log data with 10 hidden layer. The prediction result in well 1 has correlation of 0.92 and mean squared error of 5.67 x10-4. Trained network apply to other well data. The result show that correlation in well 2 and well 3 is 0.872 and 0.9077 respectively. Mean squared error in well 2 and well 3 is 11 x 10-4 and 9.539 x 10-4. From the result we can conclude that sonic log and gamma ray log could be good combination for predicting porosity with neural network.

  20. Introduction to Concepts in Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Niebur, Dagmar

    1995-01-01

    This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

  1. Design and implementation of a hybrid genetic algorithm and artificial neural network system for predicting the sizes of unerupted canines and premolars.

    PubMed

    Moghimi, S; Talebi, M; Parisay, I

    2012-08-01

    The aim of this study was to develop a novel hybrid genetic algorithm and artificial neural network (GA-ANN) system for predicting the sizes of unerupted canines and premolars during the mixed dentition period. This study was performed on 106 untreated subjects (52 girls, 54 boys, aged 13-15 years). Data were obtained from dental cast measurements. A hybrid GA-ANN algorithm was developed to find the best reference teeth and the most accurate mapping function. Based on a regression analysis, the strongest correlation was observed between the sum of the mesiodistal widths of the mandibular canines and premolars and the mesiodistal widths of the mandibular first molars and incisors (r = 0.697). In the maxilla, the highest correlation was observed between the sum of the mesiodistal widths of the canines and premolars and the mesiodistal widths of the mandibular first molars and maxillary central incisors (0.742). The hybrid GA-ANN algorithm selected the mandibular first molars and incisors and the maxillary central incisors as the reference teeth for predicting the sum of the mesiodistal widths of the canines and premolars. The prediction error rates and maximum rates of over/underestimation using the hybrid GA-ANN algorithm were smaller than those using linear regression analyses.

  2. Application of artificial neural networks to composite ply micromechanics

    NASA Technical Reports Server (NTRS)

    Brown, D. A.; Murthy, P. L. N.; Berke, L.

    1991-01-01

    Artificial neural networks can provide improved computational efficiency relative to existing methods when an algorithmic description of functional relationships is either totally unavailable or is complex in nature. For complex calculations, significant reductions in elapsed computation time are possible. The primary goal is to demonstrate the applicability of artificial neural networks to composite material characterization. As a test case, a neural network was trained to accurately predict composite hygral, thermal, and mechanical properties when provided with basic information concerning the environment, constituent materials, and component ratios used in the creation of the composite. A brief introduction on neural networks is provided along with a description of the project itself.

  3. An Artificial Neural Network Embedded Position and Orientation Determination Algorithm for Low Cost MEMS INS/GPS Integrated Sensors

    PubMed Central

    Chiang, Kai-Wei; Chang, Hsiu-Wen; Li, Chia-Yuan; Huang, Yun-Wen

    2009-01-01

    Digital mobile mapping, which integrates digital imaging with direct geo-referencing, has developed rapidly over the past fifteen years. Direct geo-referencing is the determination of the time-variable position and orientation parameters for a mobile digital imager. The most common technologies used for this purpose today are satellite positioning using Global Positioning System (GPS) and Inertial Navigation System (INS) using an Inertial Measurement Unit (IMU). They are usually integrated in such a way that the GPS receiver is the main position sensor, while the IMU is the main orientation sensor. The Kalman Filter (KF) is considered as the optimal estimation tool for real-time INS/GPS integrated kinematic position and orientation determination. An intelligent hybrid scheme consisting of an Artificial Neural Network (ANN) and KF has been proposed to overcome the limitations of KF and to improve the performance of the INS/GPS integrated system in previous studies. However, the accuracy requirements of general mobile mapping applications can’t be achieved easily, even by the use of the ANN-KF scheme. Therefore, this study proposes an intelligent position and orientation determination scheme that embeds ANN with conventional Rauch-Tung-Striebel (RTS) smoother to improve the overall accuracy of a MEMS INS/GPS integrated system in post-mission mode. By combining the Micro Electro Mechanical Systems (MEMS) INS/GPS integrated system and the intelligent ANN-RTS smoother scheme proposed in this study, a cheaper but still reasonably accurate position and orientation determination scheme can be anticipated. PMID:22574034

  4. Evaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sample

    PubMed Central

    Lyden, Kate; Kozey-Keadle, Sarah; Staudenmayer, John

    2011-01-01

    Previous work from our laboratory provided a “proof of concept” for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330–1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample (n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were +0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type. PMID:21885802

  5. Identification of Some Zeolite Group Minerals by Application of Artificial Neural Network and Decision Tree Algorithm Based on SEM-EDS Data

    NASA Astrophysics Data System (ADS)

    Akkaş, Efe; Evren Çubukçu, H.; Akin, Lutfiye; Erkut, Volkan; Yurdakul, Yasin; Karayigit, Ali Ihsan

    2016-04-01

    Identification of zeolite group minerals is complicated due to their similar chemical formulas and habits. Although the morphologies of various zeolite crystals can be recognized under Scanning Electron Microscope (SEM), it is relatively more challenging and problematic process to identify zeolites using their mineral chemical data. SEMs integrated with energy dispersive X-ray spectrometers (EDS) provide fast and reliable chemical data of minerals. However, considering elemental similarities of characteristic chemical formulae of zeolite species (e.g. Clinoptilolite ((Na,K,Ca)2 -3Al3(Al,Si)2Si13O3612H2O) and Erionite ((Na2,K2,Ca)2Al4Si14O36ṡ15H2O)) EDS data alone does not seem to be sufficient for correct identification. Furthermore, the physical properties of the specimen (e.g. roughness, electrical conductivity) and the applied analytical conditions (e.g. accelerating voltage, beam current, spot size) of the SEM-EDS should be uniform in order to obtain reliable elemental results of minerals having high alkali (Na, K) and H2O (approx. %14-18) contents. This study which was funded by The Scientific and Technological Research Council of Turkey (TUBITAK Project No: 113Y439), aims to construct a database as large as possible for various zeolite minerals and to develop a general prediction model for the identification of zeolite minerals using SEM-EDS data. For this purpose, an artificial neural network and rule based decision tree algorithm were employed. Throughout the analyses, a total of 1850 chemical data were collected from four distinct zeolite species, (Clinoptilolite-Heulandite, Erionite, Analcime and Mordenite) observed in various rocks (e.g. coals, pyroclastics). In order to obtain a representative training data set for each minerals, a selection procedure for reference mineral analyses was applied. During the selection procedure, SEM based crystal morphology data, XRD spectra and re-calculated cationic distribution, obtained by EDS have been used for the

  6. Plant Growth Models Using Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Bubenheim, David

    1997-01-01

    In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.

  7. Correcting wave predictions with artificial neural networks

    NASA Astrophysics Data System (ADS)

    Makarynskyy, O.; Makarynska, D.

    2003-04-01

    The predictions of wind waves with different lead times are necessary in a large scope of coastal and open ocean activities. Numerical wave models, which usually provide this information, are based on deterministic equations that do not entirely account for the complexity and uncertainty of the wave generation and dissipation processes. An attempt to improve wave parameters short-term forecasts based on artificial neural networks is reported. In recent years, artificial neural networks have been used in a number of coastal engineering applications due to their ability to approximate the nonlinear mathematical behavior without a priori knowledge of interrelations among the elements within a system. The common multilayer feed-forward networks, with a nonlinear transfer functions in the hidden layers, were developed and employed to forecast the wave characteristics over one hour intervals starting from one up to 24 hours, and to correct these predictions. Three non-overlapping data sets of wave characteristics, both from a buoy, moored roughly 60 miles west of the Aran Islands, west coast of Ireland, were used to train and validate the neural nets involved. The networks were trained with error back propagation algorithm. Time series plots and scatterplots of the wave characteristics as well as tables with statistics show an improvement of the results achieved due to the correction procedure employed.

  8. Isolated Speech Recognition Using Artificial Neural Networks

    DTIC Science & Technology

    2007-11-02

    In this project Artificial Neural Networks are used as research tool to accomplish Automated Speech Recognition of normal speech. A small size...the first stage of this work are satisfactory and thus the application of artificial neural networks in conjunction with cepstral analysis in isolated word recognition holds promise.

  9. Modeling the Hot Deformation Behaviors of As-Extruded 7075 Aluminum Alloy by an Artificial Neural Network with Back-Propagation Algorithm

    NASA Astrophysics Data System (ADS)

    Quan, Guo-zheng; Zou, Zhen-yu; Wang, Tong; Liu, Bo; Li, Jun-chao

    2017-01-01

    In order to investigate the hot deformation behaviors of as-extruded 7075 aluminum alloy, the isothermal compressive tests were conducted at the temperatures of 573, 623, 673 and 723 K and the strain rates of 0.01, 0.1, 1 and 10 s-1 on a Gleeble 1500 thermo-mechanical simulator. The flow behaviors showing complex characteristics are sensitive to strain, strain rate and temperature. The effects of strain, temperature and strain rate on flow stress were analyzed and dynamic recrystallization (DRX)-type softening characteristics of the flow behaviors with single peak were identified. An artificial neural network (ANN) with back-propagation (BP) algorithm was developed to deal with the complex deformation behavior characteristics based on the experimental data. The performance of ANN model has been evaluated in terms of correlation coefficient (R) and average absolute relative error (AARE). A comparative study on Arrhenius-type constitutive equation and ANN model for as-extruded 7075 aluminum alloy was conducted. Finally, the ANN model was successfully applied to the development of processing map and implanted into finite element simulation. The results have sufficiently articulated that the well-trained ANN model with BP algorithm has excellent capability to deal with the complex flow behaviors of as-extruded 7075 aluminum alloy and has great application potentiality in hot deformation processes.

  10. Modular, Hierarchical Learning By Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Baldi, Pierre F.; Toomarian, Nikzad

    1996-01-01

    Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

  11. Semantic Interpretation of An Artificial Neural Network

    DTIC Science & Technology

    1995-12-01

    success for stock market analysis/prediction is artificial neural networks. However, knowledge embedded in the neural network is not easily translated...interpret neural network knowledge. The first, called Knowledge Math, extends the use of connection weights, generating rules for general (i.e. non-binary

  12. Combination of artificial neural network and genetic algorithm method for modeling of methylene blue adsorption onto wood sawdust from water samples.

    PubMed

    Khajeh, Mostafa; Sarafraz-Yazdi, Ali; Natavan, Zahra Bameri

    2016-03-01

    The aim of this research was to develop a low price and environmentally friendly adsorbent with abundant of source to remove methylene blue (MB) from water samples. Sawdust solid-phase extraction coupled with high-performance liquid chromatography was used for the extraction and determination of MB. In this study, an experimental data-based artificial neural network model is constructed to describe the performance of sawdust solid-phase extraction method for various operating conditions. The pH, time, amount of sawdust, and temperature were the input variables, while the percentage of extraction of MB was the output. The optimum operating condition was then determined by genetic algorithm method. The optimized conditions were obtained as follows: 11.5, 22.0 min, 0.3 g, and 26.0°C for pH of the solution, extraction time, amount of adsorbent, and temperature, respectively. Under these optimum conditions, the detection limit and relative standard deviation were 0.067 μg L(-1) and <2.4%, respectively. The Langmuir and Freundlich adsorption models were applied to describe the isotherm constant and for the removal and determination of MB from water samples.

  13. Genetic algorithm-artificial neural network and adaptive neuro-fuzzy inference system modeling of antibacterial activity of annatto dye on Salmonella enteritidis.

    PubMed

    Yolmeh, Mahmoud; Habibi Najafi, Mohammad B; Salehi, Fakhreddin

    2014-01-01

    Annatto is commonly used as a coloring agent in the food industry and has antimicrobial and antioxidant properties. In this study, genetic algorithm-artificial neural network (GA-ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict the effect of annatto dye on Salmonella enteritidis in mayonnaise. The GA-ANN and ANFIS were fed with 3 inputs of annatto dye concentration (0, 0.1, 0.2 and 0.4%), storage temperature (4 and 25°C) and storage time (1-20 days) for prediction of S. enteritidis population. Both models were trained with experimental data. The results showed that the annatto dye was able to reduce of S. enteritidis and its effect was stronger at 25°C than 4°C. The developed GA-ANN, which included 8 hidden neurons, could predict S. enteritidis population with correlation coefficient of 0.999. The overall agreement between ANFIS predictions and experimental data was also very good (r=0.998). Sensitivity analysis results showed that storage temperature was the most sensitive factor for prediction of S. enteritidis population.

  14. Modeling of dispersive liquid-liquid microextraction for determination of essential oil from Borago officinalis L. by using combination of artificial neural network and genetic algorithm method.

    PubMed

    Khajeh, Mostafa; Moghaddam, Zahra Safaei; Bohlooli, Mousa; Khajeh, Ahmad

    2015-01-01

    Dispersive liquid-liquid microextraction (DLLME) coupled with gas chromatography was applied for the extraction and determination of essential oil constituents of the Borago officinalis L. In this study, an experimental data-based artificial neural network (ANN) model was constructed to describe the performance of DLLME method for various operating conditions. The volume of extraction and dispersive solvents, extraction time and salt effect were the input variables of this process, whereas the extraction efficiency was the output. The ANN method was found to be capable of modeling this procedure accurately. The overall agreement between the experimental data and ANN predictions was satisfactory showing a determination coefficient of 0.982. The optimum operating condition was then determined by the genetic algorithm method. The optimal conditions were 248 µL volume of extraction solvent, 260 µL volume of dispersive solvent, 2.5 min extraction time and 0.16 mol L(-1) of salt. The limit of detection and linear dynamic range were 0.15-24.0 and 1.2-1,800 ng mL(-1), respectively. The main components of the essential oil were δ-cadinene (31.02%), carvacrol (24.91%), α-pinene (20.89%) and α-cadinol (16.47%).

  15. Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm.

    PubMed

    Jacob, Samuel; Banerjee, Rintu

    2016-08-01

    A novel approach to overcome the acidification problem has been attempted in the present study by codigesting industrial potato waste (PW) with Pistia stratiotes (PS, an aquatic weed). The effectiveness of codigestion of the weed and PW was tested in an equal (1:1) proportion by weight with substrate concentration of 5g total solid (TS)/L (2.5gPW+2.5gPS) which resulted in enhancement of methane yield by 76.45% as compared to monodigestion of PW with a positive synergistic effect. Optimization of process parameters was conducted using central composite design (CCD) based response surface methodology (RSM) and artificial neural network (ANN) coupled genetic algorithm (GA) model. Upon comparison of these two optimization techniques, ANN-GA model obtained through feed forward back propagation methodology was found to be efficient and yielded 447.4±21.43LCH4/kgVSfed (0.279gCH4/kgCODvs) which is 6% higher as compared to the CCD-RSM based approach.

  16. Trace determination of safranin O dye using ultrasound assisted dispersive solid-phase micro extraction: Artificial neural network-genetic algorithm and response surface methodology.

    PubMed

    Dil, Ebrahim Alipanahpour; Ghaedi, Mehrorang; Asfaram, Arash; Mehrabi, Fatemeh; Bazrafshan, Ali Akbar; Ghaedi, Abdol Mohammad

    2016-11-01

    In this study, ultrasound assisted dispersive solid-phase micro extraction combined with spectrophotometry (USA-DSPME-UV) method based on activated carbon modified with Fe2O3 nanoparticles (Fe2O3-NPs-AC) was developed for pre-concentration and determination of safranin O (SO). It is known that the efficiency of USA-DSPME-UV method may be affected by pH, amount of adsorbent, ultrasound time and eluent volume and the extent and magnitude of their contribution on response (in term of main and interaction part) was studied by using central composite design (CCD) and artificial neural network-genetic algorithms (ANN-GA). Accordingly by adjustment of experimental conditions suggested by ANN-GA at pH 6.5, 1.1mg of adsorbent, 10min ultrasound and 150μL of eluent volume led to achievement of best operation performance like low LOD (6.3ngmL(-1)) and LOQ (17.5ngmL(-1)) in the range of 25-3500ngmL(-1). In following stage, the SO content in real water and wastewater samples with recoveries between 93.27-99.41% with RSD lower than 3% was successfully determined.

  17. Automated Defect Classification Using AN Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Chady, T.; Caryk, M.; Piekarczyk, B.

    2009-03-01

    The automated defect classification algorithm based on artificial neural network with multilayer backpropagation structure was utilized. The selected features of flaws were used as input data. In order to train the neural network it is necessary to prepare learning data which is representative database of defects. Database preparation requires the following steps: image acquisition and pre-processing, image enhancement, defect detection and feature extraction. The real digital radiographs of welded parts of a ship were used for this purpose.

  18. AUTOMATED DEFECT CLASSIFICATION USING AN ARTIFICIAL NEURAL NETWORK

    SciTech Connect

    Chady, T.; Caryk, M.; Piekarczyk, B.

    2009-03-03

    The automated defect classification algorithm based on artificial neural network with multilayer backpropagation structure was utilized. The selected features of flaws were used as input data. In order to train the neural network it is necessary to prepare learning data which is representative database of defects. Database preparation requires the following steps: image acquisition and pre-processing, image enhancement, defect detection and feature extraction. The real digital radiographs of welded parts of a ship were used for this purpose.

  19. Functional expansion representations of artificial neural networks

    NASA Technical Reports Server (NTRS)

    Gray, W. Steven

    1992-01-01

    In the past few years, significant interest has developed in using artificial neural networks to model and control nonlinear dynamical systems. While there exists many proposed schemes for accomplishing this and a wealth of supporting empirical results, most approaches to date tend to be ad hoc in nature and rely mainly on heuristic justifications. The purpose of this project was to further develop some analytical tools for representing nonlinear discrete-time input-output systems, which when applied to neural networks would give insight on architecture selection, pruning strategies, and learning algorithms. A long term goal is to determine in what sense, if any, a neural network can be used as a universal approximator for nonliner input-output maps with memory (i.e., realized by a dynamical system). This property is well known for the case of static or memoryless input-output maps. The general architecture under consideration in this project was a single-input, single-output recurrent feedforward network.

  20. Artificial neural network-genetic algorithm based optimization for the adsorption of methylene blue and brilliant green from aqueous solution by graphite oxide nanoparticle.

    PubMed

    Ghaedi, M; Zeinali, N; Ghaedi, A M; Teimuori, M; Tashkhourian, J

    2014-05-05

    In this study, graphite oxide (GO) nano according to Hummers method was synthesized and subsequently was used for the removal of methylene blue (MB) and brilliant green (BG). The detail information about the structure and physicochemical properties of GO are investigated by different techniques such as XRD and FTIR analysis. The influence of solution pH, initial dye concentration, contact time and adsorbent dosage was examined in batch mode and optimum conditions was set as pH=7.0, 2 mg of GO and 10 min contact time. Employment of equilibrium isotherm models for description of adsorption capacities of GO explore the good efficiency of Langmuir model for the best presentation of experimental data with maximum adsorption capacity of 476.19 and 416.67 for MB and BG dyes in single solution. The analysis of adsorption rate at various stirring times shows that both dyes adsorption followed a pseudo second-order kinetic model with cooperation with interparticle diffusion model. Subsequently, the adsorption data as new combination of artificial neural network was modeled to evaluate and obtain the real conditions for fast and efficient removal of dyes. A three-layer artificial neural network (ANN) model is applicable for accurate prediction of dyes removal percentage from aqueous solution by GO following conduction of 336 experimental data. The network was trained using the obtained experimental data at optimum pH with different GO amount (0.002-0.008 g) and 5-40 mg/L of both dyes over contact time of 0.5-30 min. The ANN model was able to predict the removal efficiency with Levenberg-Marquardt algorithm (LMA), a linear transfer function (purelin) at output layer and a tangent sigmoid transfer function (tansig) at hidden layer with 10 and 11 neurons for MB and BG dyes, respectively. The minimum mean squared error (MSE) of 0.0012 and coefficient of determination (R(2)) of 0.982 were found for prediction and modeling of MB removal, while the respective value for BG was the

  1. Artificial neural network-genetic algorithm based optimization for the adsorption of methylene blue and brilliant green from aqueous solution by graphite oxide nanoparticle

    NASA Astrophysics Data System (ADS)

    Ghaedi, M.; Zeinali, N.; Ghaedi, A. M.; Teimuori, M.; Tashkhourian, J.

    In this study, graphite oxide (GO) nano according to Hummers method was synthesized and subsequently was used for the removal of methylene blue (MB) and brilliant green (BG). The detail information about the structure and physicochemical properties of GO are investigated by different techniques such as XRD and FTIR analysis. The influence of solution pH, initial dye concentration, contact time and adsorbent dosage was examined in batch mode and optimum conditions was set as pH = 7.0, 2 mg of GO and 10 min contact time. Employment of equilibrium isotherm models for description of adsorption capacities of GO explore the good efficiency of Langmuir model for the best presentation of experimental data with maximum adsorption capacity of 476.19 and 416.67 for MB and BG dyes in single solution. The analysis of adsorption rate at various stirring times shows that both dyes adsorption followed a pseudo second-order kinetic model with cooperation with interparticle diffusion model. Subsequently, the adsorption data as new combination of artificial neural network was modeled to evaluate and obtain the real conditions for fast and efficient removal of dyes. A three-layer artificial neural network (ANN) model is applicable for accurate prediction of dyes removal percentage from aqueous solution by GO following conduction of 336 experimental data. The network was trained using the obtained experimental data at optimum pH with different GO amount (0.002-0.008 g) and 5-40 mg/L of both dyes over contact time of 0.5-30 min. The ANN model was able to predict the removal efficiency with Levenberg-Marquardt algorithm (LMA), a linear transfer function (purelin) at output layer and a tangent sigmoid transfer function (tansig) at hidden layer with 10 and 11 neurons for MB and BG dyes, respectively. The minimum mean squared error (MSE) of 0.0012 and coefficient of determination (R2) of 0.982 were found for prediction and modeling of MB removal, while the respective value for BG was the

  2. An artificial neural network based groundwater flow and transport simulator

    SciTech Connect

    Krom, T.D.; Rosbjerg, D.

    1998-07-01

    Artificial neural networks are investigated as a tool for the simulation of contaminant loss and recovery in three-dimensional heterogeneous groundwater flow and contaminant transport modeling. These methods have useful applications in expert system development, knowledge base development and optimization of groundwater pollution remediation. The numerical model runs used to develop the artificial neural networks can be re-used to develop artificial neural networks to address alternative optimization problems or changed formulations of the constraints and or objective function under optimization. Artificial neural networks have been analyzed with the goal of estimating objectives which normally require the use of traditional flow and transport codes: such as contaminant recovery, contaminant loss (unrecovered) and remediation failure. The inputs to the artificial neutral networks are variable pumping withdrawal rates at fairly unconstrained 3-D locations. A forward-feed backwards error propagation artificial neural network architecture is used. The significance of the size of the training set, network architecture, and network weight optimization algorithm with respect to the estimation accuracy and objective are shown to be important. Finally, the quality of the weight optimization is studied via cross-validation techniques. This is demonstrated to be a useful method for judging training performance for strongly under-described systems.

  3. Devices and circuits for nanoelectronic implementation of artificial neural networks

    NASA Astrophysics Data System (ADS)

    Turel, Ozgur

    Biological neural networks perform complicated information processing tasks at speeds better than conventional computers based on conventional algorithms. This has inspired researchers to look into the way these networks function, and propose artificial networks that mimic their behavior. Unfortunately, most artificial neural networks, either software or hardware, do not provide either the speed or the complexity of a human brain. Nanoelectronics, with high density and low power dissipation that it provides, may be used in developing more efficient artificial neural networks. This work consists of two major contributions in this direction. First is the proposal of the CMOL concept, hybrid CMOS-molecular hardware [1-8]. CMOL may circumvent most of the problems in posed by molecular devices, such as low yield, vet provide high active device density, ˜1012/cm 2. The second contribution is CrossNets, artificial neural networks that are based on CMOL. We showed that CrossNets, with their fault tolerance, exceptional speed (˜ 4 to 6 orders of magnitude faster than biological neural networks) can perform any task any artificial neural network can perform. Moreover, there is a hope that if their integration scale is increased to that of human cerebral cortex (˜ 1010 neurons and ˜ 1014 synapses), they may be capable of performing more advanced tasks.

  4. Mesh deformation based on artificial neural networks

    NASA Astrophysics Data System (ADS)

    Stadler, Domen; Kosel, Franc; Čelič, Damjan; Lipej, Andrej

    2011-09-01

    In the article a new mesh deformation algorithm based on artificial neural networks is introduced. This method is a point-to-point method, meaning that it does not use connectivity information for calculation of the mesh deformation. Two already known point-to-point methods, based on interpolation techniques, are also presented. In contrast to the two known interpolation methods, the new method does not require a summation over all boundary nodes for one displacement calculation. The consequence of this fact is a shorter computational time of mesh deformation, which is proven by different deformation tests. The quality of the deformed meshes with all three deformation methods was also compared. Finally, the generated and the deformed three-dimensional meshes were used in the computational fluid dynamics numerical analysis of a Francis water turbine. A comparison of the analysis results was made to prove the applicability of the new method in every day computation.

  5. Sleep scoring using artificial neural networks.

    PubMed

    Ronzhina, Marina; Janoušek, Oto; Kolářová, Jana; Nováková, Marie; Honzík, Petr; Provazník, Ivo

    2012-06-01

    Rapid development of computer technologies leads to the intensive automation of many different processes traditionally performed by human experts. One of the spheres characterized by the introduction of new high intelligence technologies substituting analysis performed by humans is sleep scoring. This refers to the classification task and can be solved - next to other classification methods - by use of artificial neural networks (ANN). ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparation of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present.

  6. Artificial neural network and medicine.

    PubMed

    Khan, Z H; Mohapatra, S K; Khodiar, P K; Ragu Kumar, S N

    1998-07-01

    The introduction of human brain functions such as perception and cognition into the computer has been made possible by the use of Artificial Neural Network (ANN). ANN are computer models inspired by the structure and behavior of neurons. Like the brain, ANN can recognize patterns, manage data and most significantly, learn. This learning ability, not seen in other computer models simulating human intelligence, constantly improves its functional accuracy as it keeps on performing. Experience is as important for an ANN as it is for man. It is being increasingly used to supplement and even (may be) replace experts, in medicine. However, there is still scope for improvement in some areas. Its ability to classify and interpret various forms of medical data comes as a helping hand to clinical decision making in both diagnosis and treatment. Treatment planning in medicine, radiotherapy, rehabilitation, etc. is being done using ANN. Morbidity and mortality prediction by ANN in different medical situations can be very helpful for hospital management. ANN has a promising future in fundamental research, medical education and surgical robotics.

  7. Implementation of artificial neural networks with optics

    NASA Astrophysics Data System (ADS)

    Yu, Francis T. S.

    1999-04-01

    Optical implementation of artificial neural nets (ANNs) with electronically addressable liquid crystal televisions (LCTVs) are presented. The major advantages of the proposed ANNs must be the low cost and the flexibility to operate. To test the performance, several artificial neural net models have been implemented in the LCTV ANNs. These models include the Hopfield, Interpattern Association, Hetero-association, and Unsupervised ANNs. System design considerations and experimental demonstrates are provided.

  8. Artificial Neural Network applied to lightning flashes

    NASA Astrophysics Data System (ADS)

    Gin, R. B.; Guedes, D.; Bianchi, R.

    2013-05-01

    The development of video cameras enabled cientists to study lightning discharges comportment with more precision. The main goal of this project is to create a system able to detect images of lightning discharges stored in videos and classify them using an Artificial Neural Network (ANN)using C Language and OpenCV libraries. The developed system, can be split in two different modules: detection module and classification module. The detection module uses OpenCV`s computer vision libraries and image processing techniques to detect if there are significant differences between frames in a sequence, indicating that something, still not classified, occurred. Whenever there is a significant difference between two consecutive frames, two main algorithms are used to analyze the frame image: brightness and shape algorithms. These algorithms detect both shape and brightness of the event, removing irrelevant events like birds, as well as detecting the relevant events exact position, allowing the system to track it over time. The classification module uses a neural network to classify the relevant events as horizontal or vertical lightning, save the event`s images and calculates his number of discharges. The Neural Network was implemented using the backpropagation algorithm, and was trained with 42 training images , containing 57 lightning events (one image can have more than one lightning). TheANN was tested with one to five hidden layers, with up to 50 neurons each. The best configuration achieved a success rate of 95%, with one layer containing 20 neurons (33 test images with 42 events were used in this phase). This configuration was implemented in the developed system to analyze 20 video files, containing 63 lightning discharges previously manually detected. Results showed that all the lightning discharges were detected, many irrelevant events were unconsidered, and the event's number of discharges was correctly computed. The neural network used in this project achieved a

  9. Artificial Neural Networks and Instructional Technology.

    ERIC Educational Resources Information Center

    Carlson, Patricia A.

    1991-01-01

    Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

  10. Dendroclimatic transfer functions revisited: Little Ice Age and Medieval Warm Period summer temperatures reconstructed using artificial neural networks and linear algorithms

    NASA Astrophysics Data System (ADS)

    Helama, S.; Makarenko, N. G.; Karimova, L. M.; Kruglun, O. A.; Timonen, M.; Holopainen, J.; Meriläinen, J.; Eronen, M.

    2009-03-01

    Tree-rings tell of past climates. To do so, tree-ring chronologies comprising numerous climate-sensitive living-tree and subfossil time-series need to be "transferred" into palaeoclimate estimates using transfer functions. The purpose of this study is to compare different types of transfer functions, especially linear and nonlinear algorithms. Accordingly, multiple linear regression (MLR), linear scaling (LSC) and artificial neural networks (ANN, nonlinear algorithm) were compared. Transfer functions were built using a regional tree-ring chronology and instrumental temperature observations from Lapland (northern Finland and Sweden). In addition, conventional MLR was compared with a hybrid model whereby climate was reconstructed separately for short- and long-period timescales prior to combining the bands of timescales into a single hybrid model. The fidelity of the different reconstructions was validated against instrumental climate data. The reconstructions by MLR and ANN showed reliable reconstruction capabilities over the instrumental period (AD 1802-1998). LCS failed to reach reasonable verification statistics and did not qualify as a reliable reconstruction: this was due mainly to exaggeration of the low-frequency climatic variance. Over this instrumental period, the reconstructed low-frequency amplitudes of climate variability were rather similar by MLR and ANN. Notably greater differences between the models were found over the actual reconstruction period (AD 802-1801). A marked temperature decline, as reconstructed by MLR, from the Medieval Warm Period (AD 931-1180) to the Little Ice Age (AD 1601-1850), was evident in all the models. This decline was approx. 0.5°C as reconstructed by MLR. Different ANN based palaeotemperatures showed simultaneous cooling of 0.2 to 0.5°C, depending on algorithm. The hybrid MLR did not seem to provide further benefit above conventional MLR in our sample. The robustness of the conventional MLR over the calibration

  11. Application of artificial neural network coupled with genetic algorithm and simulated annealing to solve groundwater inflow problem to an advancing open pit mine

    NASA Astrophysics Data System (ADS)

    Bahrami, Saeed; Doulati Ardejani, Faramarz; Baafi, Ernest

    2016-05-01

    In this study, hybrid models are designed to predict groundwater inflow to an advancing open pit mine and the hydraulic head (HH) in observation wells at different distances from the centre of the pit during its advance. Hybrid methods coupling artificial neural network (ANN) with genetic algorithm (GA) methods (ANN-GA), and simulated annealing (SA) methods (ANN-SA), were utilised. Ratios of depth of pit penetration in aquifer to aquifer thickness, pit bottom radius to its top radius, inverse of pit advance time and the HH in the observation wells to the distance of observation wells from the centre of the pit were used as inputs to the networks. To achieve the objective two hybrid models consisting of ANN-GA and ANN-SA with 4-5-3-1 arrangement were designed. In addition, by switching the last argument of the input layer with the argument of the output layer of two earlier models, two new models were developed to predict the HH in the observation wells for the period of the mining process. The accuracy and reliability of models are verified by field data, results of a numerical finite element model using SEEP/W, outputs of simple ANNs and some well-known analytical solutions. Predicted results obtained by the hybrid methods are closer to the field data compared to the outputs of analytical and simple ANN models. Results show that despite the use of fewer and simpler parameters by the hybrid models, the ANN-GA and to some extent the ANN-SA have the ability to compete with the numerical models.

  12. Isotherm and kinetics study of malachite green adsorption onto copper nanowires loaded on activated carbon: artificial neural network modeling and genetic algorithm optimization.

    PubMed

    Ghaedi, M; Shojaeipour, E; Ghaedi, A M; Sahraei, Reza

    2015-05-05

    In this study, copper nanowires loaded on activated carbon (Cu-NWs-AC) was used as novel efficient adsorbent for the removal of malachite green (MG) from aqueous solution. This new material was synthesized through simple protocol and its surface properties such as surface area, pore volume and functional groups were characterized with different techniques such XRD, BET and FESEM analysis. The relation between removal percentages with variables such as solution pH, adsorbent dosage (0.005, 0.01, 0.015, 0.02 and 0.1g), contact time (1-40min) and initial MG concentration (5, 10, 20, 70 and 100mg/L) was investigated and optimized. A three-layer artificial neural network (ANN) model was utilized to predict the malachite green dye removal (%) by Cu-NWs-AC following conduction of 248 experiments. When the training of the ANN was performed, the parameters of ANN model were as follows: linear transfer function (purelin) at output layer, Levenberg-Marquardt algorithm (LMA), and a tangent sigmoid transfer function (tansig) at the hidden layer with 11 neurons. The minimum mean squared error (MSE) of 0.0017 and coefficient of determination (R(2)) of 0.9658 were found for prediction and modeling of dye removal using testing data set. A good agreement between experimental data and predicted data using the ANN model was obtained. Fitting the experimental data on previously optimized condition confirm the suitability of Langmuir isotherm models for their explanation with maximum adsorption capacity of 434.8mg/g at 25°C. Kinetic studies at various adsorbent mass and initial MG concentration show that the MG maximum removal percentage was achieved within 20min. The adsorption of MG follows the pseudo-second-order with a combination of intraparticle diffusion model.

  13. Isotherm and kinetics study of malachite green adsorption onto copper nanowires loaded on activated carbon: Artificial neural network modeling and genetic algorithm optimization

    NASA Astrophysics Data System (ADS)

    Ghaedi, M.; Shojaeipour, E.; Ghaedi, A. M.; Sahraei, Reza

    2015-05-01

    In this study, copper nanowires loaded on activated carbon (Cu-NWs-AC) was used as novel efficient adsorbent for the removal of malachite green (MG) from aqueous solution. This new material was synthesized through simple protocol and its surface properties such as surface area, pore volume and functional groups were characterized with different techniques such XRD, BET and FESEM analysis. The relation between removal percentages with variables such as solution pH, adsorbent dosage (0.005, 0.01, 0.015, 0.02 and 0.1 g), contact time (1-40 min) and initial MG concentration (5, 10, 20, 70 and 100 mg/L) was investigated and optimized. A three-layer artificial neural network (ANN) model was utilized to predict the malachite green dye removal (%) by Cu-NWs-AC following conduction of 248 experiments. When the training of the ANN was performed, the parameters of ANN model were as follows: linear transfer function (purelin) at output layer, Levenberg-Marquardt algorithm (LMA), and a tangent sigmoid transfer function (tansig) at the hidden layer with 11 neurons. The minimum mean squared error (MSE) of 0.0017 and coefficient of determination (R2) of 0.9658 were found for prediction and modeling of dye removal using testing data set. A good agreement between experimental data and predicted data using the ANN model was obtained. Fitting the experimental data on previously optimized condition confirm the suitability of Langmuir isotherm models for their explanation with maximum adsorption capacity of 434.8 mg/g at 25 °C. Kinetic studies at various adsorbent mass and initial MG concentration show that the MG maximum removal percentage was achieved within 20 min. The adsorption of MG follows the pseudo-second-order with a combination of intraparticle diffusion model.

  14. D Coordinate Transformation Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Konakoglu, B.; Cakır, L.; Gökalp, E.

    2016-10-01

    Two coordinate systems used in Turkey, namely the ED50 (European Datum 1950) and ITRF96 (International Terrestrial Reference Frame 1996) coordinate systems. In most cases, it is necessary to conduct transformation from one coordinate system to another. The artificial neural network (ANN) is a new method for coordinate transformation. One of the biggest advantages of the ANN is that it can determine the relationship between two coordinate systems without a mathematical model. The aim of this study was to investigate the performances of three different ANN models (Feed Forward Back Propagation (FFBP), Cascade Forward Back Propagation (CFBP) and Radial Basis Function Neural Network (RBFNN)) with regard to 2D coordinate transformation. To do this, three data sets were used for the same study area, the city of Trabzon. The coordinates of data sets were measured in the ED50 and ITRF96 coordinate systems by using RTK-GPS technique. Performance of each transformation method was investigated by using the coordinate differences between the known and estimated coordinates. The results showed that the ANN algorithms can be used for 2D coordinate transformation in cases where optimum model parameters are selected.

  15. An artificial neural network for wavelet steganalysis

    NASA Astrophysics Data System (ADS)

    Davidson, Jennifer; Bergman, Clifford; Bartlett, Eric

    2005-08-01

    Hiding messages in image data, called steganography, is used for both legal and illicit purposes. The detection of hidden messages in image data stored on websites and computers, called steganalysis, is of prime importance to cyber forensics personnel. Automating the detection of hidden messages is a requirement, since the shear amount of image data stored on computers or websites makes it impossible for a person to investigate each image separately. This paper describes research on a prototype software system that automatically classifies an image as having hidden information or not, using a sophisticated artificial neural network (ANN) system. An ANN software package, the ISU ACL NetWorks Toolkit, is trained on a selection of image features that distinguish between stego and nonstego images. The novelty of this ANN is that it is a blind classifier that gives more accurate results than previous systems. It can detect messages hidden using a variety of different types of embedding algorithms. A Graphical User Interface (GUI) combines the ANN, feature selection, and embedding algorithms into a prototype software package that is not currently available to the cyber forensics community.

  16. Artificial Neural Network Analysis System

    DTIC Science & Technology

    2007-11-02

    Target detection, multi-target tracking and threat identification of ICBM and its warheads by sensor fusion and data fusion of sensors in a fuzzy neural network system based on the compound eye of a fly.

  17. Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock.

    PubMed

    Arab, Mohammad M; Yadollahi, Abbas; Shojaeiyan, Abdolali; Ahmadi, Hamed

    2016-01-01

    One of the major obstacles to the micropropagation of Prunus rootstocks has, up until now, been the lack of a suitable tissue culture medium. Therefore, reformulation of culture media or modification of the mineral content might be a breakthrough to improve in vitro multiplication of G × N15 (garnem). We found artificial neural network in combination of genetic algorithm (ANN-GA) as a very precise and powerful modeling system for optimizing the culture medium, So that modeling the effects of MS mineral salts ([Formula: see text], [Formula: see text], [Formula: see text], Ca(2+), K(+), [Formula: see text], Mg(2+), and Cl(-)) on in vitro multiplication parameters (the number of microshoots per explant, average length of microshoots, weight of calluses derived from the base of stem explants, and quality index of plantlets) of G × N15. Showed high R(2) correlation values of 87, 91, 87, and 74 between observed and predicted values were found for these four growth parameters, respectively. According to the ANN-GA results, among the input variables, [Formula: see text] and [Formula: see text] had the highest values of VSR in data set for the parameters studied. The ANN-GA showed that the best proliferation rate was obtained from medium containing (mM) 27.5 [Formula: see text], 14 [Formula: see text], 5 Ca(2+), 25.9 K(+), 0.7 Mg(2+), 1.1 [Formula: see text], 4.7 [Formula: see text], and 0.96 Cl(-). The performance of the medium optimized by ANN-GA, denoted as YAS (Yadollahi, Arab and Shojaeiyan), was compared to that of standard growth media for all Prunus rootstock, including the Murashige and Skoog (MS) medium, (specific media) EM, Quoirin and Lepoivre (QL) medium, and woody plant medium (WPM) Prunus. With respect to shoot length, shoot number per cultured explant and productivity (number of microshoots × length of microshoots), YAS was found to be superior to other media for in vitro multiplication of G × N15 rootstocks. In addition, our results indicated that by

  18. Artificial Neural Network Genetic Algorithm As Powerful Tool to Predict and Optimize In vitro Proliferation Mineral Medium for G × N15 Rootstock

    PubMed Central

    Arab, Mohammad M.; Yadollahi, Abbas; Shojaeiyan, Abdolali; Ahmadi, Hamed

    2016-01-01

    One of the major obstacles to the micropropagation of Prunus rootstocks has, up until now, been the lack of a suitable tissue culture medium. Therefore, reformulation of culture media or modification of the mineral content might be a breakthrough to improve in vitro multiplication of G × N15 (garnem). We found artificial neural network in combination of genetic algorithm (ANN-GA) as a very precise and powerful modeling system for optimizing the culture medium, So that modeling the effects of MS mineral salts (NH4+, NO3-, PO42-, Ca2+, K+, SO42-, Mg2+, and Cl−) on in vitro multiplication parameters (the number of microshoots per explant, average length of microshoots, weight of calluses derived from the base of stem explants, and quality index of plantlets) of G × N15. Showed high R2 correlation values of 87, 91, 87, and 74 between observed and predicted values were found for these four growth parameters, respectively. According to the ANN-GA results, among the input variables, NH4+ and NO3- had the highest values of VSR in data set for the parameters studied. The ANN-GA showed that the best proliferation rate was obtained from medium containing (mM) 27.5 NO3-, 14 NH4+, 5 Ca2+, 25.9 K+, 0.7 Mg2+, 1.1 PO42-, 4.7 SO42-, and 0.96 Cl−. The performance of the medium optimized by ANN-GA, denoted as YAS (Yadollahi, Arab and Shojaeiyan), was compared to that of standard growth media for all Prunus rootstock, including the Murashige and Skoog (MS) medium, (specific media) EM, Quoirin and Lepoivre (QL) medium, and woody plant medium (WPM) Prunus. With respect to shoot length, shoot number per cultured explant and productivity (number of microshoots × length of microshoots), YAS was found to be superior to other media for in vitro multiplication of G × N15 rootstocks. In addition, our results indicated that by using ANN-GA, we were able to determine a suitable culture medium formulation to achieve the best in vitro productivity. PMID:27807436

  19. Adaptive evolutionary artificial neural networks for pattern classification.

    PubMed

    Oong, Tatt Hee; Isa, Nor Ashidi Mat

    2011-11-01

    This paper presents a new evolutionary approach called the hybrid evolutionary artificial neural network (HEANN) for simultaneously evolving an artificial neural networks (ANNs) topology and weights. Evolutionary algorithms (EAs) with strong global search capabilities are likely to provide the most promising region. However, they are less efficient in fine-tuning the search space locally. HEANN emphasizes the balancing of the global search and local search for the evolutionary process by adapting the mutation probability and the step size of the weight perturbation. This is distinguishable from most previous studies that incorporate EA to search for network topology and gradient learning for weight updating. Four benchmark functions were used to test the evolutionary framework of HEANN. In addition, HEANN was tested on seven classification benchmark problems from the UCI machine learning repository. Experimental results show the superior performance of HEANN in fine-tuning the network complexity within a small number of generations while preserving the generalization capability compared with other algorithms.

  20. Time series prediction using artificial neural network for power stabilization

    SciTech Connect

    Puranik, G.; Philip, T.; Nail, B.

    1996-12-31

    Time series prediction has been applied to many business and scientific applications. Prominent among them are stock market prediction, weather forecasting, etc. Here, this technique has been applied to forecast plasma torch voltages to stabilize power using a backpropagation, a model of artificial neural network. The Extended-Delta-Bar-Delta algorithm is used to improve the convergence rate of the network and also to avoid local minima. Results from off-line data was quite promising to use in on-line.

  1. Artificial neural network modeling of p-cresol photodegradation

    PubMed Central

    2013-01-01

    Background The complexity of reactions and kinetic is the current problem of photodegradation processes. Recently, artificial neural networks have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study, an artificial neural network was applied for modeling p-cresol photodegradation. To optimize the network, the independent variables including irradiation time, pH, photocatalyst amount and concentration of p-cresol were used as the input parameters, while the photodegradation% was selected as output. The photodegradation% was obtained from the performance of the experimental design of the variables under UV irradiation. The network was trained by Quick propagation (QP) and the other three algorithms as a model. To determine the number of hidden layer nodes in the model, the root mean squared error of testing set was minimized. After minimizing the error, the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. Results The comparison indicated that the Quick propagation algorithm had minimum root mean squared error, 1.3995, absolute average deviation, 3.0478, and maximum coefficient of determination, 0.9752, for the testing data set. The validation test results of the artificial neural network based on QP indicated that the root mean squared error was 4.11, absolute average deviation was 8.071 and the maximum coefficient of determination was 0.97. Conclusion Artificial neural network based on Quick propagation algorithm with topology 4-10-1 gave the best performance in this study. PMID:23731706

  2. Forecasting Zakat collection using artificial neural network

    NASA Astrophysics Data System (ADS)

    Sy Ahmad Ubaidillah, Sh. Hafizah; Sallehuddin, Roselina

    2013-04-01

    'Zakat', "that which purifies" or "alms", is the giving of a fixed portion of one's wealth to charity, generally to the poor and needy. It is one of the five pillars of Islam, and must be paid by all practicing Muslims who have the financial means (nisab). 'Nisab' is the minimum level to determine whether there is a 'zakat' to be paid on the assets. Today, in most Muslim countries, 'zakat' is collected through a decentralized and voluntary system. Under this voluntary system, 'zakat' committees are established, which are tasked with the collection and distribution of 'zakat' funds. 'Zakat' promotes a more equitable redistribution of wealth, and fosters a sense of solidarity amongst members of the 'Ummah'. The Malaysian government has established a 'zakat' center at every state to facilitate the management of 'zakat'. The center has to have a good 'zakat' management system to effectively execute its functions especially in the collection and distribution of 'zakat'. Therefore, a good forecasting model is needed. The purpose of this study is to develop a forecasting model for Pusat Zakat Pahang (PZP) to predict the total amount of collection from 'zakat' of assets more precisely. In this study, two different Artificial Neural Network (ANN) models using two different learning algorithms are developed; Back Propagation (BP) and Levenberg-Marquardt (LM). Both models are developed and compared in terms of their accuracy performance. The best model is determined based on the lowest mean square error and the highest correlations values. Based on the results obtained from the study, BP neural network is recommended as the forecasting model to forecast the collection from 'zakat' of assets for PZP.

  3. Automated Wildfire Detection Through Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

    2005-01-01

    We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.

  4. Psychometric Measurement Models and Artificial Neural Networks

    ERIC Educational Resources Information Center

    Sese, Albert; Palmer, Alfonso L.; Montano, Juan J.

    2004-01-01

    The study of measurement models in psychometrics by means of dimensionality reduction techniques such as Principal Components Analysis (PCA) is a very common practice. In recent times, an upsurge of interest in the study of artificial neural networks apt to computing a principal component extraction has been observed. Despite this interest, the…

  5. Chaotic time series prediction using artificial neural networks

    SciTech Connect

    Bartlett, E.B.

    1991-12-31

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  6. Chaotic time series prediction using artificial neural networks

    SciTech Connect

    Bartlett, E.B.

    1991-01-01

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  7. [Application of artificial neural networks in infectious diseases].

    PubMed

    Xu, Jun-fang; Zhou, Xiao-nong

    2011-02-28

    With the development of information technology, artificial neural networks has been applied to many research fields. Due to the special features such as nonlinearity, self-adaptation, and parallel processing, artificial neural networks are applied in medicine and biology. This review summarizes the application of artificial neural networks in the relative factors, prediction and diagnosis of infectious diseases in recent years.

  8. Are artificial neural networks black boxes?

    PubMed

    Benitez, J M; Castro, J L; Requena, I

    1997-01-01

    Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Notwithstanding, one of the major criticisms is their being black boxes, since no satisfactory explanation of their behavior has been offered. In this paper, we provide such an interpretation of neural networks so that they will no longer be seen as black boxes. This is stated after establishing the equality between a certain class of neural nets and fuzzy rule-based systems. This interpretation is built with fuzzy rules using a new fuzzy logic operator which is defined after introducing the concept of f-duality. In addition, this interpretation offers an automated knowledge acquisition procedure.

  9. Pruning Neural Networks with Distribution Estimation Algorithms

    SciTech Connect

    Cantu-Paz, E

    2003-01-15

    This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algorithm (GA), the paper considers three distribution estimation algorithms (DEAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the DEAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a feed forward neural network trained with standard back propagation and public-domain and artificial data sets. The pruned networks seemed to have better or equal accuracy than the original fully-connected networks. Only in a few cases, pruning resulted in less accurate networks. We found few differences in the accuracy of the networks pruned by the four EAs, but found important differences in the execution time. The results suggest that a simple GA with a small population might be the best algorithm for pruning networks on the data sets we tested.

  10. Video data compression using artificial neural network differential vector quantization

    NASA Technical Reports Server (NTRS)

    Krishnamurthy, Ashok K.; Bibyk, Steven B.; Ahalt, Stanley C.

    1991-01-01

    An artificial neural network vector quantizer is developed for use in data compression applications such as Digital Video. Differential Vector Quantization is used to preserve edge features, and a new adaptive algorithm, known as Frequency-Sensitive Competitive Learning, is used to develop the vector quantizer codebook. To develop real time performance, a custom Very Large Scale Integration Application Specific Integrated Circuit (VLSI ASIC) is being developed to realize the associative memory functions needed in the vector quantization algorithm. By using vector quantization, the need for Huffman coding can be eliminated, resulting in superior performance against channel bit errors than methods that use variable length codes.

  11. Artificial metaplasticity neural network applied to credit scoring.

    PubMed

    Marcano-Cedeño, Alexis; Marin-de-la-Barcena, A; Jimenez-Trillo, J; Piñuela, J A; Andina, D

    2011-08-01

    The assessment of the risk of default on credit is important for financial institutions. Different Artificial Neural Networks (ANN) have been suggested to tackle the credit scoring problem, however, the obtained error rates are often high. In the search for the best ANN algorithm for credit scoring, this paper contributes with the application of an ANN Training Algorithm inspired by the neurons' biological property of metaplasticity. This algorithm is especially efficient when few patterns of a class are available, or when information inherent to low probability events is crucial for a successful application, as weight updating is overemphasized in the less frequent activations than in the more frequent ones. Two well-known and readily available such as: Australia and German data sets has been used to test the algorithm. The results obtained by AMMLP shown have been superior to state-of-the-art classification algorithms in credit scoring.

  12. Predicting stream water quality using artificial neural networks (ANN)

    SciTech Connect

    Bowers, J.A.

    2000-05-17

    Predicting point and nonpoint source runoff of dissolved and suspended materials into their receiving streams is important to protecting water quality and traditionally has been modeled using deterministic or statistical methods. The purpose of this study was to predict water quality in small streams using an Artificial Neural Network (ANN). The selected input variables were local precipitation, stream flow rates and turbidity for the initial prediction of suspended solids in the stream. A single hidden-layer feedforward neural network using backpropagation learning algorithms was developed with a detailed analysis of model design of those factors affecting successful implementation of the model. All features of a feedforward neural model were investigated including training set creation, number and layers of neurons, neural activation functions, and backpropagation algorithms. Least-squares regression was used to compare model predictions with test data sets. Most of the model configurations offered excellent predictive capabilities. Using either the logistic or the hyperbolic tangent neural activation function did not significantly affect predicted results. This was also true for the two learning algorithms tested, the Levenberg-Marquardt and Polak-Ribiere conjugate-gradient descent methods. The most important step during model development and training was the representative selection of data records for training of the model.

  13. Development of programmable artificial neural networks

    NASA Technical Reports Server (NTRS)

    Meade, Andrew J.

    1993-01-01

    Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed to mate the adaptability of the ANN with the speed and precision of the digital computer. This method was successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

  14. Non-Intrusive Gaze Tracking Using Artificial Neural Networks

    DTIC Science & Technology

    1994-01-05

    Artificial Neural Networks Shumeet Baluja & Dean...this paper appear in: Baluja, S. & Pomerleau, D.A. "Non-Intrusive Gaze Tracking Using Artificial Neural Networks ", Advances in Neural Information...document hLc-s been opproved t0T 011bhiC leleWOe cad ý’ir/4 its di stT-b’ution Ls •_nii•ite6. - Keywords Gaze Tracking, Artificial Neural Networks ,

  15. Analysis of Wideband Beamformers Designed with Artificial Neural Networks

    DTIC Science & Technology

    1990-12-01

    TECHNICAL REPORT 0-90-1 ANALYSIS OF WIDEBAND BEAMFORMERS DESIGNED WITH ARTIFICIAL NEURAL NETWORKS by Cary Cox Instrumentation Services Division...included. A briel tutorial on beamformers and neural networks is also provided. 14. SUBJECT TERMS 15, NUMBER OF PAGES Artificial neural networks Fecdforwa:,l...Beamformers Designed with Artificial Neural Networks ". The study was conducted under the general supervision of Messrs. George P. Bonner, Chief

  16. Artificial neural network cardiopulmonary modeling and diagnosis

    DOEpatents

    Kangas, L.J.; Keller, P.E.

    1997-10-28

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis. 12 figs.

  17. Artificial neural network cardiopulmonary modeling and diagnosis

    DOEpatents

    Kangas, Lars J.; Keller, Paul E.

    1997-01-01

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

  18. Medical image analysis with artificial neural networks.

    PubMed

    Jiang, J; Trundle, P; Ren, J

    2010-12-01

    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging.

  19. Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks.

    PubMed

    Erić, Slavica; Kalinić, Marko; Popović, Aleksandar; Zloh, Mire; Kuzmanovski, Igor

    2012-11-01

    In this work, we present a novel approach for the development of models for prediction of aqueous solubility, based on the implementation of an algorithm for the automatic adjustment of descriptor's relative importance (AARI) in counter-propagation artificial neural networks (CPANN). Using this approach, the interpretability of the models based on artificial neural networks, which are traditionally considered as "black box" models, was significantly improved. For the development of the model, a data set consisting of 374 diverse drug-like molecules, divided into training (n=280) and test (n=94) sets using self-organizing maps, was used. Heuristic method was applied in preselecting a small number of the most significant descriptors to serve as inputs for CPANN training. The performances of the final model based on 7 descriptors for prediction of solubility were satisfactory for both training (RMSEP(train)=0.668) and test set (RMSEP(test)=0.679). The model was found to be a highly interpretable in terms of solubility, as well as rationalizing structural features that could have an impact on the solubility of the compounds investigated. Therefore, the proposed approach can significantly enhance model usability by giving guidance for structural modifications of compounds with the aim of improving solubility in the early phase of drug discovery.

  20. Establishment of turbidity forecasting model and early-warning system for source water turbidity management using back-propagation artificial neural network algorithm and probability analysis.

    PubMed

    Yang, Tsung-Ming; Fan, Shu-Kai; Fan, Chihhao; Hsu, Nien-Sheng

    2014-08-01

    The purpose of this study is to establish a turbidity forecasting model as well as an early-warning system for turbidity management using rainfall records as the input variables. The Taipei Water Source Domain was employed as the study area, and ANOVA analysis showed that the accumulative rainfall records of 1-day Ping-lin, 2-day Ping-lin, 2-day Fei-tsui, 2-day Shi-san-gu, 2-day Tai-pin and 2-day Tong-hou were the six most significant parameters for downstream turbidity development. The artificial neural network model was developed and proven capable of predicting the turbidity concentration in the investigated catchment downstream area. The observed and model-calculated turbidity data were applied to developing the turbidity early-warning system. Using a previously determined turbidity as the threshold, the rainfall criterion, above which the downstream turbidity would possibly exceed this respective threshold turbidity, for the investigated rain gauge stations was determined. An exemplary illustration demonstrated the effectiveness of the proposed turbidity early-warning system as a precautionary alarm of possible significant increase of downstream turbidity. This study is the first report of the establishment of the turbidity early-warning system. Hopefully, this system can be applied to source water turbidity forecasting during storm events and provide a useful reference for subsequent adjustment of drinking water treatment operation.

  1. Modeling of mass transfer of Phospholipids in separation process with supercritical CO2 fluid by RBF artificial neural networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    An artificial Radial Basis Function (RBF) neural network model was developed for the prediction of mass transfer of the phospholipids from canola meal in supercritical CO2 fluid. The RBF kind of artificial neural networks (ANN) with orthogonal least squares (OLS) learning algorithm were used for mod...

  2. Adaptive conventional power system stabilizer based on artificial neural network

    SciTech Connect

    Kothari, M.L.; Segal, R.; Ghodki, B.K.

    1995-12-31

    This paper deals with an artificial neural network (ANN) based adaptive conventional power system stabilizer (PSS). The ANN comprises an input layer, a hidden layer and an output layer. The input vector to the ANN comprises real power (P) and reactive power (Q), while the output vector comprises optimum PSS parameters. A systematic approach for generating training set covering wide range of operating conditions, is presented. The ANN has been trained using back-propagation training algorithm. Investigations reveal that the dynamic performance of ANN based adaptive conventional PSS is quite insensitive to wide variations in loading conditions.

  3. An Evaluation of Artificial Neural Network Modeling for Manpower Analysis

    DTIC Science & Technology

    1993-09-01

    This thesis evaluates the capabilities of artificial neural networks in forecasting the take-rates of the Voluntary Separations Incentive/Special...Separations Benefit (VSI/SSB) programs for male, Marine Corps Enlisted Personnel in the grades of E-5 and E-6. The Artificial Neural Networks models are...results indicate that artificial neural networks provide forecasting results at least as good as, if not better than, those obtained using classical

  4. Improved Landmine Detection by Complex-Valued Artificial Neural Networks

    DTIC Science & Technology

    2002-12-04

    IMPROVED LANDMINE DETECTION BY COMPLEX-VALUED ARTIFICIAL NEURAL NETWORKS Research was Sponsored by: U. S. ARMY RESEARCH OFFICE Program Manager... artificial neural networks in conjunction with fuzzy logic for improved system performance over and above the good results already attained are...of detecting mines. One of the more promising avenues of research in this area involves the use of artificial neural networks [3]. More specifically

  5. Hadamard design and artificial neural nets

    SciTech Connect

    Kuerten, K.E. Universitaet Wien ); Klingen, N. )

    1993-04-01

    Hadamard theory is shown to play an important role in the generation of Boolean decision functions, a fundamental tool in the field of artificial neural network design. Based on a group-theoretic introduction of a complete set of Hadamard vectors, whose matrices are of the order of a power of two, the authors classify subsets according to the degree of their linear dependence. They show in the thermodynamic limit that essentially the whole Hadamard space is occupied by representatives with defect not exceeding two or three. 15 refs., 1 fig.

  6. Digital Image Compression Using Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Serra-Ricart, M.; Garrido, L.; Gaitan, V.; Aloy, A.

    1993-01-01

    The problem of storing, transmitting, and manipulating digital images is considered. Because of the file sizes involved, large amounts of digitized image information are becoming common in modern projects. Our goal is to described an image compression transform coder based on artificial neural networks techniques (NNCTC). A comparison of the compression results obtained from digital astronomical images by the NNCTC and the method used in the compression of the digitized sky survey from the Space Telescope Science Institute based on the H-transform is performed in order to assess the reliability of the NNCTC.

  7. Limited-angle tomography using artificial neural network

    NASA Astrophysics Data System (ADS)

    Yau, Sze-Fong; Wong, Shun-Him

    1996-03-01

    This paper considers the problem of limited angle tomography in which a complete sinogram is not available. This situation arises in many practical applications where tomographic projection over 180 degrees is either physically unrealizable or infeasible. When a complete sinogram is not available, it is well known that the reconstructed images using common reconstruction algorithms, such as convolution back projection (CBP), will have severe streak artifacts. In this paper, we present a linear artificial neural network to extrapolate the missing part of the sinogram. Once the complete sinogram is obtained via extrapolation, standard reconstruction techniques such as CBP can be used to generate artifact free reconstructions. The parameters of the neural network are designed using the sampling theory of signals with non-compact spectral support, the knowledge that complete sinograms have bowtie-shaped spectral support, and regularization. It is found that once designed, these parameters are data independent, especially for images of similar nature. For sinogram with 2N angular views, each having M raysum per view, if 2L views are available, the computational requirement of the neural network is 4MNL only. Hence, it is much more efficient than other iterative algorithms such as the method of projection onto convex sets, the Papoulis-Gerchberg's algorithm and the Clark-Palmer-Lawrence interpolation method.

  8. Pattern recognition, neural networks, and artificial intelligence

    NASA Astrophysics Data System (ADS)

    Bezdek, James C.

    1991-03-01

    We write about the relationship between numerical patten recognition and neural-like computation networks. Extensive research that proposes the use of neural models for a wide variety of applications has been conducted in the past few years. Sometimes justification for investigating the potential of neural nets (NNs) is obvious. On the other hand, current enthusiasm for this approach has also led to the use of neural models when the apparent rationale for their use has been justified by what is best described as 'feeding frenzy'. In this latter instance there is at times concomitant lack of concern about many 'side issues' connected with algorithms (e.g., complexity, convergence, stability, robustness and performance validation) that need attention before any computational model becomes part of an operation system. These issues are examined with a view towards guessing how best to integrate and exploit the promise of the neural approach with there efforts aimed at advancing the art and science of pattern recognition and its applications in fielded systems in the next decade.

  9. [Use of artificial neural networks in clinical psychology and psychiatry].

    PubMed

    Starzomska, Małgorzata

    2003-01-01

    Artificial neural networks make a highly specialised tools in data transformation. The human brain has become an inspiration for the makers of artificial neural networks. Although even though artificial neural networks are more frequently used in areas like financial analysis, marketing studies or economical modelling, their application in psychology and medicine has given a lot of promising and fascinating discoveries. It is worth that artificial neurol networks are successfully used in the diagnosis and etiopathogenesis description of various psychiatric disorders such as eating disorders, compulsions, depression or schizophrenia. To sum up, artificial neural networks offer a very promising option of research methodology for modern clinical psychology and psychiatry. The aim of this article is only an illustration of the applications of artificial neural networks in clinical psychology and psychiatry.

  10. Pruning artificial neural networks using neural complexity measures.

    PubMed

    Jorgensen, Thomas D; Haynes, Barry P; Norlund, Charlotte C F

    2008-10-01

    This paper describes a new method for pruning artificial neural networks, using a measure of the neural complexity of the neural network. This measure is used to determine the connections that should be pruned. The measure computes the information-theoretic complexity of a neural network, which is similar to, yet different from previous research on pruning. The method proposed here shows how overly large and complex networks can be reduced in size, whilst retaining learnt behaviour and fitness. The technique proposed here helps to discover a network topology that matches the complexity of the problem it is meant to solve. This novel pruning technique is tested in a robot control domain, simulating a racecar. It is shown, that the proposed pruning method is a significant improvement over the most commonly used pruning method Magnitude Based Pruning. Furthermore, some of the pruned networks prove to be faster learners than the benchmark network that they originate from. This means that this pruning method can also help to unleash hidden potential in a network, because the learning time decreases substantially for a pruned a network, due to the reduction of dimensionality of the network.

  11. Artificial Neural Networks Applications: from Aircraft Design Optimization to Orbiting Spacecraft On-board Environment Monitoring

    NASA Technical Reports Server (NTRS)

    Jules, Kenol; Lin, Paul P.

    2002-01-01

    This paper reviews some of the recent applications of artificial neural networks taken from various works performed by the authors over the last four years at the NASA Glenn Research Center. This paper focuses mainly on two areas. First, artificial neural networks application in design and optimization of aircraft/engine propulsion systems to shorten the overall design cycle. Out of that specific application, a generic design tool was developed, which can be used for most design optimization process. Second, artificial neural networks application in monitoring the microgravity quality onboard the International Space Station, using on-board accelerometers for data acquisition. These two different applications are reviewed in this paper to show the broad applicability of artificial intelligence in various disciplines. The intent of this paper is not to give in-depth details of these two applications, but to show the need to combine different artificial intelligence techniques or algorithms in order to design an optimized or versatile system.

  12. Analog Very Large Scale Integration (VLSI) Implementations of Artificial Neural Networks

    DTIC Science & Technology

    1992-09-01

    There has been a recent resurgence of interest in the multi- disciplinary field of artificial neural networks . Artificial neural networks , originally...e.g., backpropagation, hopfield, bidirectional associative memories, etc.). Artificial Neural Networks , Analog VLSI.

  13. Combination of uniform design with artificial neural network coupling genetic algorithm: an effective way to obtain high yield of biomass and algicidal compound of a novel HABs control actinomycete.

    PubMed

    Cai, Guanjing; Zheng, Wei; Yang, Xujun; Zhang, Bangzhou; Zheng, Tianling

    2014-05-24

    Controlling harmful algae blooms (HABs) using microbial algicides is cheap, efficient and environmental-friendly. However, obtaining high yield of algicidal microbes to meet the need of field test is still a big challenge since qualitative and quantitative analysis of algicidal compounds is difficult. In this study, we developed a protocol to increase the yield of both biomass and algicidal compound present in a novel algicidal actinomycete Streptomyces alboflavus RPS, which kills Phaeocystis globosa. To overcome the problem in algicidal compound quantification, we chose algicidal ratio as the index and used artificial neural network to fit the data, which was appropriate for this nonlinear situation. In this protocol, we firstly determined five main influencing factors through single factor experiments and generated the multifactorial experimental groups with a U15(155) uniform-design-table. Then, we used the traditional quadratic polynomial stepwise regression model and an accurate, fully optimized BP-neural network to simulate the fermentation. Optimized with genetic algorithm and verified using experiments, we successfully increased the algicidal ratio of the fermentation broth by 16.90% and the dry mycelial weight by 69.27%. These results suggested that this newly developed approach is a viable and easy way to optimize the fermentation conditions for algicidal microorganisms.

  14. Automatic segmentation of cerebral MR images using artificial neural networks

    SciTech Connect

    Alirezaie, J.; Jernigan, M.E.; Nahmias, C.

    1996-12-31

    In this paper we present an unsupervised clustering technique for multispectral segmentation of magnetic resonance (MR) images of the human brain. Our scheme utilizes the Self Organizing Feature Map (SOFM) artificial neural network for feature mapping and generates a set of codebook vectors. By extending the network with an additional layer the map will be classified and each tissue class will be labelled. An algorithm has been developed for extracting the cerebrum from the head scan prior to the segmentation. Extracting the cerebrum is performed by stripping away the skull pixels from the T2 image. Three tissue types of the brain: white matter, gray matter and cerebral spinal fluid (CSF) are segmented accurately. To compare the results with other conventional approaches we applied the c-means algorithm to the problem.

  15. Unique Applications for Artificial Neural Networks. Phase 1

    DTIC Science & Technology

    1991-08-08

    AD-A243 365’ l!1111iLI[li In M aR C ’ PHASE I FINAL REPORT Unique Applications for Artificial Neural Networks DARPA SBIR 90-115 Contract # DAAH01-91...Contents Unique Applications for Artificial Neural Networks Acknowledgments Table of Contents Abstract i 1.0 Introduction 1 2.0 The NGO-VRP Solver 2...34 solution is thus obtained through analogy. Because of this activity, artificial neural networks have emerged as a primary artificial intelligence

  16. Artificial neural network for multifunctional areas.

    PubMed

    Riccioli, Francesco; El Asmar, Toufic; El Asmar, Jean-Pierre; Fagarazzi, Claudio; Casini, Leonardo

    2016-01-01

    The issues related to the appropriate planning of the territory are particularly pronounced in highly inhabited areas (urban areas), where in addition to protecting the environment, it is important to consider an anthropogenic (urban) development placed in the context of sustainable growth. This work aims at mathematically simulating the changes in the land use, by implementing an artificial neural network (ANN) model. More specifically, it will analyze how the increase of urban areas will develop and whether this development would impact on areas with particular socioeconomic and environmental value, defined as multifunctional areas. The simulation is applied to the Chianti Area, located in the province of Florence, in Italy. Chianti is an area with a unique landscape, and its territorial planning requires a careful examination of the territory in which it is inserted.

  17. Optimization of a fermentation medium using neural networks and genetic algorithms.

    PubMed

    Nagata, Yuko; Chu, Khim Hoong

    2003-11-01

    Artificial neural networks and genetic algorithms are used to model and optimize a fermentation medium for the production of the enzyme hydantoinase by Agrobacterium radiobacter. Experimental data reported in the literature were used to build two neural network models. The concentrations of four medium components served as inputs to the neural network models, and hydantoinase or cell concentration served as a single output of each model. Genetic algorithms were used to optimize the input space of the neural network models to find the optimum settings for maximum enzyme and cell production. Using this procedure, two artificial intelligence techniques have been effectively integrated to create a powerful tool for process modeling and optimization.

  18. Artificial Neural Network-Based System for PET Volume Segmentation

    PubMed Central

    Sharif, Mhd Saeed; Abbod, Maysam; Amira, Abbes; Zaidi, Habib

    2010-01-01

    Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs), as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results. PMID:20936152

  19. Training Spiking Neural Models Using Artificial Bee Colony

    PubMed Central

    Vazquez, Roberto A.; Garro, Beatriz A.

    2015-01-01

    Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

  20. Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

    PubMed Central

    Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

    2015-01-01

    PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi’an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO2, and NO2, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors’ variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas. PMID:26426030

  1. Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS.

    PubMed

    Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

    2015-09-29

    PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi'an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO₂, and NO₂, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors' variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.

  2. A gentle introduction to artificial neural networks.

    PubMed

    Zhang, Zhongheng

    2016-10-01

    Artificial neural network (ANN) is a flexible and powerful machine learning technique. However, it is under utilized in clinical medicine because of its technical challenges. The article introduces some basic ideas behind ANN and shows how to build ANN using R in a step-by-step framework. In topology and function, ANN is in analogue to the human brain. There are input and output signals transmitting from input to output nodes. Input signals are weighted before reaching output nodes according to their respective importance. Then the combined signal is processed by activation function. I simulated a simple example to illustrate how to build a simple ANN model using nnet() function. This function allows for one hidden layer with varying number of units in that layer. The basic structure of ANN can be visualized with plug-in plot.nnet() function. The plot function is powerful that it allows for varieties of adjustment to the appearance of the neural networks. Prediction with ANN can be performed with predict() function, similar to that of conventional generalized linear models. Finally, the prediction power of ANN is examined using confusion matrix and average accuracy. It appears that ANN is slightly better than conventional linear model.

  3. Applications of artificial neural networks in medical science.

    PubMed

    Patel, Jigneshkumar L; Goyal, Ramesh K

    2007-09-01

    Computer technology has been advanced tremendously and the interest has been increased for the potential use of 'Artificial Intelligence (AI)' in medicine and biological research. One of the most interesting and extensively studied branches of AI is the 'Artificial Neural Networks (ANNs)'. Basically, ANNs are the mathematical algorithms, generated by computers. ANNs learn from standard data and capture the knowledge contained in the data. Trained ANNs approach the functionality of small biological neural cluster in a very fundamental manner. They are the digitized model of biological brain and can detect complex nonlinear relationships between dependent as well as independent variables in a data where human brain may fail to detect. Nowadays, ANNs are widely used for medical applications in various disciplines of medicine especially in cardiology. ANNs have been extensively applied in diagnosis, electronic signal analysis, medical image analysis and radiology. ANNs have been used by many authors for modeling in medicine and clinical research. Applications of ANNs are increasing in pharmacoepidemiology and medical data mining. In this paper, authors have summarized various applications of ANNs in medical science.

  4. Network traffic anomaly prediction using Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Ciptaningtyas, Hening Titi; Fatichah, Chastine; Sabila, Altea

    2017-03-01

    As the excessive increase of internet usage, the malicious software (malware) has also increase significantly. Malware is software developed by hacker for illegal purpose(s), such as stealing data and identity, causing computer damage, or denying service to other user[1]. Malware which attack computer or server often triggers network traffic anomaly phenomena. Based on Sophos's report[2], Indonesia is the riskiest country of malware attack and it also has high network traffic anomaly. This research uses Artificial Neural Network (ANN) to predict network traffic anomaly based on malware attack in Indonesia which is recorded by Id-SIRTII/CC (Indonesia Security Incident Response Team on Internet Infrastructure/Coordination Center). The case study is the highest malware attack (SQL injection) which has happened in three consecutive years: 2012, 2013, and 2014[4]. The data series is preprocessed first, then the network traffic anomaly is predicted using Artificial Neural Network and using two weight update algorithms: Gradient Descent and Momentum. Error of prediction is calculated using Mean Squared Error (MSE) [7]. The experimental result shows that MSE for SQL Injection is 0.03856. So, this approach can be used to predict network traffic anomaly.

  5. [How can an otolaryngologist benefit from artificial neural networks?].

    PubMed

    Szaleniec, Joanna; Składzień, Jacek; Tadeusiewicz, Ryszard; Oleś, Krzysztof; Konior, Marcin; Przeklasa, Robert

    2012-01-01

    Artificial neural networks are informatic systems that have unique computational capabilities. The principle of their functioning is based on the rules of data processing in the brain. This article discusses the most important features of the artificial neural networks with reference to their applications in otolaryngology. The cited studies concern the fields of rhinology, audiology, phoniatrics, vestibulology, oncology, sleep apnea and salivary gland diseases. The authors also refer to their own experience with predictive neural models designed in the Department of Otolaryngology of the Jagiellonian University Medical College in Krakow. The applications of artificial neural networks in clinical diagnosis, automated signal interpretation and outcome prediction are presented. Moreover, the article explains how the artificial neural networks work and how the otolaryngologists can use them in their clinical practice and research.

  6. An introduction to bio-inspired artificial neural network architectures.

    PubMed

    Fasel, B

    2003-03-01

    In this introduction to artificial neural networks we attempt to give an overview of the most important types of neural networks employed in engineering and explain shortly how they operate and also how they relate to biological neural networks. The focus will mainly be on bio-inspired artificial neural network architectures and specifically to neo-perceptions. The latter belong to the family of convolutional neural networks. Their topology is somewhat similar to the one of the human visual cortex and they are based on receptive fields that allow, in combination with sub-sampling layers, for an improved robustness with regard to local spatial distortions. We demonstrate the application of artificial neural networks to face analysis--a domain we human beings are particularly good at, yet which poses great difficulties for digital computers running deterministic software programs.

  7. An Analysis of Stopping Criteria in Artificial Neural Networks

    DTIC Science & Technology

    1994-03-01

    I’AD-A278 491(1 AN ANALYSIS OF STOPPING CRITERIA IN ARTIFICIAL NEURAL NETWORKS THESIS Bruce Kostal Captain, USAF AFIT/GST/ENS/94M 07 D I ELECTE APR...ANALYSIS OF STOPPING CRITERIA IN ARTIFICIAL NEURAL NETWORKS THESIS Bruce Kostal Captain, USAF AFIT/GST/ENS/94M-07 ETIC ELECTE 94-12275 APR2 1994 U Approved...for public release; distributi6 unlimited D94󈧮i •6 AFIT/GST/ENS/94M-07 AN ANALYSIS OF STOPPING CRITERIA IN ARTIFICIAL NEURAL NETWORKS THESIS

  8. Application of Artificial Neural Networks to Machine Vision Flame Detection

    DTIC Science & Technology

    1991-04-01

    AD-A242 6:12 ;AD-A42 rESL-TR-90-49 APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO MACHINE VISION FLAME DETECTION 0~~ J. A. NEAL, C. E. LAND, R. R...I7E (inctude Security C’asifica rion) A-,Plication of Artificial Neural Networks to Machine Vision Flame Detection 2. PERSONAL AUTHOR S( ,n A. ’,’al...network. Artificial neural networks are designed to simulate the physical architecture of the neurons in the human brain and have demonstrated

  9. Automatic voice recognition using traditional and artificial neural network approaches

    NASA Technical Reports Server (NTRS)

    Botros, Nazeih M.

    1989-01-01

    The main objective of this research is to develop an algorithm for isolated-word recognition. This research is focused on digital signal analysis rather than linguistic analysis of speech. Features extraction is carried out by applying a Linear Predictive Coding (LPC) algorithm with order of 10. Continuous-word and speaker independent recognition will be considered in future study after accomplishing this isolated word research. To examine the similarity between the reference and the training sets, two approaches are explored. The first is implementing traditional pattern recognition techniques where a dynamic time warping algorithm is applied to align the two sets and calculate the probability of matching by measuring the Euclidean distance between the two sets. The second is implementing a backpropagation artificial neural net model with three layers as the pattern classifier. The adaptation rule implemented in this network is the generalized least mean square (LMS) rule. The first approach has been accomplished. A vocabulary of 50 words was selected and tested. The accuracy of the algorithm was found to be around 85 percent. The second approach is in progress at the present time.

  10. Fast tomographic reconstruction from limited data using artificial neural networks.

    PubMed

    Pelt, Daniël Maria; Batenburg, Kees Joost

    2013-12-01

    Image reconstruction from a small number of projections is a challenging problem in tomography. Advanced algorithms that incorporate prior knowledge can sometimes produce accurate reconstructions, but they typically require long computation times. Furthermore, the required prior knowledge can be very specific, limiting the type of images that can be reconstructed. Here, we present a reconstruction method that automatically learns prior knowledge using an artificial neural network. We show that this method can be viewed as a combination of filtered backprojection steps, and, therefore, has a relatively low computational cost. Results for two different cases show that the new method is able to use the learned information to produce high quality reconstructions in a short time, even when presented with a small number of projections.

  11. Ionic-liquid-based hollow-fiber liquid-phase microextraction method combined with hybrid artificial neural network-genetic algorithm for speciation and optimized determination of ferro and ferric in environmental water samples.

    PubMed

    Saeidi, Iman; Barfi, Behruz; Asghari, Alireza; Gharahbagh, Abdorreza Alavi; Barfi, Azadeh; Peyrovi, Moazameh; Afsharzadeh, Maryam; Hojatinasab, Mostafa

    2015-10-01

    A novel and environmentally friendly ionic-liquid-based hollow-fiber liquid-phase microextraction method combined with a hybrid artificial neural network (ANN)-genetic algorithm (GA) strategy was developed for ferro and ferric ions speciation as model analytes. Different parameters such as type and volume of extraction solvent, amounts of chelating agent, volume and pH of sample, ionic strength, stirring rate, and extraction time were investigated. Much more effective parameters were firstly examined based on one-variable-at-a-time design, and obtained results were used to construct an independent model for each parameter. The models were then applied to achieve the best and minimum numbers of candidate points as inputs for the ANN process. The maximum extraction efficiencies were achieved after 9 min using 22.0 μL of 1-hexyl-3-methylimidazolium hexafluorophosphate ([C6MIM][PF6]) as the acceptor phase and 10 mL of sample at pH = 7.0 containing 64.0 μg L(-1) of benzohydroxamic acid (BHA) as the complexing agent, after the GA process. Once optimized, analytical performance of the method was studied in terms of linearity (1.3-316 μg L(-1), R (2) = 0.999), accuracy (recovery = 90.1-92.3%), and precision (relative standard deviation (RSD) <3.1). Finally, the method was successfully applied to speciate the iron species in the environmental and wastewater samples.

  12. Geophysical phenomena classification by artificial neural networks

    NASA Technical Reports Server (NTRS)

    Gough, M. P.; Bruckner, J. R.

    1995-01-01

    Space science information systems involve accessing vast data bases. There is a need for an automatic process by which properties of the whole data set can be assimilated and presented to the user. Where data are in the form of spectrograms, phenomena can be detected by pattern recognition techniques. Presented are the first results obtained by applying unsupervised Artificial Neural Networks (ANN's) to the classification of magnetospheric wave spectra. The networks used here were a simple unsupervised Hamming network run on a PC and a more sophisticated CALM network run on a Sparc workstation. The ANN's were compared in their geophysical data recognition performance. CALM networks offer such qualities as fast learning, superiority in generalizing, the ability to continuously adapt to changes in the pattern set, and the possibility to modularize the network to allow the inter-relation between phenomena and data sets. This work is the first step toward an information system interface being developed at Sussex, the Whole Information System Expert (WISE). Phenomena in the data are automatically identified and provided to the user in the form of a data occurrence morphology, the Whole Information System Data Occurrence Morphology (WISDOM), along with relationships to other parameters and phenomena.

  13. Geoacoustic model inversion using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Benson, Jeremy; Chapman, N. Ross; Antoniou, Andreas

    2000-12-01

    An inversion technique using artificial neural networks (ANNs) is described for estimating geoacoustic model parameters of the ocean bottom and information about the sound source from acoustic field data. The method is applied to transmission loss data from the TRIAL SABLE experiment that was carried out in shallow water off Nova Scotia. The inversion is designed to incorporate the a priori information available for the site in order to improve the estimation accuracy. The inversion scheme involves training feedforward ANNs to estimate the geoacoustic and geometric parameters using simulated input/output training pairs generated with a forward acoustic propagation model. The inputs to the ANNs are the spectral components of the transmission loss at each sensor of a vertical hydrophone array for the two lowest frequencies that were transmitted in the experiment, 35 and 55 Hz. The output is the set of environmental model parameters, both geometric and geoacoustic, corresponding to the received field. In order to decrease the training time, a separate network was trained for each parameter. The errors for the parallel estimation are 10% lower than for those obtained using a single network to estimate all the parameters simultaneously, and the training time is decreased by a factor of six. When the experimental data are presented to the ANNs the geometric parameters, such as source range and depth, are estimated with a high accuracy. Geoacoustic parameters, such as the compressional speed in the sediment and the sediment thickness, are found with a moderate accuracy.

  14. Automated Wildfire Detection Through Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Miller, Jerry; Borne, Kirk; Thomas, Brian; Huang, Zhenping; Chi, Yuechen

    2005-01-01

    Wildfires have a profound impact upon the biosphere and our society in general. They cause loss of life, destruction of personal property and natural resources and alter the chemistry of the atmosphere. In response to the concern over the consequences of wildland fire and to support the fire management community, the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS) located in Camp Springs, Maryland gradually developed an operational system to routinely monitor wildland fire by satellite observations. The Hazard Mapping System, as it is known today, allows a team of trained fire analysts to examine and integrate, on a daily basis, remote sensing data from Geostationary Operational Environmental Satellite (GOES), Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors and generate a 24 hour fire product for the conterminous United States. Although assisted by automated fire detection algorithms, N O M has not been able to eliminate the human element from their fire detection procedures. As a consequence, the manually intensive effort has prevented NOAA from transitioning to a global fire product as urged particularly by climate modelers. NASA at Goddard Space Flight Center in Greenbelt, Maryland is helping N O M more fully automate the Hazard Mapping System by training neural networks to mimic the decision-making process of the frre analyst team as well as the automated algorithms.

  15. Classifying auroras using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Rydesater, Peter; Brandstrom, Urban; Steen, Ake; Gustavsson, Bjorn

    1999-03-01

    In Auroral Large Imaging System (ALIS) there is need of stable methods for analysis and classification of auroral images and images with for example mother of pearl clouds. This part of ALIS is called Selective Imaging Techniques (SIT) and is intended to sort out images of scientific interest. It's also used to find out what and where in the images there is for example different auroral phenomena's. We will discuss some about the SIT units main functionality but this work is mainly concentrated on how to find auroral arcs and how they are placed in images. Special case have been taken to make the algorithm robust since it's going to be implemented in a SIT unit which will work automatic and often unsupervised and some extends control the data taking of ALIS. The method for finding auroral arcs is based on a local operator that detects intensity differens. This gives arc orientation values as a preprocessing which is fed to a neural network classifier. We will show some preliminary results and possibilities to use and improve this algorithm for use in the future SIT unit.

  16. Genetic algorithm for neural networks optimization

    NASA Astrophysics Data System (ADS)

    Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

    2004-11-01

    This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

  17. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    ERIC Educational Resources Information Center

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  18. Identifing Atmospheric Pollutant Sources Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Paes, F. F.; Campos, H. F.; Luz, E. P.; Carvalho, A. R.

    2008-05-01

    The estimation of the area source pollutant strength is a relevant issue for atmospheric environment. This characterizes an inverse problem in the atmospheric pollution dispersion. In the inverse analysis, an area source domain is considered, where the strength of such area source term is assumed unknown. The inverse problem is solved by using a supervised artificial neural network: multi-layer perceptron. The conection weights of the neural network are computed from delta rule - learning process. The neural network inversion is compared with results from standard inverse analysis (regularized inverse solution). In the regularization method, the inverse problem is formulated as a non-linear optimization approach, whose the objective function is given by the square difference between the measured pollutant concentration and the mathematical models, associated with a regularization operator. In our numerical experiments, the forward problem is addressed by a source-receptor scheme, where a regressive Lagrangian model is applied to compute the transition matrix. The second order maximum entropy regularization is used, and the regularization parameter is calculated by the L-curve technique. The objective function is minimized employing a deterministic scheme (a quasi-Newton algorithm) [1] and a stochastic technique (PSO: particle swarm optimization) [2]. The inverse problem methodology is tested with synthetic observational data, from six measurement points in the physical domain. The best inverse solutions were obtained with neural networks. References: [1] D. R. Roberti, D. Anfossi, H. F. Campos Velho, G. A. Degrazia (2005): Estimating Emission Rate and Pollutant Source Location, Ciencia e Natura, p. 131-134. [2] E.F.P. da Luz, H.F. de Campos Velho, J.C. Becceneri, D.R. Roberti (2007): Estimating Atmospheric Area Source Strength Through Particle Swarm Optimization. Inverse Problems, Desing and Optimization Symposium IPDO-2007, April 16-18, Miami (FL), USA, vol 1, p

  19. Multiple image sensor data fusion through artificial neural networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    With multisensor data fusion technology, the data from multiple sensors are fused in order to make a more accurate estimation of the environment through measurement, processing and analysis. Artificial neural networks are the computational models that mimic biological neural networks. With high per...

  20. Using Artificial Neural Networks to Predict Malignancy of Ovarian Tumors

    DTIC Science & Technology

    2007-11-02

    This paper discusses the application of artificial neural networks (ANNs) to preoperative discrimination between benign and malignant ovarian tumors...With the input variables selected by logistic regression analysis, two types of feed-forward neural networks were built: multi-layer perceptrons

  1. An Examination of Application of Artificial Neural Network in Cognitive Radios

    NASA Astrophysics Data System (ADS)

    Bello Salau, H.; Onwuka, E. N.; Aibinu, A. M.

    2013-12-01

    Recent advancement in software radio technology has led to the development of smart device known as cognitive radio. This type of radio fuses powerful techniques taken from artificial intelligence, game theory, wideband/multiple antenna techniques, information theory and statistical signal processing to create an outstanding dynamic behavior. This cognitive radio is utilized in achieving diverse set of applications such as spectrum sensing, radio parameter adaptation and signal classification. This paper contributes by reviewing different cognitive radio implementation that uses artificial intelligence such as the hidden markov models, metaheuristic algorithm and artificial neural networks (ANNs). Furthermore, different areas of application of ANNs and their performance metrics based approach are also examined.

  2. Prediction of Buffet Loads Using Artificial Neural Networks

    DTIC Science & Technology

    2001-09-01

    The use of artificial neural networks (ANN) for predicting the empennage buffet pressures as a function of aircraft state has been investigated. The...with additional data. The study confirmed that neural networks have a great potential as a method for modelling buffet data. The ability of neural ... networks to accurately predict magnitude and spectral content of unsteady buffet pressures was demonstrated. Based on the ANN methodology investigated, a

  3. On Design and Implementation of Neural-Machine Interface for Artificial Legs

    PubMed Central

    Zhang, Xiaorong; Liu, Yuhong; Zhang, Fan; Ren, Jin; Sun, Yan (Lindsay); Yang, Qing

    2011-01-01

    The quality of life of leg amputees can be improved dramatically by using a cyber physical system (CPS) that controls artificial legs based on neural signals representing amputees’ intended movements. The key to the CPS is the neural-machine interface (NMI) that senses electromyographic (EMG) signals to make control decisions. This paper presents a design and implementation of a novel NMI using an embedded computer system to collect neural signals from a physical system - a leg amputee, provide adequate computational capability to interpret such signals, and make decisions to identify user’s intent for prostheses control in real time. A new deciphering algorithm, composed of an EMG pattern classifier and a post-processing scheme, was developed to identify the user’s intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Real time experiments on a leg amputee subject and an able-bodied subject have been carried out to test the control accuracy of the new NMI. Our extensive experiments have shown promising results on both subjects, paving the way for clinical feasibility of neural controlled artificial legs. PMID:22389637

  4. Artificial neural network simulation of battery performance

    SciTech Connect

    O`Gorman, C.C.; Ingersoll, D.; Jungst, R.G.; Paez, T.L.

    1998-12-31

    Although they appear deceptively simple, batteries embody a complex set of interacting physical and chemical processes. While the discrete engineering characteristics of a battery such as the physical dimensions of the individual components, are relatively straightforward to define explicitly, their myriad chemical and physical processes, including interactions, are much more difficult to accurately represent. Within this category are the diffusive and solubility characteristics of individual species, reaction kinetics and mechanisms of primary chemical species as well as intermediates, and growth and morphology characteristics of reaction products as influenced by environmental and operational use profiles. For this reason, development of analytical models that can consistently predict the performance of a battery has only been partially successful, even though significant resources have been applied to this problem. As an alternative approach, the authors have begun development of a non-phenomenological model for battery systems based on artificial neural networks. Both recurrent and non-recurrent forms of these networks have been successfully used to develop accurate representations of battery behavior. The connectionist normalized linear spline (CMLS) network has been implemented with a self-organizing layer to model a battery system with the generalized radial basis function net. Concurrently, efforts are under way to use the feedforward back propagation network to map the {open_quotes}state{close_quotes} of a battery system. Because of the complexity of battery systems, accurate representation of the input and output parameters has proven to be very important. This paper describes these initial feasibility studies as well as the current models and makes comparisons between predicted and actual performance.

  5. Toward implementation of artificial neural networks that "really work".

    PubMed Central

    Leon, M. A.; Keller, J.

    1997-01-01

    Artificial neural networks are established analytical methods in bio-medical research. They have repeatedly outperformed traditional tools for pattern recognition and clinical outcome prediction while assuring continued adaptation and learning. However, successful experimental neural networks systems seldom reach a production state. That is, they are not incorporated into clinical information systems. It could be speculated that neural networks simply must undergo a lengthy acceptance process before they become part of the day to day operations of health care systems. However, our experience trying to incorporate experimental neural networks into information systems lead us to believe that there are technical and operational barriers that greatly difficult neural network implementation. A solution for these problems may be the delineation of policies and procedures for neural network implementation and the development a new class of neural network client/server applications that fit the needs of current clinical information systems. PMID:9357613

  6. Adaptive Neurons For Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul

    1990-01-01

    Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

  7. A hardware implementation of artificial neural networks using field programmable gate arrays

    NASA Astrophysics Data System (ADS)

    Won, E.

    2007-11-01

    An artificial neural network algorithm is implemented using a low-cost field programmable gate array hardware. One hidden layer is used in the feed-forward neural network structure in order to discriminate one class of patterns from the other class in real time. In this work, the training of the network is performed in the off-line computing environment and the results of the training are configured to the hardware in order to minimize the latency of the neural computation. With five 8-bit input patterns, six hidden nodes, and one 8-bit output, the implemented hardware neural network makes decisions on a set of input patterns in 11 clock cycles, or less than 200 ns with a 60 MHz clock. The result from the hardware neural computation is well predictable based on the off-line computation. This implementation may be used in level 1 hardware triggers in high energy physics experiments.

  8. Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols.

    PubMed

    Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong

    2013-11-01

    In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols.

  9. On the relationships between generative encodings, regularity, and learning abilities when evolving plastic artificial neural networks.

    PubMed

    Tonelli, Paul; Mouret, Jean-Baptiste

    2013-01-01

    A major goal of bio-inspired artificial intelligence is to design artificial neural networks with abilities that resemble those of animal nervous systems. It is commonly believed that two keys for evolving nature-like artificial neural networks are (1) the developmental process that links genes to nervous systems, which enables the evolution of large, regular neural networks, and (2) synaptic plasticity, which allows neural networks to change during their lifetime. So far, these two topics have been mainly studied separately. The present paper shows that they are actually deeply connected. Using a simple operant conditioning task and a classic evolutionary algorithm, we compare three ways to encode plastic neural networks: a direct encoding, a developmental encoding inspired by computational neuroscience models, and a developmental encoding inspired by morphogen gradients (similar to HyperNEAT). Our results suggest that using a developmental encoding could improve the learning abilities of evolved, plastic neural networks. Complementary experiments reveal that this result is likely the consequence of the bias of developmental encodings towards regular structures: (1) in our experimental setup, encodings that tend to produce more regular networks yield networks with better general learning abilities; (2) whatever the encoding is, networks that are the more regular are statistically those that have the best learning abilities.

  10. A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders.

    PubMed

    Rapoport, Benjamin I; Wattanapanitch, Woradorn; Penagos, Hector L; Musallam, Sam; Andersen, Richard A; Sarpeshkar, Rahul

    2009-01-01

    Algorithmically and energetically efficient computational architectures that operate in real time are essential for clinically useful neural prosthetic devices. Such devices decode raw neural data to obtain direct control signals for external devices. They can also perform data compression and vastly reduce the bandwidth and consequently power expended in wireless transmission of raw data from implantable brain-machine interfaces. We describe a biomimetic algorithm and micropower analog circuit architecture for decoding neural cell ensemble signals. The decoding algorithm implements a continuous-time artificial neural network, using a bank of adaptive linear filters with kernels that emulate synaptic dynamics. The filters transform neural signal inputs into control-parameter outputs, and can be tuned automatically in an on-line learning process. We provide experimental validation of our system using neural data from thalamic head-direction cells in an awake behaving rat.

  11. Artificial neural networks reveal efficiency in genetic value prediction.

    PubMed

    Peixoto, L A; Bhering, L L; Cruz, C D

    2015-06-18

    The objective of this study was to evaluate the efficiency of artificial neural networks (ANNs) for predicting genetic value in experiments carried out in randomized blocks. Sixteen scenarios were simulated with different values of heritability (10, 20, 30, and 40%), coefficient of variation (5 and 10%), and the number of genotypes per block (150 and 200 for validation, and 5000 for neural network training). One hundred validation populations were used in each scenario. Accuracy of ANNs was evaluated by comparing the correlation of network value with genetic value, and of phenotypic value with genetic value. Neural networks were efficient in predicting genetic value with a 0.64 to 10.3% gain compared to the phenotypic value, regardless the simulated population size, heritability, or coefficient of variation. Thus, the artificial neural network is a promising technique for predicting genetic value in balanced experiments.

  12. Simulation of nonlinear random vibrations using artificial neural networks

    SciTech Connect

    Paez, T.L.; Tucker, S.; O`Gorman, C.

    1997-02-01

    The simulation of mechanical system random vibrations is important in structural dynamics, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks provide a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and random vibration simulation, via component modeling with artificial neural networks, a much simpler problem. A numerical example is presented.

  13. Functional approximation using artificial neural networks in structural mechanics

    NASA Technical Reports Server (NTRS)

    Alam, Javed; Berke, Laszlo

    1993-01-01

    The artificial neural networks (ANN) methodology is an outgrowth of research in artificial intelligence. In this study, the feed-forward network model that was proposed by Rumelhart, Hinton, and Williams was applied to the mapping of functions that are encountered in structural mechanics problems. Several different network configurations were chosen to train the available data for problems in materials characterization and structural analysis of plates and shells. By using the recall process, the accuracy of these trained networks was assessed.

  14. Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission.

    PubMed

    Zeng, Jun; Guo, Hua-fang; Hu, Yue-ming

    2007-01-01

    Vehicle emission has been the major source of air pollution in urban areas in the past two decades. This article proposes an artificial neural network model for identifying the taxi gross emitters based on the remote sensing data. After carrying out the field test in Guangzhou and analyzing various factors from the emission data, the artificial neural network modeling was proved to be an advisable method of identifying the gross emitters. On the basis of the principal component analysis and the selection of algorithm and architecture, the Back-Propagation neural network model with 8-17-1 architecture was established as the optimal approach for this purpose. It gave a percentage of hits of 93%. Our previous research result and the result from aggression analysis were compared, and they provided respectively the percentage of hits of 81.63% and 75%. This comparison demonstrates the potentiality and validity of the proposed method in the identification of taxi gross emitters.

  15. Application of artificial neural networks to gaming

    NASA Astrophysics Data System (ADS)

    Baba, Norio; Kita, Tomio; Oda, Kazuhiro

    1995-04-01

    Recently, neural network technology has been applied to various actual problems. It has succeeded in producing a large number of intelligent systems. In this article, we suggest that it could be applied to the field of gaming. In particular, we suggest that the neural network model could be used to mimic players' characters. Several computer simulation results using a computer gaming system which is a modified version of the COMMONS GAME confirm our idea.

  16. Synchronous machine steady-state stability analysis using an artificial neural network

    SciTech Connect

    Chen, C.R.; Hsu, Y.Y. . Dept. of Electrical Engineering)

    1991-03-01

    A new type of artificial neural network is proposed for the steady-state stability analysis of a synchronous generator. In the developed artificial neutral network, those system variables which play an important role in steady-state stability such as generator outputs and power system stabilizer parameters are employed as the inputs. The output of the neural net provides the information on steady-state stability. Once the connection weights of the neural network have been learned using a set of training data derived off-line, the neural net can be applied to analyze the steady-state stability of the system time. To demonstrate the effectiveness of the proposed neural net, steady-state stability analysis is performed on a synchronous generator connected to a large power system. It is found that the proposed neural net requires much less training time than the multilayer feedforward network with backpropagation-momentum learning algorithm. It is also concluded from the test results that correct stability assessment can be achieved by the neural network.

  17. Macro cell placement with neural net algorithms

    NASA Astrophysics Data System (ADS)

    Storti-Gajani, Giancarlo

    Placement of VLSI (Very Large Scale Integration) macro cells is one of the hard problems encountered in the process of integrated circuits design. Since the problem is essentially NP-complete a solution must be searched for with the aid of heuristics using, maybe, non deterministic strategies. A new algorithm for cell preplacement based on neural nets that may be very well extended to find solution of the final placement problem is presented. Simulations for the part of the algorithm concerning preplacement are carried out on several different examples giving always a sharply decreasing cost function (where cost is evaluated essentially on total length of wires given a rectangular boundary). The direct mapping between neural units and VLSI blocks that is adopted in the algorithm makes the extension to the final placement problem quite simple. Simulation programs are implemented in a interpreted mathematical simulation language and a C language implementation is currently under way.

  18. Use of artificial neural network for spatial rainfall analysis

    NASA Astrophysics Data System (ADS)

    Paraskevas, Tsangaratos; Dimitrios, Rozos; Andreas, Benardos

    2014-04-01

    In the present study, the precipitation data measured at 23 rain gauge stations over the Achaia County, Greece, were used to estimate the spatial distribution of the mean annual precipitation values over a specific catchment area. The objective of this work was achieved by programming an Artificial Neural Network (ANN) that uses the feed-forward back-propagation algorithm as an alternative interpolating technique. A Geographic Information System (GIS) was utilized to process the data derived by the ANN and to create a continuous surface that represented the spatial mean annual precipitation distribution. The ANN introduced an optimization procedure that was implemented during training, adjusting the hidden number of neurons and the convergence of the ANN in order to select the best network architecture. The performance of the ANN was evaluated using three standard statistical evaluation criteria applied to the study area and showed good performance. The outcomes were also compared with the results obtained from a previous study in the area of research which used a linear regression analysis for the estimation of the mean annual precipitation values giving more accurate results. The information and knowledge gained from the present study could improve the accuracy of analysis concerning hydrology and hydrogeological models, ground water studies, flood related applications and climate analysis studies.

  19. A new data mining scheme using artificial neural networks.

    PubMed

    Kamruzzaman, S M; Jehad Sarkar, A M

    2011-01-01

    Classification is one of the data mining problems receiving enormous attention in the database community. Although artificial neural networks (ANNs) have been successfully applied in a wide range of machine learning applications, they are however often regarded as black boxes, i.e., their predictions cannot be explained. To enhance the explanation of ANNs, a novel algorithm to extract symbolic rules from ANNs has been proposed in this paper. ANN methods have not been effectively utilized for data mining tasks because how the classifications were made is not explicitly stated as symbolic rules that are suitable for verification or interpretation by human experts. With the proposed approach, concise symbolic rules with high accuracy, that are easily explainable, can be extracted from the trained ANNs. Extracted rules are comparable with other methods in terms of number of rules, average number of conditions for a rule, and the accuracy. The effectiveness of the proposed approach is clearly demonstrated by the experimental results on a set of benchmark data mining classification problems.

  20. Artificial neural network model for earthquake prediction with radon monitoring.

    PubMed

    Külahci, Fatih; Inceöz, Murat; Doğru, Mahmut; Aksoy, Ercan; Baykara, Oktay

    2009-01-01

    Apart from the linear monitoring studies concerning the relationship between radon and earthquake, an artificial neural networks (ANNs) model approach is presented starting out from non-linear changes of the eight different parameters during the earthquake occurrence. A three-layer Levenberg-Marquardt feedforward learning algorithm is used to model the earthquake prediction process in the East Anatolian Fault System (EAFS). The proposed ANN system employs individual training strategy with fixed-weight and supervised models leading to estimations. The average relative error between the magnitudes of the earthquakes acquired by ANN and measured data is about 2.3%. The relative error between the test and earthquake data varies between 0% and 12%. In addition, the factor analysis was applied on all data and the model output values to see the statistical variation. The total variance of 80.18% was explained with four factors by this analysis. Consequently, it can be concluded that ANN approach is a potential alternative to other models with complex mathematical operations.

  1. Appraisal of artificial neural network for forecasting of economic parameters

    NASA Astrophysics Data System (ADS)

    Kordanuli, Bojana; Barjaktarović, Lidija; Jeremić, Ljiljana; Alizamir, Meysam

    2017-01-01

    The main aim of this research is to develop and apply artificial neural network (ANN) with extreme learning machine (ELM) and back propagation (BP) to forecast gross domestic product (GDP) and Hirschman-Herfindahl Index (HHI). GDP could be developed based on combination of different factors. In this investigation GDP forecasting based on the agriculture and industry added value in gross domestic product (GDP) was analysed separately. Other inputs are final consumption expenditure of general government, gross fixed capital formation (investments) and fertility rate. The relation between product market competition and corporate investment is contentious. On one hand, the relation can be positive, but on the other hand, the relation can be negative. Several methods have been proposed to monitor market power for the purpose of developing procedures to mitigate or eliminate the effects. The most widely used methods are based on indices such as the Hirschman-Herfindahl Index (HHI). The reliability of the ANN models were accessed based on simulation results and using several statistical indicators. Based upon simulation results, it was presented that ELM shows better performances than BP learning algorithm in applications of GDP and HHI forecasting.

  2. Artificial neural networks: theoretical background and pharmaceutical applications: a review.

    PubMed

    Wesolowski, Marek; Suchacz, Bogdan

    2012-01-01

    In recent times, there has been a growing interest in artificial neural networks, which are a rough simulation of the information processing ability of the human brain, as modern and vastly sophisticated computational techniques. This interest has also been reflected in the pharmaceutical sciences. This paper presents a review of articles on the subject of the application of neural networks as effective tools assisting the solution of various problems in science and the pharmaceutical industry, especially those characterized by multivariate and nonlinear dependencies. After a short description of theoretical background and practical basics concerning the computations performed by means of neural networks, the most important pharmaceutical applications of neural networks, with suitable references, are demonstrated. The huge role played by neural networks in pharmaceutical analysis, pharmaceutical technology, and searching for the relationships between the chemical structure and the properties of newly synthesized compounds as candidates for drugs is discussed.

  3. Simple artificial neural networks that match probability and exploit and explore when confronting a multiarmed bandit.

    PubMed

    Dawson, Michael R W; Dupuis, Brian; Spetch, Marcia L; Kelly, Debbie M

    2009-08-01

    The matching law (Herrnstein 1961) states that response rates become proportional to reinforcement rates; this is related to the empirical phenomenon called probability matching (Vulkan 2000). Here, we show that a simple artificial neural network generates responses consistent with probability matching. This behavior was then used to create an operant procedure for network learning. We use the multiarmed bandit (Gittins 1989), a classic problem of choice behavior, to illustrate that operant training balances exploiting the bandit arm expected to pay off most frequently with exploring other arms. Perceptrons provide a medium for relating results from neural networks, genetic algorithms, animal learning, contingency theory, reinforcement learning, and theories of choice.

  4. Artificial Neural Networks in Policy Research: A Current Assessment.

    ERIC Educational Resources Information Center

    Woelfel, Joseph

    1993-01-01

    Suggests that artificial neural networks (ANNs) exhibit properties that promise usefulness for policy researchers. Notes that ANNs have found extensive use in areas once reserved for multivariate statistical programs such as regression and multiple classification analysis and are developing an extensive community of advocates for processing text…

  5. Artificial Neural Networks for Modeling Knowing and Learning in Science.

    ERIC Educational Resources Information Center

    Roth, Wolff-Michael

    2000-01-01

    Advocates artificial neural networks as models for cognition and development. Provides an example of how such models work in the context of a well-known Piagetian developmental task and school science activity: balance beam problems. (Contains 59 references.) (Author/WRM)

  6. Prediction of bioactive peptides using artificial neural networks.

    PubMed

    Andreu, David; Torrent, Marc

    2015-01-01

    Peptides are molecules of varying complexity, with different functions in the organism and with remarkable therapeutic interest. Predicting peptide activity by computational means can help us to understand their mechanism of action and deliver powerful drug-screening methodologies. In this chapter, we describe how to apply artificial neural networks to predict antimicrobial peptide activity.

  7. [Artificial neural networks for decision making in urologic oncology].

    PubMed

    Remzi, M; Djavan, B

    2007-06-01

    This chapter presents a detailed introduction regarding Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. It includes a description of ANNs methodology and points out the differences between Artifical Intelligence and traditional statistic models in terms of usefulness for patients and clinicians, and its advantages over current statistical analysis.

  8. Introducing Artificial Neural Networks through a Spreadsheet Model

    ERIC Educational Resources Information Center

    Rienzo, Thomas F.; Athappilly, Kuriakose K.

    2012-01-01

    Business students taking data mining classes are often introduced to artificial neural networks (ANN) through point and click navigation exercises in application software. Even if correct outcomes are obtained, students frequently do not obtain a thorough understanding of ANN processes. This spreadsheet model was created to illuminate the roles of…

  9. Recurrent Artificial Neural Networks and Finite State Natural Language Processing.

    ERIC Educational Resources Information Center

    Moisl, Hermann

    It is argued that pessimistic assessments of the adequacy of artificial neural networks (ANNs) for natural language processing (NLP) on the grounds that they have a finite state architecture are unjustified, and that their adequacy in this regard is an empirical issue. First, arguments that counter standard objections to finite state NLP on the…

  10. Application of artificial neural networks to the design optimization of aerospace structural components

    NASA Technical Reports Server (NTRS)

    Berke, Laszlo; Patnaik, Surya N.; Murthy, Pappu L. N.

    1993-01-01

    The application of artificial neural networks to capture structural design expertise is demonstrated. The principal advantage of a trained neural network is that it requires trivial computational effort to produce an acceptable new design. For the class of problems addressed, the development of a conventional expert system would be extremely difficult. In the present effort, a structural optimization code with multiple nonlinear programming algorithms and an artificial neural network code NETS were used. A set of optimum designs for a ring and two aircraft wings for static and dynamic constraints were generated by using the optimization codes. The optimum design data were processed to obtain input and output pairs, which were used to develop a trained artificial neural network with the code NETS. Optimum designs for new design conditions were predicted by using the trained network. Neural net prediction of optimum designs was found to be satisfactory for most of the output design parameters. However, results from the present study indicate that caution must be exercised to ensure that all design variables are within selected error bounds.

  11. Artificial neural network to search for metal-poor galaxies

    NASA Astrophysics Data System (ADS)

    Shi, Fei; Liu, Yu-Yan; Kong, Xu; Chen, Yang

    2014-02-01

    Aims: To find a fast and reliable method for selecting metal-poor galaxies (MPGs), especially in large surveys and huge databases, an artificial neural network (ANN) method is applied to a sample of star-forming galaxies from the Sloan Digital Sky Survey (SDSS) data release 9 (DR9) provided by the Max Planck Institute and the Johns Hopkins University (MPA/JHU). Methods: A two-step approach is adopted: (i) The ANN network must be trained with a subset of objects that are known to be either MPGs or metal rich galaxies (MRGs), treating the strong emission line flux measurements as input feature vectors in n-dimensional space, where n is the number of strong emission line flux ratios. (ii) After the network is trained on a sample of star-forming galaxies, the remaining galaxies are classified in the automatic test analysis as either MPGs or MRGs. We consider several random divisions of the data into training and testing sets; for instance, for our sample, a total of 70 percent of the data are involved in training the algorithm, 15 percent are involved in validating the algorithm, and the remaining 15 percent are used for blind testing the resulting classifier. Results: For target selection, we have achieved an acquisition rate for MPGs of 96 percent and 92 percent for an MPGs threshold of 12 + log (O/H) = 8.00 and 12 + log (O/H) = 8.39, respectively. Running the code takes minutes in most cases under the Matlab 2013a software environment. The ANN method can easily be extended to any MPGs target selection task when the physical property of the target can be expressed as a quantitative variable. The code in the paper is available on the web (http://fshi5388.blog.163.com).

  12. An artificial neural network controller for intelligent transportation systems applications

    SciTech Connect

    Vitela, J.E.; Hanebutte, U.R.; Reifman, J.

    1996-04-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems applications. The AICC is based on a simple nonlinear model of the vehicle dynamics. A Neural Network Controller (NNC) code developed at Argonne National Laboratory to control discrete dynamical systems was used for this purpose. In order to test the NNC, an AICC-simulator containing graphical displays was developed for a system of two vehicles driving in a single lane. Two simulation cases are shown, one involving a lead vehicle with constant velocity and the other a lead vehicle with varying acceleration. More realistic vehicle dynamic models will be considered in future work.

  13. Artificial neural networks for decision-making in urologic oncology.

    PubMed

    Anagnostou, Theodore; Remzi, Mesut; Lykourinas, Michael; Djavan, Bob

    2003-06-01

    The authors are presenting a thorough introduction in Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. The article covers a description of Artificial Neural Network methodology and points out the differences of Artificial Intelligence to traditional statistic models in terms of serving patients and clinicians, in a different way than current statistical analysis. Since Artificial Intelligence is not yet fully understood by many practicing clinicians, the authors have reviewed a careful selection of articles in order to explore the clinical benefit of Artificial Intelligence applications in modern Urology questions and decision-making. The data are from real patients and reflect attempts to achieve more accurate diagnosis and prognosis, especially in prostate cancer that stands as a good example of difficult decision-making in everyday practice. Experience from current use of Artificial Intelligence is also being discussed, and the authors address future developments as well as potential problems such as medical record quality, precautions in using ANNs or resistance to system use, in an attempt to point out future demands and the need for common standards. The authors conclude that both methods should continue to be used in a complementary manner. ANNs still do not prove always better as to replace standard statistical analysis as the method of choice in interpreting medical data.

  14. Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn.

    PubMed

    Patra, Tarak K; Meenakshisundaram, Venkatesh; Hung, Jui-Hsiang; Simmons, David S

    2017-02-13

    Machine learning has the potential to dramatically accelerate high-throughput approaches to materials design, as demonstrated by successes in biomolecular design and hard materials design. However, in the search for new soft materials exhibiting properties and performance beyond those previously achieved, machine learning approaches are frequently limited by two shortcomings. First, because they are intrinsically interpolative, they are better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require large pre-existing data sets, which are frequently unavailable and prohibitively expensive to produce. Here we describe a new strategy, the neural-network-biased genetic algorithm (NBGA), for combining genetic algorithms, machine learning, and high-throughput computation or experiment to discover materials with extremal properties in the absence of pre-existing data. Within this strategy, predictions from a progressively constructed artificial neural network are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct simulation or experiment. In effect, this strategy gives the evolutionary algorithm the ability to "learn" and draw inferences from its experience to accelerate the evolutionary process. We test this algorithm against several standard optimization problems and polymer design problems and demonstrate that it matches and typically exceeds the efficiency and reproducibility of standard approaches including a direct-evaluation genetic algorithm and a neural-network-evaluated genetic algorithm. The success of this algorithm in a range of test problems indicates that the NBGA provides a robust strategy for employing informatics-accelerated high-throughput methods to accelerate materials design in the absence of pre-existing data.

  15. Neural Network-Based Hyperspectral Algorithms

    DTIC Science & Technology

    2016-06-07

    our effort is development of robust numerical inversion algorithms, which will retrieve inherent optical properties of the water column as well as...combination of in-situ and model data of water column variables (IOP’s, depth, bottom type, upwelling radiance, etc.) a neural network non-linear...function approximation model will be used to establish the inverse relationship between upwelling surface radiance and the water column variables, 2

  16. Research on WNN modeling for gold price forecasting based on improved artificial bee colony algorithm.

    PubMed

    Li, Bai

    2014-01-01

    Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme.

  17. Comparative analysis of regression and artificial neural network models for wind speed prediction

    NASA Astrophysics Data System (ADS)

    Bilgili, Mehmet; Sahin, Besir

    2010-11-01

    In this study, wind speed was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. A three-layer feedforward artificial neural network structure was constructed and a backpropagation algorithm was used for the training of ANNs. To get a successful simulation, firstly, the correlation coefficients between all of the meteorological variables (wind speed, ambient temperature, atmospheric pressure, relative humidity and rainfall) were calculated taking two variables in turn for each calculation. All independent variables were added to the simple regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and also used in the input layer of the ANN. The results obtained by all methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods.

  18. Confidence intervals in Flow Forecasting by using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Panagoulia, Dionysia; Tsekouras, George

    2014-05-01

    One of the major inadequacies in implementation of Artificial Neural Networks (ANNs) for flow forecasting is the development of confidence intervals, because the relevant estimation cannot be implemented directly, contrasted to the classical forecasting methods. The variation in the ANN output is a measure of uncertainty in the model predictions based on the training data set. Different methods for uncertainty analysis, such as bootstrap, Bayesian, Monte Carlo, have already proposed for hydrologic and geophysical models, while methods for confidence intervals, such as error output, re-sampling, multi-linear regression adapted to ANN have been used for power load forecasting [1-2]. The aim of this paper is to present the re-sampling method for ANN prediction models and to develop this for flow forecasting of the next day. The re-sampling method is based on the ascending sorting of the errors between real and predicted values for all input vectors. The cumulative sample distribution function of the prediction errors is calculated and the confidence intervals are estimated by keeping the intermediate value, rejecting the extreme values according to the desired confidence levels, and holding the intervals symmetrical in probability. For application of the confidence intervals issue, input vectors are used from the Mesochora catchment in western-central Greece. The ANN's training algorithm is the stochastic training back-propagation process with decreasing functions of learning rate and momentum term, for which an optimization process is conducted regarding the crucial parameters values, such as the number of neurons, the kind of activation functions, the initial values and time parameters of learning rate and momentum term etc. Input variables are historical data of previous days, such as flows, nonlinearly weather related temperatures and nonlinearly weather related rainfalls based on correlation analysis between the under prediction flow and each implicit input

  19. Application of artificial neural networks in nonlinear analysis of trusses

    NASA Technical Reports Server (NTRS)

    Alam, J.; Berke, L.

    1991-01-01

    A method is developed to incorporate neural network model based upon the Backpropagation algorithm for material response into nonlinear elastic truss analysis using the initial stiffness method. Different network configurations are developed to assess the accuracy of neural network modeling of nonlinear material response. In addition to this, a scheme based upon linear interpolation for material data, is also implemented for comparison purposes. It is found that neural network approach can yield very accurate results if used with care. For the type of problems under consideration, it offers a viable alternative to other material modeling methods.

  20. Numerical solution of differential equations by artificial neural networks

    NASA Technical Reports Server (NTRS)

    Meade, Andrew J., Jr.

    1995-01-01

    Conventionally programmed digital computers can process numbers with great speed and precision, but do not easily recognize patterns or imprecise or contradictory data. Instead of being programmed in the conventional sense, artificial neural networks (ANN's) are capable of self-learning through exposure to repeated examples. However, the training of an ANN can be a time consuming and unpredictable process. A general method is being developed by the author to mate the adaptability of the ANN with the speed and precision of the digital computer. This method has been successful in building feedforward networks that can approximate functions and their partial derivatives from examples in a single iteration. The general method also allows the formation of feedforward networks that can approximate the solution to nonlinear ordinary and partial differential equations to desired accuracy without the need of examples. It is believed that continued research will produce artificial neural networks that can be used with confidence in practical scientific computing and engineering applications.

  1. An automated diagnosis system of liver disease using artificial immune and genetic algorithms.

    PubMed

    Liang, Chunlin; Peng, Lingxi

    2013-04-01

    The rise of health care cost is one of the world's most important problems. Disease prediction is also a vibrant research area. Researchers have approached this problem using various techniques such as support vector machine, artificial neural network, etc. This study typically exploits the immune system's characteristics of learning and memory to solve the problem of liver disease diagnosis. The proposed system applies a combination of two methods of artificial immune and genetic algorithm to diagnose the liver disease. The system architecture is based on artificial immune system. The learning procedure of system adopts genetic algorithm to interfere the evolution of antibody population. The experiments use two benchmark datasets in our study, which are acquired from the famous UCI machine learning repository. The obtained diagnosis accuracies are very promising with regard to the other diagnosis system in the literatures. These results suggest that this system may be a useful automatic diagnosis tool for liver disease.

  2. Elements of an algorithm for optimizing a parameter-structural neural network

    NASA Astrophysics Data System (ADS)

    Mrówczyńska, Maria

    2016-06-01

    The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.

  3. THE CHOICE OF OPTIMAL STRUCTURE OF ARTIFICIAL NEURAL NETWORK CLASSIFIER INTENDED FOR CLASSIFICATION OF WELDING FLAWS

    SciTech Connect

    Sikora, R.; Chady, T.; Baniukiewicz, P.; Caryk, M.; Piekarczyk, B.

    2010-02-22

    Nondestructive testing and evaluation are under continuous development. Currently researches are concentrated on three main topics: advancement of existing methods, introduction of novel methods and development of artificial intelligent systems for automatic defect recognition (ADR). Automatic defect classification algorithm comprises of two main tasks: creating a defect database and preparing a defect classifier. Here, the database was built using defect features that describe all geometrical and texture properties of the defect. Almost twenty carefully selected features calculated for flaws extracted from real radiograms were used. The radiograms were obtained from shipbuilding industry and they were verified by qualified operator. Two weld defect's classifiers based on artificial neural networks were proposed and compared. First model consisted of one neural network model, where each output neuron corresponded to different defect group. The second model contained five neural networks. Each neural network had one neuron on output and was responsible for detection of defects from one group. In order to evaluate the effectiveness of the neural networks classifiers, the mean square errors were calculated for test radiograms and compared.

  4. Electricity price short-term forecasting using artificial neural networks

    SciTech Connect

    Szkuta, B.R.; Sanabria, L.A.; Dillon, T.S.

    1999-08-01

    This paper presents the System Marginal Price (SMP) short-term forecasting implementation using the Artificial Neural Networks (ANN) computing technique. The described approach uses the three-layered ANN paradigm with back-propagation. The retrospective SMP real-world data, acquired from the deregulated Victorian power system, was used for training and testing the ANN. The results presented in this paper confirm considerable value of the ANN based approach in forecasting the SMP.

  5. Using Artificial Neural Networks to Assess Changes in Microbial Communities

    SciTech Connect

    Brandt, C.C.; Macnaughton, S.; Palumbo, A.V.; Pfiffner, S.M.; Schryver, J.C.

    1999-04-19

    We evaluated artificial neural networks (ANNs) as a technique for assessing changes in soil microbial communities following exposure to metals. We analyzed signature lipid biomarker (SLB) data collected from two soil microcosm experiments using traditional statistical techniques and ANN. Two phases of data analysis were done; pattern recognition and prediction. In general, the ANNs were better able to detect patterns and relationships in the SLB data than were the traditional statistical techniques.

  6. The importance of artificial neural networks in biomedicine

    SciTech Connect

    Burke, H.B.

    1995-12-31

    The future explanatory power in biomedicine will be at the molecular-genetic level of analysis (rather than the epidemiologic-demographic or anatomic-cellular levels). This is the level of complex systems. Complex systems are characterized by nonlinearity and complex interactions. It is difficult for traditional statistical methods to capture complex systems because traditional methods attempt to find the model that best fits the statistician`s understanding of the phenomenon; complex systems are difficult to understand and therefore difficult to fit with a simple model. Artificial neural networks are nonparametric regression models. They can capture any phenomena, to any degree of accuracy (depending on the adequacy of the data and the power of the predictors), without prior knowledge of the phenomena. Further, artificial neural networks can be represented, not only as formulae, but also as graphical models. Graphical models can increase analytic power and flexibility. Artificial neural networks are a powerful method for capturing complex phenomena, but their use requires a paradigm shift, from exploratory analysis of the data to exploratory analysis of the model.

  7. Predicting developmental disorder in infants using an artificial neural network.

    PubMed

    Soleimani, Farin; Teymouri, Robab; Biglarian, Akbar

    2013-07-13

    Early recognition of developmental disorders is an important goal, and equally important is avoiding misdiagnosing a disorder in a healthy child without pathology. The aim of the present study was to develop an artificial neural network using perinatal information to predict developmental disorder at infancy. A total of 1,232 mother-child dyads were recruited from 6,150 in the original data of Karaj, Alborz Province, Iran. Thousands of variables are examined in this data including basic characteristics, medical history, and variables related to infants.  The validated Infant Neurological International Battery test was employed to assess the infant's development. The concordance indexes showed that true prediction of developmental disorder in the artificial neural network model, compared to the logistic regression model, was 83.1% vs. 79.5% and the area under ROC curves, calculated from testing data, were 0.79 and 0.68, respectively. In addition, specificity and sensitivity of the ANN model vs. LR model was calculated 93.2% vs. 92.7% and 39.1% vs. 21.7%. An artificial neural network performed significantly better than a logistic regression model.

  8. Visualizing the Hidden Activity of Artificial Neural Networks.

    PubMed

    Rauber, Paulo E; Fadel, Samuel G; Falcao, Alexandre X; Telea, Alexandru C

    2017-01-01

    In machine learning, pattern classification assigns high-dimensional vectors (observations) to classes based on generalization from examples. Artificial neural networks currently achieve state-of-the-art results in this task. Although such networks are typically used as black-boxes, they are also widely believed to learn (high-dimensional) higher-level representations of the original observations. In this paper, we propose using dimensionality reduction for two tasks: visualizing the relationships between learned representations of observations, and visualizing the relationships between artificial neurons. Through experiments conducted in three traditional image classification benchmark datasets, we show how visualization can provide highly valuable feedback for network designers. For instance, our discoveries in one of these datasets (SVHN) include the presence of interpretable clusters of learned representations, and the partitioning of artificial neurons into groups with apparently related discriminative roles.

  9. Artificial neural network modeling of dissolved oxygen in reservoir.

    PubMed

    Chen, Wei-Bo; Liu, Wen-Cheng

    2014-02-01

    The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.

  10. Resource constrained design of artificial neural networks using comparator neural network

    NASA Technical Reports Server (NTRS)

    Wah, Benjamin W.; Karnik, Tanay S.

    1992-01-01

    We present a systematic design method executed under resource constraints for automating the design of artificial neural networks using the back error propagation algorithm. Our system aims at finding the best possible configuration for solving the given application with proper tradeoff between the training time and the network complexity. The design of such a system is hampered by three related problems. First, there are infinitely many possible network configurations, each may take an exceedingly long time to train; hence, it is impossible to enumerate and train all of them to completion within fixed time, space, and resource constraints. Second, expert knowledge on predicting good network configurations is heuristic in nature and is application dependent, rendering it difficult to characterize fully in the design process. A learning procedure that refines this knowledge based on examples on training neural networks for various applications is, therefore, essential. Third, the objective of the network to be designed is ill-defined, as it is based on a subjective tradeoff between the training time and the network cost. A design process that proposes alternate configurations under different cost-performance tradeoff is important. We have developed a Design System which schedules the available time, divided into quanta, for testing alternative network configurations. Its goal is to select/generate and test alternative network configurations in each quantum, and find the best network when time is expended. Since time is limited, a dynamic schedule that determines the network configuration to be tested in each quantum is developed. The schedule is based on relative comparison of predicted training times of alternative network configurations using comparator network paradigm. The comparator network has been trained to compare training times for a large variety of traces of TSSE-versus-time collected during back-propagation learning of various applications.

  11. The application of artificial neural networks in astronomy

    NASA Astrophysics Data System (ADS)

    Li, Li-Li; Zhang, Yan-Xia; Zhao, Yong-Heng; Yang, Da-Wei

    2006-12-01

    Artificial Neural Networks (ANNs) are computer algorithms inspired from simple models of human central nervous system activity. They can be roughly divided into two main kinds: supervised and unsupervised. The supervised approach lays the stress on "teaching" a machine to do the work of a mention human expert, usually by showing examples for which the true answer is supplied by the expert. The unsupervised one is aimed at learning new things from the data, and most useful when the data cannot easily be plotted in a two or three dimensional space. ANNs have been used widely and successfully in various fields, for instance, pattern recognition, financial analysis, biology, engineering and so on, because they have many merits such as self-learning, self-adapting, good robustness and dynamically rapid response as well as strong capability of dealing with non-linear problems. In the last few years there has been an increasing interest toward the astronomical applications of ANNs. In this paper, the authors firstly introduce the fundamental principle of ANNs together with the architecture of the network and outline various kinds of learning algorithms and network toplogies. The specific aspects of the applications of ANNs in astronomical problems are also listed, which contain the strong capabilities of approximating to arbitrary accuracy, any nonlinear functional mapping, parallel and distributed storage, tolerance of faulty and generalization of results. They summarize the advantages and disadvantages of main ANN models available to the astronomical community. Furthermore, the application cases of ANNs in astronomy are mainly described in detail. Here, the focus is on some of the most interesting fields of its application, for example: object detection, star/galaxy classification, spectral classification, galaxy morphology classification, the estimation of photometric redshifts of galaxies and time series analysis. In addition, other kinds of applications have been

  12. Training product unit neural networks with genetic algorithms

    NASA Technical Reports Server (NTRS)

    Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

    1991-01-01

    The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

  13. TEDANN: Turbine engine diagnostic artificial neural network

    SciTech Connect

    Kangas, L.J.; Greitzer, F.L.; Illi, O.J. Jr.

    1994-03-17

    The initial focus of TEDANN is on AGT-1500 fuel flow dynamics: that is, fuel flow faults detectable in the signals from the Electronic Control Unit`s (ECU) diagnostic connector. These voltage signals represent the status of the Electro-Mechanical Fuel System (EMFS) in response to ECU commands. The EMFS is a fuel metering device that delivers fuel to the turbine engine under the management of the ECU. The ECU is an analog computer whose fuel flow algorithm is dependent upon throttle position, ambient air and turbine inlet temperatures, and compressor and turbine speeds. Each of these variables has a representative voltage signal available at the ECU`s J1 diagnostic connector, which is accessed via the Automatic Breakout Box (ABOB). The ABOB is a firmware program capable of converting 128 separate analog data signals into digital format. The ECU`s J1 diagnostic connector provides 32 analog signals to the ABOB. The ABOB contains a 128 to 1 multiplexer and an analog-to-digital converter, CP both operated by an 8-bit embedded controller. The Army Research Laboratory (ARL) developed and published the hardware specifications as well as the micro-code for the ABOB Intel EPROM processor and the internal code for the multiplexer driver subroutine. Once the ECU analog readings are converted into a digital format, the data stream will be input directly into TEDANN via the serial RS-232 port of the Contact Test Set (CTS) computer. The CTS computer is an IBM compatible personal computer designed and constructed for tactical use on the battlefield. The CTS has a 50MHz 32-bit Intel 80486DX processor. It has a 200MB hard drive and 8MB RAM. The CTS also has serial, parallel and SCSI interface ports. The CTS will also host a frame-based expert system for diagnosing turbine engine faults (referred to as TED; not shown in Figure 1).

  14. An Investigation of the Application of Artificial Neural Networks to Adaptive Optics Imaging Systems

    DTIC Science & Technology

    1991-12-01

    Recurrent and feedforward artificial neural networks are developed as wavefront reconstructors. The recurrent neural network studied is the Hopfield...input features are just the wavefront sensor slope outputs. Both artificial neural networks use their inputs to calculate deformable mirror actuator commands. The effects of training are examined.

  15. Artificial neural networks applied to forecasting time series.

    PubMed

    Montaño Moreno, Juan J; Palmer Pol, Alfonso; Muñoz Gracia, Pilar

    2011-04-01

    This study offers a description and comparison of the main models of Artificial Neural Networks (ANN) which have proved to be useful in time series forecasting, and also a standard procedure for the practical application of ANN in this type of task. The Multilayer Perceptron (MLP), Radial Base Function (RBF), Generalized Regression Neural Network (GRNN), and Recurrent Neural Network (RNN) models are analyzed. With this aim in mind, we use a time series made up of 244 time points. A comparative study establishes that the error made by the four neural network models analyzed is less than 10%. In accordance with the interpretation criteria of this performance, it can be concluded that the neural network models show a close fit regarding their forecasting capacity. The model with the best performance is the RBF, followed by the RNN and MLP. The GRNN model is the one with the worst performance. Finally, we analyze the advantages and limitations of ANN, the possible solutions to these limitations, and provide an orientation towards future research.

  16. Inverse simulation system for manual-controlled rendezvous and docking based on artificial neural network

    NASA Astrophysics Data System (ADS)

    Zhou, Wanmeng; Wang, Hua; Tang, Guojin; Guo, Shuai

    2016-09-01

    The time-consuming experimental method for handling qualities assessment cannot meet the increasing fast design requirements for the manned space flight. As a tool for the aircraft handling qualities research, the model-predictive-control structured inverse simulation (MPC-IS) has potential applications in the aerospace field to guide the astronauts' operations and evaluate the handling qualities more effectively. Therefore, this paper establishes MPC-IS for the manual-controlled rendezvous and docking (RVD) and proposes a novel artificial neural network inverse simulation system (ANN-IS) to further decrease the computational cost. The novel system was obtained by replacing the inverse model of MPC-IS with the artificial neural network. The optimal neural network was trained by the genetic Levenberg-Marquardt algorithm, and finally determined by the Levenberg-Marquardt algorithm. In order to validate MPC-IS and ANN-IS, the manual-controlled RVD experiments on the simulator were carried out. The comparisons between simulation results and experimental data demonstrated the validity of two systems and the high computational efficiency of ANN-IS.

  17. Analysis of Mars Express Ionogram Data via a Multilayer Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Wilkinson, Collin; Potter, Arron; Palmer, Greg; Duru, Firdevs

    2017-01-01

    Mars Advanced Radar for Subsurface and Ionospheric Sounding (MARSIS), which is a low frequency radar on the Mars Express (MEX) Spacecraft, can provide electron plasma densities of the ionosphere local at the spacecraft in addition to densities obtained with remote sounding. The local electron densities are obtained, with a standard error of about 2%, by measuring the electron plasma frequencies with an electronic ruler on ionograms, which are plots of echo intensity as a function of time and frequency. This is done by using a tool created at the University of Iowa (Duru et al., 2008). This approach is time consuming due to the rapid accumulation of ionogram data. In 2013, results from an algorithm-based analysis of ionograms were reported by Andrews et al., but this method did not improve the human error. In the interest of fast, accurate data interpretation, a neural network (NN) has been created based on the Fast Artificial Neural Network C libraries. This NN consists of artificial neurons, with 4 layers of 12960, 10000, 1000 and 1 neuron(s) each, consecutively. This network was trained using 40 iterations of 1000 orbits. The algorithm-based method of Andrews et al. had a standard error of 40%, while the neural network has achieved error on the order of 20%.

  18. Benchmark for Peak Detection Algorithms in Fiber Bragg Grating Interrogation and a New Neural Network for its Performance Improvement

    PubMed Central

    Negri, Lucas; Nied, Ademir; Kalinowski, Hypolito; Paterno, Aleksander

    2011-01-01

    This paper presents a benchmark for peak detection algorithms employed in fiber Bragg grating spectrometric interrogation systems. The accuracy, precision, and computational performance of currently used algorithms and those of a new proposed artificial neural network algorithm are compared. Centroid and gaussian fitting algorithms are shown to have the highest precision but produce systematic errors that depend on the FBG refractive index modulation profile. The proposed neural network displays relatively good precision with reduced systematic errors and improved computational performance when compared to other networks. Additionally, suitable algorithms may be chosen with the general guidelines presented. PMID:22163806

  19. Design of a Thermoacoustic Sensor for Low Intensity Ultrasound Measurements Based on an Artificial Neural Network.

    PubMed

    Xing, Jida; Chen, Jie

    2015-06-23

    In therapeutic ultrasound applications, accurate ultrasound output intensities are crucial because the physiological effects of therapeutic ultrasound are very sensitive to the intensity and duration of these applications. Although radiation force balance is a benchmark technique for measuring ultrasound intensity and power, it is costly, difficult to operate, and compromised by noise vibration. To overcome these limitations, the development of a low-cost, easy to operate, and vibration-resistant alternative device is necessary for rapid ultrasound intensity measurement. Therefore, we proposed and validated a novel two-layer thermoacoustic sensor using an artificial neural network technique to accurately measure low ultrasound intensities between 30 and 120 mW/cm2. The first layer of the sensor design is a cylindrical absorber made of plexiglass, followed by a second layer composed of polyurethane rubber with a high attenuation coefficient to absorb extra ultrasound energy. The sensor determined ultrasound intensities according to a temperature elevation induced by heat converted from incident acoustic energy. Compared with our previous one-layer sensor design, the new two-layer sensor enhanced the ultrasound absorption efficiency to provide more rapid and reliable measurements. Using a three-dimensional model in the K-wave toolbox, our simulation of the ultrasound propagation process demonstrated that the two-layer design is more efficient than the single layer design. We also integrated an artificial neural network algorithm to compensate for the large measurement offset. After obtaining multiple parameters of the sensor characteristics through calibration, the artificial neural network is built to correct temperature drifts and increase the reliability of our thermoacoustic measurements through iterative training about ten seconds. The performance of the artificial neural network method was validated through a series of experiments. Compared to our previous

  20. Design of a Thermoacoustic Sensor for Low Intensity Ultrasound Measurements Based on an Artificial Neural Network

    PubMed Central

    Xing, Jida; Chen, Jie

    2015-01-01

    In therapeutic ultrasound applications, accurate ultrasound output intensities are crucial because the physiological effects of therapeutic ultrasound are very sensitive to the intensity and duration of these applications. Although radiation force balance is a benchmark technique for measuring ultrasound intensity and power, it is costly, difficult to operate, and compromised by noise vibration. To overcome these limitations, the development of a low-cost, easy to operate, and vibration-resistant alternative device is necessary for rapid ultrasound intensity measurement. Therefore, we proposed and validated a novel two-layer thermoacoustic sensor using an artificial neural network technique to accurately measure low ultrasound intensities between 30 and 120 mW/cm2. The first layer of the sensor design is a cylindrical absorber made of plexiglass, followed by a second layer composed of polyurethane rubber with a high attenuation coefficient to absorb extra ultrasound energy. The sensor determined ultrasound intensities according to a temperature elevation induced by heat converted from incident acoustic energy. Compared with our previous one-layer sensor design, the new two-layer sensor enhanced the ultrasound absorption efficiency to provide more rapid and reliable measurements. Using a three-dimensional model in the K-wave toolbox, our simulation of the ultrasound propagation process demonstrated that the two-layer design is more efficient than the single layer design. We also integrated an artificial neural network algorithm to compensate for the large measurement offset. After obtaining multiple parameters of the sensor characteristics through calibration, the artificial neural network is built to correct temperature drifts and increase the reliability of our thermoacoustic measurements through iterative training about ten seconds. The performance of the artificial neural network method was validated through a series of experiments. Compared to our previous

  1. Vibration monitoring of EDF rotating machinery using artificial neural networks

    SciTech Connect

    Alguindigue, I.E.; Loskiewicz-Buczak, A.; Uhrig, R.E. . Dept. of Nuclear Engineering); Hamon, L.; Lefevre, F. . Direction des Etudes et Recherches)

    1991-01-01

    Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected by Electricite de France (EDF). Two neural networks algorithms were used in our project: the Recirculation algorithm and the Backpropagation algorithm. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results are very encouraging.

  2. Vibration monitoring of EDF rotating machinery using artificial neural networks

    SciTech Connect

    Alguindigue, I.E.; Loskiewicz-Buczak, A.; Uhrig, R.E.; Hamon, L.; Lefevre, F.

    1991-12-31

    Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected by Electricite de France (EDF). Two neural networks algorithms were used in our project: the Recirculation algorithm and the Backpropagation algorithm. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results are very encouraging.

  3. A Winner Determination Algorithm for Combinatorial Auctions Based on Hybrid Artificial Fish Swarm Algorithm

    NASA Astrophysics Data System (ADS)

    Zheng, Genrang; Lin, ZhengChun

    The problem of winner determination in combinatorial auctions is a hotspot electronic business, and a NP hard problem. A Hybrid Artificial Fish Swarm Algorithm(HAFSA), which is combined with First Suite Heuristic Algorithm (FSHA) and Artificial Fish Swarm Algorithm (AFSA), is proposed to solve the problem after probing it base on the theories of AFSA. Experiment results show that the HAFSA is a rapidly and efficient algorithm for The problem of winner determining. Compared with Ant colony Optimization Algorithm, it has a good performance with broad and prosperous application.

  4. Recurrent neural networks training with stable bounding ellipsoid algorithm.

    PubMed

    Yu, Wen; de Jesús Rubio, José

    2009-06-01

    Bounding ellipsoid (BE) algorithms offer an attractive alternative to traditional training algorithms for neural networks, for example, backpropagation and least squares methods. The benefits include high computational efficiency and fast convergence speed. In this paper, we propose an ellipsoid propagation algorithm to train the weights of recurrent neural networks for nonlinear systems identification. Both hidden layers and output layers can be updated. The stability of the BE algorithm is proven.

  5. Structural damage identification based on rough sets and artificial neural network.

    PubMed

    Liu, Chengyin; Wu, Xiang; Wu, Ning; Liu, Chunyu

    2014-01-01

    This paper investigates potential applications of the rough sets (RS) theory and artificial neural network (ANN) method on structural damage detection. An information entropy based discretization algorithm in RS is applied for dimension reduction of the original damage database obtained from finite element analysis (FEA). The proposed approach is tested with a 14-bay steel truss model for structural damage detection. The experimental results show that the damage features can be extracted efficiently from the combined utilization of RS and ANN methods even the volume of measurement data is enormous and with uncertainties.

  6. Artificial neural network for the determination of Hubble Space Telescope aberration from stellar images

    NASA Technical Reports Server (NTRS)

    Barrett, Todd K.; Sandler, David G.

    1993-01-01

    An artificial-neural-network method, first developed for the measurement and control of atmospheric phase distortion, using stellar images, was used to estimate the optical aberration of the Hubble Space Telescope. A total of 26 estimates of distortion was obtained from 23 stellar images acquired at several secondary-mirror axial positions. The results were expressed as coefficients of eight orthogonal Zernike polynomials: focus through third-order spherical. For all modes other than spherical the measured aberration was small. The average spherical aberration of the estimates was -0.299 micron rms, which is in good agreement with predictions obtained when iterative phase-retrieval algorithms were used.

  7. Artificial neural network for the determination of Hubble Space Telescope aberration from stellar images.

    PubMed

    Barrett, T K; Sandler, D G

    1993-04-01

    An artificial-neural-network method, first developed for the measurement and control of atmospheric phase distortion, using stellar images, was used to estimate the optical aberration of the Hubble Space Telescope. A total of 26 estimates of distortion was obtained from 23 stellar images acquired at several secondary-mirror axial positions. The results were expressed as coefficients of eight orthogonal Zernike polynomials: focus through third-order spherical. For all modes other than spherical the measured aberration was small. The average spherical aberration of the estimates was -0.299 microm rms, which is in good agreement with predictions obtained when iterative phase-retrieval algorithms were used.

  8. EEG signal classification based on artificial neural networks and amplitude spectra features

    NASA Astrophysics Data System (ADS)

    Chojnowski, K.; FrÄ czek, J.

    BCI (called Brain-Computer Interface) is an interface that allows direct communication between human brain and an external device. It bases on EEG signal collection, processing and classification. In this paper a complete BCI system is presented which classifies EEG signal using artificial neural networks. For this purpose we used a multi-layered perceptron architecture trained with the RProp algorithm. Furthermore a simple multi-threaded method for automatic network structure optimizing was shown. We presented the results of our system in the opening and closing eyes recognition task. We also showed how our system could be used for controlling devices basing on imaginary hand movements.

  9. Application of artificial neural network for prediction of marine diesel engine performance

    NASA Astrophysics Data System (ADS)

    Mohd Noor, C. W.; Mamat, R.; Najafi, G.; Nik, W. B. Wan; Fadhil, M.

    2015-12-01

    This study deals with an artificial neural network (ANN) modelling of a marine diesel engine to predict the brake power, output torque, brake specific fuel consumption, brake thermal efficiency and volumetric efficiency. The input data for network training was gathered from engine laboratory testing running at various engine speed. The prediction model was developed based on standard back-propagation Levenberg-Marquardt training algorithm. The performance of the model was validated by comparing the prediction data sets with the measured experiment data. Results showed that the ANN model provided good agreement with the experimental data with high accuracy.

  10. Structural Damage Identification Based on Rough Sets and Artificial Neural Network

    PubMed Central

    Liu, Chengyin; Wu, Xiang; Wu, Ning; Liu, Chunyu

    2014-01-01

    This paper investigates potential applications of the rough sets (RS) theory and artificial neural network (ANN) method on structural damage detection. An information entropy based discretization algorithm in RS is applied for dimension reduction of the original damage database obtained from finite element analysis (FEA). The proposed approach is tested with a 14-bay steel truss model for structural damage detection. The experimental results show that the damage features can be extracted efficiently from the combined utilization of RS and ANN methods even the volume of measurement data is enormous and with uncertainties. PMID:25013847

  11. Prediction of radiation induced hardening of reactor pressure vessel steels using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Castin, N.; Malerba, L.; Chaouadi, R.

    2011-01-01

    In this paper, we use an artificial neural network approach to obtain predictions of neutron irradiation induced hardening, more precisely of the change in the yield stress, for reactor pressure vessel steels of pressurized water nuclear reactors. Different training algorithms are proposed and compared, with the goal of identifying the best procedure to follow depending on the needs of the user. The numerical importance of some input variables is also studied. Very accurate numerical regressions are obtained, by taking only four input variables into account: neutron fluence, irradiation temperature, and chemical composition (Cu and Ni content). Accurate extrapolations in term of neutron fluence are obtained.

  12. Classification of Communication Signals and Detection of Unknown Formats Using Artificial Neural Networks

    DTIC Science & Technology

    2006-12-01

    Using Artificial Neural Networks Alexander Iversen, Nicholas K. Taylor and Keith E. Brown Intelligent Systems Laboratory Heriot-Watt University...Brown, K.E. (2006) Classification of Communication Signals and Detection of Unknown Formats Using Artificial Neural Networks . In Military...Classification of Communication Signals and Detection of Unknown Formats Using Artificial Neural Networks 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM

  13. Angle of Arrival Detection Through Artificial Neural Network Analysis of Optical Fiber Intensity Patterns

    DTIC Science & Technology

    1990-12-01

    ARTIFICIAL NEURAL NETWORK ANALYSIS OF OPTICAL FIBER INTENSITY PATTERNS THESIS Scott Thomas Captain, USAF AFIT/GE/ENG/90D-62 DTIC...ELECTE ao • JAN08 1991 Approved for public release; distribution unlimited. AFIT/GE/ENG/90D-62 ANGLE OF ARRIVAL DETECTION THROUGH ARTIFICIAL NEURAL NETWORK ANALYSIS... ARTIFICIAL NEURAL NETWORK ANALYSIS OF OPTICAL FIBER INTENSITY PATTERNS L Introduction The optical sensors of United States Air Force reconnaissance

  14. Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach.

    DTIC Science & Technology

    1998-05-01

    Coverage Probability with a Random Optimization Procedure: An Artificial Neural Network Approach by Biing T. Guan, George Z. Gertner, and Alan B...Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach 6. AUTHOR(S) Biing...coverage based on past coverage. Approach A literature survey was conducted to identify artificial neural network analysis techniques applicable for

  15. Dynamic modeling of physical phenomena for probabilistic risk assessments using artificial neural networks

    SciTech Connect

    Benjamin, A.S.; Paez, T.L.; Brown, N.N.

    1998-01-01

    In most probabilistic risk assessments, there is a subset of accident scenarios that involves physical challenges to the system, such as high heat rates and/or accelerations. The system`s responses to these challenges may be complicated, and their prediction may require the use of long-running computer codes. To deal with the many scenarios demanded by a risk assessment, the authors have been investigating the use of artificial neural networks (ANNs) as a fast-running estimation tool. They have developed a multivariate linear spline algorithm by extending previous ANN methods that use radial basis functions. They have applied the algorithm to problems involving fires, shocks, and vibrations. They have found that within the parameter range for which it is trained, the algorithm can simulate the nonlinear responses of complex systems with high accuracy. Running times per case are less than one second.

  16. Learning ensembles of neural networks by means of a Bayesian artificial immune system.

    PubMed

    Castro, Pablo A Dalbem; Von Zuben, Fernando José

    2011-02-01

    In this paper, we apply an immune-inspired approach to design ensembles of heterogeneous neural networks for classification problems. Our proposal, called Bayesian artificial immune system, is an estimation of distribution algorithm that replaces the traditional mutation and cloning operators with a probabilistic model, more specifically a Bayesian network, representing the joint distribution of promising solutions. Among the additional attributes provided by the Bayesian framework inserted into an immune-inspired search algorithm are the automatic control of the population size along the search and the inherent ability to promote and preserve diversity among the candidate solutions. Both are attributes generally absent from alternative estimation of distribution algorithms, and both were shown to be useful attributes when implementing the generation and selection of components of the ensemble, thus leading to high-performance classifiers. Several aspects of the design are illustrated in practical applications, including a comparative analysis with other attempts to synthesize ensembles.

  17. Improved Glomerular Filtration Rate Estimation by an Artificial Neural Network

    PubMed Central

    Zhang, Yunong; Zhang, Xiang; Chen, Jinxia; Lv, Linsheng; Ma, Huijuan; Wu, Xiaoming; Zhao, Weihong; Lou, Tanqi

    2013-01-01

    Background Accurate evaluation of glomerular filtration rates (GFRs) is of critical importance in clinical practice. A previous study showed that models based on artificial neural networks (ANNs) could achieve a better performance than traditional equations. However, large-sample cross-sectional surveys have not resolved questions about ANN performance. Methods A total of 1,180 patients that had chronic kidney disease (CKD) were enrolled in the development data set, the internal validation data set and the external validation data set. Additional 222 patients that were admitted to two independent institutions were externally validated. Several ANNs were constructed and finally a Back Propagation network optimized by a genetic algorithm (GABP network) was chosen as a superior model, which included six input variables; i.e., serum creatinine, serum urea nitrogen, age, height, weight and gender, and estimated GFR as the one output variable. Performance was then compared with the Cockcroft-Gault equation, the MDRD equations and the CKD-EPI equation. Results In the external validation data set, Bland-Altman analysis demonstrated that the precision of the six-variable GABP network was the highest among all of the estimation models; i.e., 46.7 ml/min/1.73 m2 vs. a range from 71.3 to 101.7 ml/min/1.73 m2, allowing improvement in accuracy (15% accuracy, 49.0%; 30% accuracy, 75.1%; 50% accuracy, 90.5% [P<0.001 for all]) and CKD stage classification (misclassification rate of CKD stage, 32.4% vs. a range from 47.3% to 53.3% [P<0.001 for all]). Furthermore, in the additional external validation data set, precision and accuracy were improved by the six-variable GABP network. Conclusions A new ANN model (the six-variable GABP network) for CKD patients was developed that could provide a simple, more accurate and reliable means for the estimation of GFR and stage of CKD than traditional equations. Further validations are needed to assess the ability of the ANN model in diverse

  18. Science of the science, drug discovery and artificial neural networks.

    PubMed

    Patel, Jigneshkumar

    2013-03-01

    Drug discovery process many times encounters complex problems, which may be difficult to solve by human intelligence. Artificial Neural Networks (ANNs) are one of the Artificial Intelligence (AI) technologies used for solving such complex problems. ANNs are widely used for primary virtual screening of compounds, quantitative structure activity relationship studies, receptor modeling, formulation development, pharmacokinetics and in all other processes involving complex mathematical modeling. Despite having such advanced technologies and enough understanding of biological systems, drug discovery is still a lengthy, expensive, difficult and inefficient process with low rate of new successful therapeutic discovery. In this paper, author has discussed the drug discovery science and ANN from very basic angle, which may be helpful to understand the application of ANN for drug discovery to improve efficiency.

  19. A brief overview and introduction to artificial neural networks.

    PubMed

    Buscema, Massimo

    2002-01-01

    This article is designed to acquaint professionals working in the field of substance use intervention with a range of artificial intelligence nonlinear, powerful tools, artificial neural networks, concepts, and paradigms. The family of ANNs, when appropriately selected and used, permits the maximization of what can be derived from available data as well as our studying and understanding the many people, processes, and phenomena which comprise substance use and its intervention. The latter represent complex, dynamic, multidimensional phenomena which are unpredictable and uncontrollable in the traditional "cause and effect" sense. As such they are likely to be nonlinear in their very essence. Using linear-based paradigms for planned intervention with nonlinear phenomena brooks the all-too-common possibility of using inappropriate intervention paradigms and/or drawing misleading conclusions about what is and/or has happened.

  20. Experiments in dysarthric speech recognition using artificial neural networks.

    PubMed

    Jayaram, G; Abdelhamied, K

    1995-05-01

    In this study, we investigated the use of artificial neural networks (ANNs) to recognize dysarthric speech. Two multilayer neural networks were developed, trained, and tested using isolated words spoken by a dysarthric speaker. One network had the fast Fourier transform (FFT) coefficients as inputs, while the other network had the formant frequencies as inputs. The effect of additional features in the input vector on the recognition rate was also observed. The recognition rate was evaluated against the intelligibility rating obtained by five human listeners and also against the recognition rate of the Introvoice commercial speech-recognition system. Preliminary results demonstrated the ability of the developed networks to successfully recognize dysarthric speech despite its large variability. These networks clearly outperformed both the human listeners and the Introvoice commercial system.

  1. Prediction of Universal Time using the artificial neural network

    NASA Astrophysics Data System (ADS)

    Richard, J. Y.; Lopes, P.; Barache, C.; Bizouard, C.; Gambis, D.

    2014-12-01

    The monitoring of the Earth Orientation Parameters (EOP) variations is the main task of the Earth orientation Center of the IERS. In addition, for various applications linked in particular to navigation, precise orbit determination or leap seconds announcements, short and long term predictions are required. The method which is currently applied for predictions is based on deterministic processes, Least Square fitting, autoregressive filtering (Gambis and Luzum 2011). We present an alternative method, the Artificial Neural Networks (ANN) which has have already been successfully applied for pattern recognition. It has been tested as well by various authors for EOP predictions but with so far no real improvement compared to the current methods (Schuh et. al. 2002). New formalisms recently developed allow reconsidering the use of neural networks for EOP predictions. Series of simulations were performed for both short and long term predictions. Statistics and comparisons with the current methods are presented.

  2. Design of Jetty Piles Using Artificial Neural Networks

    PubMed Central

    2014-01-01

    To overcome the complication of jetty pile design process, artificial neural networks (ANN) are adopted. To generate the training samples for training ANN, finite element (FE) analysis was performed 50 times for 50 different design cases. The trained ANN was verified with another FE analysis case and then used as a structural analyzer. The multilayer neural network (MBPNN) with two hidden layers was used for ANN. The framework of MBPNN was defined as the input with the lateral forces on the jetty structure and the type of piles and the output with the stress ratio of the piles. The results from the MBPNN agree well with those from FE analysis. Particularly for more complex modes with hundreds of different design cases, the MBPNN would possibly substitute parametric studies with FE analysis saving design time and cost. PMID:25177724

  3. Classification of cardiac patient states using artificial neural networks

    PubMed Central

    Kannathal, N; Acharya, U Rajendra; Lim, Choo Min; Sadasivan, PK; Krishnan, SM

    2003-01-01

    Electrocardiogram (ECG) is a nonstationary signal; therefore, the disease indicators may occur at random in the time scale. This may require the patient be kept under observation for long intervals in the intensive care unit of hospitals for accurate diagnosis. The present study examined the classification of the states of patients with certain diseases in the intensive care unit using their ECG and an Artificial Neural Networks (ANN) classification system. The states were classified into normal, abnormal and life threatening. Seven significant features extracted from the ECG were fed as input parameters to the ANN for classification. Three neural network techniques, namely, back propagation, self-organizing maps and radial basis functions, were used for classification of the patient states. The ANN classifier in this case was observed to be correct in approximately 99% of the test cases. This result was further improved by taking 13 features of the ECG as input for the ANN classifier. PMID:19649222

  4. Fault Tolerant Characteristics of Artificial Neural Network Electronic Hardware

    NASA Technical Reports Server (NTRS)

    Zee, Frank

    1995-01-01

    The fault tolerant characteristics of analog-VLSI artificial neural network (with 32 neurons and 532 synapses) chips are studied by exposing them to high energy electrons, high energy protons, and gamma ionizing radiations under biased and unbiased conditions. The biased chips became nonfunctional after receiving a cumulative dose of less than 20 krads, while the unbiased chips only started to show degradation with a cumulative dose of over 100 krads. As the total radiation dose increased, all the components demonstrated graceful degradation. The analog sigmoidal function of the neuron became steeper (increase in gain), current leakage from the synapses progressively shifted the sigmoidal curve, and the digital memory of the synapses and the memory addressing circuits began to gradually fail. From these radiation experiments, we can learn how to modify certain designs of the neural network electronic hardware without using radiation-hardening techniques to increase its reliability and fault tolerance.

  5. Analysis of pellet properties with use of artificial neural networks.

    PubMed

    Mendyk, Aleksander; Kleinebudde, Peter; Thommes, Markus; Yoo, Angelina; Szlęk, Jakub; Jachowicz, Renata

    2010-11-20

    The objective was to prepare neural models identifying relationships between formulation characteristics and pellet properties based on algorithmic approach of crucial variables selection and neuro-fuzzy systems application. The database consisted of information about 227 pellet formulations prepared by extrusion/spheronization method, with various model drugs and excipients. Cheminformatic description of excipients and model drugs was employed for numerical description of pellet formulations. Initial numbers of neural model inputs were up to around 3000. The inputs reduction procedure based on sensitivity analysis allowed to obtain less than 40 inputs for each model. The reduced models were subjects of fuzzy logic implementation resulting in logical rules tables providing human-readable rule sets applicable in future development of pellet formulations. Neural modeling enhanced knowledge about pelletization process and provided means for future computer-guided search for the optimal formulation.

  6. Artificial neural networks in Space Station optimal attitude control

    NASA Astrophysics Data System (ADS)

    Kumar, Renjith R.; Seywald, Hans; Deshpande, Samir M.; Rahman, Zia

    1992-08-01

    Innovative techniques of using 'Artificial Neural Networks' (ANN) for improving the performance of the pitch axis attitude control system of Space Station Freedom using Control Moment Gyros (CMGs) are investigated. The first technique uses a feedforward ANN with multilayer perceptrons to obtain an on-line controller which improves the performance of the control system via a model following approach. The second techique uses a single layer feedforward ANN with a modified back propagation scheme to estimate the internal plant variations and the external disturbances separately. These estimates are then used to solve two differential Riccati equations to obtain time varying gains which improve the control system performance in successive orbits.

  7. Weld modeling and control using artificial neural networks

    SciTech Connect

    Cook, G.E.; Barnett, R.J.; Andersen, K.; Strauss, A.M.

    1995-11-01

    The arc welding processes play an important role in modern manufacturing. Despite the widespread use of arc welding for joining metals, controlling most welding processes still requires considerable skills and experience on behalf of the human welder. Total automation of welding has not yet been achieved, largely because the physics which determine the success of any welding task, are not yet fully understood and quantified. Artificial neural networks were evaluated for monitoring and control of the variable polarity plasma arc welding process. Three areas of welding application were investigated: weld process modeling, weld process control, and weld bead profile analysis for quality control.

  8. Using Artificial Neural Networks to Assess Microbial Communities

    SciTech Connect

    Almeida, J.S.; Brand, C.C.; Palumbo, A.V.; Pfiffner, S.M.; Schryver, J.C.

    1998-09-08

    We are evaluating artificial neural networks (ANNs) as tools for assessing changes in soil microbial communities following exposure to metals. We analyzed signature lipid biomarker data collected from two soil microcosm experiments using an autoassociative ANN. In one experiment, the microcosms were exposed to O, 100, or 250 ppm of metals, and in the other experiment the microcosms were exposed to O or 500 ppm of metals. The ANNs were able to distinguish between microcosms exposed and not exposed to metals in both experiments.

  9. Application of artificial neural nets to Shashlik calorimetry

    NASA Astrophysics Data System (ADS)

    Bonesini, M.; Gumenyuk, S.; Paganoni, M.; Petrovykh, L.; Terranova, F.

    1997-02-01

    Artificial Neural Networks (ANN) are powerful tools widely used in high-energy physics to solve track finding and particle identification problems. An entirely new class of application is related to the problem of recovering the information lost during data taking or signal transmission. Good performances can be reached by ANN when the events are described by quite regular patterns. Such a method was used for the DELPHI luminosity monitor (STIC) to recover calorimeter dead channels. A comparison with more traditional techniques is also given.

  10. Simulation of nonlinear strutures with artificial neural networks

    SciTech Connect

    Paez, T.L.

    1996-03-01

    Structural system simulation is important in analysis, design, testing, control, and other areas, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks offer a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and simulation a much simpler problem. A numerical example is also presented.

  11. Communication: Separable potential energy surfaces from multiplicative artificial neural networks

    SciTech Connect

    Koch, Werner Zhang, Dong H.

    2014-07-14

    We present a potential energy surface fitting scheme based on multiplicative artificial neural networks. It has the sum of products form required for efficient computation of the dynamics of multidimensional quantum systems with the multi configuration time dependent Hartree method. Moreover, it results in analytic potential energy matrix elements when combined with quantum dynamics methods using Gaussian basis functions, eliminating the need for a local harmonic approximation. Scaling behavior with respect to the complexity of the potential as well as the requested accuracy is discussed.

  12. Forecasting the portuguese stock market time series by using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Isfan, Monica; Menezes, Rui; Mendes, Diana A.

    2010-04-01

    In this paper, we show that neural networks can be used to uncover the non-linearity that exists in the financial data. First, we follow a traditional approach by analysing the deterministic/stochastic characteristics of the Portuguese stock market data and some typical features are studied, like the Hurst exponents, among others. We also simulate a BDS test to investigate nonlinearities and the results are as expected: the financial time series do not exhibit linear dependence. Secondly, we trained four types of neural networks for the stock markets and used the models to make forecasts. The artificial neural networks were obtained using a three-layer feed-forward topology and the back-propagation learning algorithm. The quite large number of parameters that must be selected to develop a neural network forecasting model involves some trial and as a consequence the error is not small enough. In order to improve this we use a nonlinear optimization algorithm to minimize the error. Finally, the output of the 4 models is quite similar, leading to a qualitative forecast that we compare with the results of the application of k-nearest-neighbor for the same time series.

  13. Autonomous Defensive Space Control via On-Board Artificial Neural Networks

    DTIC Science & Technology

    2007-04-01

    by investing the necessary resources for the development of space-based neural networks . An artificial neural network (ANN) or commonly just neural...processing capability could potentially enable the placement of neural networks , requiring significant processing power and storage capacity, on-board

  14. Prediction of friction factor of pure water flowing inside vertical smooth and microfin tubes by using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Çebi, A.; Akdoğan, E.; Celen, A.; Dalkilic, A. S.

    2016-06-01

    An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.

  15. Prediction of friction factor of pure water flowing inside vertical smooth and microfin tubes by using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Çebi, A.; Akdoğan, E.; Celen, A.; Dalkilic, A. S.

    2017-02-01

    An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.

  16. The use of artificial neural networks in experimental data acquisition and aerodynamic design

    NASA Technical Reports Server (NTRS)

    Meade, Andrew J., Jr.

    1991-01-01

    It is proposed that an artificial neural network be used to construct an intelligent data acquisition system. The artificial neural networks (ANN) model has a potential for replacing traditional procedures as well as for use in computational fluid dynamics validation. Potential advantages of the ANN model are listed. As a proof of concept, the author modeled a NACA 0012 airfoil at specific conditions, using the neural network simulator NETS, developed by James Baffes of the NASA Johnson Space Center. The neural network predictions were compared to the actual data. It is concluded that artificial neural networks can provide an elegant and valuable class of mathematical tools for data analysis.

  17. Total solar irradiance reconstruction using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Tebabal Yirdaw, Ambelu; Damtie, Baylie; Nigussie, Melessew; Bires, Abiyot; Yizengaw, Endawoke

    2015-08-01

    A feed-forward neural network which can account for nonlinear relationships was used to reconstruct total solar irradiance (TSI). A single layer feed forward neural network with back-propagation algorithm have been implemented for reconstructing daily total solar irradiance from daily photometric sunspot index, and core to wing ratio of Mg II index data. The data year from 1978 to 2013 was used for the training, validation and testing purpose. In order to obtain the optimum neural network for TSI reconstruction, the root mean square error (RMSE), mean absolute error (MAE) and regression coefficient have been taken into account. We have carried out the analysis is made by comparing the reconstructed TSI from neural networks (NNs ) and TSI measurement from satellite. We have found out that the reconstructed TSI and the PMOD composite have the correlation coefficient of about R=0.9307 over the span of the recorded, 1978 to 2013. The NNs model output indicates that reconstructed TSI from solar proxies (photometric index and MgII ) can explain 86.6% of the variance of TSI. Neural network is able to recreate TSI observations on a time scale of a day. This reconstructed TSI using NNs further strengthens the view that surface magnetism indeed plays a dominant role in modulating solar irradiance.

  18. Metaheuristic Algorithms for Convolution Neural Network

    PubMed Central

    Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

    2016-01-01

    A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent). PMID:27375738

  19. Model-free prediction of atmospheric warp based on artificial neural network.

    PubMed

    Halder, Kalyan Kumar; Tahtali, Murat; Anavatti, Sreenatha G

    2014-10-20

    This paper presents the application of artificial neural network for predicting the warping of images of remote objects or scenes ahead of time. The algorithm is based on estimating the pattern of warping of previously captured short-exposure frames through a generalized regression neural network (GRNN) and then predicting the warping of the upcoming frame. A high-accuracy optical flow technique is employed to estimate the dense motion fields of the captured frames, which are considered as training data for the GRNN. The proposed approach is independent of the pixel-oscillatory model unlike the state-of-the-art Kalman filter (KF) approach. Simulation experiments on synthetic and real-world turbulence degraded videos show that the proposed GRNN-based approach performs better than the KF approach in atmospheric warp prediction.

  20. Landslide susceptibility analysis using an artificial neural network model

    NASA Astrophysics Data System (ADS)

    Mansor, Shattri; Pradhan, Biswajeet; Daud, Mohamed; Jamaludin, Normalina; Khuzaimah, Zailani

    2007-10-01

    This paper deals with landslide susceptibility analysis using an artificial neural network model for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for the landslide hazards. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide hazard was analyzed using landslide occurrence factors employing the logistic regression model. The results of the analysis were verified using the landslide location data and compared with logistic regression model. The accuracy of hazard map observed was 85.73%. The qualitative landslide susceptibility analysis was carried out using an artificial neural network model by doing map overlay analysis in GIS environment. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.

  1. Prediction aluminum corrosion inhibitor efficiency using artificial neural network (ANN)

    NASA Astrophysics Data System (ADS)

    Ebrahimi, Sh; Kalhor, E. G.; Nabavi, S. R.; Alamiparvin, L.; Pogaku, R.

    2016-06-01

    In this study, activity of some Schiff bases as aluminum corrosion inhibitor was investigated using artificial neural network (ANN). Hence, corrosion inhibition efficiency of Schiff bases (in any type) were gathered from different references. Then these molecules were drawn and optimized in Hyperchem software. Molecular descriptors generating and descriptors selection were fulfilled by Dragon software and principal component analysis (PCA) method, respectively. These structural descriptors along with environmental descriptors (ambient temperature, time of exposure, pH and the concentration of inhibitor) were used as input variables. Furthermore, aluminum corrosion inhibition efficiency was used as output variable. Experimental data were split into three sets: training set (for model building) and test set (for model validation) and simulation (for general model). Modeling was performed by Multiple linear regression (MLR) methods and artificial neural network (ANN). The results obtained in linear models showed poor correlation between experimental and theoretical data. However nonlinear model presented satisfactory results. Higher correlation coefficient of ANN (R > 0.9) revealed that ANN can be successfully applied for prediction of aluminum corrosion inhibitor efficiency of Schiff bases in different environmental conditions.

  2. The Application of Artificial Neural Networks to Astronomical Classification

    NASA Astrophysics Data System (ADS)

    Naim, A.

    1995-12-01

    Galaxies are fundamental to the understanding of the structure and evolution of the universe. They contain stars, gas and dust, and serve as an astrophysical laboratory in which physical processes can be examined. In the context of the large scale structure of the universe galaxies can be viewed as test particles. They are bright and therefore visible at very large distances, and also numerous and so can be used to provide reliable statistics. In previous decades the major obstacle to studying the large scale structure of the universe was the relatively sparse data samples, because obtaining large quantities of galaxian images and spectra requires a lot of observing time, and the accumulation of significant data bases was therefore a slow process. This obstacle is in the process of being removed today, with the advent of large-scale surveys (e.g., the APM galaxy survey, the Sloan Digital Sky Survey and the 2 degree Field survey). The new challenge with which the astronomical community is faced is the management and analysis of the forthcoming extragalactic data bases. On top of the obvious need for better hardware to give large storage volumes and quick access, one needs to devise automated tools for data analysis. The sheer volume of the data renders manual analysis impractical. It would be best if one could somehow transfer the knowledge and expertise accumulated over years of painstaking manual analysis to a machine. This thesis is part of an effort to achieve this goal. I borrowed techniques that have proved useful in other fields (e.g., engineering) and applied them to astronomical datasets. The major tool I used was Artificial Neural Networks (ANNs), which was originally conceived as a simplified computational model for the brain. The scope of methods and algorithms referred to as ANNs is quite wide. In particular, a distinction is made between Supervised Learning algorithms and Unsupervised methods. The former put the emphasis on ``teaching'' a machine to do

  3. Improved artificial bee colony algorithm based gravity matching navigation method.

    PubMed

    Gao, Wei; Zhao, Bo; Zhou, Guang Tao; Wang, Qiu Ying; Yu, Chun Yang

    2014-07-18

    Gravity matching navigation algorithm is one of the key technologies for gravity aided inertial navigation systems. With the development of intelligent algorithms, the powerful search ability of the Artificial Bee Colony (ABC) algorithm makes it possible to be applied to the gravity matching navigation field. However, existing search mechanisms of basic ABC algorithms cannot meet the need for high accuracy in gravity aided navigation. Firstly, proper modifications are proposed to improve the performance of the basic ABC algorithm. Secondly, a new search mechanism is presented in this paper which is based on an improved ABC algorithm using external speed information. At last, modified Hausdorff distance is introduced to screen the possible matching results. Both simulations and ocean experiments verify the feasibility of the method, and results show that the matching rate of the method is high enough to obtain a precise matching position.

  4. Fuzzy learning vector quantization neural network and its application for artificial odor recognition system

    NASA Astrophysics Data System (ADS)

    Kusumoputro, Benyamin; Budiarto, Hary; Jatmiko, Wisnu

    2000-03-01

    In this paper, a kind of fuzzy algorithm for learning vector quantization is developed and used as pattern classifiers with a supervised learning paradigm in artificial odor discrimination system. In this type of FLVQ, the neuron activation is derived through fuzziness of the input data, so that the neural system could deal with the statistical of the measurement error directly. During learning,the similarity between the training vector and the reference vectors are calculated, and the winning reference vector is updated in two ways. Firstly, by shifting the central position of the fuzzy reference vector toward or away from the input vector, and secondly, by modifying its fuzziness. Two types of fuzziness modifications are used, i.e., a constant modification factor and a variable modification factor. This type of FLVQ is different in nature with FALVQ, and in this paper, the performance of FNLVQ network is compared with that of FALVQ in artificial odor recognition system. Experimental results show that both FALVQ and FNLVQ provided high recognition probability in determining various learn-category of odors, however, the FNLVQ neural system has the ability to recognize the unlearn-category of odor that could not recognized by FALVQ neural system.

  5. Pipelined chebyshev functional link artificial recurrent neural network for nonlinear adaptive filter.

    PubMed

    Zhao, Haiquan; Zhang, Jiashu

    2010-02-01

    A novel nonlinear adaptive filter with pipelined Chebyshev functional link artificial recurrent neural network (PCFLARNN) is presented in this paper, which uses a modification real-time recurrent learning algorithm. The PCFLARNN consists of a number of simple small-scale Chebyshev functional link artificial recurrent neural network (CFLARNN) modules. Compared to the standard recurrent neural network (RNN), those modules of PCFLARNN can simultaneously be performed in a pipelined parallelism fashion, and this would lead to a significant improvement in its total computational efficiency. Furthermore, contrasted with the architecture of a pipelined RNN (PRNN), each module of PCFLARNN is a CFLARNN whose nonlinearity is introduced by enhancing the input pattern with Chebyshev functional expansion, whereas the RNN of each module in PRNN utilizing linear input and first-order recurrent term only fails to utilize the high-order terms of inputs. Therefore, the performance of PCFLARNN can further be improved at the cost of a slightly increased computational complexity. In addition, due to the introduced nonlinear functional expansion of each module in PRNN, the number of input signals can be reduced. Computer simulations have demonstrated that the proposed filter performs better than PRNN and RNN for nonlinear colored signal prediction, nonstationary speech signal prediction, and chaotic time series prediction.

  6. Proposed health state awareness of helicopter blades using an artificial neural network strategy

    NASA Astrophysics Data System (ADS)

    Lee, Andrew; Habtour, Ed; Gadsden, S. A.

    2016-05-01

    Structural health prognostics and diagnosis strategies can be classified as either model or signal-based. Artificial neural network strategies are popular signal-based techniques. This paper proposes the use of helicopter blades in order to study the sensitivity of an artificial neural network to structural fatigue. The experimental setup consists of a scale aluminum helicopter blade exposed to transverse vibratory excitation at the hub using single axis electrodynamic shaker. The intent of this study is to optimize an algorithm for processing high-dimensional data while retaining important information content in an effort to select input features and weights, as well as health parameters, for training a neural network. Data from accelerometers and piezoelectric transducers is collected from a known system designated as healthy. Structural damage will be introduced to different blades, which they will be designated as unhealthy. A variety of different tests will be performed to track the evolution and severity of the damage. A number of damage detection and diagnosis strategies will be implemented. A preliminary experiment was performed on aluminum cantilever beams providing a simpler model for implementation and proof of concept. Future work will look at utilizing the detection information as part of a hierarchical control system in order to mitigate structural damage and fatigue. The proposed approach may eliminate massive data storage on board of an aircraft through retaining relevant information only. The control system can then employ the relevant information to intelligently reconfigure adaptive maneuvers to avoid harmful regimes, thus, extending the life of the aircraft.

  7. Supervised artificial neural network-based method for conversion of solar radiation data (case study: Algeria)

    NASA Astrophysics Data System (ADS)

    Laidi, Maamar; Hanini, Salah; Rezrazi, Ahmed; Yaiche, Mohamed Redha; El Hadj, Abdallah Abdallah; Chellali, Farouk

    2016-01-01

    In this study, a backpropagation artificial neural network (BP-ANN) model is used as an alternative approach to predict solar radiation on tilted surfaces (SRT) using a number of variables involved in physical process. These variables are namely the latitude of the site, mean temperature and relative humidity, Linke turbidity factor and Angstrom coefficient, extraterrestrial solar radiation, solar radiation data measured on horizontal surfaces (SRH), and solar zenith angle. Experimental solar radiation data from 13 stations spread all over Algeria around the year (2004) were used for training/validation and testing the artificial neural networks (ANNs), and one station was used to make the interpolation of the designed ANN. The ANN model was trained, validated, and tested using 60, 20, and 20 % of all data, respectively. The configuration 8-35-1 (8 inputs, 35 hidden, and 1 output neurons) presented an excellent agreement between the prediction and the experimental data during the test stage with determination coefficient of 0.99 and root meat squared error of 5.75 Wh/m2, considering a three-layer feedforward backpropagation neural network with Levenberg-Marquardt training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. This novel model could be used by researchers or scientists to design high-efficiency solar devices that are usually tilted at an optimum angle to increase the solar incident on the surface.

  8. Third-dimension information retrieval from a single convergent-beam transmission electron diffraction pattern using an artificial neural network

    NASA Astrophysics Data System (ADS)

    Pennington, Robert S.; Van den Broek, Wouter; Koch, Christoph T.

    2014-05-01

    We have reconstructed third-dimension specimen information from convergent-beam electron diffraction (CBED) patterns simulated using the stacked-Bloch-wave method. By reformulating the stacked-Bloch-wave formalism as an artificial neural network and optimizing with resilient back propagation, we demonstrate specimen orientation reconstructions with depth resolutions down to 5 nm. To show our algorithm's ability to analyze realistic data, we also discuss and demonstrate our algorithm reconstructing from noisy data and using a limited number of CBED disks. Applicability of this reconstruction algorithm to other specimen parameters is discussed.

  9. Monthly evaporation forecasting using artificial neural networks and support vector machines

    NASA Astrophysics Data System (ADS)

    Tezel, Gulay; Buyukyildiz, Meral

    2016-04-01

    Evaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Artificial neural network methods and support vector machines (SVM) are frequently utilized to estimate evaporation and other hydrological variables. In this study, usability of artificial neural networks (ANNs) (multilayer perceptron (MLP) and radial basis function network (RBFN)) and ɛ-support vector regression (SVR) artificial intelligence methods was investigated to estimate monthly pan evaporation. For this aim, temperature, relative humidity, wind speed, and precipitation data for the period 1972 to 2005 from Beysehir meteorology station were used as input variables while pan evaporation values were used as output. The Romanenko and Meyer method was also considered for the comparison. The results were compared with observed class A pan evaporation data. In MLP method, four different training algorithms, gradient descent with momentum and adaptive learning rule backpropagation (GDX), Levenberg-Marquardt (LVM), scaled conjugate gradient (SCG), and resilient backpropagation (RBP), were used. Also, ɛ-SVR model was used as SVR model. The models were designed via 10-fold cross-validation (CV); algorithm performance was assessed via mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2). According to the performance criteria, the ANN algorithms and ɛ-SVR had similar results. The ANNs and ɛ-SVR methods were found to perform better than the Romanenko and Meyer methods. Consequently, the best performance using the test data was obtained using SCG(4,2,2,1) with R 2 = 0.905.

  10. A hybrid artificial bee colony algorithm for numerical function optimization

    NASA Astrophysics Data System (ADS)

    Alqattan, Zakaria N.; Abdullah, Rosni

    2015-02-01

    Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

  11. Low-power hardware implementation of artificial neural network strain detection for extrinsic Fabry-Pérot interferometric sensors under sinusoidal excitation

    NASA Astrophysics Data System (ADS)

    Mitchell, Kyle; Ebel, William J.; Watkins, Steve E.

    2009-11-01

    Artificial neural networks are studied for use in estimating strain in extrinsic Fabry-Pérot interferometric sensors. These networks can require large memory spaces and a large number of calculations for implementation. We describe a modified neural network solution that is suitable for implementation on relatively low cost, low-power hardware. Moreover, we give strain estimates resulting from an implementation of the artificial neural network algorithm on an 8-bit 8051 processor with 64 kbytes of memory. For example, one of our results shows that for 2048 samples of the transmittance signal, the presented neural network algorithm requires around 24,622 floating point multiplies and 35,835 adds, and where the data and algorithm fit within the 64-kbyte memory.

  12. Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis.

    PubMed

    Fei, Y; Hu, J; Li, W-Q; Wang, W; Zong, G-Q

    2017-03-01

    Essentials Predicting the occurrence of portosplenomesenteric vein thrombosis (PSMVT) is difficult. We studied 72 patients with acute pancreatitis. Artificial neural networks modeling was more accurate than logistic regression in predicting PSMVT. Additional predictive factors may be incorporated into artificial neural networks.

  13. Toward IMRT 2D dose modeling using artificial neural networks: A feasibility study

    SciTech Connect

    Kalantzis, Georgios; Vasquez-Quino, Luis A.; Zalman, Travis; Pratx, Guillem; Lei, Yu

    2011-10-15

    Purpose: To investigate the feasibility of artificial neural networks (ANN) to reconstruct dose maps for intensity modulated radiation treatment (IMRT) fields compared with those of the treatment planning system (TPS). Methods: An artificial feed forward neural network and the back-propagation learning algorithm have been used to replicate dose calculations of IMRT fields obtained from PINNACLE{sup 3} v9.0. The ANN was trained with fluence and dose maps of IMRT fields for 6 MV x-rays, which were obtained from the amorphous silicon (a-Si) electronic portal imaging device of Novalis TX. Those fluence distributions were imported to the TPS and the dose maps were calculated on the horizontal midpoint plane of a water equivalent homogeneous cylindrical virtual phantom. Each exported 2D dose distribution from the TPS was classified into two clusters of high and low dose regions, respectively, based on the K-means algorithm and the Euclidian metric in the fluence-dose domain. The data of each cluster were divided into two sets for the training and validation phase of the ANN, respectively. After the completion of the ANN training phase, 2D dose maps were reconstructed by the ANN and isodose distributions were created. The dose maps reconstructed by ANN were evaluated and compared with the TPS, where the mean absolute deviation of the dose and the {gamma}-index were used. Results: A good agreement between the doses calculated from the TPS and the trained ANN was achieved. In particular, an average relative dosimetric difference of 4.6% and an average {gamma}-index passing rate of 93% were obtained for low dose regions, and a dosimetric difference of 2.3% and an average {gamma}-index passing rate of 97% for high dose region. Conclusions: An artificial neural network has been developed to convert fluence maps to corresponding dose maps. The feasibility and potential of an artificial neural network to replicate complex convolution kernels in the TPS for IMRT dose calculations

  14. Online signature recognition using principal component analysis and artificial neural network

    NASA Astrophysics Data System (ADS)

    Hwang, Seung-Jun; Park, Seung-Je; Baek, Joong-Hwan

    2016-12-01

    In this paper, we propose an algorithm for on-line signature recognition using fingertip point in the air from the depth image acquired by Kinect. We extract 10 statistical features from X, Y, Z axis, which are invariant to changes in shifting and scaling of the signature trajectories in three-dimensional space. Artificial neural network is adopted to solve the complex signature classification problem. 30 dimensional features are converted into 10 principal components using principal component analysis, which is 99.02% of total variances. We implement the proposed algorithm and test to actual on-line signatures. In experiment, we verify the proposed method is successful to classify 15 different on-line signatures. Experimental result shows 98.47% of recognition rate when using only 10 feature vectors.

  15. Satellite image processing for precision agriculture and agroindustry using convolutional neural network and genetic algorithm

    NASA Astrophysics Data System (ADS)

    Firdaus; Arkeman, Y.; Buono, A.; Hermadi, I.

    2017-01-01

    Translating satellite imagery to a useful data for decision making during this time are usually done manually by human. In this research, we are going to translate satellite imagery by using artificial intelligence method specifically using convolutional neural network and genetic algorithm to become a useful data for decision making, especially for precision agriculture and agroindustry. In this research, we are focused on how to made a sustainable land use planning with 3 objectives. The first is maximizing economic factor. Second is minimizing CO2 emission and the last is minimizing land degradation. Results show that by using artificial intelligence method, can produced a good pareto optimum solutions in a short time.

  16. Artificial Neural Networks Based Controller for Glucose Monitoring during Clamp Test

    PubMed Central

    Catalogna, Merav; Cohen, Eyal; Fishman, Sigal; Halpern, Zamir; Nevo, Uri; Ben-Jacob, Eshel

    2012-01-01

    Insulin resistance (IR) is one of the most widespread health problems in modern times. The gold standard for quantification of IR is the hyperinsulinemic-euglycemic glucose clamp technique. During the test, a regulated glucose infusion is delivered intravenously to maintain a constant blood glucose concentration. Current control algorithms for regulating this glucose infusion are based on feedback control. These models require frequent sampling of blood, and can only partly capture the complexity associated with regulation of glucose. Here we present an improved clamp control algorithm which is motivated by the stochastic nature of glucose kinetics, while using the minimal need in blood samples required for evaluation of IR. A glucose pump control algorithm, based on artificial neural networks model was developed. The system was trained with a data base collected from 62 rat model experiments, using a back-propagation Levenberg-Marquardt optimization. Genetic algorithm was used to optimize network topology and learning features. The predictive value of the proposed algorithm during the temporal period of interest was significantly improved relative to a feedback control applied at an equivalent low sampling interval. Robustness to noise analysis demonstrates the applicability of the algorithm in realistic situations. PMID:22952998

  17. Adaptively combined FIR and functional link artificial neural network equalizer for nonlinear communication channel.

    PubMed

    Zhao, Haiquan; Zhang, Jiashu

    2009-04-01

    This paper proposes a novel computational efficient adaptive nonlinear equalizer based on combination of finite impulse response (FIR) filter and functional link artificial neural network (CFFLANN) to compensate linear and nonlinear distortions in nonlinear communication channel. This convex nonlinear combination results in improving the speed while retaining the lower steady-state error. In addition, since the CFFLANN needs not the hidden layers, which exist in conventional neural-network-based equalizers, it exhibits a simpler structure than the traditional neural networks (NNs) and can require less computational burden during the training mode. Moreover, appropriate adaptation algorithm for the proposed equalizer is derived by the modified least mean square (MLMS). Results obtained from the simulations clearly show that the proposed equalizer using the MLMS algorithm can availably eliminate various intensity linear and nonlinear distortions, and be provided with better anti-jamming performance. Furthermore, comparisons of the mean squared error (MSE), the bit error rate (BER), and the effect of eigenvalue ratio (EVR) of input correlation matrix are presented.

  18. Nuclide identification algorithm based on K-L transform and neural networks

    NASA Astrophysics Data System (ADS)

    Chen, Liang; Wei, Yi-Xiang

    2009-01-01

    Traditional spectrum analysis algorithm based on peak search is hard to deal with complex overlapped peaks, especially in bad resolution and high background conditions. This paper described a new nuclide identification method based on the Karhunen-Loeve transform (K-L transform) and artificial neural networks. By the K-L transform and feature extraction, the nuclide gamma spectrum was compacted. The K-L transform coefficients were used as the neural network's input. The linear associative memory and ADALINE were discussed. Lots of experiments and tests showed that the method was credible and practical, especially suitable for fast nuclide identification.

  19. Application of artificial neural network to search for gravitational-wave signals associated with short gamma-ray bursts

    NASA Astrophysics Data System (ADS)

    Kim, Kyungmin; Harry, Ian W.; Hodge, Kari A.; Kim, Young-Min; Lee, Chang-Hwan; Lee, Hyun Kyu; Oh, John J.; Oh, Sang Hoon; Son, Edwin J.

    2015-12-01

    We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts (GRBs). The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability (FAP) is improved by the artificial neural network in comparison to the conventional detection statistic. Specifically, the distance at 50% detection probability at a fixed false positive rate is increased about 8%-14% for the considered waveform models. We also evaluate a few seconds of the gravitational-wave data segment using the trained networks and obtain the FAP. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short GRBs.

  20. Artificial immune algorithm for multi-depot vehicle scheduling problems

    NASA Astrophysics Data System (ADS)

    Wu, Zhongyi; Wang, Donggen; Xia, Linyuan; Chen, Xiaoling

    2008-10-01

    In the fast-developing logistics and supply chain management fields, one of the key problems in the decision support system is that how to arrange, for a lot of customers and suppliers, the supplier-to-customer assignment and produce a detailed supply schedule under a set of constraints. Solutions to the multi-depot vehicle scheduling problems (MDVRP) help in solving this problem in case of transportation applications. The objective of the MDVSP is to minimize the total distance covered by all vehicles, which can be considered as delivery costs or time consumption. The MDVSP is one of nondeterministic polynomial-time hard (NP-hard) problem which cannot be solved to optimality within polynomial bounded computational time. Many different approaches have been developed to tackle MDVSP, such as exact algorithm (EA), one-stage approach (OSA), two-phase heuristic method (TPHM), tabu search algorithm (TSA), genetic algorithm (GA) and hierarchical multiplex structure (HIMS). Most of the methods mentioned above are time consuming and have high risk to result in local optimum. In this paper, a new search algorithm is proposed to solve MDVSP based on Artificial Immune Systems (AIS), which are inspirited by vertebrate immune systems. The proposed AIS algorithm is tested with 30 customers and 6 vehicles located in 3 depots. Experimental results show that the artificial immune system algorithm is an effective and efficient method for solving MDVSP problems.

  1. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    SciTech Connect

    Saini, K. K.; Saini, Sanju

    2008-10-07

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  2. The Application of Neural Networks with Artificial Intelligence Technique in the Modeling of Industrial Processes

    NASA Astrophysics Data System (ADS)

    Saini, K. K.; Saini, Sanju

    2008-10-01

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can "learn," automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  3. Direct Inverse Control using an Artificial Neural Network for the Autonomous Hover of a Helicopter

    DTIC Science & Technology

    2014-10-05

    Inverse Control technique using an Artificial Neural Network to learn and then cancel out the Hover dynamics of the quadrotor UAV... Inverse Control , Neural Network , Flight Control , and UAV helicopter REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 10. SPONSOR...Helicopter. The goal of the project is to investigate the effectiveness of the Direct Inverse Control technique using an Artificial Neural Network to

  4. Applications of Artificial Neural Networks (ANNs) in Food Science

    SciTech Connect

    HUang, Yiqun; Kangas, Lars J.; Rasco, Barbara A.

    2007-02-01

    Abstract Artificial neural networks (ANNs) have been applied in almost every aspect of food science over the past two decade, although most applications are in the development stage. ANNs are useful tools for food safety and quality analyses, which include modeling of microbial growth and from this predicting food safety, interpreting spectroscopic data, and predicting physical, chemical, functional and sensory properties of various food products during processing and distribution. ANNs have a great deal of promise for modeling complex tasks in process control and simulation, and in applications of machine perception including machine vision and the electronic nose for food safety and quality control. This review discusses the basic theory of the ANN technology and its applications in food science, providing food scientists and the research community an overview of the current research and future trend of the applications of ANN technology in this field.

  5. Artificial neural networks in Space Station optimal attitude control

    NASA Astrophysics Data System (ADS)

    Kumar, Renjith R.; Seywald, Hans; Deshpande, Samir M.; Rahman, Zia

    1995-01-01

    Innovative techniques of using "artificial neural networks" (ANN) for improving the performance of the pitch axis attitude control system of Space Station Freedom using control moment gyros (CMGs) are investigated. The first technique uses a feed-forward ANN with multi-layer perceptrons to obtain an on-line controller which improves the performance of the control system via a model following approach. The second technique uses a single layer feed-forward ANN with a modified back propagation scheme to estimate the internal plant variations and the external disturbances separately. These estimates are then used to solve two differential Riccati equations to obtain time varying gains which improve the control system performance in successive orbits.

  6. Correlated EEG Signals Simulation Based on Artificial Neural Networks.

    PubMed

    Tomasevic, Nikola M; Neskovic, Aleksandar M; Neskovic, Natasa J

    2016-09-30

    In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper, a novel approach to simulation of two cross-correlated EEG signals is proposed. The proposed method is based on the principles of artificial neural networks (ANN). Contrary to the existing EEG data simulators, the ANN-based approach was leveraged solely on the experimentally acquired EEG data. More precisely, measured EEG data were utilized to optimize the simulator which consisted of two ANN models (each model responsible for generation of one EEG sequence). In order to acquire the EEG recordings, the measurement campaign was carried out on a healthy awake adult having no cognitive, physical or mental load. For the evaluation of the proposed approach, comprehensive quantitative and qualitative statistical analysis was performed considering probability distribution, correlation properties and spectral characteristics of generated EEG processes. The obtained results clearly indicated the satisfactory agreement with the measurement data.

  7. Magnesium degradation as determined by artificial neural networks.

    PubMed

    Willumeit, Regine; Feyerabend, Frank; Huber, Norbert

    2013-11-01

    Magnesium degradation under physiological conditions is a highly complex process in which temperature, the use of cell culture growth medium and the presence of CO2, O2 and proteins can influence the corrosion rate and the composition of the resulting corrosion layer. Due to the complexity of this process it is almost impossible to predict the parameters that are most important and whether some parameters have a synergistic effect on the corrosion rate. Artificial neural networks are a mathematical tool that can be used to approximate and analyse non-linear problems with multiple inputs. In this work we present the first analysis of corrosion data obtained using this method, which reveals that CO2 and the composition of the buffer system play a crucial role in the corrosion of magnesium, whereas O2, proteins and temperature play a less prominent role.

  8. Practical application of artificial neural networks in the neurosciences

    NASA Astrophysics Data System (ADS)

    Pinti, Antonio

    1995-04-01

    This article presents a practical application of artificial multi-layer perceptron (MLP) neural networks in neurosciences. The data that are processed are labeled data from the visual analysis of electrical signals of human sleep. The objective of this work is to automatically classify into sleep stages the electrophysiological signals recorded from electrodes placed on a sleeping patient. Two large data bases were designed by experts in order to realize this study. One data base was used to train the network and the other to test its generalization capacity. The classification results obtained with the MLP network were compared to a type K nearest neighbor Knn non-parametric classification method. The MLP network gave a better result in terms of classification than the Knn method. Both classification techniques were implemented on a transputer system. With both networks in their final configuration, the MLP network was 160 times faster than the Knn model in classifying a sleep period.

  9. Application of artificial neural networks in computer-aided diagnosis.

    PubMed

    Liu, Bei

    2015-01-01

    Computer-aided diagnosis is a diagnostic procedure in which a radiologist uses the outputs of computer analysis of medical images as a second opinion in the interpretation of medical images, either to help with lesion detection or to help determine if the lesion is benign or malignant. Artificial neural networks (ANNs) are usually employed to formulate the statistical models for computer analysis. Receiver operating characteristic curves are used to evaluate the performance of the ANN alone, as well as the diagnostic performance of radiologists who take into account the ANN output as a second opinion. In this chapter, we use mammograms to illustrate how an ANN model is trained, tested, and evaluated, and how a radiologist should use the ANN output as a second opinion in CAD.

  10. Semantic characteristics prediction of pulmonary nodule using Artificial Neural Networks.

    PubMed

    Li, Guangxu; Kim, Hyoungseop; Tan, Joo Kooi; Ishikawa, Seiji; Hirano, Yasushi; Kido, Shoji; Tachibana, Rie

    2013-01-01

    Since it is difficult to choose which computer calculated features are effective to predict the malignancy of pulmonary nodules, in this study, we add a semantic-level of Artificial Neural Networks (ANNs) structure to improve intuition of features selection. The works of this study include two: 1) seeking the relationships between computer-calculated features and medical semantic concepts which could be understood by human; 2) providing an objective assessment method to predict the malignancy from semantic characteristics. We used 60 thoracic CT scans collected from the Lung Image Database Consortium (LIDC) database, in which the suspicious lesions had been delineated and annotated by 4 radiologists independently. Corresponding to the two works of this study, correlation analysis experiment and agreement experiment were performed separately.

  11. Incomplete fuzzy data processing systems using artificial neural network

    NASA Technical Reports Server (NTRS)

    Patyra, Marek J.

    1992-01-01

    In this paper, the implementation of a fuzzy data processing system using an artificial neural network (ANN) is discussed. The binary representation of fuzzy data is assumed, where the universe of discourse is decartelized into n equal intervals. The value of a membership function is represented by a binary number. It is proposed that incomplete fuzzy data processing be performed in two stages. The first stage performs the 'retrieval' of incomplete fuzzy data, and the second stage performs the desired operation on the retrieval data. The method of incomplete fuzzy data retrieval is proposed based on the linear approximation of missing values of the membership function. The ANN implementation of the proposed system is presented. The system was computationally verified and showed a relatively small total error.

  12. Inflow forecasting using Artificial Neural Networks for reservoir operation

    NASA Astrophysics Data System (ADS)

    Chiamsathit, Chuthamat; Adeloye, Adebayo J.; Bankaru-Swamy, Soundharajan

    2016-05-01

    In this study, multi-layer perceptron (MLP) artificial neural networks have been applied to forecast one-month-ahead inflow for the Ubonratana reservoir, Thailand. To assess how well the forecast inflows have performed in the operation of the reservoir, simulations were carried out guided by the systems rule curves. As basis of comparison, four inflow situations were considered: (1) inflow known and assumed to be the historic (Type A); (2) inflow known and assumed to be the forecast (Type F); (3) inflow known and assumed to be the historic mean for month (Type M); and (4) inflow is unknown with release decision only conditioned on the starting reservoir storage (Type N). Reservoir performance was summarised in terms of reliability, resilience, vulnerability and sustainability. It was found that Type F inflow situation produced the best performance while Type N was the worst performing. This clearly demonstrates the importance of good inflow information for effective reservoir operation.

  13. Nuclear power plant status diagnostics using artificial neural networks

    SciTech Connect

    Bartlett, E.B. . Dept. of Mechanical Engineering); Uhrig, R.E. . Dept. of Nuclear Engineering)

    1991-01-01

    In this work, the nuclear power plant operating status recognition issue is investigated using artificial neural networks (ANNs). The objective is to train an ANN to classify nuclear power plant accident conditions and to assess the potential of future work in the area of plant diagnostics with ANNS. To this end, an ANN was trained to recognize normal operating conditions as well as potentially unsafe conditions based on nuclear power plant training simulator generated accident scenarios. These scenarios include; hot and cold leg loss of coolant, control rod ejection, loss of offsite power, main steam line break, main feedwater line break and steam generator tube leak accidents. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results.

  14. Nuclear power plant status diagnostics using artificial neural networks

    SciTech Connect

    Bartlett, E.B.; Uhrig, R.E.

    1991-12-31

    In this work, the nuclear power plant operating status recognition issue is investigated using artificial neural networks (ANNs). The objective is to train an ANN to classify nuclear power plant accident conditions and to assess the potential of future work in the area of plant diagnostics with ANNS. To this end, an ANN was trained to recognize normal operating conditions as well as potentially unsafe conditions based on nuclear power plant training simulator generated accident scenarios. These scenarios include; hot and cold leg loss of coolant, control rod ejection, loss of offsite power, main steam line break, main feedwater line break and steam generator tube leak accidents. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results.

  15. Modeling biodegradation and kinetics of glyphosate by artificial neural network.

    PubMed

    Nourouzi, Mohsen M; Chuah, Teong G; Choong, Thomas S Y; Rabiei, F

    2012-01-01

    An artificial neural network (ANN) model was developed to simulate the biodegradation of herbicide glyphosate [2-(Phosphonomethylamino) acetic acid] in a solution with varying parameters pH, inoculum size and initial glyphosate concentration. The predictive ability of ANN model was also compared with Monod model. The result showed that ANN model was able to accurately predict the experimental results. A low ratio of self-inhibition and half saturation constants of Haldane equations (< 8) exhibited the inhibitory effect of glyphosate on bacteria growth. The value of K(i)/K(s) increased when the mixed inoculum size was increased from 10(4) to 10(6) bacteria/mL. It was found that the percentage of glyphosate degradation reached a maximum value of 99% at an optimum pH 6-7 while for pH values higher than 9 or lower than 4, no degradation was observed.

  16. Artificial neural network for the configuration problem in solids

    NASA Astrophysics Data System (ADS)

    Ji, Hyunjun; Jung, Yousung

    2017-02-01

    A machine learning approach based on the artificial neural network (ANN) is applied for the configuration problem in solids. The proposed method provides a direct mapping from configuration vectors to energies. The benchmark conducted for the M1 phase of Mo-V-Te-Nb oxide showed that only a fraction of configurations needs to be calculated, thus the computational burden significantly decreased, by a factor of 20-50, with R2 = 0.96 and MAD = 0.12 eV. It is shown that ANN can also handle the effects of geometry relaxation when properly trained, resulting in R2 = 0.95 and MAD = 0.13 eV.

  17. Application of artificial neural networks for prediction of photocatalytic reactor.

    PubMed

    Delnavaz, Mohammad

    2015-02-01

    In this paper, forecasting of kinetic constant and efficiency of photocatalytic process of TiO2 nano powder immobilized on light expanded clay aggregates (LECA) was investigated. Synthetic phenolic wastewater, which is toxic and not easily biodegradable, was selected as the pollutant. The efficiency of the process in various operation conditions, including initial phenol concentration, pH, TiO2 concentration, retention time, and UV lamp intensity, was then measured. The TiO2 nano powder was immobilized on LECA using slurry and sol-gel methods. Kinetics of photocatalytic reactions has been proposed to follow the Langmuir-Hinshelwood model in different initial phenol concentration and pH. Several steps of training and testing of the models were used to determine the appropriate architecture of the artificial neural network models (ANNs). The ANN-based models were found to provide an efficient and robust tool in predicting photocatalytic reactor efficiency and kinetic constant for treating phenolic compounds.

  18. Landslide Susceptibility Zonation through ratings derived from Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Chauhan, Shivani; Sharma, Mukta; Arora, M. K.; Gupta, N. K.

    2010-10-01

    In the present study, Artificial Neural Network (ANN) has been implemented to derive ratings of categories of causative factors, which are then integrated to produce a landslide susceptibility zonation map in an objective manner. The results have been evaluated with an ANN based black box approach for Landslide Susceptibility Zonation (LSZ) proposed earlier by the authors. Seven causative factors, namely, slope, slope aspect, relative relief, lithology, structural features (e.g., thrusts and faults), landuse landcover, and drainage density, were placed in 42 categories for which ratings were determined. The results indicate that LSZ map based on ratings derived from ANN performs exceedingly better than that produced from the earlier ANN based approach. The landslide density analysis clearly showed that susceptibility zones were in close agreement with actual landslide areas in the field.

  19. Prediction of Dried Durian Moisture Content Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Husna, Marati; Purqon, Acep

    2016-08-01

    Moisture content has a crucial issue in post-harvest processing since it plays main role to estimate a quality of dried product. However, estimating the moisture content is difficult since it shows mathematically nonlinear systems and complex physical processes. We investigate the prediction of moisture content of dried product by using Artificial Neural Networks (ANN). Our sample is a Bengkulu's local durian that is dried using a microwave oven. Our results show that ANN can predict the moisture content by performing with R2 value is 98.47%. Moreover, the RMSE values is 3.97% and MSE values is 0.16%. Our results indicate that ANN model have high capability for predicting moisture content and it is potentially applied in post-harvest product, especially in drying product quality control.

  20. Artificial Neural Network Modeling to Evaluate the Dynamic Flow Stress of 7050 Aluminum Alloy

    NASA Astrophysics Data System (ADS)

    Quan, Guo-zheng; Wang, Tong; Li, Yong-le; Zhan, Zong-yang; Xia, Yu-feng

    2016-02-01

    The flow stress data have been obtained by a set of isothermal hot compression tests, which were carried out in the temperature range of 573-723 K and strain rates of 0.01, 0.1, 1, and 10 s-1 with a reduction of 60% on a Gleeble-1500 thermo-mechanical simulator. On the basis of the experimental data, constitutive equation and an artificial neural network model were developed for the analysis and simulation of the flow behavior of the 7050 aluminum alloy. After training with standard back-propagation learning algorithm, the artificial neural network model has the ability to present the intrinsic relationship between the flow stress and the processing variables. In the present model, the temperature, strain, and strain rate were chosen as inputs, and the flow stress was chosen as output. By comparing the values of correlation coefficient and average absolute relative error, the prediction accuracy of the model and the improved Arrhenius-type model can be evaluated. The results indicated that the well-trained artificial neural network model is more accurate than the improved Arrhenius-type model in predicting the hot compressive behavior of the as-extruded 7050 aluminum alloy. Based on the predicted stress data and experimental stress data, the 3D continuous stress-strain maps at different strains, temperatures, and strain rates were plotted subsequently. Besides, the flow stress values at arbitrary temperature, strain rate, and strain are explicit on the 3D continuous stress-strain maps, which would be beneficial to articulate working processes more validly.

  1. Prediction of Austenite Formation Temperatures Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Schulze, P.; Schmidl, E.; Grund, T.; Lampke, T.

    2016-03-01

    For the modeling and design of heat treatments, in consideration of the development/ transformation of the microstructure, different material data depending on the chemical composition, the respective microstructure/phases and the temperature are necessary. Material data are, e.g. the thermal conductivity, heat capacity, thermal expansion and transformation data etc. The quality of thermal simulations strongly depends on the accuracy of the material data. For many materials, the required data - in particular for different microstructures and temperatures - are rare in the literature. In addition, a different chemical composition within the permitted limits of the considered steel alloy cannot be predicted. A solution for this problem is provided by the calculation of material data using Artificial Neural Networks (ANN). In the present study, the start and finish temperatures of the transformation from the bcc lattice to the fcc lattice structure of hypoeutectoid steels are calculated using an Artificial Neural Network. An appropriate database containing different transformation temperatures (austenite formation temperatures) to train the ANN is selected from the literature. In order to find a suitable feedforward network, the network topologies as well as the activation functions of the hidden layers are varied and subsequently evaluated in terms of the prediction accuracy. The transformation temperatures calculated by the ANN exhibit a very good compliance compared to the experimental data. The results show that the prediction performance is even higher compared to classical empirical equations such as Andrews or Brandis. Therefore, it can be assumed that the presented ANN is a convenient tool to distinguish between bcc and fcc phases in hypoeutectoid steels.

  2. Review of Artificial Neural Networks (ANN) applied to corrosion monitoring

    NASA Astrophysics Data System (ADS)

    Mabbutt, S.; Picton, P.; Shaw, P.; Black, S.

    2012-05-01

    The assessment of corrosion within an engineering system often forms an important aspect of condition monitoring but it is a parameter that is inherently difficult to measure and predict. The electrochemical nature of the corrosion process allows precise measurements to be made. Advances in instruments, techniques and software have resulted in devices that can gather data and perform various analysis routines that provide parameters to identify corrosion type and corrosion rate. Although corrosion rates are important they are only useful where general or uniform corrosion dominates. However, pitting, inter-granular corrosion and environmentally assisted cracking (stress corrosion) are examples of corrosion mechanisms that can be dangerous and virtually invisible to the naked eye. Electrochemical noise (EN) monitoring is a very useful technique for detecting these types of corrosion and it is the only non-invasive electrochemical corrosion monitoring technique commonly available. Modern instrumentation is extremely sensitive to changes in the system and new experimental configurations for gathering EN data have been proven. In this paper the identification of localised corrosion by different data analysis routines has been reviewed. In particular the application of Artificial Neural Network (ANN) analysis to corrosion data is of key interest. In most instances data needs to be used with conventional theory to obtain meaningful information and relies on expert interpretation. Recently work has been carried out using artificial neural networks to investigate various types of corrosion data in attempts to predict corrosion behaviour with some success. This work aims to extend this earlier work to identify reliable electrochemical indicators of localised corrosion onset and propagation stages.

  3. A novel technology for fabricating customizable VLSI artificial neural network chips

    SciTech Connect

    Fu, C.Y.; Law, B.; Chapline, G.; Swenson, D.

    1992-02-05

    This paper describes an implementation of hardware neural networks using highly linear thin-film resistor technology and an 8-bit binary weight circuit to produce customizable artificial neural network chips and systems. These neural networks are programmed using precision laser cutting and deposition. The fast turnaround of laser-based customization allows us to explore different neural network architectures and to rapidly program the synaptic weights. Our customizable chip allows us to expand an artificial network laterally and vertically. This flexibility permits us to build very large neural network systems.

  4. Surface daytime net radiation estimation using artificial neural networks

    DOE PAGES

    Jiang, Bo; Zhang, Yi; Liang, Shunlin; ...

    2014-11-11

    Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical Rn estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate Rn globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. Rn estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991–2010more » both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R2) of 0.92, a root mean square error (RMSE) of 34.27 W·m–2 , and a bias of –0.61 W·m–2 in global mode based on the validation dataset. In conclusion, ANN methods are a potentially powerful tool for global Rn estimation.« less

  5. Classification of Images Acquired with Colposcopy Using Artificial Neural Networks

    PubMed Central

    Simões, Priscyla W; Izumi, Narjara B; Casagrande, Ramon S; Venson, Ramon; Veronezi, Carlos D; Moretti, Gustavo P; da Rocha, Edroaldo L; Cechinel, Cristian; Ceretta, Luciane B; Comunello, Eros; Martins, Paulo J; Casagrande, Rogério A; Snoeyer, Maria L; Manenti, Sandra A

    2014-01-01

    OBJECTIVE To explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy. PURPOSE Transversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database (n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images were used for the validation. A hybrid neural network based on Kohonen self-organizing maps and multilayer perceptron (MLP) networks was used. RESULTS After 126 cycles, the validation was performed. The best results reached an accuracy of 72.15%, a sensibility of 69.78%, and a specificity of 68%. CONCLUSION Although the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study. PMID:25374454

  6. Reliability and risk analysis using artificial neural networks

    SciTech Connect

    Robinson, D.G.

    1995-12-31

    This paper discusses preliminary research at Sandia National Laboratories into the application of artificial neural networks for reliability and risk analysis. The goal of this effort is to develop a reliability based methodology that captures the complex relationship between uncertainty in material properties and manufacturing processes and the resulting uncertainty in life prediction estimates. The inputs to the neural network model are probability density functions describing system characteristics and the output is a statistical description of system performance. The most recent application of this methodology involves the comparison of various low-residue, lead-free soldering processes with the desire to minimize the associated waste streams with no reduction in product reliability. Model inputs include statistical descriptions of various material properties such as the coefficients of thermal expansion of solder and substrate. Consideration is also given to stochastic variation in the operational environment to which the electronic components might be exposed. Model output includes a probabilistic characterization of the fatigue life of the surface mounted component.

  7. Eye tracking using artificial neural networks for human computer interaction.

    PubMed

    Demjén, E; Aboši, V; Tomori, Z

    2011-01-01

    This paper describes an ongoing project that has the aim to develop a low cost application to replace a computer mouse for people with physical impairment. The application is based on an eye tracking algorithm and assumes that the camera and the head position are fixed. Color tracking and template matching methods are used for pupil detection. Calibration is provided by neural networks as well as by parametric interpolation methods. Neural networks use back-propagation for learning and bipolar sigmoid function is chosen as the activation function. The user's eye is scanned with a simple web camera with backlight compensation which is attached to a head fixation device. Neural networks significantly outperform parametric interpolation techniques: 1) the calibration procedure is faster as they require less calibration marks and 2) cursor control is more precise. The system in its current stage of development is able to distinguish regions at least on the level of desktop icons. The main limitation of the proposed method is the lack of head-pose invariance and its relative sensitivity to illumination (especially to incidental pupil reflections).

  8. Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

    NASA Astrophysics Data System (ADS)

    Chen, Wei

    2015-07-01

    In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

  9. Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

    PubMed Central

    Manikandan, Narayanan; Subha, Srinivasan

    2016-01-01

    Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used. PMID:26881271

  10. Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks.

    PubMed

    Manikandan, Narayanan; Subha, Srinivasan

    2016-01-01

    Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

  11. Modeling a densely urbanized watershed with an artificial neural network, weather radar and telemetric data

    NASA Astrophysics Data System (ADS)

    Pereira Filho, Augusto José; dos Santos, Cláudia Cristina

    2006-02-01

    Artificial neural networks (ANN) are widely used in a myriad of fields of research and development, including the predictability of time series. This work is concerned with one of such applications to simulate and to forecast stage level and streamflow at the Tamanduateí river watershed, one of the main tributaries of the Alto Tietê river watershed in São Paulo State, Brazil. This heavily urbanized watershed is within the Metropolitan Area of São Paulo (MASP) where recurrent flash floods affect a population of more than 17 million inhabitants. Flash floods events between 1991 and 1995 were selected and divided up into three groups for training, verification and forecasting purposes. Weather radar rainfall estimation and telemetric stage level and streamflow data were input to a three-layer feed forward ANN trained with the Linear Least Square Simplex training algorithm (LLSSIM) by Hsu et al. [Hsu, K.L., Gupta, H.V., Sorooshian, S., 1996. A superior training strategy for three-layer feed forward artificial neural networks. Tucson, University of Arizona. (Technique report, HWR no. 96-030, Department of Hydrology and Water Resources)]. The performance of the ANN is improved by 40% when either streamflow or stage level were input together with the rainfall. The ANN simulated flood waves tend to be dominated by phase errors. The ANN showed slightly better results then a multi-parameter auto-regression model and indicates its usefulness in flash flood forecasting.

  12. Analyte species and concentration identification using differentially functionalized microcantilever arrays and artificial neural networks

    SciTech Connect

    Senesac, Larry R; Datskos, Panos G; Sepaniak, Michael J

    2006-01-01

    In the present work, we have performed analyte species and concentration identification using an array of ten differentially functionalized microcantilevers coupled with a back-propagation artificial neural network pattern recognition algorithm. The array consists of ten nanostructured silicon microcantilevers functionalized by polymeric and gas chromatography phases and macrocyclic receptors as spatially dense, differentially responding sensing layers for identification and quantitation of individual analyte(s) and their binary mixtures. The array response (i.e. cantilever bending) to analyte vapor was measured by an optical readout scheme and the responses were recorded for a selection of individual analytes as well as several binary mixtures. An artificial neural network (ANN) was designed and trained to recognize not only the individual analytes and binary mixtures, but also to determine the concentration of individual components in a mixture. To the best of our knowledge, ANNs have not been applied to microcantilever array responses previously to determine concentrations of individual analytes. The trained ANN correctly identified the eleven test analyte(s) as individual components, most with probabilities greater than 97%, whereas it did not misidentify an unknown (untrained) analyte. Demonstrated unique aspects of this work include an ability to measure binary mixtures and provide both qualitative (identification) and quantitative (concentration) information with array-ANN-based sensor methodologies.

  13. Software Development Cost and Time Forecasting Using a High Performance Artificial Neural Network Model

    NASA Astrophysics Data System (ADS)

    Attarzadeh, Iman; Ow, Siew Hock

    Nowadays, mature software companies are more interested to have a precise estimation of software metrics such as project time, cost, quality, and risk at the early stages of software development process. The ability to precisely estimate project time and costs by project managers is one of the essential tasks in software development activities, and it named software effort estimation. The estimated effort at the early stage of project development process is uncertain, vague, and often the least accurate. It is because that very little information is available at the beginning stage of project. Therefore, a reliable and precise effort estimation model is an ongoing challenge for project managers and software engineers. This research work proposes a novel soft computing model incorporating Constructive Cost Model (COCOMO) to improve the precision of software time and cost estimation. The proposed artificial neural network model has good generalisation, adaption capability, and it can be interpreted and validated by software engineers. The experimental results show that applying the desirable features of artificial neural networks on the algorithmic estimation model improves the accuracy of time and cost estimation and estimated effort can be very close to the actual effort.

  14. Modeling of frost crystal growth over a flat plate using artificial neural networks and fractal geometries

    NASA Astrophysics Data System (ADS)

    Tahavvor, Ali Reza

    2017-03-01

    In the present study artificial neural network and fractal geometry are used to predict frost thickness and density on a cold flat plate having constant surface temperature under forced convection for different ambient conditions. These methods are very applicable in this area because phase changes such as melting and solidification are simulated by conventional methods but frost formation is a most complicated phase change phenomenon consists of coupled heat and mass transfer. Therefore conventional mathematical techniques cannot capture the effects of all parameters on its growth and development because this process influenced by many factors and it is a time dependent process. Therefore, in this work soft computing method such as artificial neural network and fractal geometry are used to do this manner. The databases for modeling are generated from the experimental measurements. First, multilayer perceptron network is used and it is found that the back-propagation algorithm with Levenberg-Marquardt learning rule is the best choice to estimate frost growth properties due to accurate and faster training procedure. Second, fractal geometry based on the Von-Koch curve is used to model frost growth procedure especially in frost thickness and density. Comparison is performed between experimental measurements and soft computing methods. Results show that soft computing methods can be used more efficiently to determine frost properties over a flat plate. Based on the developed models, wide range of frost formation over flat plates can be determined for various conditions.

  15. Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network

    NASA Astrophysics Data System (ADS)

    Nantasenamat, Chanin; Naenna, Thanakorn; Ayudhya, Chartchalerm Isarankura Na; Prachayasittikul, Virapong

    2005-07-01

    Artificial neural network (ANN) implementing the back-propagation algorithm was applied for the calculation of the imprinting factors (IF) of molecularly imprinted polymers (MIP) as a function of the computed molecular descriptors of template and functional monomer molecules and mobile phase descriptors. The dataset used in our study were obtained from the literature and classified into two distinctive datasets on the basis of the polymer's morphology, irregularly sized MIP and uniformly sized MIP datasets. Results revealed that artificial neural network was able to perform well on datasets derived from uniformly sized MIP ( n=23, r=0.946, RMS=2.944) while performing poorly on datasets derived from irregularly sized MIP ( n=75, r=0.382, RMS=6.123). The superior performance of the uniformly sized MIP dataset over the irregularly sized MIP dataset could be attributed to its more predictable nature owing to the consistency of MIP particles, uniform number and association constant of binding sites, and minimal deviation of the imprinted polymers. The ability to predict the imprinting factor of imprinted polymer prior to performing actual experimental work provide great insights on the feasibility of the interaction between template-functional monomer pairs.

  16. Groundwater level forecasting using an artificial neural network trained with particle swarm optimization.

    NASA Astrophysics Data System (ADS)

    Tapoglou, E.; Trichakis, I. C.; Dokou, Z.; Karatzas, G. P.

    2012-04-01

    The purpose of this study is to examine the use of particle swarm optimization algorithm in order to train a feed-forward multi-layer artificial neural network, which can simulate hydraulic head change at an observation well. Particle swarm optimization is a relatively new evolutionary algorithm, developed by Eberhart and Kennedy (1995), which is used to find optimal solutions to numerical and quantitative problems. Three different variations of particle swarm optimization algorithm are considered, the classic algorithm with the improvement of inertia weight, PSO-TVAC and GLBest-PSO. The best performance among all the algorithms was achieved by GLBest-PSO, where the distance between the overall best solution found and the best solution of each particle plays a major role in updating each particle's velocity. The algorithm is implemented using field data from the region of Agia, Chania, Greece. The particle swarm optimization algorithm shows an improvement of 9.3% and 18% in training and test errors respectively, compared to the errors of the back propagation algorithm. The trained neural network can predict the hydraulic head change at a well, without being able to predict extreme and transitional phenomena. The maximum divergence from the observed values is 0.35m. When the hydraulic head change is converted into hydraulic head, using the observed hydraulic head of the previous day, the deviations of simulated values from the actual hydraulic head appear comparatively smaller, with an average deviation of 0.041m. The trained neural network was also used for midterm prediction. In this case, the hydraulic head of the first day of the simulation is used together with the hydraulic head change derived from the simulation. The values obtained by this process were smaller than the observed, while the maximum difference is approximately 1m. However, this error, is not accumulated during the two hydrological years of simulation, and the error at the end of the simulation

  17. A radial basis function neural network based on artificial immune systems for remote sensing image classification

    NASA Astrophysics Data System (ADS)

    Yan, Qin; Zhong, Yanfei

    2008-12-01

    The radial basis function (RBF) neural network is a powerful method for remote sensing image classification. It has a simple architecture and the learning algorithm corresponds to the solution of a linear regression problem, resulting in a fast training process. The main drawback of this strategy is the requirement of an efficient algorithm to determine the number, position, and dispersion of the RBF. Traditional methods to determine the centers are: randomly choose input vectors from the training data set; vectors obtained from unsupervised clustering algorithms, such as k-means, applied to the input data. These conduce that traditional RBF neural network is sensitive to the center initialization. In this paper, the artificial immune network (aiNet) model, a new computational intelligence based on artificial immune networks (AIN), is applied to obtain appropriate centers for remote sensing image classification. In the aiNet-RBF algorihtm, each input pattern corresonds to an antigenic stimulus, while each RBF candidate center is considered to be an element, or cell, of the immune network model. The steps are as follows: A set of candidate centers is initialized at random, where the initial number of candidates and their positions is not crucial to the performance. Then, the clonal selection principle will control which candidates will be selected and how they will be upadated. Note that the clonal selection principle will be responsible for how the centers will represent the training data set. Finally, the immune network will identify and eliminate or suppress self-recognizing individuals to control the number of candidate centers. After the above learning phase, the aiNet network centers represent internal images of the inuput patterns presented to it. The algorithm output is taken to be the matrix of memory cells' coordinates that represent the final centers to be adopted by the RBF network. The stopping criterion of the proposed algorithm is given by a pre

  18. Improved Quantum Artificial Fish Algorithm Application to Distributed Network Considering Distributed Generation.

    PubMed

    Du, Tingsong; Hu, Yang; Ke, Xianting

    2015-01-01

    An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA), the basic artificial fish swarm algorithm (BAFSA), and the global edition artificial fish swarm algorithm (GAFSA) to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA.

  19. Communications and control for electric power systems: Power system stability applications of artificial neural networks

    NASA Technical Reports Server (NTRS)

    Toomarian, N.; Kirkham, Harold

    1994-01-01

    This report investigates the application of artificial neural networks to the problem of power system stability. The field of artificial intelligence, expert systems, and neural networks is reviewed. Power system operation is discussed with emphasis on stability considerations. Real-time system control has only recently been considered as applicable to stability, using conventional control methods. The report considers the use of artificial neural networks to improve the stability of the power system. The networks are considered as adjuncts and as replacements for existing controllers. The optimal kind of network to use as an adjunct to a generator exciter is discussed.

  20. Nuclear power plant status diagnostics using an artificial neural network

    SciTech Connect

    Bartlett, E.B.; Uhrig, R.E. )

    1992-03-01

    In this paper, nuclear power plant operating status recognition is investigated using a self-optimizing stochastic learning algorithm artificial neutral network (ANN) with dynamic node architecture learning. The objective is to train the ANN to classify selected nuclear power plant accident conditions and assess the potential for future success in this area. The network is trained on normal operating conditions as well as on potentially unsafe conditions based on nuclear power plant training simulator-generated accident scenarios. These scenarios include hot-and cold-leg loss of coolant, control rod ejection, total loss of off-site power, main streamline break, main feedwater line break, and steam generator tube leak accidents as well as the normal operating condition. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results. continued research work indicated.

  1. International experience on the use of artificial neural networks in gastroenterology.

    PubMed

    Grossi, E; Mancini, A; Buscema, M

    2007-03-01

    In this paper, we reconsider the scientific background for the use of artificial intelligence tools in medicine. A review of some recent significant papers shows that artificial neural networks, the more advanced and effective artificial intelligence technique, can improve the classification accuracy and survival prediction of a number of gastrointestinal diseases. We discuss the 'added value' the use of artificial neural networks-based tools can bring in the field of gastroenterology, both at research and clinical application level, when compared with traditional statistical or clinical-pathological methods.

  2. Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor.

    PubMed

    Pandey, Daya Shankar; Das, Saptarshi; Pan, Indranil; Leahy, James J; Kwapinski, Witold

    2016-12-01

    In this paper, multi-layer feed forward neural networks are used to predict the lower heating value of gas (LHV), lower heating value of gasification products including tars and entrained char (LHVp) and syngas yield during gasification of municipal solid waste (MSW) during gasification in a fluidized bed reactor. These artificial neural networks (ANNs) with different architectures are trained using the Levenberg-Marquardt (LM) back-propagation algorithm and a cross validation is also performed to ensure that the results generalise to other unseen datasets. A rigorous study is carried out on optimally choosing the number of hidden layers, number of neurons in the hidden layer and activation function in a network using multiple Monte Carlo runs. Nine input and three output parameters are used to train and test various neural network architectures in both multiple output and single output prediction paradigms using the available experimental datasets. The model selection procedure is carried out to ascertain the best network architecture in terms of predictive accuracy. The simulation results show that the ANN based methodology is a viable alternative which can be used to predict the performance of a fluidized bed gasifier.

  3. A novel neural-inspired learning algorithm with application to clinical risk prediction.

    PubMed

    Tay, Darwin; Poh, Chueh Loo; Kitney, Richard I

    2015-04-01

    Clinical risk prediction - the estimation of the likelihood an individual is at risk of a disease - is a coveted and exigent clinical task, and a cornerstone to the recommendation of life saving management strategies. This is especially important for individuals at risk of cardiovascular disease (CVD) given the fact that it is the leading causes of death in many developed counties. To this end, we introduce a novel learning algorithm - a key factor that influences the performance of machine learning-based prediction models - and utilities it to develop CVD risk prediction tool. This novel neural-inspired algorithm, called the Artificial Neural Cell System for classification (ANCSc), is inspired by mechanisms that develop the brain and empowering it with capabilities such as information processing/storage and recall, decision making and initiating actions on external environment. Specifically, we exploit on 3 natural neural mechanisms responsible for developing and enriching the brain - namely neurogenesis, neuroplasticity via nurturing and apoptosis - when implementing ANCSc algorithm. Benchmark testing was conducted using the Honolulu Heart Program (HHP) dataset and results are juxtaposed with 2 other algorithms - i.e. Support Vector Machine (SVM) and Evolutionary Data-Conscious Artificial Immune Recognition System (EDC-AIRS). Empirical experiments indicate that ANCSc algorithm (statistically) outperforms both SVM and EDC-AIRS algorithms. Key clinical markers identified by ANCSc algorithm include risk factors related to diet/lifestyle, pulmonary function, personal/family/medical history, blood data, blood pressure, and electrocardiography. These clinical markers, in general, are also found to be clinically significant - providing a promising avenue for identifying potential cardiovascular risk factors to be evaluated in clinical trials.

  4. An Improved Back Propagation Neural Network Algorithm on Classification Problems

    NASA Astrophysics Data System (ADS)

    Nawi, Nazri Mohd; Ransing, R. S.; Salleh, Mohd Najib Mohd; Ghazali, Rozaida; Hamid, Norhamreeza Abdul

    The back propagation algorithm is one the most popular algorithms to train feed forward neural networks. However, the convergence of this algorithm is slow, it is mainly because of gradient descent algorithm. Previous research demonstrated that in 'feed forward' algorithm, the slope of the activation function is directly influenced by a parameter referred to as 'gain'. This research proposed an algorithm for improving the performance of the back propagation algorithm by introducing the adaptive gain of the activation function. The gain values change adaptively for each node. The influence of the adaptive gain on the learning ability of a neural network is analysed. Multi layer feed forward neural networks have been assessed. Physical interpretation of the relationship between the gain value and the learning rate and weight values is given. The efficiency of the proposed algorithm is compared with conventional Gradient Descent Method and verified by means of simulation on four classification problems. In learning the patterns, the simulations result demonstrate that the proposed method converged faster on Wisconsin breast cancer with an improvement ratio of nearly 2.8, 1.76 on diabetes problem, 65% better on thyroid data sets and 97% faster on IRIS classification problem. The results clearly show that the proposed algorithm significantly improves the learning speed of the conventional back-propagation algorithm.

  5. Surrogate modeling of deformable joint contact using artificial neural networks.

    PubMed

    Eskinazi, Ilan; Fregly, Benjamin J

    2015-09-01

    Deformable joint contact models can be used to estimate loading conditions for cartilage-cartilage, implant-implant, human-orthotic, and foot-ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input-output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models.

  6. Surrogate Modeling of Deformable Joint Contact using Artificial Neural Networks

    PubMed Central

    Eskinazi, Ilan; Fregly, Benjamin J.

    2016-01-01

    Deformable joint contact models can be used to estimate loading conditions for cartilage-cartilage, implant-implant, human-orthotic, and foot-ground interactions. However, contact evaluations are often so expensive computationally that they can be prohibitive for simulations or optimizations requiring thousands or even millions of contact evaluations. To overcome this limitation, we developed a novel surrogate contact modeling method based on artificial neural networks (ANNs). The method uses special sampling techniques to gather input-output data points from an original (slow) contact model in multiple domains of input space, where each domain represents a different physical situation likely to be encountered. For each contact force and torque output by the original contact model, a multi-layer feed-forward ANN is defined, trained, and incorporated into a surrogate contact model. As an evaluation problem, we created an ANN-based surrogate contact model of an artificial tibiofemoral joint using over 75,000 evaluations of a fine-grid elastic foundation (EF) contact model. The surrogate contact model computed contact forces and torques about 1000 times faster than a less accurate coarse grid EF contact model. Furthermore, the surrogate contact model was seven times more accurate than the coarse grid EF contact model within the input domain of a walking motion. For larger input domains, the surrogate contact model showed the expected trend of increasing error with increasing domain size. In addition, the surrogate contact model was able to identify out-of-contact situations with high accuracy. Computational contact models created using our proposed ANN approach may remove an important computational bottleneck from musculoskeletal simulations or optimizations incorporating deformable joint contact models. PMID:26220591

  7. Surface daytime net radiation estimation using artificial neural networks

    SciTech Connect

    Jiang, Bo; Zhang, Yi; Liang, Shunlin; Zhang, Xiaotong; Xiao, Zhiqiang

    2014-11-11

    Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical Rn estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate Rn globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. Rn estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991–2010 both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R2) of 0.92, a root mean square error (RMSE) of 34.27 W·m–2 , and a bias of –0.61 W·m–2 in global mode based on the validation dataset. In conclusion, ANN methods are a potentially powerful tool for global Rn estimation.

  8. Biological and bionic hands: natural neural coding and artificial perception.

    PubMed

    Bensmaia, Sliman J

    2015-09-19

    The first decade and a half of the twenty-first century brought about two major innovations in neuroprosthetics: the development of anthropomorphic robotic limbs that replicate much of the function of a native human arm and the refinement of algorithms that decode intended movements from brain activity. However, skilled manipulation of objects requires somatosensory feedback, for which vision is a poor substitute. For upper-limb neuroprostheses to be clinically viable, they must therefore provide for the restoration of touch and proprioception. In this review, I discuss efforts to elicit meaningful tactile sensations through stimulation of neurons in somatosensory cortex. I focus on biomimetic approaches to sensory restoration, which leverage our current understanding about how information about grasped objects is encoded in the brain of intact individuals. I argue that not only can sensory neuroscience inform the development of sensory neuroprostheses, but also that the converse is true: stimulating the brain offers an exceptional opportunity to causally interrogate neural circuits and test hypotheses about natural neural coding.

  9. Biological and bionic hands: natural neural coding and artificial perception

    PubMed Central

    Bensmaia, Sliman J.

    2015-01-01

    The first decade and a half of the twenty-first century brought about two major innovations in neuroprosthetics: the development of anthropomorphic robotic limbs that replicate much of the function of a native human arm and the refinement of algorithms that decode intended movements from brain activity. However, skilled manipulation of objects requires somatosensory feedback, for which vision is a poor substitute. For upper-limb neuroprostheses to be clinically viable, they must therefore provide for the restoration of touch and proprioception. In this review, I discuss efforts to elicit meaningful tactile sensations through stimulation of neurons in somatosensory cortex. I focus on biomimetic approaches to sensory restoration, which leverage our current understanding about how information about grasped objects is encoded in the brain of intact individuals. I argue that not only can sensory neuroscience inform the development of sensory neuroprostheses, but also that the converse is true: stimulating the brain offers an exceptional opportunity to causally interrogate neural circuits and test hypotheses about natural neural coding. PMID:26240424

  10. A hybrid calibration-free/artificial neural networks approach to the quantitative analysis of LIBS spectra

    NASA Astrophysics Data System (ADS)

    D'Andrea, Eleonora; Pagnotta, Stefano; Grifoni, Emanuela; Legnaioli, Stefano; Lorenzetti, Giulia; Palleschi, Vincenzo; Lazzerini, Beatrice

    2015-03-01

    A `hybrid' method is proposed for the quantitative analysis of materials by LIBS, combining the precision of the calibration-free LIBS (CF-LIBS) algorithm with the quickness of artificial neural networks. The method allows the precise determination of the samples' composition even in the presence of relatively large laser fluctuations and matrix effects. To show the strength and robustness of this approach, a number of synthetic LIBS spectra of Cu-Ni binary alloys with different composition were computer-simulated, in correspondence of different plasma temperatures, electron number densities and ablated mass. The CF-LIBS/ANN approach here proposed demonstrated to be capable, after appropriate training, of `learning' the basic physical relations between the experimentally measured line intensities and the plasma parameters. Because of that the composition of the sample can be correctly determined, as in CF-LIBS measurements, but in a much shorter time.

  11. Deep dreaming, aberrant salience and psychosis: Connecting the dots by artificial neural networks.

    PubMed

    Keshavan, Matcheri S; Sudarshan, Mukund

    2017-01-24

    Why some individuals, when presented with unstructured sensory inputs, develop altered perceptions not based in reality, is not well understood. Machine learning approaches can potentially help us understand how the brain normally interprets sensory inputs. Artificial neural networks (ANN) progressively extract higher and higher-level features of sensory input and identify the nature of an object based on a priori information. However, some ANNs which use algorithms such as the "deep-dreaming" developed by Google, allow the network to over-emphasize some objects it "thinks" it recognizes in those areas, and iteratively enhance such outputs leading to representations that appear farther and farther from "reality". We suggest that such "deep dreaming" ANNs may model aberrant salience, a mechanism suggested for pathogenesis of psychosis. Such models can generate testable predictions for psychosis.

  12. Artificial neural network modeling of the water quality index using land use areas as predictors.

    PubMed

    Gazzaz, Nabeel M; Yusoff, Mohd Kamil; Ramli, Mohammad Firuz; Juahir, Hafizan; Aris, Ahmad Zaharin

    2015-02-01

    This paper describes the design of an artificial neural network (ANN) model to predict the water quality index (WQI) using land use areas as predictors. Ten-year records of land use statistics and water quality data for Kinta River (Malaysia) were employed in the modeling process. The most accurate WQI predictions were obtained with the network architecture 7-23-1; the back propagation training algorithm; and a learning rate of 0.02. The WQI forecasts of this model had significant (p < 0.01), positive, very high correlation (ρs = 0.882) with the measured WQI values. Sensitivity analysis revealed that the relative importance of the land use classes to WQI predictions followed the order: mining > rubber > forest > logging > urban areas > agriculture > oil palm. These findings show that the ANNs are highly reliable means of relating water quality to land use, thus integrating land use development with river water quality management.

  13. Short-term acoustic forecasting via artificial neural networks for neonatal intensive care units.

    PubMed

    Young, Jason; Macke, Christopher J; Tsoukalas, Lefteri H

    2012-11-01

    Noise levels in hospitals, especially neonatal intensive care units (NICUs), have become of great concern for hospital designers. This paper details an artificial neural network (ANN) approach to forecasting the sound loads in NICUs. The ANN is used to learn the relationship between past, present, and future noise levels. By training the ANN with data specific to the location and device used to measure the sound, the ANN is able to produce reasonable predictions of noise levels in the NICU. Best case results show average absolute errors of 5.06 ± 4.04% when used to predict the noise levels one hour ahead, which correspond to 2.53 dBA ± 2.02 dBA. The ANN has the tendency to overpredict during periods of stability and underpredict during large transients. This forecasting algorithm could be of use in any application where prediction and prevention of harmful noise levels are of the utmost concern.

  14. Artificial neural network study of whole-cell bacterial bioreporter response determined using fluorescence flow cytometry.

    PubMed

    Busam, Sirisha; McNabb, Maia; Wackwitz, Anke; Senevirathna, Wasana; Beggah, Siham; Meer, Jan Roelof van der; Wells, Mona; Breuer, Uta; Harms, Hauke

    2007-12-01

    Genetically engineered bioreporters are an excellent complement to traditional methods of chemical analysis. The application of fluorescence flow cytometry to detection of bioreporter response enables rapid and efficient characterization of bacterial bioreporter population response on a single-cell basis. In the present study, intrapopulation response variability was used to obtain higher analytical sensitivity and precision. We have analyzed flow cytometric data for an arsenic-sensitive bacterial bioreporter using an artificial neural network-based adaptive clustering approach (a single-layer perceptron model). Results for this approach are far superior to other methods that we have applied to this fluorescent bioreporter (e.g., the arsenic detection limit is 0.01 microM, substantially lower than for other detection methods/algorithms). The approach is highly efficient computationally and can be implemented on a real-time basis, thus having potential for future development of high-throughput screening applications.

  15. Failure forecast of Boeing 737 bleed air system using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Al-Wadiee, Waheed

    In this study, the failure rate of different types of bleed air control valves for the Boeing 737 aircraft is modeled. Two approaches are utilized to perform this work. In the first approach, Weibull model, in which different parameters are utilized and tested, is used. In the second one, a common type of the Artificial Neural Network (ANN) modeling is used. A Feed-forward back-propagation algorithm is implemented to train the network. Subsequently, the optimum number of neurons and layers that give the best result compared to the actual data are determined. Finally, the outputs from both models are compared against the actual data. The final results show a high level of accuracy of the ANN's predictions compared to the more traditional Weibull modeling. The developed verified model lends itself to applications that extend from scheduling replacements operations of these valves, to developing plans for inventory management in any aviation engines maintenance facility.

  16. Diagnosis and Prognosis of Bearing Failure in Rotating Machinery Using Acoustic Emission and Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Mahamad, Abd Kadir; Hiyama, Takashi; Ghazali, Mohd Imran

    Bearing failure is well-known as a common problem in industries. Therefore, timely diagnosis and prognosis (DAP) of bearing fault is very crucial in order to prevent sudden damages. This paper proposes the practical method of bearing fault DAP using acoustic emission (AE) technique assisted with artificial neural network (ANN). The bearings failure data is measured based on the AE in terms of decibel (dB) and Distress levels, which are used as input for ANN of a bearing fault DAP. For this purpose, an experimental rig is setup to collect data from target bearing by using Machine Health Checker (MHC) Memo assisted with MHC Analysis software. In this work, Elman network with training algorithm, Levenberg-Marquardt Back- propagation is used for ANN DAP. The obtained results indicates that the proposed methods are suitable to inform the remaining useful life time of a faulty bearing.

  17. Towards Artificial Speech Therapy: A Neural System for Impaired Speech Segmentation.

    PubMed

    Iliya, Sunday; Neri, Ferrante

    2016-09-01

    This paper presents a neural system-based technique for segmenting short impaired speech utterances into silent, unvoiced, and voiced sections. Moreover, the proposed technique identifies those points of the (voiced) speech where the spectrum becomes steady. The resulting technique thus aims at detecting that limited section of the speech which contains the information about the potential impairment of the speech. This section is of interest to the speech therapist as it corresponds to the possibly incorrect movements of speech organs (lower lip and tongue with respect to the vocal tract). Two segmentation models to detect and identify the various sections of the disordered (impaired) speech signals have been developed and compared. The first makes use of a combination of four artificial neural networks. The second is based on a support vector machine (SVM). The SVM has been trained by means of an ad hoc nested algorithm whose outer layer is a metaheuristic while the inner layer is a convex optimization algorithm. Several metaheuristics have been tested and compared leading to the conclusion that some variants of the compact differential evolution (CDE) algorithm appears to be well-suited to address this problem. Numerical results show that the SVM model with a radial basis function is capable of effective detection of the portion of speech that is of interest to a therapist. The best performance has been achieved when the system is trained by the nested algorithm whose outer layer is hybrid-population-based/CDE. A population-based approach displays the best performance for the isolation of silence/noise sections, and the detection of unvoiced sections. On the other hand, a compact approach appears to be clearly well-suited to detect the beginning of the steady state of the voiced signal. Both the proposed segmentation models display outperformed two modern segmentation techniques based on Gaussian mixture model and deep learning.

  18. Instrumentation for Scientific Computing in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics.

    DTIC Science & Technology

    1987-10-01

    This was an instrumentation grant to purchase equipment of support of research in neural networks, information science , artificial intelligence, and applied mathematics. Computer lab equipment, motor control and robotics lab equipment, speech analysis equipment and computational vision equipment were purchased.

  19. Multiobjective analysis of a public wellfield using artificial neural networks

    USGS Publications Warehouse

    Coppola, E.A.; Szidarovszky, F.; Davis, D.; Spayd, S.; Poulton, M.M.; Roman, E.

    2007-01-01

    As competition for increasingly scarce ground water resources grows, many decision makers may come to rely upon rigorous multiobjective techniques to help identify appropriate and defensible policies, particularly when disparate stakeholder groups are involved. In this study, decision analysis was conducted on a public water supply wellfield to balance water supply needs with well vulnerability to contamination from a nearby ground water contaminant plume. With few alternative water sources, decision makers must balance the conflicting objectives of maximizing water supply volume from noncontaminated wells while minimizing their vulnerability to contamination from the plume. Artificial neural networks (ANNs) were developed with simulation data from a numerical ground water flow model developed for the study area. The ANN-derived state transition equations were embedded into a multiobjective optimization model, from which the Pareto frontier or trade-off curve between water supply and wellfield vulnerability was identified. Relative preference values and power factors were assigned to the three stakeholders, namely the company whose waste contaminated the aquifer, the community supplied by the wells, and the water utility company that owns and operates the wells. A compromise pumping policy that effectively balances the two conflicting objectives in accordance with the preferences of the three stakeholder groups was then identified using various distance-based methods. ?? 2006 National Ground Water Association.

  20. Prediction of Soil Deformation in Tunnelling Using Artificial Neural Networks

    PubMed Central

    Lai, Jinxing

    2016-01-01

    In the past few decades, as a new tool for analysis of the tough geotechnical problems, artificial neural networks (ANNs) have been successfully applied to address a number of engineering problems, including deformation due to tunnelling in various types of rock mass. Unlike the classical regression methods in which a certain form for the approximation function must be presumed, ANNs do not require the complex constitutive models. Additionally, it is traced that the ANN prediction system is one of the most effective ways to predict the rock mass deformation. Furthermore, it could be envisaged that ANNs would be more feasible for the dynamic prediction of displacements in tunnelling in the future, especially if ANN models are combined with other research methods. In this paper, we summarized the state-of-the-art and future research challenges of ANNs on the tunnel deformation prediction. And the application cases as well as the improvement of ANN models were also presented. The presented ANN models can serve as a benchmark for effective prediction of the tunnel deformation with characters of nonlinearity, high parallelism, fault tolerance, learning, and generalization capability. PMID:26819587

  1. Molnets: An Artificial Chemistry Based on Neural Networks

    NASA Technical Reports Server (NTRS)

    Colombano, Silvano; Luk, Johnny; Segovia-Juarez, Jose L.; Lohn, Jason; Clancy, Daniel (Technical Monitor)

    2002-01-01

    The fundamental problem in the evolution of matter is to understand how structure-function relationships are formed and increase in complexity from the molecular level all the way to a genetic system. We have created a system where structure-function relationships arise naturally and without the need of ad hoc function assignments to given structures. The idea was inspired by neural networks, where the structure of the net embodies specific computational properties. In this system networks interact with other networks to create connections between the inputs of one net and the outputs of another. The newly created net then recomputes its own synaptic weights, based on anti-hebbian rules. As a result some connections may be cut, and multiple nets can emerge as products of a 'reaction'. The idea is to study emergent reaction behaviors, based on simple rules that constitute a pseudophysics of the system. These simple rules are parameterized to produce behaviors that emulate chemical reactions. We find that these simple rules show a gradual increase in the size and complexity of molecules. We have been building a virtual artificial chemistry laboratory for discovering interesting reactions and for testing further ideas on the evolution of primitive molecules. Some of these ideas include the potential effect of membranes and selective diffusion according to molecular size.

  2. Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making

    PubMed Central

    Burnside, Elizabeth S.

    2013-01-01

    Screening mammography is the most effective means for early detection of breast cancer. Although general rules for discriminating malignant and benign lesions exist, radiologists are unable to perfectly detect and classify all lesions as malignant and benign, for many reasons which include, but are not limited to, overlap of features that distinguish malignancy, difficulty in estimating disease risk, and variability in recommended management. When predictive variables are numerous and interact, ad hoc decision making strategies based on experience and memory may lead to systematic errors and variability in practice. The integration of computer models to help radiologists increase the accuracy of mammography examinations in diagnostic decision making has gained increasing attention in the last two decades. In this study, we provide an overview of one of the most commonly used models, artificial neural networks (ANNs), in mammography interpretation and diagnostic decision making and discuss important features in mammography interpretation. We conclude by discussing several common limitations of existing research on ANN-based detection and diagnostic models and provide possible future research directions. PMID:23781276

  3. Automatic labeling and characterization of objects using artificial neural networks

    NASA Technical Reports Server (NTRS)

    Campbell, William J.; Hill, Scott E.; Cromp, Robert F.

    1989-01-01

    Existing NASA supported scientific data bases are usually developed, managed and populated in a tedious, error prone and self-limiting way in terms of what can be described in a relational Data Base Management System (DBMS). The next generation Earth remote sensing platforms, i.e., Earth Observation System, (EOS), will be capable of generating data at a rate of over 300 Mbs per second from a suite of instruments designed for different applications. What is needed is an innovative approach that creates object-oriented databases that segment, characterize, catalog and are manageable in a domain-specific context and whose contents are available interactively and in near-real-time to the user community. Described here is work in progress that utilizes an artificial neural net approach to characterize satellite imagery of undefined objects into high-level data objects. The characterized data is then dynamically allocated to an object-oriented data base where it can be reviewed and assessed by a user. The definition, development, and evolution of the overall data system model are steps in the creation of an application-driven knowledge-based scientific information system.

  4. Application of artificial neural network in medical geochemistry.

    PubMed

    Fajčíková, K; Stehlíková, B; Cvečková, V; Rapant, S

    2017-03-28

    For the evaluation of various adverse health effects of chemical elements occurring in the environment on humans, the comparison and linking of geochemical data (chemical composition of groundwater, soils, and dusts) with data on health status of population (so-called health indicators) play a key role. Geochemical and health data are predominantly nonlinear, and the use of standard statistical methods can lead to wrong conclusions. For linking such data, we find appropriate the use method of artificial neural networks (ANNs) which enable to eliminate data inhomogeneity and also potential data errors. Through method of ANNs, we are able to determine the order of influence of chemical elements on health indicators as well as to define limit values for the influential elements at which the health status of population is the most favourable (i.e. the lowest mortality, the highest life expectancy). For determination of dependence between the groundwater contents of chemical elements and health indicators, we recommend to create 200 ANNs. In further calculations performed for identification of order of influence of chemical elements as well as definition of limit values, we propose to work with median or mean values from calculated 200 ANNs. The ANN represents an appropriate method to be used for environmental and health data analysis in medical geochemistry.

  5. Design The Cervical Cancer Detector Use The Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Intan Af'idah, Dwi; Didik Widianto, Eko; Setyawan, Budi

    2013-06-01

    Cancer is one of the contagious diseases that become a public health issue, both in the world and in Indonesia. In the world, 12% of all deaths caused by cancer and is the second killer after cardiovascular disease. Early detection using the IVA is a practical and inexpensive (only requiring acetic acid). However, the accuracy of the method is quite low, as it can not detect the stage of the cancer. While other methods have a better sensitivity than the IVA method, is a method of PAP smear. However, this method is relatively expensive, and requires an experienced pathologist-cytologist. According to the case above, Considered important to make the cancer cervics detector that is used to detect the abnormality and cervical cancer stage and consists of a digital microscope, as well as a computer application based on artificial neural network. The use of cervical cancer detector software and hardware are integrated each other. After the specifications met, the steps to design the cervical cancer detection are: Modifying a conventional microscope by adding a lens, image recording, and the lights, Programming the tools, designing computer applications, Programming features abnormality detection and staging of cancer.

  6. Honey characterization using computer vision system and artificial neural networks.

    PubMed

    Shafiee, Sahameh; Minaei, Saeid; Moghaddam-Charkari, Nasrollah; Barzegar, Mohsen

    2014-09-15

    This paper reports the development of a computer vision system (CVS) for non-destructive characterization of honey based on colour and its correlated chemical attributes including ash content (AC), antioxidant activity (AA), and total phenolic content (TPC). Artificial neural network (ANN) models were applied to transform RGB values of images to CIE L*a*b* colourimetric measurements and to predict AC, TPC and AA from colour features of images. The developed ANN models were able to convert RGB values to CIE L*a*b* colourimetric parameters with low generalization error of 1.01±0.99. In addition, the developed models for prediction of AC, TPC and AA showed high performance based on colour parameters of honey images, as the R(2) values for prediction were 0.99, 0.98, and 0.87, for AC, AA and TPC, respectively. The experimental results show the effectiveness and possibility of applying CVS for non-destructive honey characterization by the industry.

  7. Classification of breast abnormalities using artificial neural network

    NASA Astrophysics Data System (ADS)

    Zaman, Nur Atiqah Kamarul; Rahman, Wan Eny Zarina Wan Abdul; Jumaat, Abdul Kadir; Yasiran, Siti Salmah

    2015-05-01

    Classification is the process of recognition, differentiation and categorizing objects into groups. Breast abnormalities are calcifications which are tumor markers that indicate the presence of cancer in the breast. The aims of this research are to classify the types of breast abnormalities using artificial neural network (ANN) classifier and to evaluate the accuracy performance using receiver operating characteristics (ROC) curve. The methods used in this research are ANN for breast abnormalities classifications and Canny edge detector as a feature extraction method. Previously the ANN classifier provides only the number of benign and malignant cases without providing information for specific cases. However in this research, the type of abnormality for each image can be obtained. The existing MIAS MiniMammographic database classified the mammogram images into three features only namely characteristic of background tissues, class of abnormality and radius of abnormality. However, in this research three other features are added-in. These three features are number of spots, area and shape of abnormalities. Lastly the performance of the ANN classifier is evaluated using ROC curve. It is found that ANN has an accuracy of 97.9% which is considered acceptable.

  8. Predicting concrete corrosion of sewers using artificial neural network.

    PubMed

    Jiang, Guangming; Keller, Jurg; Bond, Philip L; Yuan, Zhiguo

    2016-04-01

    Corrosion is often a major failure mechanism for concrete sewers and under such circumstances the sewer service life is largely determined by the progression of microbially induced concrete corrosion. The modelling of sewer processes has become possible due to the improved understanding of in-sewer transformation. Recent systematic studies about the correlation between the corrosion processes and sewer environment factors should be utilized to improve the prediction capability of service life by sewer models. This paper presents an artificial neural network (ANN)-based approach for modelling the concrete corrosion processes in sewers. The approach included predicting the time for the corrosion to initiate and then predicting the corrosion rate after the initiation period. The ANN model was trained and validated with long-term (4.5 years) corrosion data obtained in laboratory corrosion chambers, and further verified with field measurements in real sewers across Australia. The trained model estimated the corrosion initiation time and corrosion rates very close to those measured in Australian sewers. The ANN model performed better than a multiple regression model also developed on the same dataset. Additionally, the ANN model can serve as a prediction framework for sewer service life, which can be progressively improved and expanded by including corrosion rates measured in different sewer conditions. Furthermore, the proposed methodology holds promise to facilitate the construction of analytical models associated with corrosion processes of concrete sewers.

  9. Superiority of artificial neural networks for a genetic classification procedure.

    PubMed

    Sant'Anna, I C; Tomaz, R S; Silva, G N; Nascimento, M; Bhering, L L; Cruz, C D

    2015-08-19

    The correct classification of individuals is extremely important for the preservation of genetic variability and for maximization of yield in breeding programs using phenotypic traits and genetic markers. The Fisher and Anderson discriminant functions are commonly used multivariate statistical techniques for these situations, which allow for the allocation of an initially unknown individual to predefined groups. However, for higher levels of similarity, such as those found in backcrossed populations, these methods have proven to be inefficient. Recently, much research has been devoted to developing a new paradigm of computing known as artificial neural networks (ANNs), which can be used to solve many statistical problems, including classification problems. The aim of this study was to evaluate the feasibility of ANNs as an evaluation technique of genetic diversity by comparing their performance with that of traditional methods. The discriminant functions were equally ineffective in discriminating the populations, with error rates of 23-82%, thereby preventing the correct discrimination of individuals between populations. The ANN was effective in classifying populations with low and high differentiation, such as those derived from a genetic design established from backcrosses, even in cases of low differentiation of the data sets. The ANN appears to be a promising technique to solve classification problems, since the number of individuals classified incorrectly by the ANN was always lower than that of the discriminant functions. We envisage the potential relevant application of this improved procedure in the genomic classification of markers to distinguish between breeds and accessions.

  10. Sensor calibration and compensation using artificial neural network.

    PubMed

    Khan, Shakeb A; Shahani, D T; Agarwala, A K

    2003-07-01

    Artificial neural network (ANN) based inverse modeling technique is used for sensor response linearization. The choice of the order of the model and the number of the calibration points are important design parameters in this technique. An intensive study of the effect of the order of the model and number of calibration points on the lowest asymptotic root-mean-square (RMS) error has been reported in this paper. Starting from the initial value of the nonlinearity in the characteristics of a sensor and required RMS error, it is possible to quickly fix the order of the model and the number of calibration points required using results of this paper. The number of epochs needed to calibrate the sensor, and thereafter the epochs needed to recalibrate in event of sensitivity or offset drifts, are also presented to bring out the convergence time of the technique. More importantly, the advantages of the ANN technique over traditional regression based modeling are also discussed from the point of view of its advantage in hardware simplicity in microcontroller based implementation. Results presented in this paper would be of interest to instrumentation design engineers.

  11. Multiobjective analysis of a public wellfield using artificial neural networks.

    PubMed

    Coppola, Emery A; Szidarovszky, Ferenc; Davis, Donald; Spayd, Steven; Poulton, Mary M; Roman, Eric

    2007-01-01

    As competition for increasingly scarce ground water resources grows, many decision makers may come to rely upon rigorous multiobjective techniques to help identify appropriate and defensible policies, particularly when disparate stakeholder groups are involved. In this study, decision analysis was conducted on a public water supply wellfield to balance water supply needs with well vulnerability to contamination from a nearby ground water contaminant plume. With few alternative water sources, decision makers must balance the conflicting objectives of maximizing water supply volume from noncontaminated wells while minimizing their vulnerability to contamination from the plume. Artificial neural networks (ANNs) were developed with simulation data from a numerical ground water flow model developed for the study area. The ANN-derived state transition equations were embedded into a multiobjective optimization model, from which the Pareto frontier or trade-off curve between water supply and wellfield vulnerability was identified. Relative preference values and power factors were assigned to the three stakeholders, namely the company whose waste contaminated the aquifer, the community supplied by the wells, and the water utility company that owns and operates the wells. A compromise pumping policy that effectively balances the two conflicting objectives in accordance with the preferences of the three stakeholder groups was then identified using various distance-based methods.

  12. Automatic classification of DMSA scans using an artificial neural network

    NASA Astrophysics Data System (ADS)

    Wright, J. W.; Duguid, R.; Mckiddie, F.; Staff, R. T.

    2014-04-01

    DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from ‘definitely normal’ to ‘definitely abnormal’. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α < 0.05) in performance between the network and operators. A further result from this work was that when suitably optimized, a negative predictive value of 100% for renal defects was achieved by the network, while still managing to identify 93% of the negative cases in the dataset. These results are encouraging for application of such a network as a screening tool or quality assurance assistant in clinical practice.

  13. Prediction of problematic wine fermentations using artificial neural networks.

    PubMed

    Román, R César; Hernández, O Gonzalo; Urtubia, U Alejandra

    2011-11-01

    Artificial neural networks (ANNs) have been used for the recognition of non-linear patterns, a characteristic of bioprocesses like wine production. In this work, ANNs were tested to predict problems of wine fermentation. A database of about 20,000 data from industrial fermentations of Cabernet Sauvignon and 33 variables was used. Two different ways of inputting data into the model were studied, by points and by fermentation. Additionally, different sub-cases were studied by varying the predictor variables (total sugar, alcohol, glycerol, density, organic acids and nitrogen compounds) and the time of fermentation (72, 96 and 256 h). The input of data by fermentations gave better results than the input of data by points. In fact, it was possible to predict 100% of normal and problematic fermentations using three predictor variables: sugars, density and alcohol at 72 h (3 days). Overall, ANNs were capable of obtaining 80% of prediction using only one predictor variable at 72 h; however, it is recommended to add more fermentations to confirm this promising result.

  14. Atmospheric controls on Puerto Rico precipitation using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Ramseyer, Craig A.; Mote, Thomas L.

    2016-10-01

    The growing need for local climate change scenarios has given rise to a wide range of empirical climate downscaling techniques. One of the most critical decisions in these methodologies is the selection of appropriate predictor variables for the downscaled surface predictand. A systematic approach to selecting predictor variables should be employed to ensure that the most important variables are utilized for the study site where the climate change scenarios are being developed. Tropical study areas have been far less examined than mid- and high-latitudes in the climate downscaling literature. As a result, studies analyzing optimal predictor variables for tropics are limited. The objectives of this study include developing artificial neural networks for six sites around Puerto Rico to develop nonlinear functions between 37 atmospheric predictor variables and local rainfall. The relative importance of each predictor is analyzed to determine the most important inputs in the network. Randomized ANNs are produced to determine the statistical significance of the relative importance of each predictor variable. Lower tropospheric moisture and winds are shown to be the most important variables at all sites. Results show inter-site variability in u- and v-wind importance depending on the unique geographic situation of the site. Lower tropospheric moisture and winds are physically linked to variability in sea surface temperatures (SSTs) and the strength and position of the North Atlantic High Pressure cell (NAHP). The changes forced by anthropogenic climate change in regional SSTs and the NAHP will impact rainfall variability in Puerto Rico.

  15. An artificial neural network predictor for tropospheric surface duct phenomena

    NASA Astrophysics Data System (ADS)

    Isaakidis, S. A.; Dimou, I. N.; Xenos, T. D.; Dris, N. A.

    2007-09-01

    In this work, an artificial neural network (ANN) model is developed and used to predict the presence of ducting phenomena for a specific time, taking into account ground values of atmospheric pressure, relative humidity and temperature. A feed forward backpropagation ANN is implemented, which is trained, validated and tested using atmospheric radiosonde data from the Helliniko airport, for the period from 1991 to 2004. The network's quality and generality is assessed using the Area Under the Receiver Operating Characteristics (ROC) Curves (AUC), which resulted to a mean value of about 0.86 to 0.90, depending on the observation time. In order to validate the ANN results and to evaluate any further improvement options of the proposed method, the problem was additionally treated using Least Squares Support Vector Machine (LS-SVM) classifiers, trained and tested with identical data sets for direct performance comparison with the ANN. Furthermore, time series prediction and the effect of surface wind to the presence of tropospheric ducts appearance are discussed. The results show that the ANN model presented here performs efficiently and gives successful tropospheric ducts predictions.

  16. Signal processing using artificial neural network for BOTDA sensor system.

    PubMed

    Azad, Abul Kalam; Wang, Liang; Guo, Nan; Tam, Hwa-Yaw; Lu, Chao

    2016-03-21

    We experimentally demonstrate the use of artificial neural network (ANN) to process sensing signals obtained from Brillouin optical time domain analyzer (BOTDA). The distributed temperature information is extracted directly from the local Brillouin gain spectra (BGSs) along the fiber under test without the process of determination of Brillouin frequency shift (BFS) and hence conversion from BFS to temperature. Unlike our previous work for short sensing distance where ANN is trained by measured BGSs, here we employ ideal BGSs with different linewidths to train the ANN in order to take the linewidth variation due to different conditions from the training and testing phases into account, making it feasible for long distance sensing. Moreover, the performance of ANN is compared with other two techniques, Lorentzian curve fitting and cross-correlation method, and our results show that ANN has higher accuracy and larger tolerance to measurement error, especially at large frequency scanning step. We also show that the temperature extraction from BOTDA measurements employing ANN is significantly faster than the other two approaches. Hence ANN can be an excellent alternative tool to process BGSs measured by BOTDA and obtain temperature distribution along the fiber, especially when large frequency scanning step is adopted to significantly reduce the measurement time but without sacrifice of sensing accuracy.

  17. Pharyngeal wall vibration detection using an artificial neural network.

    PubMed

    Behbehani, K; Lopez, F; Yen, F C; Lucas, E A; Burk, J R; Axe, J P; Kamangar, F

    1997-05-01

    An artificial-neural-network-based detector of pharyngeal wall vibration (PWV) is presented. PWV signals the imminent occurrence of obstructive sleep apnoea (OSA) in adults who suffer from OSA syndrome. Automated detection of PWV is very important in enhancing continuous positive airway pressure (CPAP) therapy by allowing automatic adjustment of the applied airway pressure by a procedure called automatic positive airway pressure (APAP) therapy. A network with 15 inputs, one output, and two hidden layers, each with two Adaline-nodes, is used as part of a PWV detection scheme. The network is initially trained using nasal mask pressure data from five positively diagnosed OSA patients. The performance of the ANN-based detector is evaluated using data from five different OSA patients. The results show that on the average it correctly detects the presence of PWV events at a rate of approximately 92% and correctly distinguishes normal breaths approximately 98% of the time. Further, the ANN-based detector accuracy is not affected by the pressure level required for therapy.

  18. Automatic classification of DMSA scans using an artificial neural network.

    PubMed

    Wright, J W; Duguid, R; McKiddie, F; Staff, R T

    2014-04-07

    DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from 'definitely normal' to 'definitely abnormal'. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α < 0.05) in performance between the network and operators. A further result from this work was that when suitably optimized, a negative predictive value of 100% for renal defects was achieved by the network, while still managing to identify 93% of the negative cases in the dataset. These results are encouraging for application of such a network as a screening tool or quality assurance assistant in clinical practice.

  19. Geochemical characterization of oceanic basalts using Artificial Neural Network.

    PubMed

    Das, Pranab; Iyer, Sridhar D

    2009-12-23

    The geochemical discriminate diagrams help to distinguish the volcanics recovered from different tectonic settings but these diagrams tend to group the ocean floor basalts (OFB) under one class i.e., as mid-oceanic ridge basalts (MORB). Hence, a method is specifically needed to identify the OFB as normal (N-MORB), enriched (E-MORB) and ocean island basalts (OIB). We have applied Artificial Neural Network (ANN) technique as a supervised Learning Vector Quantisation (LVQ) to identify the inherent geochemical signatures present in the Central Indian Ocean Basin (CIOB) basalts. A range of N-MORB, E-MORB and OIB dataset was used for training and testing of the network. Although the identification of the characters as N-MORB, E-MORB and OIB is completely dependent upon the training data set for the LVQ, but to a significant extent this method is found to be successful in identifying the characters within the CIOB basalts. The study helped to geochemically delineate the CIOB basalts as N-MORB with perceptible imprints of E-MORB and OIB characteristics in the form of moderately enriched rare earth and incompatible elements. Apart from the fact that the magmatic processes are difficult to be deciphered, the architecture performs satisfactorily.

  20. Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok?

    NASA Astrophysics Data System (ADS)

    Vu, Minh Tue; Aribarg, Thannob; Supratid, Siriporn; Raghavan, Srivatsan V.; Liong, Shie-Yui

    2016-11-01

    Artificial neural network (ANN) is an established technique with a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data. The present study utilizes ANN as a method of statistically downscaling global climate models (GCMs) during the rainy season at meteorological site locations in Bangkok, Thailand. The study illustrates the applications of the feed forward back propagation using large-scale predictor variables derived from both the ERA-Interim reanalyses data and present day/future GCM data. The predictors are first selected over different grid boxes surrounding Bangkok region and then screened by using principal component analysis (PCA) to filter the best correlated predictors for ANN training. The reanalyses downscaled results of the present day climate show good agreement against station precipitation with a correlation coefficient of 0.8 and a Nash-Sutcliffe efficiency of 0.65. The final downscaled results for four GCMs show an increasing trend of precipitation for rainy season over Bangkok by the end of the twenty-first century. The extreme values of precipitation determined using statistical indices show strong increases of wetness. These findings will be useful for policy makers in pondering adaptation measures due to flooding such as whether the current drainage network system is sufficient to meet the changing climate and to plan for a range of related adaptation/mitigation measures.

  1. Using an Artificial Neural Bypass to Restore Cortical Control of Rhythmic Movements in a Human with Quadriplegia

    NASA Astrophysics Data System (ADS)

    Sharma, Gaurav; Friedenberg, David A.; Annetta, Nicholas; Glenn, Bradley; Bockbrader, Marcie; Majstorovic, Connor; Domas, Stephanie; Mysiw, W. Jerry; Rezai, Ali; Bouton, Chad

    2016-09-01

    Neuroprosthetic technology has been used to restore cortical control of discrete (non-rhythmic) hand movements in a paralyzed person. However, cortical control of rhythmic movements which originate in the brain but are coordinated by Central Pattern Generator (CPG) neural networks in the spinal cord has not been demonstrated previously. Here we show a demonstration of an artificial neural bypass technology that decodes cortical activity and emulates spinal cord CPG function allowing volitional rhythmic hand movement. The technology uses a combination of signals recorded from the brain, machine-learning algorithms to decode the signals, a numerical model of CPG network, and a neuromuscular electrical stimulation system to evoke rhythmic movements. Using the neural bypass, a quadriplegic participant was able to initiate, sustain, and switch between rhythmic and discrete finger movements, using his thoughts alone. These results have implications in advancing neuroprosthetic technology to restore complex movements in people living with paralysis.

  2. Using an Artificial Neural Bypass to Restore Cortical Control of Rhythmic Movements in a Human with Quadriplegia

    PubMed Central

    Sharma, Gaurav; Friedenberg, David A.; Annetta, Nicholas; Glenn, Bradley; Bockbrader, Marcie; Majstorovic, Connor; Domas, Stephanie; Mysiw, W. Jerry; Rezai, Ali; Bouton, Chad

    2016-01-01

    Neuroprosthetic technology has been used to restore cortical control of discrete (non-rhythmic) hand movements in a paralyzed person. However, cortical control of rhythmic movements which originate in the brain but are coordinated by Central Pattern Generator (CPG) neural networks in the spinal cord has not been demonstrated previously. Here we show a demonstration of an artificial neural bypass technology that decodes cortical activity and emulates spinal cord CPG function allowing volitional rhythmic hand movement. The technology uses a combination of signals recorded from the brain, machine-learning algorithms to decode the signals, a numerical model of CPG network, and a neuromuscular electrical stimulation system to evoke rhythmic movements. Using the neural bypass, a quadriplegic participant was able to initiate, sustain, and switch between rhythmic and discrete finger movements, using his thoughts alone. These results have implications in advancing neuroprosthetic technology to restore complex movements in people living with paralysis. PMID:27658585

  3. An artificial bee colony algorithm for uncertain portfolio selection.

    PubMed

    Chen, Wei

    2014-01-01

    Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.

  4. An Artificial Bee Colony Algorithm for Uncertain Portfolio Selection

    PubMed Central

    Chen, Wei

    2014-01-01

    Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm. PMID:25089292

  5. Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

    NASA Astrophysics Data System (ADS)

    Masri Husam Fayiz, Al

    2017-01-01

    The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms.

  6. Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

    NASA Astrophysics Data System (ADS)

    Li, Hong; Ding, Xue

    2017-03-01

    This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

  7. Using Artificial Neural Networks in Educational Research: Some Comparisons with Linear Statistical Models.

    ERIC Educational Resources Information Center

    Everson, Howard T.; And Others

    This paper explores the feasibility of neural computing methods such as artificial neural networks (ANNs) and abductory induction mechanisms (AIM) for use in educational measurement. ANNs and AIMS methods are contrasted with more traditional statistical techniques, such as multiple regression and discriminant function analyses, for making…

  8. Modeling Career Counselor Decisions with Artificial Neural Networks: Predictions of Fit across a Comprehensive Occupational Map.

    ERIC Educational Resources Information Center

    Carson, Andrew D.; Bizot, Elizabeth B.; Hendershot, Peggy E.; Barton, Margaret G.; Garvin, Mary K.; Kraemer, Barbara

    1999-01-01

    Career recommendations were made based on aptitude scores of 335 high school freshmen. Artificial neural networks were used to map recommendations to 12 occupational clusters. Overall accuracy of neural networks (.80) approached that of discriminant function analysis (.84). The two methods had different strengths and weaknesses. (SK)

  9. Artificial Neural Networks applied to landslide susceptibility assessment

    NASA Astrophysics Data System (ADS)

    Ermini, Leonardo; Catani, Filippo; Casagli, Nicola

    2005-03-01

    Landslide hazard mapping is often performed through the identification and analysis of hillslope instability factors, usually managed as thematic data within geographic information systems (GIS). In heuristic approaches, these factors are rated by the attribution of scores based on the assumed role played by each of them in controlling the development of a sliding process. Other more refined methods, based on the principle that the present and the past are keys to the future, have also been developed, thus allowing less subjective analyses in which landslide susceptibility is assessed by statistical relationships between past landslide events and hillslope instability factors. The objective of this research is to define a method with the ability to forecast landslide susceptibility through the application of Artificial Neural Networks (ANNs). The Riomaggiore catchment, a subwatershed of the Reno River basin located in the Northern Apennines (Italy), was chosen as an ideal test site, as it is representative of many of the geomorphological settings within this region. In the present application, two different ANNs, used in classification problems, were set up and applied: one belonging to the category of Multi-Layered Perceptron (MLP) and the other to the Probabilistic Neural Network (PNN) family. The hillslope factors that have been taken into account in the analysis were the following: (a) lithology, (b) slope angle, (c), profile curvature, (d) land cover and (e) upslope contributing area. These factors have been classified on nominal scales, and their intersection allowed 3342 homogeneous domains (Unique Condition Unit, UCU) to be singled out, which correspond to the terrain units utilized in this analysis. The model vector used to train the ANNs is a subset of that derived from the production of Unique Condition Units and consists of 3342 records organized in input and output variable vectors. In particular, the hillslope factors, once classified on nominal

  10. Diagnostic Accuracy Comparison of Artificial Immune Algorithms for Primary Headaches

    PubMed Central

    Çelik, Ufuk; Yurtay, Nilüfer; Koç, Emine Rabia; Tepe, Nermin; Güllüoğlu, Halil; Ertaş, Mustafa

    2015-01-01

    The present study evaluated the diagnostic accuracy of immune system algorithms with the aim of classifying the primary types of headache that are not related to any organic etiology. They are divided into four types: migraine, tension, cluster, and other primary headaches. After we took this main objective into consideration, three different neurologists were required to fill in the medical records of 850 patients into our web-based expert system hosted on our project web site. In the evaluation process, Artificial Immune Systems (AIS) were used as the classification algorithms. The AIS are classification algorithms that are inspired by the biological immune system mechanism that involves significant and distinct capabilities. These algorithms simulate the specialties of the immune system such as discrimination, learning, and the memorizing process in order to be used for classification, optimization, or pattern recognition. According to the results, the accuracy level of the classifier used in this study reached a success continuum ranging from 95% to 99%, except for the inconvenient one that yielded 71% accuracy. PMID:26075014

  11. Network intrusion detection by the coevolutionary immune algorithm of artificial immune systems with clonal selection

    NASA Astrophysics Data System (ADS)

    Salamatova, T.; Zhukov, V.

    2017-02-01

    The paper presents the application of the artificial immune systems apparatus as a heuristic method of network intrusion detection for algorithmic provision of intrusion detection systems. The coevolutionary immune algorithm of artificial immune systems with clonal selection was elaborated. In testing different datasets the empirical results of evaluation of the algorithm effectiveness were achieved. To identify the degree of efficiency the algorithm was compared with analogs. The fundamental rules based of solutions generated by this algorithm are described in the article.

  12. Estimating complicated baselines in analytical signals using the iterative training of Bayesian regularized artificial neural networks.

    PubMed

    Mani-Varnosfaderani, Ahmad; Kanginejad, Atefeh; Gilany, Kambiz; Valadkhani, Abolfazl

    2016-10-12

    The present work deals with the development of a new baseline correction method based on the comparative learning capabilities of artificial neural networks. The developed method uses the Bayes probability theorem for prevention of the occurrence of the over-fitting and finding a generalized baseline. The developed method has been applied on simulated and real metabolomic gas-chromatography (GC) and Raman data sets. The results revealed that the proposed method can be used to handle different types of baselines with cave, convex, curvelinear, triangular and sinusoidal patterns. For further evaluation of the performances of this method, it has been compared with benchmarking baseline correction methods such as corner-cutting (CC), morphological weighted penalized least squares (MPLS), adaptive iteratively-reweighted penalized least squares (airPLS) and iterative polynomial fitting (iPF). In order to compare the methods, the projected difference resolution (PDR) criterion has been calculated for the data before and after the baseline correction procedure. The calculated values of PDR after the baseline correction using iBRANN, airPLS, MPLS, iPF and CC algorithms for the GC metabolomic data were 4.18, 3.64, 3.88, 1.88 and 3.08, respectively. The obtained results in this work demonstrated that the developed iterative Bayesian regularized neural network (iBRANN) method in this work thoroughly detects the baselines and is superior over the CC, MPLS, airPLS and iPF techniques. A graphical user interface has been developed for the suggested algorithm and can be used for easy implementation of the iBRANN algorithm for the correction of different chromatography, NMR and Raman data sets.

  13. Locus minimization in breed prediction using artificial neural network approach.

    PubMed

    Iquebal, M A; Ansari, M S; Sarika; Dixit, S P; Verma, N K; Aggarwal, R A K; Jayakumar, S; Rai, A; Kumar, D

    2014-12-01

    Molecular markers, viz. microsatellites and single nucleotide polymorphisms, have revolutionized breed identification through the use of small samples of biological tissue or germplasm, such as blood, carcass samples, embryos, ova and semen, that show no evident phenotype. Classical tools of molecular data analysis for breed identification have limitations, such as the unavailability of referral breed data, causing increased cost of collection each time, compromised computational accuracy and complexity of the methodology used. We report here the successful use of an artificial neural network (ANN) in background to decrease the cost of genotyping by locus minimization. The webserver is freely accessible (http://nabg.iasri.res.in/bisgoat) to the research community. We demonstrate that the machine learning (ANN) approach for breed identification is capable of multifold advantages such as locus minimization, leading to a drastic reduction in cost, and web availability of reference breed data, alleviating the need for repeated genotyping each time one investigates the identity of an unknown breed. To develop this model web implementation based on ANN, we used 51,850 samples of allelic data of microsatellite-marker-based DNA fingerprinting on 25 loci covering 22 registered goat breeds of India for training. Minimizing loci to up to nine loci through the use of a multilayer perceptron model, we achieved 96.63% training accuracy. This server can be an indispensable tool for identification of existing breeds and new synthetic commercial breeds, leading to protection of intellectual property in case of sovereignty and bio-piracy disputes. This server can be widely used as a model for cost reduction by locus minimization for various other flora and fauna in terms of variety, breed and/or line identification, especially in conservation and improvement programs.

  14. Use of artificial neural networks as estimators and controllers

    NASA Astrophysics Data System (ADS)

    Concilio, Antonio; Sorrentino, A.

    1996-04-01

    Active noise control is one among the most promising applications of the so-called Smart Structures, because it ensures, or promises, lower weight, lower cost, more effectiveness and all what is desirable in a vehicle design process, with respect to the current solutions. More and more attention in the research world has been devoting to this argument, pushed by both political, economical and environmental reasons, the one connected to the others. Piezoceramic actuators, integrated into the structure, seem to offer the most fashionable and practical solutions among all the proposed architectures, [1-2]. As sensors, microphones demonstrated to be the most performing, above all because they give the most suitable representation of the field that has to be cancelled, [3-4]. This approach is known as Acousto-Structural Active Control, ASAC, [5]. However, according to Fuller's definition, [6] , an intelligent controller is needed to ensure the development of an "Intelligent Structure" . Its main characteristic should be represented by the capability of learning by examples, of following the structure during its evolution, of being the system "brain" . This peculiarity may be offered by Artificial Neural Networks (ANN's), [7-8]. They present other important features, like the capability, in principle, of treating non-linear as well as linear problems, [9], of identifying dynamic systems, [10], of properly acting as a controller. Then, such a net could integrate in itself the function of "system estimator" or "observer" ,and of interpolator - extrapolator and controller, contemporarily. The authors have been working on such subjects for a long time, proposing for instance ANN's as time-domain structural parameters estimators on a simple 2D element ( a framed plate), [11], as noise and vibration controllers in a FF system, [12-13], as materials damping parameters extractors from experimental data, [14]. All these applications were aimed at noise reduction problems. The

  15. Evaluation of the efficiency of artificial neural networks for genetic value prediction.

    PubMed

    Silva, G N; Tomaz, R S; Sant'Anna, I C; Carneiro, V Q; Cruz, C D; Nascimento, M

    2016-03-28

    Artificial neural networks have shown great potential when applied to breeding programs. In this study, we propose the use of artificial neural networks as a viable alternative to conventional prediction methods. We conduct a thorough evaluation of the efficiency of these networks with respect to the prediction of breeding values. Therefore, we considered eight simulated scenarios, and for the purpose of genetic value prediction, seven statistical parameters in addition to the phenotypic mean in a network designed as a multilayer perceptron. After an evaluation of different network configurations, the results demonstrated the superiority of neural networks compared to estimation procedures based on linear models, and indicated high predictive accuracy and network efficiency.

  16. Calibration of a shock wave position sensor using artificial neural networks

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Weiland, Kenneth E.

    1993-01-01

    This report discusses the calibration of a shock wave position sensor. The position sensor works by using artificial neural networks to map cropped CCD frames of the shadows of the shock wave into the value of the shock wave position. This project was done as a tutorial demonstration of method and feasibility. It used a laboratory shadowgraph, nozzle, and commercial neural network package. The results were quite good, indicating that artificial neural networks can be used efficiently to automate the semi-quantitative applications of flow visualization.

  17. Flaws Identification Using Eddy Current Differential Transducer and Artificial Neural Networks

    SciTech Connect

    Chady, T.; Lopato, P.

    2006-03-06

    In this paper we present a multi-frequency excitation eddy current differential transducer and dynamic neural models which were used to detect and identify artificial flaws in thin conducting plates. Plates are made of Inconel600. EDM notches have relative depth from 10% to 80% and length from 2 mm to 7 mm. All flaws were located on the opposite surface of the examined specimen. Measured signals were used as input for training and verifying dynamic neural networks with a moving window. Wide range of ANN (Artificial Neural Network) structures are examined for different window length and different number of frequency components in excitation signal. Observed trends are presented in this paper.

  18. Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks

    NASA Astrophysics Data System (ADS)

    Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solís Sánches, L. O.; Miranda, R. Castañeda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

    2013-07-01

    In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetry with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural

  19. Evaluating the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks

    SciTech Connect

    Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solis Sanches, L. O.; Miranda, R. Castaneda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

    2013-07-03

    In this work the performance of two neutron spectrum unfolding codes based on iterative procedures and artificial neural networks is evaluated. The first one code based on traditional iterative procedures and called Neutron spectrometry and dosimetry from the Universidad Autonoma de Zacatecas (NSDUAZ) use the SPUNIT iterative algorithm and was designed to unfold neutron spectrum and calculate 15 dosimetric quantities and 7 IAEA survey meters. The main feature of this code is the automated selection of the initial guess spectrum trough a compendium of neutron spectrum compiled by the IAEA. The second one code known as Neutron spectrometry and dosimetry with artificial neural networks (NDSann) is a code designed using neural nets technology. The artificial intelligence approach of neural net does not solve mathematical equations. By using the knowledge stored at synaptic weights on a neural net properly trained, the code is capable to unfold neutron spectrum and to simultaneously calculate 15 dosimetric quantities, needing as entrance data, only the rate counts measured with a Bonner spheres system. Similarities of both NSDUAZ and NSDann codes are: they follow the same easy and intuitive user's philosophy and were designed in a graphical interface under the LabVIEW programming environment. Both codes unfold the neutron spectrum expressed in 60 energy bins, calculate 15 dosimetric quantities and generate a full report in HTML format. Differences of these codes are: NSDUAZ code was designed using classical iterative approaches and needs an initial guess spectrum in order to initiate the iterative procedure. In NSDUAZ, a programming routine was designed to calculate 7 IAEA instrument survey meters using the fluence-dose conversion coefficients. NSDann code use artificial neural networks for solving the ill-conditioned equation system of neutron spectrometry problem through synaptic weights of a properly trained neural network. Contrary to iterative procedures, in neural

  20. Applications of artificial neural nets in structural mechanics

    NASA Technical Reports Server (NTRS)

    Berke, Laszlo; Hajela, Prabhat

    1990-01-01

    A brief introduction to the fundamental of Neural Nets is given, followed by two applications in structural optimization. In the first case, the feasibility of simulating with neural nets the many structural analyses performed during optimization iterations was studied. In the second case, the concept of using neural nets to capture design expertise was studied.

  1. Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications.

    PubMed

    Pastur-Romay, Lucas Antón; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana Belén

    2016-08-11

    Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure-Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron-Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.

  2. Artificial neural networks in gynaecological diseases: current and potential future applications.

    PubMed

    Siristatidis, Charalampos S; Chrelias, Charalampos; Pouliakis, Abraham; Katsimanis, Evangelos; Kassanos, Dimitrios

    2010-10-01

    Current (and probably future) practice of medicine is mostly associated with prediction and accurate diagnosis. Especially in clinical practice, there is an increasing interest in constructing and using valid models of diagnosis and prediction. Artificial neural networks (ANNs) are mathematical systems being used as a prospective tool for reliable, flexible and quick assessment. They demonstrate high power in evaluating multifactorial data, assimilating information from multiple sources and detecting subtle and complex patterns. Their capability and difference from other statistical techniques lies in performing nonlinear statistical modelling. They represent a new alternative to logistic regression, which is the most commonly used method for developing predictive models for outcomes resulting from partitioning in medicine. In combination with the other non-algorithmic artificial intelligence techniques, they provide useful software engineering tools for the development of systems in quantitative medicine. Our paper first presents a brief introduction to ANNs, then, using what we consider the best available evidence through paradigms, we evaluate the ability of these networks to serve as first-line detection and prediction techniques in some of the most crucial fields in gynaecology. Finally, through the analysis of their current application, we explore their dynamics for future use.

  3. Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

    PubMed Central

    Pastur-Romay, Lucas Antón; Cedrón, Francisco; Pazos, Alejandro; Porto-Pazos, Ana Belén

    2016-01-01

    Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods. PMID:27529225

  4. Identification of Propionibacteria to the species level using Fourier transform infrared spectroscopy and artificial neural networks.

    PubMed

    Dziuba, B

    2013-01-01

    Fourier transform infrared spectroscopy (FTIR) and artificial neural networks (ANN's) were used to identify species of Propionibacteria strains. The aim of the study was to improve the methodology to identify species of Propionibacteria strains, in which the differentiation index D, calculated based on Pearson's correlation and cluster analyses were used to describe the correlation between the Fourier transform infrared spectra and bacteria as molecular systems brought unsatisfactory results. More advanced statistical methods of identification of the FTIR spectra with application of artificial neural networks (ANN's) were used. In this experiment, the FTIR spectra of Propionibacteria strains stored in the library were used to develop artificial neural networks for their identification. Several multilayer perceptrons (MLP) and probabilistic neural networks (PNN) were tested. The practical value of selected artificial neural networks was assessed based on identification results of spectra of 9 reference strains and 28 isolates. To verify results of isolates identification, the PCR based method with the pairs of species-specific primers was used. The use of artificial neural networks in FTIR spectral analyses as the most advanced chemometric method supported correct identification of 93% bacteria of the genus Propionibacterium to the species level.

  5. A Bayesian regularized artificial neural network for adaptive optics forecasting

    NASA Astrophysics Data System (ADS)

    Sun, Zhi; Chen, Ying; Li, Xinyang; Qin, Xiaolin; Wang, Huiyong

    2017-01-01

    Real-time adaptive optics is a technology for enhancing the resolution of ground-based optical telescopes and overcoming the disturbance of atmospheric turbulence. The performance of the system is limited by delay errors induced by the servo system and photoelectrons noise of wavefront sensor. In order to cut these delay errors, this paper proposes a novel model to forecast the future control voltages of the deformable mirror. The predictive model is constructed by a multi-layered back propagation network with Bayesian regularization (BRBP). For the purpose of parallel computation and less disturbance, we adopt a number of sub-BP neural networks to substitute the whole network. The Bayesian regularized network assigns a probability to the network weights, allowing the network to automatically and optimally penalize excessively complex models. The simulation results show that the BRBP introduces smaller mean absolute percentage error (MAPE) and mean square errors (MSE) than other typical algorithms. Meanwhile, real data analysis results show that the BRBP model has strong generalization capability and parallelism.

  6. DETECTING ACTIVE GALACTIC NUCLEI USING MULTI-FILTER IMAGING DATA. II. INCORPORATING ARTIFICIAL NEURAL NETWORKS

    SciTech Connect

    Dong, X. Y.; De Robertis, M. M.

    2013-10-01

    This is the second paper of the series Detecting Active Galactic Nuclei Using Multi-filter Imaging Data. In this paper we review shapelets, an image manipulation algorithm, which we employ to adjust the point-spread function (PSF) of galaxy images. This technique is used to ensure the image in each filter has the same and sharpest PSF, which is the preferred condition for detecting AGNs using multi-filter imaging data as we demonstrated in Paper I of this series. We apply shapelets on Canada-France-Hawaii Telescope Legacy Survey Wide Survey ugriz images. Photometric parameters such as effective radii, integrated fluxes within certain radii, and color gradients are measured on the shapelets-reconstructed images. These parameters are used by artificial neural networks (ANNs) which yield: photometric redshift with an rms of 0.026 and a regression R-value of 0.92; galaxy morphological types with an uncertainty less than 2 T types for z ≤ 0.1; and identification of galaxies as AGNs with 70% confidence, star-forming/starburst (SF/SB) galaxies with 90% confidence, and passive galaxies with 70% confidence for z ≤ 0.1. The incorporation of ANNs provides a more reliable technique for identifying AGN or SF/SB candidates, which could be very useful for large-scale multi-filter optical surveys that also include a modest set of spectroscopic data sufficient to train neural networks.

  7. Real-time tumor tracking with an artificial neural networks-based method: a feasibility study.

    PubMed

    Seregni, Matteo; Pella, Andrea; Riboldi, Marco; Orecchia, Roberto; Cerveri, Pietro; Baroni, Guido

    2013-01-01

    The purpose of this study was to develop and assess the performance of a tumor tracking method designed for application in radiation therapy. This motion compensation strategy is currently applied clinically only in conventional photon radiotherapy but not in particle therapy, as greater accuracy in dose delivery is required. We proposed a tracking method that exploits artificial neural networks to estimate the internal tumor trajectory as a function of external surrogate signals. The developed algorithm was tested by means of a retrospective clinical data analysis in 20 patients, who were treated with state of the art infra-red motion tracking for photon radiotherapy, which is used as a benchmark. Integration into a hardware platform for motion tracking in particle therapy was performed and then tested on a moving phantom, specifically developed for this purpose. Clinical data show that a median tracking error reduction up to 0.7 mm can be achieved with respect to state of the art technologies. The phantom study demonstrates that a real-time tumor position estimation is feasible when the external signals are acquired at 60 Hz. The results of this work show that neural networks can be considered a valuable tool for the implementation of high accuracy real-time tumor tracking methodologies.

  8. Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network.

    PubMed

    Pendashteh, Ali Reza; Fakhru'l-Razi, A; Chaibakhsh, Naz; Abdullah, Luqman Chuah; Madaeni, Sayed Siavash; Abidin, Zurina Zainal

    2011-08-30

    A membrane sequencing batch reactor (MSBR) treating hypersaline oily wastewater was modeled by artificial neural network (ANN). The MSBR operated at different total dissolved solids (TDSs) (35,000; 50,000; 100,000; 150,000; 200,000; 250,000mg/L), various organic loading rates (OLRs) (0.281, 0.563, 1.124, 2.248, and 3.372kg COD/(m(3)day)) and cyclic time (12, 24, and 48h). A feed-forward neural network trained by batch back propagation algorithm was employed to model the MSBR. A set of 193 operational data from the wastewater treatment with the MSBR was used to train the network. The training, validating and testing procedures for the effluent COD, total organic carbon (TOC) and oil and grease (O&G) concentrations were successful and a good correlation was observed between the measured and predicted values. The results showed that at OLR of 2.44kg COD/(m(3)day), TDS of 78,000mg/L and reaction time (RT) of 40h, the average removal rate of COD was 98%. In these conditions, the average effluent COD concentration was less than 100mg/L and met the discharge limits.

  9. eFurniture for home-based frailty detection using artificial neural networks and wireless sensors.

    PubMed

    Chang, Yu-Chuan; Lin, Chung-Chih; Lin, Pei-Hsin; Chen, Chun-Chang; Lee, Ren-Guey; Huang, Jing-Siang; Tsai, Tsai-Hsuan

    2013-02-01

    The purpose of this study is to integrate wireless sensor technologies and artificial neural networks to develop a system to manage personal frailty information automatically. The system consists of five parts: (1) an eScale to measure the subject's reaction time; (2) an eChair to detect slowness in movement, weakness and weight loss; (3) an ePad to measure the subject's balancing ability; (4) an eReach to measure body extension; and (5) a Home-based Information Gateway, which collects all the data and predicts the subject's frailty. Using a furniture-based measuring device to provide home-based measurement means that health checks are not confined to health institutions. We designed two experiments to obtain optimum frailty prediction model and test overall system performance: (1) We developed a three-step process to adjust different parameters to obtain an optimized neural identification network whose parameters include initialization, L.R. dec and L.R. inc. The post-process identification rate increased from 77.85% to 83.22%. (2) We used 149 cases to evaluate the sensitivity and specificity of our frailty prediction algorithm. The sensitivity and specificity of this system are 79.71% and 86.25% respectively. These results show that our system is a high specificity prediction tool that can be used to assess frailty.

  10. Prediction of monthly mean daily global solar radiation using Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Sivamadhavi, V.; Selvaraj, R. Samuel

    2012-12-01

    In this study, a multilayer feed forward (MLFF) neural network based on back propagation algorithm was developed, trained, and tested to predict monthly mean daily global radiation in Tamil Nadu, India. Various geographical, solar and meteorological parameters of three different locations with diverse climatic conditions were used as input parameters. Out of 565 available data, 530 were used for training and the rest were used for testing the artificial neural network (ANN). A 3-layer and a 4-layer MLFF networks were developed and the performance of the developed models was evaluated based on mean bias error, mean absolute percentage error, root mean squared error and Student's t-test. The 3-layer MLFF network developed in this study did not give uniform results for the three chosen locations. Hence, a 4-layer MLFF network was developed and the average value of the mean absolute percentage error was found to be 5.47%. Values of global radiation obtained using the model were in excellent agreement with measured values. Results of this study show that the designed ANN model can be used to estimate monthly mean daily global radiation of any place in Tamil Nadu where measured global radiation data are not available.

  11. Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

    PubMed

    Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

    2014-01-01

    Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

  12. Detection and identification of seismic signals recorded at Krakatau volcano (Indonesia) using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Ibs-von Seht, M.

    2008-10-01

    The Anak Krakatau volcano (Indonesia) has been monitored by a multi-parametric system since 2005. A variety of signal types can be observed in the records of the seismic stations installed on the island volcano. These include volcano-induced signals such as LP, VT, and tremor-type events as well as signals not originating from the volcano such as regional tectonic earthquakes and transient noise signals. The work presented here aims at the realization of a system that automatically detects and identifies the signals in order to estimate and monitor current activity states of the volcano. An artificial neural network approach was chosen for the identification task. A set of parameters was defined, describing waveform and spectrogram properties of events detected by an amplitude-ratio-based (STA/LTA) algorithm. The parameters are fed into a neural network which is, after a training phase, able to generalize input data and identify corresponding event types. The success of the identification depends on the network architecture and training strategy. Several tests have been performed in order to determine appropriate network layout and training for the given problem. The performance of the final system is found to be well suited to get an overview of the seismic activity recorded at the volcano. The reliability of the network classifier, as well as general drawbacks of the methods used, are discussed.

  13. Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

    PubMed Central

    Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

    2014-01-01

    Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an “optimal” weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds. PMID:27382627

  14. An artificial neural network for estimating scatter exposures in portable chest radiography.

    PubMed

    Lo, J Y; Floyd, C E; Baker, J A; Ravin, C E

    1993-01-01

    An adaptive linear element (Adaline) was developed to estimate the two-dimensional scatter exposure distribution in digital portable chest radiographs (DPCXR). DPCXRs and quantitative scatter exposure measurements at 64 locations throughout the chest were acquired for ten radiographically normal patients. The Adaline is an artificial neural network which has only a single node and linear thresholding. The Adaline was trained using DPCXR-scatter measurement pairs from five patients. The spatially invariant network would take a portion of the image as its input and estimate the scatter content as output. The trained network was applied to the other five images, and errors were evaluated between estimated and measured scatter values. Performance was compared against a convolution scatter estimation algorithm. The network was evaluated as a function of network size, initial values, and duration of training. Network performance was evaluated qualitatively by the correlation of network weights to physical models, and quantitatively by training and evaluation errors. Using DPCXRs as input, the network learned to describe known scatter exposures accurately (7% error) and estimate scatter in new images (< 8% error) slightly better than convolution methods. Regardless of size and initial shape, all networks adapted into radial exponentials with magnitude of 0.75, perhaps implying an ideal point spread function and average scatter fraction, respectively. To implement scatter compensation, the two-dimensional scatter distribution estimated by the neural network is subtracted from the original DPCXR.

  15. Two artificial neural systems for generation of gait swing by means of neuromuscular electrical stimulation.

    PubMed

    Sepulveda, F; Granat, M H; Cliquet, A

    1997-01-01

    A three-layer artificial neural network was used for adaptive control of gait swing generated by neuromuscular electrical stimulation (NMES) in a spinal cord injured subject. Network inputs consisted of knee and ankle goniometer signals for System 1, and knee and hip angular data for System 2. Controller output was proportional to changes in applied NMES pulse width (PW). Stimulation was applied to the left femoral and common peroneal nerves. The neural networks were trained off-line and on-line. Network performance was assessed by applying a number of different stimulation PWs and later comparing the resulting motion to a sample good step observed during the same test session. On-line training consisted of negative and positive reinforcement applied at chosen times. Both on-line and off-line training algorithms consisted of an enhanced supervised backpropagation scheme. Performance evaluation results favour the use of System 1 over System 2. Also, a network trained off-line and later submitted to on-line punishment appears to be more reliable (in automatic mode) than the same network after it is submitted to on-line reward or to off-line training alone. Finally, the systems' immediate response to on-line learning was favourable in all cases. Based on the results, a version of System 1 was used to generate walking in the test subject. This test indicated that the system is promising.

  16. Automation of Some Operations of a Wind Tunnel Using Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Decker, Arthur J.; Buggele, Alvin E.

    1996-01-01

    Artificial neural networks were used successfully to sequence operations in a small, recently modernized, supersonic wind tunnel at NASA-Lewis Research Center. The neural nets generated correct estimates of shadowgraph patterns, pressure sensor readings and mach numbers for conditions occurring shortly after startup and extending to fully developed flow. Artificial neural networks were trained and tested for estimating: sensor readings from shadowgraph patterns, shadowgraph patterns from shadowgraph patterns and sensor readings from sensor readings. The 3.81 by 10 in. (0.0968 by 0.254 m) tunnel was operated with its mach 2.0 nozzle, and shadowgraph was recorded near the nozzle exit. These results support the thesis that artificial neural networks can be combined with current workstation technology to automate wind tunnel operations.

  17. Structure-response relationship in electrospray ionization-mass spectrometry of sartans by artificial neural networks.

    PubMed

    Golubović, Jelena; Birkemeyer, Claudia; Protić, Ana; Otašević, Biljana; Zečević, Mira

    2016-03-18

    Quantitative structure-property relationship (QSPR) methods are based on the hypothesis that changes in the molecular structure are reflected in changes in the observed property of the molecule. Artificial neural network is a technique of data analysis, which sets out to emulate the human brain's way of working. For the first time a quantitative structure-response relationship in electrospray ionization-mass spectrometry (ESI-MS) by means of artificial neural networks (ANN) on the group of angiotensin II receptor antagonists--sartans has been established. The investigated descriptors correspond to different properties of the analytes: polarity (logP), ionizability (pKa), surface area (solvent excluded volume) and number of proton acceptors. The influence of the instrumental parameters: methanol content in mobile phase, mobile phase pH and flow rate was also examined. Best performance showed a multilayer perceptron network with the architecture 6-3-3-1, trained with backpropagation algorithm. It showed high prediction ability on the previously unseen (test) data set with a coefficient of determination of 0.994. High prediction ability of the model would enable prediction of ESI-MS responsiveness under different conditions. This is particularly important in the method development phase. Also, prediction of responsiveness can be important in case of gradient-elution LC-MS and LC-MS/MS methods in which instrumental conditions are varied during time. Polarity, chargeability and surface area all appeared to be crucial for electrospray ionization whereby signal intensity appeared to be the result of a simultaneous influence of the molecular descriptors and their interactions. Percentage of organic phase in the mobile phase showed a positive, while flow rate showed a negative impact on signal intensity.

  18. Automatic brain MR image denoising based on texture feature-based artificial neural networks.

    PubMed

    Chang, Yu-Ning; Chang, Herng-Hua

    2015-01-01

    Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

  19. Hierarchical artificial bee colony algorithm for RFID network planning optimization.

    PubMed

    Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

    2014-01-01

    This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

  20. Simultaneous determination of some components in detergent washing powder by mid-infrared spectrometry and artificial neural network.

    PubMed

    Khanmohammadi, Mohammadreza; Garmarudi, Amir Bagheri; Rouchi, Mohammad Babaei; Khoddami, Nafiseh

    2011-01-01

    A method has been established for simultaneous determination of sodium sulfate, sodium carbonate, and sodium tripolyphosphate in detergent washing powder samples based on attenuated total reflectance Fourier transform IR spectrometry in the mid-IR spectral region (800-1550 cm(-1)). Genetic algorithm (GA) wavelength selection followed by feed forward back-propagation artificial neural network (BP-ANN) was the chemometric approach. Root mean square error of prediction for BP-ANN and GA-BP-ANN was 0.0051 and 0.0048, respectively. The proposed method is simple, with no tedious pretreatment step, for simultaneous determination of the above-mentioned components in commercial washing powder samples.

  1. Application of a hybrid method combining grey model and back propagation artificial neural networks to forecast hepatitis B in china.

    PubMed

    Gan, Ruijing; Chen, Xiaojun; Yan, Yu; Huang, Daizheng

    2015-01-01

    Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM) and back propagation artificial neural networks (BP-ANN) to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method's feasibility. The results showed that the proposal method has advantages over GM (1, 1) and GM (2, 1) in all the evaluation indexes.

  2. Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

    PubMed Central

    Chen, Tinggui; Xiao, Renbin

    2014-01-01

    Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023

  3. Projection learning algorithm for threshold - controlled neural networks

    SciTech Connect

    Reznik, A.M.

    1995-03-01

    The projection learning algorithm proposed in [1, 2] and further developed in [3] substantially improves the efficiency of memorizing information and accelerates the learning process in neural networks. This algorithm is compatible with the completely connected neural network architecture (the Hopfield network [4]), but its application to other networks involves a number of difficulties. The main difficulties include constraints on interconnection structure and the need to eliminate the state uncertainty of latent neurons if such are present in the network. Despite the encouraging preliminary results of [3], further extension of the applications of the projection algorithm therefore remains problematic. In this paper, which is a continuation of the work begun in [3], we consider threshold-controlled neural networks. Networks of this type are quite common. They represent the receptor neuron layers in some neurocomputer designs. A similar structure is observed in the lower divisions of biological sensory systems [5]. In multilayer projection neural networks with lateral interconnections, the neuron layers or parts of these layers may also have the structure of a threshold-controlled completely connected network. Here the thresholds are the potentials delivered through the projection connections from other parts of the network. The extension of the projection algorithm to the class of threshold-controlled networks may accordingly prove to be useful both for extending its technical applications and for better understanding of the operation of the nervous system in living organisms.

  4. Adaptation algorithms for 2-D feedforward neural networks.

    PubMed

    Kaczorek, T

    1995-01-01

    The generalized weight adaptation algorithms presented by J.G. Kuschewski et al. (1993) and by S.H. Zak and H.J. Sira-Ramirez (1990) are extended for 2-D madaline and 2-D two-layer feedforward neural nets (FNNs).

  5. Improved Fault Classification in Series Compensated Transmission Line: Comparative Evaluation of Chebyshev Neural Network Training Algorithms.

    PubMed

    Vyas, Bhargav Y; Das, Biswarup; Maheshwari, Rudra Prakash

    2016-08-01

    This paper presents the Chebyshev neural network (ChNN) as an improved artificial intelligence technique for power system protection studies and examines the performances of two ChNN learning algorithms for fault classification of series compensated transmission line. The training algorithms are least-square Levenberg-Marquardt (LSLM) and recursive least-square algorithm with forgetting factor (RLSFF). The performances of these algorithms are assessed based on their generalization capability in relating the fault current parameters with an event of fault in the transmission line. The proposed algorithm is fast in response as it utilizes postfault samples of three phase currents measured at the relaying end corresponding to half-cycle duration only. After being trained with only a small part of the generated fault data, the algorithms have been tested over a large number of fault cases with wide variation of system and fault parameters. Based on the studies carried out in this paper, it has been found that although the RLSFF algorithm is faster for training the ChNN in the fault classification application for series compensated transmission lines, the LSLM algorithm has the best accuracy in testing. The results prove that the proposed ChNN-based method is accurate, fast, easy to design, and immune to the level of compensations. Thus, it is suitable for digital relaying applications.

  6. Closed-Loop Artificial Pancreas Systems: Engineering the Algorithms

    PubMed Central

    Huyett, Lauren M.; Lee, Joon Bok; Zisser, Howard C.; Dassau, Eyal

    2014-01-01

    In this two-part Bench to Clinic narrative, recent advances in both the preclinical and clinical aspects of artificial pancreas (AP) development are described. In the preceding Bench narrative, Kudva and colleagues provide an in-depth understanding of the modified glucoregulatory physiology of type 1 diabetes that will help refine future AP algorithms. In the Clinic narrative presented here, we compare and evaluate AP technology to gain further momentum toward outpatient trials and eventual approval for widespread use. We enumerate the design objectives, variables, and challenges involved in AP development, concluding with a discussion of recent clinical advancements. Thanks to the effective integration of engineering and medicine, the dream of automated glucose regulation is nearing reality. Consistent and methodical presentation of results will accelerate this success, allowing head-to-head comparisons that will facilitate adoption of the AP as a standard therapy for type 1 diabetes. PMID:24757226

  7. [Artificial neural networks for the identification of infrared spectra of Ilex Kudingcha].

    PubMed

    Pang, Tao-Tao; Yao, Jian-Bin; Du, Li-Ming

    2007-07-01

    In order to identify Ilex Kudingcha, two kinds of models of artificial neural networks (ANN), i.e. competitive neural network and back propagation neural network, were used to analyze their infrared spectra. Ilex Kudingcha samples were collected by Fourier transform infrared (FTIR) spectra. Twenty five samples were gathered as a train set, and 11 samples as a test set, then their training was performed using two networks each. The results show that the identification of Ilex Kudingcha from different areas can be effectively performed with the competitive neural network and BP network, but the competitive neural network is used in the identification of different grades of Ilex Kudingcha. The results were better in training speed and accuracy with the competitive neural network. In conclusion, the competitive neural network combined with FTIR spectroscopy is a good method for the identification of Ilex Kudingcha.

  8. Designing laboratory wind simulations using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Križan, Josip; Gašparac, Goran; Kozmar, Hrvoje; Antonić, Oleg; Grisogono, Branko

    2015-05-01

    While experiments in boundary layer wind tunnels remain to be a major research tool in wind engineering and environmental aerodynamics, designing the modeling hardware required for a proper atmospheric boundary layer (ABL) simulation can be costly and time consuming. Hence, possibilities are sought to speed-up this process and make it more time-efficient. In this study, two artificial neural networks (ANNs) are developed to determine an optimal design of the Counihan hardware, i.e., castellated barrier wall, vortex generators, and surface roughness, in order to simulate the ABL flow developing above urban, suburban, and rural terrains, as previous ANN models were created for one terrain type only. A standard procedure is used in developing those two ANNs in order to further enhance best-practice possibilities rather than to improve existing ANN designing methodology. In total, experimental results obtained using 23 different hardware setups are used when creating ANNs. In those tests, basic barrier height, barrier castellation height, spacing density, and height of surface roughness elements are the parameters that were varied to create satisfactory ABL simulations. The first ANN was used for the estimation of mean wind velocity, turbulent Reynolds stress, turbulence intensity, and length scales, while the second one was used for the estimation of the power spectral density of velocity fluctuations. This extensive set of studied flow and turbulence parameters is unmatched in comparison to the previous relevant studies, as it includes here turbulence intensity and power spectral density of velocity fluctuations in all three directions, as well as the Reynolds stress profiles and turbulence length scales. Modeling results agree well with experiments for all terrain types, particularly in the lower ABL within the height range of the most engineering structures, while exhibiting sensitivity to abrupt changes and data scattering in profiles of wind-tunnel results. The

  9. Phytoremediation of palm oil mill secondary effluent (POMSE) by Chrysopogon zizanioides (L.) using artificial neural networks.

    PubMed

    Darajeh, Negisa; Idris, Azni; Fard Masoumi, Hamid Reza; Nourani, Abolfazl; Truong, Paul; Rezania, Shahabaldin

    2017-05-04

    Artificial neural networks (ANNs) have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the nonlinear relationships between variables in complex systems. In this study, ANN was applied for modeling of Chemical Oxygen Demand (COD) and biodegradable organic matter (BOD) removal from palm oil mill secondary effluent (POMSE) by vetiver system. The independent variable, including POMSE concentration, vetiver slips density, and removal time, has been considered as input parameters to optimize the network, while the removal percentage of COD and BOD were selected as output. To determine the number of hidden layer nodes, the root mean squared error of testing set was minimized, and the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. The comparison indicated that the quick propagation (QP) algorithm had minimum root mean squared error and absolute average deviation, and maximum coefficient of determination. The importance values of the variables was included vetiver slips density with 42.41%, time with 29.8%, and the POMSE concentration with 27.79%, which showed none of them, is negligible. Results show that the ANN has great potential ability in prediction of COD and BOD removal from POMSE with residual standard error (RSE) of less than 0.45%.

  10. Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks.

    PubMed

    Lin, Lan; Jin, Cong; Fu, Zhenrong; Zhang, Baiwen; Bin, Guangyu; Wu, Shuicai

    2016-03-01

    Brain ageing is followed by changes of the connectivity of white matter (WM) and changes of the grey matter (GM) concentration. Neurodegenerative disease is more vulnerable to an accelerated brain ageing, which is associated with prospective cognitive decline and disease severity. Accurate detection of accelerated ageing based on brain network analysis has a great potential for early interventions designed to hinder atypical brain changes. To capture the brain ageing, we proposed a novel computational approach for modeling the 112 normal older subjects (aged 50-79 years) brain age by connectivity analyses of networks of the brain. Our proposed method applied principal component analysis (PCA) to reduce the redundancy in network topological parameters. Back propagation artificial neural network (BPANN) improved by hybrid genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithm is established to model the relation among principal components (PCs) and brain age. The predicted brain age is strongly correlated with chronological age (r=0.8). The model has mean absolute error (MAE) of 4.29 years. Therefore, we believe the method can provide a possible way to quantitatively describe the typical and atypical network organization of human brain and serve as a biomarker for presymptomatic detection of neurodegenerative diseases in the future.

  11. Artificial neural network approach for moiré fringe center determination

    NASA Astrophysics Data System (ADS)

    Woo, Wing Hon; Ratnam, Mani Maran; Yen, Kin Sam

    2015-11-01

    The moiré effect has been used in high-accuracy positioning and alignment systems for decades. Various methods have been proposed to identify and locate moiré fringes in order to relate the pattern information to dimensional and displacement measurement. These methods can be broadly categorized into manual interpretation based on human knowledge and image processing based on computational algorithms. An artificial neural network (ANN) is proposed to locate moiré fringe centers within circular grating moiré patterns. This ANN approach aims to mimic human decision making by eliminating complex mathematical computations or time-consuming image processing algorithms in moiré fringe recognition. A feed-forward backpropagation ANN architecture was adopted in this work. Parametric studies were performed to optimize the ANN architecture. The finalized ANN approach was able to determine the location of the fringe centers with average deviations of 3.167 pixels out of 200 pixels (≈1.6%) and 6.166 pixels out of 200 pixels (≈3.1%) for real moiré patterns that lie within and outside the training intervals, respectively. In addition, a reduction of 43.4% in the computational time was reported using the ANN approach. Finally, the applicability of the ANN approach for moiré fringe center determination was confirmed.

  12. Non-linear global optimization via parameterization and inverse function approximation: an artificial neural networks approach.

    PubMed

    Mayorga, René V; Arriaga, Mariano

    2007-10-01

    In this article, a novel technique for non-linear global optimization is presented. The main goal is to find the optimal global solution of non-linear problems avoiding sub-optimal local solutions or inflection points. The proposed technique is based on a two steps concept: properly keep decreasing the value of the objective function, and calculating the corresponding independent variables by approximating its inverse function. The decreasing process can continue even after reaching local minima and, in general, the algorithm stops when converging to solutions near the global minimum. The implementation of the proposed technique by conventional numerical methods may require a considerable computational effort on the approximation of the inverse function. Thus, here a novel Artificial Neural Network (ANN) approach is implemented to reduce the computational requirements of the proposed optimization technique. This approach is successfully tested on some highly non-linear functions possessing several local minima. The results obtained demonstrate that the proposed approach compares favorably over some current conventional numerical (Matlab functions) methods, and other non-conventional (Evolutionary Algorithms, Simulated Annealing) optimization methods.

  13. Prediction of temperature performance of a two-phase closed thermosyphon using Artificial Neural Network

    NASA Astrophysics Data System (ADS)

    Shanbedi, Mehdi; Jafari, Dariush; Amiri, Ahmad; Heris, Saeed Zeinali; Baniadam, Majid

    2013-01-01

    Here, the temperature performance of a two-phase closed thermosyphon (TPCT) was investigated using two synthesized nanofluids, including carbon nano-tube (CNT)/water and CNT-Ag/water. In order to determine the temperature performance of a TPCT, the experiments were performed for various values of weight fraction and input power. To predict the other experimental conditions, a reliable and accurate tool should be applied. Therefore Artificial Neural Network (ANN) was applied to predict the process performance. Using ANN, the operating parameters, including distribution of wall temperature (T) and the temperature difference between the input and the output water streams of condenser section (∆T) were determined. To achieve this goal, the multi-layer perceptron network was employed. The Levenberg-Marquardt algorithm was chosen as learning algorithm of this network. The results of simulation showed an excellent agreement with the data resulted from the experiments. Therefore it is possible to say that ANN is a powerful tool to predict the performance of different processes.

  14. Comparison of polynomial approximations and artificial neural nets for response surfaces in engineering optimization

    NASA Technical Reports Server (NTRS)

    Carpenter, William C.

    1991-01-01

    Engineering optimization problems involve minimizing some function subject to constraints. In areas such as aircraft optimization, the constraint equations may be from numerous disciplines such as transfer of information between these disciplines and the optimization algorithm. They are also suited to problems which may require numerous re-optimizations such as in multi-objective function optimization or to problems where the design space contains numerous local minima, thus requiring repeated optimizations from different initial designs. Their use has been limited, however, by the fact that development of response surfaces randomly selected or preselected points in the design space. Thus, they have been thought to be inefficient compared to algorithms to the optimum solution. A development has taken place in the last several years which may effect the desirability of using response surfaces. It may be possible that artificial neural nets are more efficient in developing response surfaces than polynomial approximations which have been used in the past. This development is the concern of the work.

  15. Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks

    PubMed Central

    Rivera, José; Carrillo, Mariano; Chacón, Mario; Herrera, Gilberto; Bojorquez, Gilberto

    2007-01-01

    The development of smart sensors involves the design of reconfigurable systems capable of working with different input sensors. Reconfigurable systems ideally should spend the least possible amount of time in their calibration. An autocalibration algorithm for intelligent sensors should be able to fix major problems such as offset, variation of gain and lack of linearity, as accurately as possible. This paper describes a new autocalibration methodology for nonlinear intelligent sensors based on artificial neural networks, ANN. The methodology involves analysis of several network topologies and training algorithms. The proposed method was compared against the piecewise and polynomial linearization methods. Method comparison was achieved using different number of calibration points, and several nonlinear levels of the input signal. This paper also shows that the proposed method turned out to have a better overall accuracy than the other two methods. Besides, experimentation results and analysis of the complete study, the paper describes the implementation of the ANN in a microcontroller unit, MCU. In order to illustrate the method capability to build autocalibration and reconfigurable systems, a temperature measurement system was designed and tested. The proposed method is an improvement over the classic autocalibration methodologies, because it impacts on the design process of intelligent sensors, autocalibration methodologies and their associated factors, like time and cost.

  16. Emergence of gamma motor activity in an artificial neural network model of the corticospinal system.

    PubMed

    Grandjean, Bernard; Maier, Marc A

    2017-02-01

    Muscle spindle discharge during active movement is a function of mechanical and neural parameters. Muscle length changes (and their derivatives) represent its primary mechanical, fusimotor drive its neural component. However, neither the action nor the function of fusimotor and in particular of γ-drive, have been clearly established, since γ-motor activity during voluntary, non-locomotor movements remains largely unknown. Here, using a computational approach, we explored whether γ-drive emerges in an artificial neural network model of the corticospinal system linked to a biomechanical antagonist wrist simulator. The wrist simulator included length-sensitive and γ-drive-dependent type Ia and type II muscle spindle activity. Network activity and connectivity were derived by a gradient descent algorithm to generate reciprocal, known target α-motor unit activity during wrist flexion-extension (F/E) movements. Two tasks were simulated: an alternating F/E task and a slow F/E tracking task. Emergence of γ-motor activity in the alternating F/E network was a function of α-motor unit drive: if muscle afferent (together with supraspinal) input was required for driving α-motor units, then γ-drive emerged in the form of α-γ coactivation, as predicted by empirical studies. In the slow F/E tracking network, γ-drive emerged in the form of α-γ dissociation and provided critical, bidirectional muscle afferent activity to the cortical network, containing known bidirectional target units. The model thus demonstrates the complementary aspects of spindle output and hence γ-drive: i) muscle spindle activity as a driving force of α-motor unit activity, and ii) afferent activity providing continuous sensory information, both of which crucially depend on γ-drive.

  17. An Artificial Neural Network Control System for Spacecraft Attitude Stabilization

    DTIC Science & Technology

    1990-06-01

    training is based on the concept of enforced performance. A neural network will learn to meet a specific performance goal if the performance standard...is the only solution to a problem. Performance index training is devised to teach the neural network the time-optimal control law for the system. Real...time adaptation of a neural network in closed loop control of the Crew/Equipment Retriever was demonstrated in computer simulations.

  18. Structural optimisation and input selection of an artificial neural network for river level prediction

    NASA Astrophysics Data System (ADS)

    Leahy, Paul; Kiely, Ger; Corcoran, Gearóid

    2008-06-01

    SummaryAccurate river level prediction, necessary for reliable flood forecasting, is a difficult task due to the complexity and inherent nonlinearity of the catchment hydrological system. Although artificial neural networks (ANNs) offer advantages over mechanistic or conceptual hydrological models for river level prediction, their applicability is limited by the fact that each ANN has to be specifically optimised and trained for a particular prediction problem and suitable input vectors selected. A recently developed novel optimisation algorithm combining properties of simulated annealing and tabu search is used to arrive at an optimal ANN for the prediction of river levels 5 h in advance. The algorithm seeks to minimise the value of a cost function based on the complexity and performance of the ANN. This is done by removing inter-neuron connections and adjusting the weights of the remaining connections. The candidate inputs presented to the algorithm were: current values of river levels at the flood location and two upstream locations; the change in level over the previous 4 h at the flood point, mean sea level pressure (SLP) and the change in SLP over the previous 24 h. The optimisation removed 79% of the network connections and three of the candidate inputs, leaving the current levels at the two upstream locations and at the flood point as the only inputs. The optimised ANN was then trained using the standard backpropagation algorithm. This methodology produces an ANN of greatly reduced complexity albeit with a reduced performance compared to an unoptimised ANN trained with backpropagation only. However, it has the advantage of being generally applicable and represents an improvement over trial and error as a method of ANN structural optimisation and input selection. For this prediction problem, current levels at two upstream locations and at the flood point are the best predictors of the level at the flood point.

  19. Volcanic hot spot detection from optical multispectral remote sensing data using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Piscini, Alessandro; Lombardo, Valerio

    2014-03-01

    This paper describes an application of artificial neural networks for the recognition of volcanic lava flow hot spots using remote sensing data. Satellite remote sensing is a very effective and safe way to monitor volcanic eruptions in order to safeguard the environment and the people affected by such natural hazards. Neural networks are an effective and well-established technique for the classification of satellite images. In addition, once well trained, they prove to be very fast in the application stage. In our study a back propagation neural network was used for the recognition of thermal anomalies affecting hot lava pixels. The network was trained using the three thermal channels of the Advanced Very High Resolution Radiometer (AVHRR) sensor as inputs and the corresponding values of heat flux, estimated using a two thermal component model, as reference outputs. As a case study the volcano Etna (Eastern Sicily, Italy) was chosen, and in particular the effusive eruption which took place during the month of 2006 July. The neural network was trained with a time-series of 15 images (12 nighttime images and 3 daytime images) and validated on three independent data sets of AVHRR images of the same eruption and on two relative to an eruption occurred the following month. While for both nighttime and daytime validation images the neural network identified the image pixels affected by hot lava with a 100 per cent success rate, for the daytime images also adjacent pixels were included, apparently not interested by lava flow. Despite these performance differences under different illumination conditions, the proposed method can be considered effective both in terms of classification accuracy and generalization capability. In particular our approach proved to be robust in the rejection of false positives, often corresponding to noisy or cloudy pixels, whose presence in multispectral images can often undermine the performance of traditional classification algorithms. Future

  20. Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm

    PubMed Central

    Wu, Haizhou; Luo, Qifang

    2016-01-01

    Symbiotic organisms search (SOS) is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs). In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared. PMID:28105044

  1. Towards multifocal ultrasonic neural stimulation: pattern generation algorithms.

    PubMed

    Hertzberg, Yoni; Naor, Omer; Volovick, Alexander; Shoham, Shy

    2010-10-01

    Focused ultrasound (FUS) waves directed onto neural structures have been shown to dynamically modulate neural activity and excitability, opening up a range of possible systems and applications where the non-invasiveness, safety, mm-range resolution and other characteristics of FUS are advantageous. As in other neuro-stimulation and modulation modalities, the highly distributed and parallel nature of neural systems and neural information processing call for the development of appropriately patterned stimulation strategies which could simultaneously address multiple sites in flexible patterns. Here, we study the generation of sparse multi-focal ultrasonic distributions using phase-only modulation in ultrasonic phased arrays. We analyse the relative performance of an existing algorithm for generating multifocal ultrasonic distributions and new algorithms that we adapt from the field of optical digital holography, and find that generally the weighted Gerchberg-Saxton algorithm leads to overall superior efficiency and uniformity in the focal spots, without significantly increasing the computational burden. By combining phased-array FUS and magnetic-resonance thermometry we experimentally demonstrate the simultaneous generation of tightly focused multifocal distributions in a tissue phantom, a first step towards patterned FUS neuro-modulation systems and devices.

  2. Artificial neural networks as a useful tool to predict the risk level of Betula pollen in the air

    NASA Astrophysics Data System (ADS)

    Castellano-Méndez, M.; Aira, M. J.; Iglesias, I.; Jato, V.; González-Manteiga, W.

    2005-05-01

    An increasing percentage of the European population suffers from allergies to pollen. The study of the evolution of air pollen concentration supplies prior knowledge of the levels of pollen in the air, which can be useful for the prevention and treatment of allergic symptoms, and the management of medical resources. The symptoms of Betula pollinosis can be associated with certain levels of pollen in the air. The aim of this study was to predict the risk of the concentration of pollen exceeding a given level, using previous pollen and meteorological information, by applying neural network techniques. Neural networks are a widespread statistical tool useful for the study of problems associated with complex or poorly understood phenomena. The binary response variable associated with each level requires a careful selection of the neural network and the error function associated with the learning algorithm used during the training phase. The performance of the neural network with the validation set showed that the risk of the pollen level exceeding a certain threshold can be successfully forecasted using artificial neural networks. This prediction tool may be implemented to create an automatic system that forecasts the risk of suffering allergic symptoms.

  3. Resonant hopping of a robot controlled by an artificial neural oscillator.

    PubMed

    Pelc, Evan H; Daley, Monica A; Ferris, Daniel P

    2008-06-01

    The bouncing gaits of terrestrial animals (hopping, running, trotting) can be modeled as a hybrid dynamic system, with spring-mass dynamics during stance and ballistic motion during the aerial phase. We used a simple hopping robot controlled by an artificial neural oscillator to test the ability of the neural oscillator to adaptively drive this hybrid dynamic system. The robot had a single joint, actuated by an artificial pneumatic muscle in series with a tendon spring. We examined how the oscillator-robot system responded to variation in two neural control parameters: descending neural drive and neuromuscular gain. We also tested the ability of the oscillator-robot system to adapt to variations in mechanical properties by changing the series and parallel spring stiffnesses. Across a 100-fold variation in both supraspinal gain and muscle gain, hopping frequency changed by less than 10%. The neural oscillator consistently drove the system at the resonant half-period for the stance phase, and adapted to a new resonant half-period when the muscle series and parallel stiffnesses were altered. Passive cycling of elastic energy in the tendon accounted for 70-79% of the mechanical work done during each hop cycle. Our results demonstrate that hopping dynamics were largely determined by the intrinsic properties of the mechanical system, not the specific choice of neural oscillator parameters. The findings provide the first evidence that an artificial neural oscillator will drive a hybrid dynamic system at partial resonance.

  4. Constitutive flow behaviour of austenitic stainless steels under hot deformation: artificial neural network modelling to understand, evaluate and predict

    NASA Astrophysics Data System (ADS)

    Mandal, Sumantra; Sivaprasad, P. V.; Venugopal, S.; Murthy, K. P. N.

    2006-09-01

    An artificial neural network (ANN) model is developed to predict the constitutive flow behaviour of austenitic stainless steels during hot deformation. The input parameters are alloy composition and process variables whereas flow stress is the output. The model is based on a three-layer feed-forward ANN with a back-propagation learning algorithm. The neural network is trained with an in-house database obtained from hot compression tests on various grades of austenitic stainless steels. The performance of the model is evaluated using a wide variety of statistical indices. Good agreement between experimental and predicted data is obtained. The correlation between individual alloying elements and high temperature flow behaviour is investigated by employing the ANN model. The results are found to be consistent with the physical phenomena. The model can be used as a guideline for new alloy development.

  5. Modelling of multi-nutrient interactions in growth of the dinoflagellate microalga Protoceratium reticulatum using artificial neural networks.

    PubMed

    López-Rosales, L; Gallardo-Rodríguez, J J; Sánchez-Mirón, A; Contreras-Gómez, A; García-Camacho, F; Molina-Grima, E

    2013-10-01

    This study examines the use of artificial neural networks as predictive tools for the growth of the dinoflagellate microalga Protoceratium reticulatum. Feed-forward back-propagation neural networks (FBN), using Levenberg-Marquardt back-propagation or Bayesian regularization as training functions, offered the best results in terms of representing the nonlinear interactions among all nutrients in a culture medium containing 26 different components. A FBN configuration of 26-14-1 layers was selected. The FBN model was trained using more than 500 culture experiments on a shake flask scale. Garson's algorithm provided a valuable means of evaluating the relative importance of nutrients in terms of microalgal growth. Microelements and vitamins had a significant importance (approximately 70%) in relation to macronutrients (nearly 25%), despite their concentrations in the culture medium being various orders of magnitude smaller. The approach presented here may be useful for modelling multi-nutrient interactions in photobioreactors.

  6. Simulating and Synthesizing Substructures Using Neural Network and Genetic Algorithms

    NASA Technical Reports Server (NTRS)

    Liu, Youhua; Kapania, Rakesh K.; VanLandingham, Hugh F.

    1997-01-01

    The feasibility of simulating and synthesizing substructures by computational neural network models is illustrated by investigating a statically indeterminate beam, using both a 1-D and a 2-D plane stress modelling. The beam can be decomposed into two cantilevers with free-end loads. By training neural networks to simulate the cantilever responses to different loads, the original beam problem can be solved as a match-up between two subsystems under compatible interface conditions. The genetic algorithms are successfully used to solve the match-up problem. Simulated results are found in good agreement with the analytical or FEM solutions.

  7. Artificial neural networks in evaluation and optimization of modified release solid dosage forms.

    PubMed

    Ibrić, Svetlana; Djuriš, Jelena; Parojčić, Jelena; Djurić, Zorica

    2012-10-18

    Implementation of the Quality by Design (QbD) approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE) as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM) is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN) in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization of modified release solid dosage forms.

  8. Macrobenthos habitat potential mapping using GIS-based artificial neural network models.

    PubMed

    Lee, Saro; Park, Inhye; Koo, Bon Joo; Ryu, Joo-Hyung; Choi, Jong-Kuk; Woo, Han Jun

    2013-02-15

    This paper proposes and tests a method of producing macrobenthos habitat potential maps in Hwangdo tidal flat, Korea based on an artificial neural network. Samples of macrobenthos were collected during field work, and eight control factors were compiled as a spatial database from remotely sensed data and GIS analysis. The macrobenthos habitat potential maps were produced using an artificial neural network model. Macrobenthos habitat potential maps were made for Macrophthalmus dilatatus, Cerithideopsilla cingulata, and Armandia lanceolata. The maps were validated by compared with the surveyed habitat locations. A strong correlation between the potential maps and species locations was revealed. The validation result showed average accuracies of 74.9%, 78.32%, and 73.27% for M. dilatatus, C. cingulata, and A. lanceolata, respectively. A GIS-based artificial neural network model combined with remote sensing techniques is an effective tool for mapping the areas of macrobenthos habitat potential in tidal flats.

  9. Decoding the brain's algorithm for categorization from its neural implementation.

    PubMed

    Mack, Michael L; Preston, Alison R; Love, Bradley C

    2013-10-21

    Acts of cognition can be described at different levels of analysis: what behavior should characterize the act, what algorithms and representations underlie the behavior, and how the algorithms are physically realized in neural activity [1]. Theories that bridge levels of analysis offer more complete explanations by leveraging the constraints present at each level [2-4]. Despite the great potential for theoretical advances, few studies of cognition bridge levels of analysis. For example, formal cognitive models of category decisions accurately predict human decision making [5, 6], but whether model algorithms and representations supporting category decisions are consistent with underlying neural implementation remains unknown. This uncertainty is largely due to the hurdle of forging links between theory and brain [7-9]. Here, we tackle this critical problem by using brain response to characterize the nature of mental computations that support category decisions to evaluate two dominant, and opposing, models of categorization. We found that brain states during category decisions were significantly more consistent with latent model representations from exemplar [5] rather than prototype theory [10, 11]. Representations of individual experiences, not the abstraction of experiences, are critical for category decision making. Holding models accountable for behavior and neural implementation provides a means for advancing more complete descriptions of the algorithms of cognition.

  10. Analysis and improvement of estimated snow water equivalent (SWE) using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    E Azar, A.; Ghedira, H.; Khanbilvardi, R.

    2005-12-01

    The goal of this study is to improve the retrieval of SWE/Snow depth in Great lakes area, United States using passive microwave images along with Normalized Difference Vegetation Index NDVI and Artificial Neural Networks (ANNs). Passive microwave images have been successfully used to estimate snow characteristics such as Snow Water Equivalent (SWE) and snow depth. Despite considerable progress, challenges still exist with respect to accuracy and reliability. In this study, Special Sensor Microwave Imager (SSM/I) channels which are available in Equal-Area Scalable Earth Grid (EASE-GRID) format are used. The study area is covered by a 28 by 35 grid of EASE-Grid pixels, 25km by 25km each. To have a comprehensive data set of brightness temperatures (Tb) of SSM/I channels, an assortment of pixels were selected based on latitude and land cover. A time series analysis was conducted for three winter seasons to assess the SSM/I capability to estimates snow depth and SWE for various land covers. Ground truth data' were obtained from the National Climate Data Center (NCDC) and the National Operational Hydrological Remote Sensing Center (NOHRSC). The NCDC provided daily snow depth measurements reported from various stations located in the study area. Measurements were recorded and projected to match EASE-GRID formatting. The NOHRSC produces SNODAS dataset using airborne Gamma radiation and gauge measurements combined with a physical model. The data set consisted of different snow characteristics such as SWE and snow depth. Landcover characteristics are introduced by using Normalized Difference Vegetation Index (NDVI). An Artificial Neural Network (ANN) algorithm has been employed to evaluate the effect of landcover in estimating snow depth and Snow Water Equivalent (SWE). The model is trained using SSM/I channels (19v, 19h, 37v, 37h, 22v, 85v, 85h) and the mean and standard deviation of NDVI for the each pixel. The preliminary time series results showed various degrees of

  11. Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes

    PubMed Central

    Borgs, Christian; Chayes, Jennifer T.; Ingrosso, Alessandro; Lucibello, Carlo; Saglietti, Luca; Zecchina, Riccardo

    2016-01-01

    In artificial neural networks, learning from data is a computationally demanding task in which a large number of connection weights are iteratively tuned through stochastic-gradient-based heuristic processes over a cost function. It is not well understood how learning occurs in these systems, in particular how they avoid getting trapped in configurations with poor computational performance. Here, we study the difficult case of networks with discrete weights, where the optimization landscape is very rough even for simple architectures, and provide theoretical and numerical evidence of the existence of rare—but extremely dense and accessible—regions of configurations in the network weight space. We define a measure, the robust ensemble (RE), which suppresses trapping by isolated configurations and amplifies the role of these dense regions. We analytically compute the RE in some exactly solvable models and also provide a general algorithmic scheme that is straightforward to implement: define a cost function given by a sum of a finite number of replicas of the original cost function, with a constraint centering the replicas around a driving assignment. To illustrate this, we derive several powerful algorithms, ranging from Markov Chains to message passing to gradient descent processes, where the algorithms target the robust dense states, resulting in substantial improvements in performance. The weak dependence on the number of precision bits of the weights leads us to conjecture that very similar reasoning applies to more conventional neural networks. Analogous algorithmic schemes can also be applied to other optimization problems. PMID:27856745

  12. Pattern recognition and data mining software based on artificial neural networks applied to proton transfer in aqueous environments

    NASA Astrophysics Data System (ADS)

    Amani, Tahat; Jordi, Marti; Ali, Khwaldeh; Kaher, Tahat

    2014-04-01

    In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories: transfer ‘occurred’ and transfer ‘not occurred’. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation (multilayer perceptron algorithms) could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties (e.g., temperature, density) show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.

  13. Beneficial role of noise in artificial neural networks

    SciTech Connect

    Monterola, Christopher; Saloma, Caesar; Zapotocky, Martin

    2008-06-18

    We demonstrate enhancement of neural networks efficacy to recognize frequency encoded signals and/or to categorize spatial patterns of neural activity as a result of noise addition. For temporal information recovery, noise directly added to the receiving neurons allow instantaneous improvement of signal-to-noise ratio [Monterola and Saloma, Phys. Rev. Lett. 2002]. For spatial patterns however, recurrence is necessary to extend and homogenize the operating range of a feed-forward neural network [Monterola and Zapotocky, Phys. Rev. E 2005]. Finally, using the size of the basin of attraction of the networks learned patterns (dynamical fixed points), a procedure for estimating the optimal noise is demonstrated.

  14. Artificial neural networks for processing fluorescence spectroscopy data in skin cancer diagnostics

    NASA Astrophysics Data System (ADS)

    Lenhardt, L.; Zeković, I.; Dramićanin, T.; Dramićanin, M. D.

    2013-11-01

    Over the years various optical spectroscopic techniques have been widely used as diagnostic tools in the discrimination of many types of malignant diseases. Recently, synchronous fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer diagnostics. The SFS method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. This method is fast, relatively inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal skin, nevus and melanoma samples were used as input for training of artificial neural networks. Two different types of artificial neural networks were trained, the self-organizing map and the feed-forward neural network. Histopathology results of investigated skin samples were used as the gold standard for network output. Based on the obtained classification success rate of neural networks, we concluded that both networks provided high sensitivity with classification errors between 2 and 4%.

  15. Frequency-coded artificial neural networks: An approach to self-organizing systems

    SciTech Connect

    Dress, W.B.

    1987-01-01

    A frequency-based model of an artificial neural network is being explored for active learning in a simulated environment and for its response to multiple modalities of input data. Physical sensors couple naturally to such a network, providing an easy migration path from simulation to application. The combination of an artificial neural network processing frequency-coded sensor information and implemented on advanced computer architectures is seen as an answer to the problems arising in robotics and the fusion of large quantities of multisensory data.

  16. Modeling sulphur dioxide due to vehicular traffic using artificial neural network.

    PubMed

    Singh, B K; Singh, A K; Prasad, S C

    2009-10-01

    The dispersion characteristics of vehicular exhaust are highly non-linear. The deterministic as well as numerical models are unable to predict these air pollutants precisely. Artificial neural network (ANN), having the capability to recognize the non-linearity present in the noisy data, has been used in the present work to model the emission concentration of sulphur dioxide from vehicular source in an urban area. ANN model is developed with different combinations of traffic and meteorological parameters. The model prediction reveals that the artificial neural network trained with both traffic and meteorological parameters together shows better performance in predicting SO2 concentration.

  17. Classification of Acousto-Optic Correlation Signatures of Spread Spectrum Signals Using Artificial Neural Networks

    DTIC Science & Technology

    1989-12-01

    Ohio ’aPw iorlipuab muo i 0I2, AFIT/GE/ENG/89D-10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION SIGNATURES OF SPREAD SPECTRUM SIGNALS USING ARTIFICIAL...ENG/89D- 10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION SIGNATURES OF SPREAD SPECTRUM SIGNALS USING ARTIFICIAL NEURAL NETWORKS THESIS John W. DeBerry...Captain, USAF AFIT/GE/ENG/89D- 10 Approved for public release; distribution unlimited. AFIT/GE/ENG/89D-10 CLASSIFICATION OF ACOUSTO - OPTIC CORRELATION

  18. The use of artificial neural networks in PVT-based radiation portal monitors

    SciTech Connect

    Kangas, Lars J.; Keller, Paul E.; Siciliano, Edward R.; Kouzes, Richard T.; Ely, James H.

    2008-03-21

    Polyvinyl toluene (PVT) based gamma-ray scintillation detectors are cost effective for use in radiation portal monitors (RPMs) applied to screening for illicit radioactive materials at international border crossings. While PVT detectors provide good sensitivity in detecting the presence of radioactive materials, they provide poor spectral resolution, limiting their ability to identify the isotopic content of the source of radiation. Thus using only total-spectrum or gross-count alarm algorithms, PVT-based RPMs cannot distinguish innocent materials that contain low-levels of normally occurring radioactivity from special nuclear materials of concern. To reduce the number of “nuisance” alarms produced in PVT-based RPMs by innocent materials, algorithms that analyze spectra from PVT detectors must be optimized to make use of the limited information contained in their energy spectra. This paper discusses how artificial neural networks (ANNs) can be used in such an analysis. The objective was to reduce the nuisance/false alarm probability while maintaining high detection probabilities, thus allowing gross count alarm thresholds to be raised without loss of performance and sensitivity to radioactive materials of interest. The spectra used in this study were obtained from actual PVT-based RPM data, and included cases where simulated spectra were inserted into the measured spectra. This paper also includes an analysis of spectral channel importance and shows evaluations of two methods used to rebin energy spectra into smaller sets. The results show that ANNs can be used with RPMs to reduce nuisance alarms. The algorithms described can be used in analyzing PVT spectra, and potentially sodium iodide spectra.

  19. Classification of Respiratory Sounds by Using An Artificial Neural Network

    DTIC Science & Technology

    2007-11-02

    are selected by using dynamic programming. Grow and Learn (GAL) neural network is used for the classification. It is observed that RSs of patients (with asthma) and healthy subjects are successfully classified by the GAL network.

  20. Adaptive artificial neural network for autonomous robot control

    NASA Technical Reports Server (NTRS)

    Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

    1992-01-01

    The topics are presented in viewgraph form and include: neural network controller for robot arm positioning with visual feedback; initial training of the arm; automatic recovery from cumulative fault scenarios; and error reduction by iterative fine movements.

  1. Risk stratification in heart failure using artificial neural networks.

    PubMed Central

    Atienza, F.; Martinez-Alzamora, N.; De Velasco, J. A.; Dreiseitl, S.; Ohno-Machado, L.

    2000-01-01

    Accurate risk stratification of heart failure patients is critical to improve management and outcomes. Heart failure is a complex multisystem disease in which several predictors are categorical. Neural network models have successfully been applied to several medical classification problems. Using a simple neural network, we assessed one-year prognosis in 132 patients, consecutively admitted with heart failure, by classifying them in 3 groups: death, readmission and one-year event-free survival. Given the small number of cases, the neural network model was trained using a resampling method. We identified relevant predictors using the Automatic Relevance Determination (ARD) method, and estimated their mean effect on the 3 different outcomes. Only 9 individuals were misclassified. Neural networks have the potential to be a useful tool for making prognosis in the domain of heart failure. PMID:11079839

  2. An application of artificial neural networks to experimental data approximation

    NASA Technical Reports Server (NTRS)

    Meade, Andrew J., Jr.

    1993-01-01

    As an initial step in the evaluation of networks, a feedforward architecture is trained to approximate experimental data by the backpropagation algorithm. Several drawbacks were detected and an alternative learning algorithm was then developed to partially address the drawbacks. This noniterative algorithm has a number of advantages over the backpropagation method and is easily implemented on existing hardware.

  3. HL-2A tokamak disruption forecasting based on an artificial neural network

    NASA Astrophysics Data System (ADS)

    Wang, Hao; Wang, Ai-Ke; Yang, Qing-Wei; Ding, Xuan-Tong; Dong, Jia-Qi; H, Sanuki; H, Itoh

    2007-12-01

    Artificial neural networks are trained to forecast the plasma disruption in HL-2A tokamak. Optimized network architecture is obtained. Saliency analysis is made to assess the relative importance of different diagnostic signals as network input. The trained networks can successfully detect the disruptive pulses of HL-2A tokamak. The results obtained show the possibility of developing a neural network predictor that intervenes well in advance for avoiding plasma disruption or mitigating its effects.

  4. Customizable VLSI artificial neural network chips based on a novel technology

    SciTech Connect

    Fu, C. Y.; Law, B.; Chapline, G.; Swenson, D.

    1993-09-14

    The human cerebral cortex contains approximately 10{sup 11} neurons and 10{sup 14} synapses. It thus seems logical that any technology intended to mimic human capabilities should have the ability to fabricate a very large number of neurons and even larger numbers of synapses. This paper describes an implementation of hardware neural networks using highly linear thin-film resistor technology and an 8-bit binary weight circuit to produce customizable artificial neural network chips and systems.

  5. Cascaded-grating holography for artificial neural networks

    NASA Astrophysics Data System (ADS)

    Owechko, Y.

    1993-03-01

    A holographic technique for implementing optical neural networks is described in which each connection weight is distributed among a series of cascaded angularly and spatially multiplexed photorefractive gratings. This greatly reduces cross talk caused by the conical Bragg degeneracy associated with a single grating, reduces distortions caused by beam coupling, and improves the utilization of pixels in the input spatial light modulator. Experimental examples of high-quality holographic storage and optical implementations of the perceptron and backpropagation neural networks are also discussed.

  6. Optimal field-scale groundwater remediation using neural networks and the genetic algorithm

    SciTech Connect

    Rogers, L.L.; Dowla, F.U.; Johnson, V.M.

    1993-05-01

    We present a new approach for field-scale nonlinear management of groundwater remediation. First, an artificial neural network (ANN) is trained to predict the outcome of a groundwater transport simulation. Then a genetic algorithm (GA) searches through possible pumping realizations, evaluating the fitness of each with a prediction from the trained ANN. Traditional approaches rely on optimization algorithms requiring sequential calls of the groundwater transport simulation. Our approach processes the transport simulations in parallel and ``recycles`` the knowledge base of these simulations, greatly reducing the computational and real-time burden, often the primary impediment to developing field-scale management models. We present results from a Superfund site suggesting that such management techniques can reduce cleanup costs by over a hundred million dollars.

  7. Application of neural based estimation algorithm for gait phases of above knee prosthesis.

    PubMed

    Tileylioğlu, E; Yilmaz, A

    2015-01-01

    In this study, two gait phase estimation methods which utilize a rule based quantization and an artificial neural network model respectively are developed and applied for the microcontroller based semi-active knee prosthesis in order to respond user demands and adapt environmental conditions. In this context, an experimental environment in which gait data collected synchronously from both inertial and image based measurement systems has been set up. The inertial measurement system that incorporates MEM accelerometers and gyroscopes is used to perform direct motion measurement through the microcontroller, while the image based measurement system is employed for producing the verification data and assessing the success of the prosthesis. Embedded algorithms dynamically normalize the input data prior to gait phase estimation. The real time analyses of two methods revealed that embedded ANN based approach performs slightly better in comparison with the rule based algorithm and has advantage of being easily-scalable, thus able to accommodate additional input parameters considering the microcontroller constraints.

  8. Towards the fabrication of artificial 3D microdevices for neural cell networks.

    PubMed

    Gill, Andrew A; Ortega, Ílida; Kelly, Stephen; Claeyssens, Frederik

    2015-04-01

    This work reports first steps towards the development of artificial neural stem cell microenvironments for the control and assessment of neural stem cell behaviour. Stem cells have been shown to be found in specific, supportive microenvironments (niches) and are believed to play an important role in tissue regeneration mechanisms. These environments are intricate spaces with chemical and biological features. Here we present work towards the development of physically defined microdevices in which neural and neural stem cells can be studied in 3-dimensions. We have approached this challenge by creating bespoke, microstructured polymer environments using both 2-photon polymerisation and soft lithography techniques. Specifically, we have designed and fabricated biodegradable microwell-shaped devices using an in house synthetized polymer (4-arm photocurable poly-lactid acid) on a bespoke 2-photon polymerisation (2PP) set-up. We have studied swelling and degradation of the constructs as well as biocompatibility. Moreover, we have explored the potential of these constructs as artificial neural cell substrates by culturing NG108-15 cells (mouse neuroblastoma; rat glioma hybrid) and human neural progenitor cells on the microstructures. Finally, we have studied the effects of our artificial microenvironments upon neurite length and cell density.

  9. Using artificial neural networks to retrieve the aerosol type from multi-spectral lidar data

    NASA Astrophysics Data System (ADS)

    Nicolae, Doina; Belegante, Livio; Talianu, Camelia; Vasilescu, Jeni

    2015-04-01

    Aerosols can influence the microphysical and macrophysical properties of clouds and hence impact the energy balance, precipitation and the hydrological cycle. They have different scattering and absorption properties depending on their origin, therefore measured optical properties can be used to retrieve their physical properties, as well as to estimate their chemical composition. Due to the measurement limitations (spectral, uncertainties, range) and high variability of the aerosol properties with environmental conditions (including mixing during transport), the identification of the aerosol type from lidar data is still not solved. However, ground, airborne and space-based lidars provide more and more observations to be exploited. Since 2000, EARLINET collected more than 20,000 aerosol vertical profiles under various meteorological conditions, concerning local or long-range transport of aerosols in the free troposphere. This paper describes the basic algorithm for aerosol typing from optical data using the benefits of artificial neural networks. A relevant database was built to provide sufficient training cases for the neural network, consisting of synthetic and measured aerosol properties. Synthetic aerosols were simulated starting from the microphysical properties of basic components, internally mixed in various proportions. The algorithm combines the GADS database (Global Aerosol DataSet) to OPAC model (Optical Properties of Aerosol and Clouds) and T-Matrix code in order to compute, in an iterative way, the intensive optical properties of each aerosol type. Both pure and mixed aerosol types were considered, as well as their particular non-sphericity and hygroscopicity. Real aerosol cases were picked up from the ESA-CALIPSO database, as well as EARLINET datasets. Specific selection criteria were applied to identify cases with accurate optical data and validated sources. Cross-check of the synthetic versus measured aerosol intensive parameters was performed in

  10. Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

    NASA Technical Reports Server (NTRS)

    Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)

    2002-01-01

    Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic

  11. Artificial Neural Networks: an overview and their use in the analysis of the AMPHORA-3 dataset.

    PubMed

    Buscema, Paolo Massimo; Massini, Giulia; Maurelli, Guido

    2014-10-01

    The Artificial Adaptive Systems (AAS) are theories with which generative algebras are able to create artificial models simulating natural phenomenon. Artificial Neural Networks (ANNs) are the more diffused and best-known learning system models in the AAS. This article describes an overview of ANNs, noting its advantages and limitations for analyzing dynamic, complex, non-linear, multidimensional processes. An example of a specific ANN application to alcohol consumption in Spain, as part of the EU AMPHORA-3 project, during 1961-2006 is presented. Study's limitations are noted and future needed research using ANN methodologies are suggested.

  12. Analogue spin-orbit torque device for artificial-neural-network-based associative memory operation

    NASA Astrophysics Data System (ADS)

    Borders, William A.; Akima, Hisanao; Fukami, Shunsuke; Moriya, Satoshi; Kurihara, Shouta; Horio, Yoshihiko; Sato, Shigeo; Ohno, Hideo

    2017-01-01

    We demonstrate associative memory operations reminiscent of the brain using nonvolatile spintronics devices. Antiferromagnet-ferromagnet bilayer-based Hall devices, which show analogue-like spin-orbit torque switching under zero magnetic fields and behave as artificial synapses, are used. An artificial neural network is used to associate memorized patterns from their noisy versions. We develop a network consisting of a field-programmable gate array and 36 spin-orbit torque devices. An effect of learning on associative memory operations is successfully confirmed for several 3 × 3-block patterns. A discussion on the present approach for realizing spintronics-based artificial intelligence is given.

  13. Neural-network diagnostic algorithm and smart sensor

    NASA Astrophysics Data System (ADS)

    Banish, Michele R.; Anderson, Roland B.; Ranganath, Heggere S.; Jones, Brian; Kirsch, James C.

    2000-05-01

    Digital image interpretation is the basis of medical diagnoses. Through extensive review of patient data, an algorithm was developed to identify features of diagnostic importance in radiological images. The algorithm is generally applicable. Results from cardiac, lung, and military imagery are reported. The algorithm uses a pulse coupled neural network. It is this neural network that is fabricated on a custom CMOS chip. Each neuron of the pulse coupled neural network accepts an external optical input. The optical input is accomplished by a photo-detector. The neurons communicate laterally through a voltage grid. The communication strength, light sensitivity and other global parameters are under external control. A programmable logic array is on the camera board. Data for a specific neuron is accessed by an addressing scheme typically used for a CCD array. The individual neuron speed ranges from 10 to 50 Mhertz, and is fixed by a digital clock. The current chip is configured to operate at 300 Hertz. The chip logic is a hybrid of analog and digital circuitry to minimize the neuron size, maximize the number of neurons at a fixed cost. The hybrid circuitry also minimized the noise level in the chip.

  14. Multi-robot task allocation based on two dimensional artificial fish swarm algorithm

    NASA Astrophysics Data System (ADS)

    Zheng, Taixiong; Li, Xueqin; Yang, Liangyi

    2007-12-01

    The problem of task allocation for multiple robots is to allocate more relative-tasks to less relative-robots so as to minimize the processing time of these tasks. In order to get optimal multi-robot task allocation scheme, a twodimensional artificial swarm algorithm based approach is proposed in this paper. In this approach, the normal artificial fish is extended to be two dimension artificial fish. In the two dimension artificial fish, each vector of primary artificial fish is extended to be an m-dimensional vector. Thus, each vector can express a group of tasks. By redefining the distance between artificial fish and the center of artificial fish, the behavior of two dimension fish is designed and the task allocation algorithm based on two dimension artificial swarm algorithm is put forward. At last, the proposed algorithm is applied to the problem of multi-robot task allocation and comparer with GA and SA based algorithm is done. Simulation and compare result shows the proposed algorithm is effective.

  15. Lévy flight artificial bee colony algorithm

    NASA Astrophysics Data System (ADS)

    Sharma, Harish; Bansal, Jagdish Chand; Arya, K. V.; Yang, Xin-She

    2016-08-01

    Artificial bee colony (ABC) optimisation algorithm is a relatively simple and recent population-based probabilistic approach for global optimisation. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the ABC, there is a high chance to skip the true solution due to its large step sizes. In order to balance between diversity and convergence in the ABC, a Lévy flight inspired search strategy is proposed and integrated with ABC. The proposed strategy is named as Lévy Flight ABC (LFABC) has both the local and global search capability simultaneously and can be achieved by tuning the Lévy flight parameters and thus automatically tuning the step sizes. In the LFABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. Furthermore, to improve the exploration capability, the numbers of scout bees are increased. The experiments on 20 test problems of different complexities and five real-world engineering optimisation problems show that the proposed strategy outperforms the basic ABC and recent variants of ABC, namely, Gbest-guided ABC, best-so-far ABC and modified ABC in most of the experiments.

  16. Forecasting geomagnetic activity indices using the Boyle index through artificial neural networks

    NASA Astrophysics Data System (ADS)

    Balasubramanian, Ramkumar

    2010-11-01

    Adverse space weather conditions affect various sectors making both human lives and technologies highly susceptible. This dissertation introduces a new set of algorithms suitable for short term space weather forecasts with an enhanced lead-time and better accuracy in predicting Kp, Dst and the AE index over some leading models. Kp is a 3-hour averaged global geomagnetic activity index good for midlatitude regions. The Dst index, an hourly index calculated using four ground based magnetic field measurements near the equator, measures the energy of the Earth's ring current. The Auroral Electrojet indices or AE indices are hourly indices used to characterize the global geomagnetic activity in the auroral zone. Our algorithms can predict these indices purely from the solar wind data with lead times up to 6 hours. We have trained and tested an ANN (Artificial Neural Network) over a complete solar cycle to serve this purpose. Over the last couple of decades, ANNs have been successful for temporal prediction problems amongst other advanced non-linear techniques. Our ANN-based algorithms receive near-real-time inputs either from ACE (Advanced Composition Explorer), located at L1, and a handful of ground-based magnetometers or only from ACE. The Boyle potential, phi = 10-4 (vkm/sec)2+ 11.7BnT sin3 (theta/2) kV, or the Boyle Index (BI) is an empirically-derived formula that approximates the Earth's polar cap potential and is easily derivable in real time using the solar wind data from ACE. The logarithms of both 3-hour and 1-hour averages of the Boyle Index correlate well with the subsequent Kp, Dst and AE: Kp = 8.93 log 10 - 12.55. Dst = 0.355 - 6.48, and AE = 5.87 - 83.46. Inputs to our ANN models have greatly benefitted from the BI and its proven record as a forecasting parameter since its initiation in October, 2003. A preconditioning event tunes the magnetosphere to a specific state before an impending geomagnetic storm. The neural net not only improves the

  17. Application of Artificial Neural Networks to the Design of Turbomachinery Airfoils

    NASA Technical Reports Server (NTRS)

    Rai, Man Mohan; Madavan, Nateri

    1997-01-01

    Artificial neural networks are widely used in engineering applications, such as control, pattern recognition, plant modeling and condition monitoring to name just a few. In this seminar we will explore the possibility of applying neural networks to aerodynamic design, in particular, the design of turbomachinery airfoils. The principle idea behind this effort is to represent the design space using a neural network (within some parameter limits), and then to employ an optimization procedure to search this space for a solution that exhibits optimal performance characteristics. Results obtained for design problems in two spatial dimensions will be presented.

  18. A Model for Improving the Learning Curves of Artificial Neural Networks.

    PubMed

    Monteiro, Roberto L S; Carneiro, Tereza Kelly G; Fontoura, José Roberto A; da Silva, Valéria L; Moret, Marcelo A; Pereira, Hernane Borges de Barros

    2016-01-01

    In this article, the performance of a hybrid artificial neural network (i.e. scale-free and small-world) was analyzed and its learning curve compared to three other topologies: random, scale-free and small-world, as well as to the chemotaxis neural network of the nematode Caenorhabditis Elegans. One hundred equivalent networks (same number of vertices and average degree) for each topology were generated and each was trained for one thousand epochs. After comparing the mean learning curves of each network topology with the C. elegans neural network, we found that the networks that exhibited preferential attachment exhibited the best learning curves.

  19. Improved Quantum Artificial Fish Algorithm Application to Distributed Network Considering Distributed Generation

    PubMed Central

    Du, Tingsong; Hu, Yang; Ke, Xianting

    2015-01-01

    An improved quantum artificial fish swarm algorithm (IQAFSA) for solving distributed network programming considering distributed generation is proposed in this work. The IQAFSA based on quantum computing which has exponential acceleration for heuristic algorithm uses quantum bits to code artificial fish and quantum revolving gate, preying behavior, and following behavior and variation of quantum artificial fish to update the artificial fish for searching for optimal value. Then, we apply the proposed new algorithm, the quantum artificial fish swarm algorithm (QAFSA), the basic artificial fish swarm algorithm (BAFSA), and the global edition artificial fish swarm algorithm (GAFSA) to the simulation experiments for some typical test functions, respectively. The simulation results demonstrate that the proposed algorithm can escape from the local extremum effectively and has higher convergence speed and better accuracy. Finally, applying IQAFSA to distributed network problems and the simulation results for 33-bus radial distribution network system show that IQAFSA can get the minimum power loss after comparing with BAFSA, GAFSA, and QAFSA. PMID:26447713

  20. Artificial neural networks for automatic modelling of the pectus excavatum corrective prosthesis

    NASA Astrophysics Data System (ADS)

    Rodrigues, Pedro L.; Moreira, António H. J.; Rodrigues, Nuno F.; Pinho, ACM; Fonseca, Jaime C.; Correia-Pinto, Jorge; Vilaça, João. L.

    2014-03-01

    Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82+/-5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7+/-4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.

  1. Sequential modelling of a full-scale wastewater treatment plant using an artificial neural network.

    PubMed

    Lee, Joong-Won; Suh, Changwon; Hong, Yoon-Seok Timothy; Shin, Hang-Sik

    2011-10-01

    This work proposes a sequential modelling approach using an artificial neural network (ANN) to develop four independent multivariate models that are able to predict the dynamics of biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solid (SS), and total nitrogen (TN) removal in a wastewater treatment plant (WWTP). Suitable structures of ANN models were automatically and conveniently optimized by a genetic algorithm rather than the conventional trial and error method. The sequential modelling approach, which is composed of two parts, a process disturbance estimator and a process behaviour predictor, was also presented to develop multivariate dynamic models. In particular, the process disturbance estimator was first employed to estimate the influent quality. The process behaviour predictor then sequentially predicted the effluent quality based on the estimated influent quality from the process disturbance estimator with other process variables. The efficiencies of the developed ANN models with a sequential modelling approach were demonstrated with a practical application using a data set collected from a full-scale WWTP during 2 years. The results show that the ANN with the sequential modelling approach successfully developed multivariate dynamic models of BOD, COD, SS, and TN removal with satisfactory estimation and prediction capability. Thus, the proposed method could be used as a powerful tool for the prediction of complex and nonlinear WWTP performance.

  2. Cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors.

    PubMed

    Sasaki, Masao S; Tachibana, Akira; Takeda, Shunichi

    2014-05-01

    Cancer risk at low doses of ionizing radiation remains poorly defined because of ambiguity in the quantitative link to doses below 0.2 Sv in atomic bomb survivors in Hiroshima and Nagasaki arising from limitations in the statistical power and information available on overall radiation dose. To deal with these difficulties, a novel nonparametric statistics based on the 'integrate-and-fire' algorithm of artificial neural networks was developed and tested in cancer databases established by the Radiation Effects Research Foundation. The analysis revealed unique features at low doses that could not be accounted for by nominal exposure dose, including (i) the presence of a threshold that varied with organ, gender and age at exposure, and (ii) a small but significant bumping increase in cancer risk at low doses in Nagasaki that probably reflects internal exposure to (239)Pu. The threshold was distinct from the canonical definition of zero effect in that it was manifested as negative excess relative risk, or suppression of background cancer rates. Such a unique tissue response at low doses of radiation exposure has been implicated in the context of the molecular basis of radiation-environment interplay in favor of recently emerging experimental evidence on DNA double-strand break repair pathway choice and its epigenetic memory by histone marking.

  3. Training Knowledge Bots for Physics-Based Simulations Using Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Samareh, Jamshid A.; Wong, Jay Ming

    2014-01-01

    Millions of complex physics-based simulations are required for design of an aerospace vehicle. These simulations are usually performed by highly trained and skilled analysts, who execute, monitor, and steer each simulation. Analysts rely heavily on their broad experience that may have taken 20-30 years to accumulate. In addition, the simulation software is complex in nature, requiring significant computational resources. Simulations of system of systems become even more complex and are beyond human capacity to effectively learn their behavior. IBM has developed machines that can learn and compete successfully with a chess grandmaster and most successful jeopardy contestants. These machines are capable of learning some complex problems much faster than humans can learn. In this paper, we propose using artificial neural network to train knowledge bots to identify the idiosyncrasies of simulation software and recognize patterns that can lead to successful simulations. We examine the use of knowledge bots for applications of computational fluid dynamics (CFD), trajectory analysis, commercial finite-element analysis software, and slosh propellant dynamics. We will show that machine learning algorithms can be used to learn the idiosyncrasies of computational simulations and identify regions of instability without including any additional information about their mathematical form or applied discretization approaches.

  4. Artificial neural network in breast lesions from fine-needle aspiration cytology smear.

    PubMed

    Subbaiah, R M; Dey, Pranab; Nijhawan, Raje

    2014-03-01

    Artificial neural networks (ANNs) are applied in engineering and certain medical fields. ANN has immense potential and is rarely been used in breast lesions. In this present study, we attempted to build up a complete robust back propagation ANN model based on cytomorphological data, morphometric data, nuclear densitometric data, and gray level co-occurrence matrix (GLCM) of ductal carcinoma and fibroadenomas of breast cases diagnosed on fine-needle aspiration cytology (FNAC). We selected 52 cases of fibroadenomas and 60 cases of infiltrating ductal carcinoma of breast diagnosed on FNAC by two cytologists. Essential cytological data was quantitated by two independent cytologists (SRM, PD). With the help of Image J software, nuclear morphomeric, densitometric, and GLCM features were measured in all the cases on hematoxylin and eosin-stained smears. With the available data, an ANN model was built up with the help of Neurointelligence software. The network was designed as 41-20-1 (41 input nodes, 20 hidden nodes, 1 output node). The network was trained by the online back propagation algorithm and 500 iterations were done. Learning was adjusted after every iteration. ANN model correctly identified all cases of fibroadenomas and infiltrating carcinomas in the test set. This is one of the first successful composite ANN models of breast carcinomas. This basic model can be used to diagnose the gray zone area of the breast lesions on FNAC. We assume that this model may have far-reaching implications in future.

  5. Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model.

    PubMed

    Qiu, Mingyue; Song, Yu

    2016-01-01

    In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.

  6. Cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors

    PubMed Central

    Sasaki, Masao S.; Tachibana, Akira; Takeda, Shunichi

    2014-01-01

    Cancer risk at low doses of ionizing radiation remains poorly defined because of ambiguity in the quantitative link to doses below 0.2 Sv in atomic bomb survivors in Hiroshima and Nagasaki arising from limitations in the statistical power and information available on overall radiation dose. To deal with these difficulties, a novel nonparametric statistics based on the ‘integrate-and-fire’ algorithm of artificial neural networks was developed and tested in cancer databases established by the Radiation Effects Research Foundation. The analysis revealed unique features at low doses that could not be accounted for by nominal exposure dose, including (i) the presence of a threshold that varied with organ, gender and age at exposure, and (ii) a small but significant bumping increase in cancer risk at low doses in Nagasaki that probably reflects internal exposure to 239Pu. The threshold was distinct from the canonical definition of zero effect in that it was manifested as negative excess relative risk, or suppression of background cancer rates. Such a unique tissue response at low doses of radiation exposure has been implicated in the context of the molecular basis of radiation–environment interplay in favor of recently emerging experimental evidence on DNA double-strand break repair pathway choice and its epigenetic memory by histone marking. PMID:24366315

  7. Microwave-vacuum drying of sour cherry: comparison of mathematical models and artificial neural networks.

    PubMed

    Motavali, Ali; Najafi, Gholam Hassan; Abbasi, Solayman; Minaei, Saeid; Ghaderi, Abdurrahman

    2013-08-01

    Drying characteristics of sour cherries were determined using microwave vacuum drier at various microwave powers (360, 600, 840, 1200 W) and absolute pressures (200, 400, 600, 800 mbars). In addition, using the artificial neural networks (ANN), trained by standard Back-Propagation algorithm, the effects of microwave power, pressure and drying time on moisture ratio (MR) and drying rate (DR) were investigated Based on the evaluation of experimental data fitting with semi-theoretical and empirical models, the Midilli et al. model was selected as the most appropriate one. Furthermore, the ANN model was able to predict the moisture ratio and drying rate quite well with determination coefficients (R(2)) of 0.9996, 0.9961 and 0.9958 for training, validation and testing, respectively. The prediction Mean Square Error of ANN was about 0.0003, 0.0071 and 0.0053 for training, validation and testing, respectively. This parameter signifies the difference between the desired outputs (as measured values) and the simulated values by the model. The good agreement between the experimental data and ANN model leads to the conclusion that the model adequately describes the drying behavior of sour cherries, in the range of operating conditions tested.

  8. Broiler weight estimation based on machine vision and artificial neural network.

    PubMed

    Amraei, S; Abdanan Mehdizadeh, S; Salari, S

    2017-03-09

    1. Machine vision and artificial neural network (ANN) procedures were used to estimate live body weight of broiler chickens in 30 1-d-old broiler chickens reared for 42 d. 2. Imaging was performed two times daily. To localise chickens within the pen, an ellipse fitting algorithm was used and the chickens' head and tail removed using the Chan-Vese method. 3. The correlations between the body weight and 6 physical extracted features indicated that there were strong correlations between body weight and the 5 features including area, perimeter, convex area, major and minor axis length. 5. According to statistical analysis there was no significant difference between morning and afternoon data over 42 d. 6. In an attempt to improve the accuracy of live weight approximation different ANN techniques, including Bayesian regulation, Levenberg-Marquardt, Scaled conjugate gradient and gradient descent were used. Bayesian regulation with R(2) value of 0.98 was the best network for prediction of broiler weight. 7. The accuracy of the machine vision technique was examined and most errors were less than 50 g.

  9. Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks.

    PubMed

    Rodrigues, Pedro L; Rodrigues, Nuno F; Pinho, A C M; Fonseca, Jaime C; Correia-Pinto, Jorge; Vilaça, João L

    2014-10-01

    Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

  10. Prediction of soil temperature using regression and artificial neural network models

    NASA Astrophysics Data System (ADS)

    Bilgili, Mehmet

    2010-12-01

    In this study, monthly soil temperature was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. The soil temperature and other meteorological parameters, which have been taken from Adana meteorological station, were observed between the years of 2000 and 2007 by the Turkish State Meteorological Service (TSMS). The soil temperatures were measured at depths of 5, 10, 20, 50 and 100 cm below the ground level. A three-layer feed-forward ANN structure was constructed and a back-propagation algorithm was used for the training of ANNs. In order to get a successful simulation, the correlation coefficients between all of the meteorological variables (soil temperature, atmospheric temperature, atmospheric pressure, relative humidity, wind speed, rainfall, global solar radiation and sunshine duration) were calculated taking them two by two. First, all independent variables were split into two time periods such as cold and warm seasons. They were added to the enter regression model. Then, the method of stepwise multiple regression was applied for the selection of the "best" regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and they were also used in the input layer of the ANN method. Results of these methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods.

  11. Creation and testing of an artificial neural network based carbonate detector for Mars rovers

    NASA Technical Reports Server (NTRS)

    Bornstein, Benjamin; Castano, Rebecca; Gilmore, Martha S.; Merrill, Matthew; Greenwood, James P.

    2005-01-01

    We have developed an artificial neural network (ANN) based carbonate detector capable of running on current and future rover hardware. The detector can identify calcite in visible/NIR (350-2500 nm) spectra of both laboratory specimens covered by ferric dust and rocks in Mars analogue field environments. The ANN was trained using the Backpropagation algorithm with sigmoid activation neurons. For the training dataset, we chose nine carbonate and eight non-carbonate representative mineral spectra from the USGS spectral library. Using these spectra as seeds, we generated 10,000 variants with up to 2% Gaussian noise in each reflectance measurement. We cross-validated several ANN architectures, training on 9,900 spectra and testing on the remaining 100. The best performing ANN correctly detected, with perfect accuracy, the presence (or absence) of carbonate in spectral data taken on field samples from the Mojave desert and clean, pure marbles from CT. Sensitivity experiments with JSC Mars-1 simulant dust suggest the carbonate detector would perform well in aeolian Martian environments.

  12. An artificial neural network approach for ranking quenching parameters in central galaxies

    NASA Astrophysics Data System (ADS)

    Teimoorinia, Hossen; Bluck, Asa F. L.; Ellison, Sara L.

    2016-04-01

    We present a novel technique for ranking the relative importance of galaxy properties in the process of quenching star formation. Specifically, we develop an artificial neural network (ANN) approach for pattern recognition and apply it to a population of over 400 000 central galaxies taken from the Sloan Digital Sky Survey Data Release 7. We utilize a variety of physical galaxy properties for training the pattern recognition algorithm to recognize star-forming and passive systems, for a `training set' of ˜100 000 galaxies. We then apply the ANN model to a `verification set' of ˜100 000 different galaxies, randomly chosen from the remaining sample. The success rate of each parameter singly, and in conjunction with other parameters, is taken as an indication of how important the parameters are to the process(es) of central galaxy quenching. We find that central velocity dispersion, bulge mass and bulge-to-total stellar mass ratio are excellent predictors of the passive state of the system, indicating that properties related to the central mass of the galaxy are most closely linked to the cessation of star formation. Larger scale galaxy properties (total or disc stellar masses), or those linked to environment (halo masses or δ5), perform significantly less well. Our results are plausibly explained by AGN feedback driving the quenching of central galaxies, although we discuss other possibilities as well.

  13. Digital image classification with the help of artificial neural network by simple histogram

    PubMed Central

    Dey, Pranab; Banerjee, Nirmalya; Kaur, Rajwant

    2016-01-01

    Background: Visual image classification is a great challenge to the cytopathologist in routine day-to-day work. Artificial neural network (ANN) may be helpful in this matter. Aims and Objectives: In this study, we have tried to classify digital images of malignant and benign cells in effusion cytology smear with the help of simple histogram data and ANN. Materials and Methods: A total of 404 digital images consisting of 168 benign cells and 236 malignant cells were selected for this study. The simple histogram data was extracted from these digital images and an ANN was constructed with the help of Neurointelligence software [Alyuda Neurointelligence 2.2 (577), Cupertino, California, USA]. The network architecture was 6-3-1. The images were classified as training set (281), validation set (63), and test set (60). The on-line backpropagation training algorithm was used for this study. Result: A total of 10,000 iterations were done to train the ANN system with the speed of 609.81/s. After the adequate training of this ANN model, the system was able to identify all 34 malignant cell images and 24 out of 26 benign cells. Conclusion: The ANN model can be used for the identification of the individual malignant cells with the help of simple histogram data. This study will be helpful in the future to identify malignant cells in unknown situations. PMID:27279679

  14. Automatic Detection and Identification of Seismic Signals Recorded at Krakatau Volcano (Indonesia) Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Ibs-von Seht, M.; Kniess, R.

    2006-12-01

    A number of different event types can be observed in the records of seismic stations operated on Krakatau volcano (Indonesia). These include volcano-induced signals such as LP, VT, and hybrid-type events as well as signals not originating from the volcano such as local and regional tectonic earthquakes and transient noise signals. The work presented here aims at the realization of a system that automatically detects and identifies the signals in order to estimate and monitor current activity states of the volcano. An artificial neural network (ANN) approach was chosen for the identification task. A set of parameters were defined, describing waveform and spectrogram properties of events detected by an STA/LTA algorithm. The parameters are fed into an ANN which is, after a training phase, able to generalize input data and identify corresponding event types. The success of the identification depends on the network architecture and training strategy. Several tests have been performed in order to determine an appropriate network layout and training intensity for the given problem. The resulting network shows a good performance. A practical implementation of the system for the volcano observatory routine is sketched.

  15. Artificial neural network model as a potential alternative for groundwater salinity forecasting

    NASA Astrophysics Data System (ADS)

    Banerjee, Pallavi; Singh, V. S.; Chatttopadhyay, Kausik; Chandra, P. C.; Singh, Bhoop

    2011-02-01

    SummaryThe paper evaluates the prospect of artificial neural network (ANN) simulation over mathematical modeling in estimating safe pumping rate to maintain groundwater salinity in island aquifers. Feed-forward ANN model with quick propagation (QP) as training algorithm has been used to forecast the salinity under varied pumping rates. The accuracy, generalization ability and reliability of the model are verified by real-time field data. The model is trained with 2 years of real-time field data and prediction on water quality with varying pumping rate is made for a span of 5 years. The same is then compared with both real-time field data and the prediction based on SUTRA (Saturated-Unsaturated Transport) computations. The proposed ANN model has surfaced as a simpler and more accurate alternative to the numerical method techniques. The ANN methodology using minimal lag and number of hidden nodes, along with the optimal number of spatial and temporal variables consistently produced the best performing network based simulation models. The prediction accuracy of the ANN model has been extended for another 5 years to further define the limit of pumping at a permissible level of groundwater salinity.

  16. Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network.

    PubMed

    Soltani, Mahmoud; Omid, Mahmoud; Alimardani, Reza

    2015-05-01

    Egg size is one of the important properties of egg that is judged by customers. Accordingly, in egg sorting and grading, the size of eggs must be considered. In this research, a new method of egg volume prediction was proposed without need to measure weight of egg. An accurate and efficient image processing algorithm was designed and implemented for computing major and minor diameters of eggs. Two methods of egg size modeling were developed. In the first method, a mathematical model was proposed based on Pappus theorem. In second method, Artificial Neural Network (ANN) technique was used to estimate egg volume. The determined egg volume by these methods was compared statistically with actual values. For mathematical modeling, the R(2), Mean absolute error and maximum absolute error values were obtained as 0.99, 0.59 cm(3) and 1.69 cm(3), respectively. To determine the best ANN, R(2) test and RMSEtest were used as selection criteria. The best ANN topology was 2-28-1 which had the R(2) test and RMSEtest of 0.992 and 0.66, respectively. After system calibration, the proposed models were evaluated. The results which indicated the mathematical modeling yielded more satisfying results. So this technique was selected for egg size determination.

  17. An artificial neural network method for lumen and media-adventitia border detection in IVUS.

    PubMed

    Su, Shengran; Hu, Zhenghui; Lin, Qiang; Hau, William Kongto; Gao, Zhifan; Zhang, Heye

    2016-11-17

    Intravascular ultrasound (IVUS) has been well recognized as one powerful imaging technique to evaluate the stenosis inside the coronary arteries. The detection of lumen border and media-adventitia (MA) border in IVUS images is the key procedure to determine the plaque burden inside the coronary arteries, but this detection could be burdensome to the doctor because of large volume of the IVUS images. In this paper, we use the artificial neural network (ANN) method as the feature learning algorithm for the detection of the lumen and MA borders in IVUS images. Two types of imaging information including spatial, neighboring features were used as the input data to the ANN method, and then the different vascular layers were distinguished accordingly through two sparse auto-encoders and one softmax classifier. Another ANN was used to optimize the result of the first network. In the end, the active contour model was applied to smooth the lumen and MA borders detected by the ANN method. The performance of our approach was compared with the manual drawing method performed by two IVUS experts on 461 IVUS images from four subjects. Results showed that our approach had a high correlation and good agreement with the manual drawing results. The detection error of the ANN method close to the error between two groups of manual drawing result. All these results indicated that our proposed approach could efficiently and accurately handle the detection of lumen and MA borders in the IVUS images.

  18. Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model

    PubMed Central

    Qiu, Mingyue; Song, Yu

    2016-01-01

    In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders’ expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day’s price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately. PMID:27196055

  19. Forecasting Plasma Disruptions Instabilities Using Artificial Neural Networks

    NASA Astrophysics Data System (ADS)

    Andresia de Oliveira, Kenya; Vannucci, Alvaro; da Silva, Elton Cesar

    2000-10-01

    Two-layer feedforward neural network was used to forecast the disruptive instabilities observed in the TEXT tokamak plasma discharges. For this task, soft X-ray experimental signals were used with very promising results. It was verified that a neural network with an architecture of the type m:2m:m:1 (where m=15) was capable, for example, to forecast the disruptive instabilities up to 4 ms in advance. This period of time is about four times larger than the one obtained previously, when experimental magnetic signals from Mirnov coils were used. From these very good forecasting results obtained it can be concluded that neural networks might be considered an important tool for making predictions over the time evolution of nonlinear systems.

  20. Vein matching using artificial neural network in vein authentication systems

    NASA Astrophysics Data System (ADS)

    Noori Hoshyar, Azadeh; Sulaiman, Riza

    2011-10-01

    Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.

  1. Artificial neural network modeling in competitive adsorption of phenol and resorcinol from water environment using some carbonaceous adsorbents.

    PubMed

    Aghav, R M; Kumar, Sunil; Mukherjee, S N

    2011-04-15

    This paper illustrates the application of artificial neural network (ANN) for prediction of performances in competitive adsorption of phenol and resorcinol from aqueous solution by conventional and low cost carbonaceous adsorbent materials, such as activated carbon (AC), wood charcoal (WC) and rice husk ash (RHA). The three layer's feed forward neural network with back propagation algorithm in MATLAB environment was used for estimation of removal efficiencies of phenol and resorcinol in bi-solute water environment based on 29 sets of laboratory batch study results. The input parameters used for training of the neural network include amount of adsorbent (g/L), initial concentrations of phenol (mg/L) and resorcinol (mg/L), contact time (h), and pH. The removal efficiencies of phenol and resorcinol were considered as an output of the neural network. The performances of the developed ANN models were also measured using statistical parameters, such as mean error, mean square error, root mean square error, and linear regression. The comparison of the removal efficiencies of pollutants using ANN model and experimental results showed that ANN modeling in competitive adsorption of phenolic compounds reasonably corroborated with the experimental results.

  2. The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process.

    PubMed

    Elmolla, Emad S; Chaudhuri, Malay; Eltoukhy, Mohamed Meselhy

    2010-07-15

    The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of antibiotic degradation in aqueous solution by the Fenton process. A three-layer backpropagation neural network was optimized to predict and simulate the degradation of amoxicillin, ampicillin and cloxacillin in aqueous solution in terms of COD removal. The configuration of the backpropagation neural network giving the smallest mean square error (MSE) was three-layer ANN with tangent sigmoid transfer function (tansig) at hidden layer with 14 neurons, linear transfer function (purelin) at output layer and Levenberg-Marquardt backpropagation training algorithm (LMA). ANN predicted results are very close to the experimental results with correlation coefficient (R(2)) of 0.997 and MSE 0.000376. The sensitivity analysis showed that all studied variables (reaction time, H(2)O(2)/COD molar ratio, H(2)O(2)/Fe(2+) molar ratio, pH and antibiotics concentration) have strong effect on antibiotic degradation in terms of COD removal. In addition, H(2)O(2)/Fe(2+) molar ratio is the most influential parameter with relative importance of 25.8%. The results showed that neural network modeling could effectively predict and simulate the behavior of the Fenton process.

  3. Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron.

    PubMed

    Costalago Meruelo, Alicia; Simpson, David M; Veres, Sandor M; Newland, Philip L

    2016-03-01

    Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to displacement of a sensory organ, the femoral chordotonal organ, which monitors movements of the tibia relative to the femur of the leg. The aim of the study was threefold: first to determine the potential value of ANNs as tools to model and investigate neural networks, second to understand the generalisation properties of ANNs across individuals and to different input signals and third, to understand individual differences in responses of an identified neuron. A metaheuristic algorithm was developed to design the ANN architectures. The performance of the models generated by the ANNs was compared with those generated through previous mathematical models of the same neuron. The results suggest that ANNs are significantly better than LNL and Wiener models in predicting specific neural responses to Gaussian White Noise, but not significantly different when tested with sinusoidal inputs. They are also able to predict responses of the same neuron in different individuals irrespective of which animal was used to develop the model, although notable differences between some individuals were evident.

  4. Temporal and Spatial prediction of groundwater levels using Artificial Neural Networks, Fuzzy logic and Kriging interpolation.

    NASA Astrophysics Data System (ADS)

    Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.

    2014-05-01

    The purpose of this study is to examine the use of Artificial Neural Networks (ANN) combined with kriging interpolation method, in order to simulate the hydraulic head both spatially and temporally. Initially, ANNs are used for the temporal simulation of the hydraulic head change. The results of the most appropriate ANNs, determined through a fuzzy logic system, are used as an input for the kriging algorithm where the spatial simulation is conducted. The proposed algorithm is tested in an area located across Isar River in Bayern, Germany and covers an area of approximately 7800 km2. The available data extend to a time period from 1/11/2008 to 31/10/2012 (1460 days) and include the hydraulic head at 64 wells, temperature and rainfall at 7 weather stations and surface water elevation at 5 monitoring stations. One feedforward ANN was trained for each of the 64 wells, where hydraulic head data are available, using a backpropagation algorithm. The most appropriate input parameters for each wells' ANN are determined considering their proximity to the measuring station, as well as their statistical characteristics. For the rainfall, the data for two consecutive time lags for best correlated weather station, as well as a third and fourth input from the second best correlated weather station, are used as an input. The surface water monitoring stations with the three best correlations for each well are also used in every case. Finally, the temperature for the best correlated weather station is used. Two different architectures are considered and the one with the best results is used henceforward. The output of the ANNs corresponds to the hydraulic head change per time step. These predictions are used in the kriging interpolation algorithm. However, not all 64 simulated values should be used. The appropriate neighborhood for each prediction point is constructed based not only on the distance between known and prediction points, but also on the training and testing error of

  5. Forecasting Stock Exchange Movements Using Artificial Neural Network Models and Hybrid Models

    NASA Astrophysics Data System (ADS)

    Güreşen, Erkam; Kayakutlu, Gülgün

    Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models; recurrent neural network (RNN), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) to extract new input variables. The comparison for each model is done in two view points: MSE and MAD using real exchange daily rate values of Istanbul Stock Exchange (ISE) index XU10).

  6. Prediction of road traffic death rate using neural networks optimised by genetic algorithm.

    PubMed

    Jafari, Seyed Ali; Jahandideh, Sepideh; Jahandideh, Mina; Asadabadi, Ebrahim Barzegari

    2015-01-01

    Road traffic injuries (RTIs) are realised as a main cause of public health problems at global, regional and national levels. Therefore, prediction of road traffic death rate will be helpful in its management. Based on this fact, we used an artificial neural network model optimised through Genetic algorithm to predict mortality. In this study, a five-fold cross-validation procedure on a data set containing total of 178 countries was used to verify the performance of models. The best-fit model was selected according to the root mean square errors (RMSE). Genetic algorithm, as a powerful model which has not been introduced in prediction of mortality to this extent in previous studies, showed high performance. The lowest RMSE obtained was 0.0808. Such satisfactory results could be attributed to the use of Genetic algorithm as a powerful optimiser which selects the best input feature set to be fed into the neural networks. Seven factors have been known as the most effective factors on the road traffic mortality rate by high accuracy. The gained results displayed that our model is very promising and may play a useful role in developing a better method for assessing the influence of road traffic mortality risk factors.

  7. A study of creep-fatigue life prediction using an artificial neural network

    NASA Astrophysics Data System (ADS)

    Kwon, Young Il; Lim, Byeong Soo

    2001-07-01

    In this study, using AISI 316 stainless steel, creep-fatigue tests were carried out under various test conditions (different total strain ranges and hold times) to verify the applicability of the artificial neural network method to creep-fatigue life prediction. Life prediction was also made by the modified Coffin-Manson method and the modified Ostegren method using 21 data points out of a total 27 experimental data points. The six verification data points were carefully chosen for the purpose of evaluating the predictability of each method. The predicted lives were compared with the experimental results and the following conclusions were obtained within the scope of this study. While the creep-fatigue life prediction by the modified Coffin-Manson method and the modified Ostegren method had average errors of 35.8% and 47.7% respectively, the artificial neural network method had only 15.6%. As a result, the artificial neural network method with the adaptive learning rate was found to be far more accurate and effective than any of the others. The validity of the artificial neural network method for life prediction checked with the six verification data points also proved to be very satisfactory.

  8. Fractional Fokker-Planck Equations and Artificial Neural Networks for Stochastic Control of Tokamak

    NASA Astrophysics Data System (ADS)

    Rastovic, Danilo

    2008-09-01

    The general form of description of Kolmogorov-Arnold-Moser (KAM) theorem in controlled plasma fusion, is obtained via the theory of artificial fuzzy neural networks. Without of the global maximum entropy principle, the complexity function is used for the Monte Carlo simulations.

  9. Reconstructing missing daily precipitation data using regression trees and artificial neural networks

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Incomplete meteorological data has been a problem in environmental modeling studies. The objective of this work was to develop a technique to reconstruct missing daily precipitation data in the central part of Chesapeake Bay Watershed using regression trees (RT) and artificial neural networks (ANN)....

  10. Artificial neural network estimation of soil erosion and nutrient concentrations in runoff from land application areas

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The transport of sediment and nutrients from land application areas is an environmental concern. New methods are needed for estimating soil and nutrient concentrations of runoff from cropland areas on which manure is applied. Artificial Neural Networks (ANN) trained with a Backpropagation (BP) algor...

  11. Artificial Neural Networks: A New Approach for Predicting Application Behavior. AIR 2001 Annual Forum Paper.

    ERIC Educational Resources Information Center

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    This paper examines how predictive modeling can be used to study application behavior. A relatively new technique, artificial neural networks (ANNs), was applied to help predict which students were likely to get into a large Research I university. Data were obtained from a university in Iowa. Two cohorts were used, each containing approximately…

  12. Predicting Item Difficulty in a Reading Comprehension Test with an Artificial Neural Network.

    ERIC Educational Resources Information Center

    Perkins, Kyle; And Others

    1995-01-01

    This article reports the results of using a three-layer back propagation artificial neural network to predict item difficulty in a reading comprehension test. Three classes of variables were examined: text structure, propositional analysis, and cognitive demand. Results demonstrate that the networks can consistently predict item difficulty. (JL)

  13. The Use of Artificial Neural Networks to Estimate Speech Intelligibility from Acoustic Variables: A Preliminary Analysis.

    ERIC Educational Resources Information Center

    Metz, Dale Evan; And Others

    1992-01-01

    A preliminary scheme for estimating the speech intelligibility of hearing-impaired speakers from acoustic parameters, using a computerized artificial neural network to process mathematically the acoustic input variables, is outlined. Tests with 60 hearing-impaired speakers found the scheme to be highly accurate in identifying speakers separated by…

  14. Predicting Item Difficulty in a Reading Comprehension Test with an Artificial Neural Network.

    ERIC Educational Resources Information Center

    Perkins, Kyle; And Others

    This paper reports the results of using a three-layer backpropagation artificial neural network to predict item difficulty in a reading comprehension test. Two network structures were developed, one with and one without a sigmoid function in the output processing unit. The data set, which consisted of a table of coded test items and corresponding…

  15. Statistical Classification for Cognitive Diagnostic Assessment: An Artificial Neural Network Approach

    ERIC Educational Resources Information Center

    Cui, Ying; Gierl, Mark; Guo, Qi

    2016-01-01

    The purpose of the current investigation was to describe how the artificial neural networks (ANNs) can be used to interpret student performance on cognitive diagnostic assessments (CDAs) and evaluate the performances of ANNs using simulation results. CDAs are designed to measure student performance on problem-solving tasks and provide useful…

  16. Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

    NASA Technical Reports Server (NTRS)

    Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

    1995-01-01

    This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system.

  17. An Artificial Neural Network-Based Decision-Support System for Integrated Network Security

    DTIC Science & Technology

    2014-09-01

    AN ARTIFICIAL NEURAL NETWORK-BASED DECISION-SUPPORT SYSTEM FOR INTEGRATED NETWORK SECURITY THESIS ...The views expressed in this thesis are those of the author and do not reflect the official policy or position of the United States Air Force... THESIS Presented to the Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force

  18. Application of an artificial neural network and morphing techniques in the redesign of dysplastic trochlea.

    PubMed

    Cho, Kyung Jin; Müller, Jacobus H; Erasmus, Pieter J; DeJour, David; Scheffer, Cornie

    2014-01-01

    Segmentation and computer assisted design tools have the potential to test the validity of simulated surgical procedures, e.g., trochleoplasty. A repeatable measurement method for three dimensional femur models that enables quantification of knee parameters of the distal femur is presented. Fifteen healthy knees are analysed using the method to provide a training set for an artificial neural network. The aim is to use this artificial neural network for the prediction of parameter values that describe the shape of a normal trochlear groove geometry. This is achieved by feeding the artificial neural network with the unaffected parameters of a dysplastic knee. Four dysplastic knees (Type A through D) are virtually redesigned by way of morphing the groove geometries based on the suggested shape from the artificial neural network. Each of the four resulting shapes is analysed and compared to its initial dysplastic shape in terms of three anteroposterior dimensions: lateral, central and medial. For the four knees the trochlear depth is increased, the ventral trochlear prominence reduced and the sulcus angle corrected to within published normal ranges. The results show a lateral facet elevation inadequate, with a sulcus deepening or a depression trochleoplasty more beneficial to correct trochlear dysplasia.

  19. Predicting Final GPA of Graduate School Students: Comparing Artificial Neural Networking and Simultaneous Multiple Regression

    ERIC Educational Resources Information Center

    Anderson, Joan L.

    2006-01-01

    Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models…

  20. Automatic Keyword Identification by Artificial Neural Networks Compared to Manual Identification by Users of Filtering Systems.

    ERIC Educational Resources Information Center

    Boger, Zvi; Kuflik, Tsvi; Shoval, Peretz; Shapira, Bracha

    2001-01-01

    Discussion of information filtering (IF) and information retrieval focuses on the use of an artificial neural network (ANN) as an alternative method for both IF and term selection and compares its effectiveness to that of traditional methods. Results show that the ANN relevance prediction out-performs the prediction of an IF system. (Author/LRW)