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1

Evapotranspiration estimation by two different neuro-fuzzy inference systems

NASA Astrophysics Data System (ADS)

SummaryThe potential of two different adaptive network-based fuzzy inference systems (ANFIS) based neuro-fuzzy systems in modeling of reference evapotranspiration (ET 0) are investigated in this paper. The two neuro-fuzzy systems are: (1) grid partition based fuzzy inference system, named G-ANFIS, and (2) subtractive clustering based fuzzy inference system, named S-ANFIS. In the first part of the study, the performance of resultant FIS was compared and the effect of parameters was investigated. Various daily climatic data, that is, solar radiation, air temperature, relative humidity and wind speed from Santa Monica, in Los Angeles, USA, are used as inputs to the FIS models so as to estimate ET 0 obtained using the FAO-56 Penman-Monteith equation. In the second part of the study, the estimates of the FIS models are compared with those of artificial neural network (ANN) approach, namely, multi-layer perceptron (MLP), and three empirical models, namely, CIMIS Penman, Hargreaves and Ritchie methods. Root mean square error, mean absolute error and determination coefficient statistics are used as comparing criteria for the evaluation of the models' performances. Based on the comparisons, it is found that the S-ANFIS model yields plausible accuracy with fewer amounts of computations as compared to the G-ANFIS and MLP models in modeling the ET 0 process.

Cobaner, Murat

2011-02-01

2

Adaptive Neuro-Fuzzy Inference System for mid term prognostic error stabilization

prediction errors appears to be essential. For that purpose a neuro-fuzzy predictor based on the ANFIS model Introduction Maintenance activity combines dierent methods, tools and techniques to reduce costs while justied and an illustra- tion based on the adaptive neuro-fuzzy inference system is given. The proposed

Paris-Sud XI, UniversitÃ© de

3

A new learning algorithm for a fully connected neuro-fuzzy inference system.

A traditional neuro-fuzzy system is transformed into an equivalent fully connected three layer neural network (NN), namely, the fully connected neuro-fuzzy inference systems (F-CONFIS). The F-CONFIS differs from traditional NNs by its dependent and repeated weights between input and hidden layers and can be considered as the variation of a kind of multilayer NN. Therefore, an efficient learning algorithm for the F-CONFIS to cope these repeated weights is derived. Furthermore, a dynamic learning rate is proposed for neuro-fuzzy systems via F-CONFIS where both premise (hidden) and consequent portions are considered. Several simulation results indicate that the proposed approach achieves much better accuracy and fast convergence. PMID:25291730

Chen, C L Philip; Wang, Jing; Wang, Chi-Hsu; Chen, Long

2014-10-01

4

To date, cancer of uterine cervix is still a leading cause of cancer-related deaths in women worldwide. The current methods (i.e., Pap smear and liquid-based cytology (LBC)) to screen for cervical cancer are time-consuming and dependent on the skill of the cytopathologist and thus are rather subjective. Therefore, this paper presents an intelligent computer vision system to assist pathologists in overcoming these problems and, consequently, produce more accurate results. The developed system consists of two stages. In the first stage, the automatic features extraction (AFE) algorithm is performed. In the second stage, a neuro-fuzzy model called multiple adaptive neuro-fuzzy inference system (MANFIS) is proposed for recognition process. The MANFIS contains a set of ANFIS models which are arranged in parallel combination to produce a model with multi-input-multioutput structure. The system is capable of classifying cervical cell image into three groups, namely, normal, low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL). The experimental results prove the capability of the AFE algorithm to be as effective as the manual extraction by human experts, while the proposed MANFIS produces a good classification performance with 94.2% accuracy. PMID:24707316

Al-batah, Mohammad Subhi; Isa, Nor Ashidi Mat; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi

2014-01-01

5

Review of Medical Image Classification using the Adaptive Neuro-Fuzzy Inference System

Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and reducing their workload. This paper reviews the application of the adaptive neuro-fuzzy inference system (ANFIS) as a classifier in medical image classification during the past 16 years. ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of an FIS with the learning power of artificial neural networks. The objective of ANFIS is to integrate the best features of fuzzy systems and neural networks. A brief comparison with other classifiers, main advantages and drawbacks of this classifier are investigated. PMID:23493054

Hosseini, Monireh Sheikh; Zekri, Maryam

2012-01-01

6

This article presents a comparison of two adaptive neuro-fuzzy inference systems (ANFIS)-based neuro-fuzzy models applied for modeling dissolved oxygen (DO) concentration. The two models are developed using experimental data collected from the bottom (USGS station no: 420615121533601) and top (USGS station no: 420615121533600) stations at Klamath River at site KRS12a nr Rock Quarry, Oregon, USA. The input variables used for the ANFIS models are water pH, temperature, specific conductance, and sensor depth. Two ANFIS-based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system, named ANFIS_GRID, and (2) subtractive-clustering-based fuzzy inference system, named ANFIS_SUB. In both models, 60 % of the data set was randomly assigned to the training set, 20 % to the validation set, and 20 % to the test set. The ANFIS results are compared with multiple linear regression models. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models for DO concentration modeling. PMID:24057665

Heddam, Salim

2014-01-01

7

Training Hybrid Neuro-Fuzzy System to Infer Permeability in Wells on Maracaibo Lake, Venezuela

The high accuracy on inferrring of rocks properties, such as permeability ($k$), is a very useful study in the analysis of wells. This has led to development and use of empirical equations like Tixier, Timur, among others. In order to improve the inference of permeability we used a hybrid Neuro-Fuzzy System (NFS). The NFS allowed us to infer permeability of well, from data of porosity ($\\phi$) and water saturation ($Sw$). The work was performed with data from wells VCL-1021 (P21) and VCL-950 (P50), Block III, Maracaibo Lake, Venezuela. We evaluated the NFS equations ($k_{P50,i}(\\phi_i,Sw_i)$) with neighboring well data ($P21$), in order to verify the validity of the equations in the area. We have used ANFIS in MatLab.

Hurtado, Nuri; Torres, Julio

2014-01-01

8

Proton Exchange Membrane Fuel Cell degradation prediction based on Adaptive Neuro Fuzzy Inference online XX XX XXXX Keywords: Proton Exchange Membrane fuel cell degradation, Prognostic and Health nominal operating condition of a PEM fuel cell stack. It proposes a methodology based on Adaptive Neuro

Paris-Sud XI, UniversitÃ© de

9

Memristive Neuro-Fuzzy System.

In this paper, a novel neuro-fuzzy computing system is proposed where its learning is based on the creation of fuzzy relations by using a new implication method without utilizing any exact mathematical techniques. Then, a simple memristor crossbar-based analog circuit is designed to implement this neuro-fuzzy system which offers very interesting properties. In addition to high connectivity between neurons and being fault tolerant, all synaptic weights in our proposed method are always non-negative, and there is no need to adjust them precisely. Finally, this structure is hierarchically expandable, and it can do fuzzy operations in real time since it is implemented through analog circuits. Simulation results confirm the efficiency and applicability of our neuro-fuzzy computing system. They also indicate that this system can be a good candidate to be used for creating artificial brain. PMID:22851278

Merrikh-Bayat, Farnood; Bagheri Shouraki, Saeed

2012-07-25

10

Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands.

Monitoring large-scale treatment wetlands is costly and time-consuming, but required by regulators. Some analytical results are available only after 5 days or even longer. Thus, adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the effluent concentrations of 5-day biochemical oxygen demand (BOD5) and NH4-N from a full-scale integrated constructed wetland (ICW) treating domestic wastewater. The ANFIS models were developed and validated with a 4-year data set from the ICW system. Cost-effective, quicker and easier to measure variables were selected as the possible predictors based on their goodness of correlation with the outputs. A self-organizing neural network was applied to extract the most relevant input variables from all the possible input variables. Fuzzy subtractive clustering was used to identify the architecture of the ANFIS models and to optimize fuzzy rules, overall, improving the network performance. According to the findings, ANFIS could predict the effluent quality variation quite strongly. Effluent BOD5 and NH4-N concentrations were predicted relatively accurately by other effluent water quality parameters, which can be measured within a few hours. The simulated effluent BOD5 and NH4-N concentrations well fitted the measured concentrations, which was also supported by relatively low mean squared error. Thus, ANFIS can be useful for real-time monitoring and control of ICW systems. PMID:25607665

Dzakpasu, Mawuli; Scholz, Miklas; McCarthy, Valerie; Jordan, Siobhán; Sani, Abdulkadir

2015-01-01

11

Application of an adaptive neuro-fuzzy inference system to ground subsidence hazard mapping

NASA Astrophysics Data System (ADS)

We constructed hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok City, Korea, using an adaptive neuro-fuzzy inference system (ANFIS) and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, and ground subsidence maps. An attribute database was also constructed from field investigations and reports on existing ground subsidence areas at the study site. Five major factors causing ground subsidence were extracted: (1) depth of drift; (2) distance from drift; (3) slope gradient; (4) geology; and (5) land use. The adaptive ANFIS model with different types of membership functions (MFs) was then applied for ground subsidence hazard mapping in the study area. Two ground subsidence hazard maps were prepared using the different MFs. Finally, the resulting ground subsidence hazard maps were validated using the ground subsidence test data which were not used for training the ANFIS. The validation results showed 95.12% accuracy using the generalized bell-shaped MF model and 94.94% accuracy using the Sigmoidal2 MF model. These accuracy results show that an ANFIS can be an effective tool in ground subsidence hazard mapping. Analysis of ground subsidence with the ANFIS model suggests that quantitative analysis of ground subsidence near AUCMs is possible.

Park, Inhye; Choi, Jaewon; Jin Lee, Moung; Lee, Saro

2012-11-01

12

NASA Astrophysics Data System (ADS)

Modified 9Cr-1Mo ferritic steel is used as a structural material for steam generator components of power plants. Generally, tungsten inert gas (TIG) welding is preferred for welding of these steels in which the depth of penetration achievable during autogenous welding is limited. Therefore, activated flux TIG (A-TIG) welding, a novel welding technique, has been developed in-house to increase the depth of penetration. In modified 9Cr-1Mo steel joints produced by the A-TIG welding process, weld bead width, depth of penetration, and heat-affected zone (HAZ) width play an important role in determining the mechanical properties as well as the performance of the weld joints during service. To obtain the desired weld bead geometry and HAZ width, it becomes important to set the welding process parameters. In this work, adaptative neuro fuzzy inference system is used to develop independent models correlating the welding process parameters like current, voltage, and torch speed with weld bead shape parameters like depth of penetration, bead width, and HAZ width. Then a genetic algorithm is employed to determine the optimum A-TIG welding process parameters to obtain the desired weld bead shape parameters and HAZ width.

Gowtham, K. N.; Vasudevan, M.; Maduraimuthu, V.; Jayakumar, T.

2011-04-01

13

Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents.

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electrocardiogram (ECG) signals. Decision making was performed in two stages: feature extraction by computation of Lyapunov exponents and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Four types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, and atrial fibrillation beat) obtained from the PhysioBank database were classified by four ANFIS classifiers. To improve diagnostic accuracy, the fifth ANFIS classifier (combining ANFIS) was trained using the outputs of the four ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on classification of the ECG signals were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. PMID:19084286

Ubeyli, Elif Derya

2009-03-01

14

Prediction of Scour Depth around Bridge Piers using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)

NASA Astrophysics Data System (ADS)

Earth's surface is continuously shaped due to the action of geophysical flows. Erosion due to the flow of water in river systems has been identified as a key problem in preserving ecological health of river systems but also a threat to our built environment and critical infrastructure, worldwide. As an example, it has been estimated that a major reason for bridge failure is due to scour. Even though the flow past bridge piers has been investigated both experimentally and numerically, and the mechanisms of scouring are relatively understood, there still lacks a tool that can offer fast and reliable predictions. Most of the existing formulas for prediction of bridge pier scour depth are empirical in nature, based on a limited range of data or for piers of specific shape. In this work, the application of a Machine Learning model that has been successfully employed in Water Engineering, namely an Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to estimate the scour depth around bridge piers. In particular, various complexity architectures are sequentially built, in order to identify the optimal for scour depth predictions, using appropriate training and validation subsets obtained from the USGS database (and pre-processed to remove incomplete records). The model has five variables, namely the effective pier width (b), the approach velocity (v), the approach depth (y), the mean grain diameter (D50) and the skew to flow. Simulations are conducted with data groups (bed material type, pier type and shape) and different number of input variables, to produce reduced complexity and easily interpretable models. Analysis and comparison of the results indicate that the developed ANFIS model has high accuracy and outstanding generalization ability for prediction of scour parameters. The effective pier width (as opposed to skew to flow) is amongst the most relevant input parameters for the estimation.

Valyrakis, Manousos; Zhang, Hanqing

2014-05-01

15

NASA Astrophysics Data System (ADS)

The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and ANFIS) has limited success.

Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.

2015-01-01

16

Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Finding pathogenic factors and how they are spread in the environment has become a global demand, recently. Cutaneous Leishmaniasis (CL) created by Leishmania is a special parasitic disease which can be passed on to human through phlebotomus of vector-born. Studies show that economic situation, cultural issues, as well as environmental and ecological conditions can affect the prevalence of this disease. In this study, Data Mining is utilized in order to predict CL prevalence rate and obtain a risk map. This case is based on effective environmental parameters on CL and a Neuro-Fuzzy system was also used. Learning capacity of Neuro-Fuzzy systems in neural network on one hand and reasoning power of fuzzy systems on the other, make it very efficient to use. In this research, in order to predict CL prevalence rate, an adaptive Neuro-fuzzy inference system with fuzzy inference structure of fuzzy C Means clustering was applied to determine the initial membership functions. Regarding to high incidence of CL in Ilam province, counties of Ilam, Mehran, and Dehloran have been examined and evaluated. The CL prevalence rate was predicted in 2012 by providing effective environmental map and topography properties including temperature, moisture, annual, rainfall, vegetation and elevation. Results indicate that the model precision with fuzzy C Means clustering structure rises acceptable RMSE values of both training and checking data and support our analyses. Using the proposed data mining technology, the pattern of disease spatial distribution and vulnerable areas become identifiable and the map can be used by experts and decision makers of public health as a useful tool in management and optimal decision-making.

Akhavan, P.; Karimi, M.; Pahlavani, P.

2014-10-01

17

Ultrasonic drilling of hard and brittle ceramic materials is a mechanical material removal process which is complex in nature and generally characterised by comparatively slow material removal rates. A precise modeling approach is required to simulate the material removal of ceramics by ultrasonic drilling to recompense the affect of sluggish material removal rates. The present paper uses Adaptive Neuro-Fuzzy Inference

Simranpreet Singh Gill; Jagdev Singh

2010-01-01

18

Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning

ERIC Educational Resources Information Center

With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy…

Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun

2012-01-01

19

This study represents a first attempt at applying a fuzzy inference system (FIS) and an adaptive neuro-fuzzy inference system (ANFIS) to the field of aquatic biomonitoring for classification of the dosage and time of benzo[a]pyrene (BaP) injection through selected biomarkers in African catfish (Clarias gariepinus). Fish were injected either intramuscularly (i.m.) or intraperitoneally (i.p.) with BaP. Hepatic glutathione S-transferase (GST) activities, relative visceral fat weights (LSI), and four biliary fluorescent aromatic compounds (FACs) concentrations were used as the inputs in the modeling study. Contradictory rules in FIS and ANFIS models appeared after conversion of bioassay results into human language (rule-based system). A "data trimming" approach was proposed to eliminate the conflicts prior to fuzzification. However, the model produced was relevant only to relatively low exposures to BaP, especially through the i.m. route of exposure. Furthermore, sensitivity analysis was unable to raise the classification rate to an acceptable level. In conclusion, FIS and ANFIS models have limited applications in the field of fish biomarker studies. PMID:22752811

Karami, Ali; Keiter, Steffen; Hollert, Henner; Courtenay, Simon C

2013-03-01

20

This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order non-linear hydrogenerator model.

Djukanovic, M.B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems] [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M.S. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering] [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Vesovic, B.V. [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control] [Inst. Mihajlo Pupin, Belgrade (Yugoslavia). Dept. of Automatic Control; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)] [Electric Power Research Inst., Palo Alto, CA (United States)

1997-12-01

21

Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS) is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR) models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A) and electronegativity (?) of flavylium cation, and ionization potential (I) of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively. PMID:25153627

Jhin, Changho; Hwang, Keum Taek

2014-01-01

22

In external radiotherapy of dynamic targets such as lung and breast cancers, accurate correlation models are utilized to extract real time tumor position by means of external surrogates in correlation with the internal motion of tumors. In this study, a correlation method based on the neuro-fuzzy model is proposed to correlate the input external motion data with internal tumor motion estimation in real-time mode, due to its robustness in motion tracking. An initial test of the performance of this model was reported in our previous studies. In this work by implementing some modifications it is resulted that ANFIS is still robust to track tumor motion more reliably by reducing the motion estimation error remarkably. After configuring new version of our ANFIS model, its performance was retrospectively tested over ten patients treated with Synchrony Cyberknife system. In order to assess the performance of our model, the predicted tumor motion as model output was compared with respect to the state of the art model. Final analyzed results show that our adaptive neuro-fuzzy model can reduce tumor tracking errors more significantly, as compared with ground truth database and even tumor tracking methods presented in our previous works. PMID:25412886

Torshabi, Ahmad Esmaili

2014-12-01

23

be used as a range extender in an electric car, where a liquid fuel is a great advantage as opposed. The system is used as a battery charger and the fuel cell current can therefore be different to the reference implemented with the system of the electric vehicle. The presented method for controlling the reformer

Andreasen, SÃ¸ren Juhl

24

Intelligent Transformer Monitoring System Utilizing Neuro-Fuzzy

Intelligent Transformer Monitoring System Utilizing Neuro-Fuzzy Technique Approach Intelligent Center Intelligent Transformer Monitoring System Utilizing Neuro-Fuzzy Technique Approach Final Project transformers and circuit breakers off-line, in order to assess whether the equipment is operating normally

25

NASA Astrophysics Data System (ADS)

This work presents a simulation based study by Monte Carlo which uses two adaptive neuro-fuzzy inference systems (ANFIS) for cross talk compensation of simultaneous 99mTc/201Tl dual-radioisotope SPECT imaging. We have compared two neuro-fuzzy systems based on fuzzy c-means (FCM) and subtractive (SUB) clustering. Our approach incorporates eight energy-windows image acquisition from 28 keV to 156 keV and two main photo peaks of 201Tl (77±10% keV) and 99mTc (140±10% keV). The Geant4 application in emission tomography (GATE) is used as a Monte Carlo simulator for three cylindrical and a NURBS Based Cardiac Torso (NCAT) phantom study. Three separate acquisitions including two single-isotopes and one dual isotope were performed in this study. Cross talk and scatter corrected projections are reconstructed by an iterative ordered subsets expectation maximization (OSEM) algorithm which models the non-uniform attenuation in the projection/back-projection. ANFIS-FCM/SUB structures are tuned to create three to sixteen fuzzy rules for modeling the photon cross-talk of the two radioisotopes. Applying seven to nine fuzzy rules leads to a total improvement of the contrast and the bias comparatively. It is found that there is an out performance for the ANFIS-FCM due to its acceleration and accurate results.

Heidary, Saeed; Setayeshi, Saeed

2015-01-01

26

NASA Astrophysics Data System (ADS)

In HVDC Light transmission systems, converter control is one of the major fields of present day research works. In this paper, fuzzy logic controller is utilized for controlling both the converters of the space vector pulse width modulation (SVPWM) based HVDC Light transmission systems. Due to its complexity in the rule base formation, an intelligent controller known as adaptive neuro fuzzy inference system (ANFIS) controller is also introduced in this paper. The proposed ANFIS controller changes the PI gains automatically for different operating conditions. A hybrid learning method which combines and exploits the best features of both the back propagation algorithm and least square estimation method is used to train the 5-layer ANFIS controller. The performance of the proposed ANFIS controller is compared and validated with the fuzzy logic controller and also with the fixed gain conventional PI controller. The simulations are carried out in the MATLAB/SIMULINK environment. The results reveal that the proposed ANFIS controller is reducing power fluctuations at both the converters. It also improves the dynamic performance of the test power system effectively when tested for various ac fault conditions.

Ajay Kumar, M.; Srikanth, N. V.

2014-03-01

27

NASA Astrophysics Data System (ADS)

The battery management system (BMS) of a battery-electric road vehicle must ensure an optimal operation of the electrochemical storage system to guarantee for durability and reliability. In particular, the BMS must provide precise information about the battery's state-of-functionality, i.e. how much dis-/charging power can the battery accept at current state and condition while at the same time preventing it from operating outside its safe operating area. These critical limits have to be calculated in a predictive manner, which serve as a significant input factor for the supervising vehicle energy management (VEM). The VEM must provide enough power to the vehicle's drivetrain for certain tasks and especially in critical driving situations. Therefore, this paper describes a new approach which can be used for state-of-available-power estimation with respect to lowest/highest cell voltage prediction using an adaptive neuro-fuzzy inference system (ANFIS). The estimated voltage for a given time frame in the future is directly compared with the actual voltage, verifying the effectiveness and accuracy of a relative voltage prediction error of less than 1%. Moreover, the real-time operating capability of the proposed algorithm was verified on a battery test bench while running on a real-time system performing voltage prediction.

Fleischer, Christian; Waag, Wladislaw; Bai, Ziou; Sauer, Dirk Uwe

2013-12-01

28

Recurrent neuro-fuzzy system for fault detection and isolation in nuclear reactors

This paper presents an application of recurrent neuro-fuzzy systems to fault detection and isolation in nuclear reactors. A general framework is adopted, in which a fuzzification module is linked to an inference module that is actually a neural network adapted to the recognition of the dynamic evolution of process variables and related faults. Process data is fuzzified in order to

Alexandre Evsukoff; Sylviane Gentil

2005-01-01

29

NASA Astrophysics Data System (ADS)

The purpose of this study was to investigate the associations between gait performance, postural stability, and depression in patients with Parkinson's disease (PD) by using an adaptive neuro-fuzzy inference system (ANFIS). Twenty-two idiopathic PD patients were assessed during outpatient physical therapy by using three clinical tests: the Berg balance scale (BBS), Dynamic gait index (DGI), and Geriatric depression scale (GDS). Scores were determined from clinical observation and patient interviews, and associations among gait performance, postural stability, and depression in this PD population were evaluated. The DGI showed significant positive correlation with the BBS scores, and negative correlation with the GDS score. We assessed the relationship between the BBS score and the DGI results by using a multiple regression analysis. In this case, the GDS score was not significantly associated with the DGI, but the BBS and DGI results were. Strikingly, the ANFIS-estimated value of the DGI, based on the BBS and the GDS scores, significantly correlated with the walking ability determined by using the DGI in patients with Parkinson's disease. These findings suggest that the ANFIS techniques effectively reflect and explain the multidirectional phenomena or conditions of gait performance in patients with PD.

Woo, Youngkeun; Lee, Juwon; Hwang, Sujin; Hong, Cheol Pyo

2013-03-01

30

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. PMID:24566279

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

2014-01-01

31

NASA Astrophysics Data System (ADS)

The aim of this study is to predict the peak particle velocity (PPV) values from both presently constructed simple regression model and fuzzy-based model. For this purpose, vibrations induced by bench blasting operations were measured in an open-pit mine operated by the most important magnesite producing company (MAS) in Turkey. After gathering the ordered pairs of distance and PPV values, the site-specific parameters were determined using traditional regression method. Also, an attempt has been made to investigate the applicability of a relatively new soft computing method called as the adaptive neuro-fuzzy inference system (ANFIS) to predict PPV. To achieve this objective, data obtained from the blasting measurements were evaluated by constructing an ANFIS-based prediction model. The distance from the blasting site to the monitoring stations and the charge weight per delay were selected as the input parameters of the constructed model, the output parameter being the PPV. Valid for the site, the PPV prediction capability of the constructed ANFIS-based model has proved to be successful in terms of statistical performance indices such as variance account for (VAF), root mean square error (RMSE), standard error of estimation, and correlation between predicted and measured PPV values. Also, using these statistical performance indices, a prediction performance comparison has been made between the presently constructed ANFIS-based model and the classical regression-based prediction method, which has been widely used in the literature. Although the prediction performance of the regression-based model was high, the comparison has indicated that the proposed ANFIS-based model exhibited better prediction performance than the classical regression-based model.

Iphar, Melih; Yavuz, Mahmut; Ak, Hakan

2008-11-01

32

In this research, a novel adsorbent gold nanoparticle loaded on activated carbon (Au-NP-AC) was synthesized by ultrasound energy as a low cost routing protocol. Subsequently, this novel material characterization and identification followed by different techniques such as scanning electron microscope(SEM), Brunauer-Emmett-Teller(BET) and transmission electron microscopy (TEM) analysis. Unique properties such as high BET surface area (>1229.55m(2)/g) and low pore size (<22.46Å) and average particle size lower than 48.8Å in addition to high reactive atoms and the presence of various functional groups make it possible for efficient removal of 1,3,4-thiadiazole-2,5-dithiol (TDDT). Generally, the influence of variables, including the amount of adsorbent, initial pollutant concentration, contact time on pollutants removal percentage has great effect on the removal percentage that their influence was optimized. The optimum parameters for adsorption of 1,3,4-thiadiazole-2, 5-dithiol onto gold nanoparticales-activated carbon were 0.02g adsorbent mass, 10mgL(-1) initial 1,3,4-thiadiazole-2,5-dithiol concentration, 30min contact time and pH 7. The Adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, have been applied for prediction of removal of 1,3,4-thiadiazole-2,5-dithiol using gold nanoparticales-activated carbon (Au-NP-AC) in a batch study. The input data are included adsorbent dosage (g), contact time (min) and pollutant concentration (mg/l). The coefficient of determination (R(2)) and mean squared error (MSE) for the training data set of optimal ANFIS model were achieved to be 0.9951 and 0.00017, respectively. These results show that ANFIS model is capable of predicting adsorption of 1,3,4-thiadiazole-2,5-dithiol using Au-NP-AC with high accuracy in an easy, rapid and cost effective way. PMID:24858196

Ghaedi, M; Hosaininia, R; Ghaedi, A M; Vafaei, A; Taghizadeh, F

2014-10-15

33

NASA Astrophysics Data System (ADS)

Data driven models are very useful for river flow forecasting when the underlying physical relationships are not fully understand, but it is not clear whether these data driven models still have a good performance in the small river basin of semiarid mountain regions where have complicated topography. In this study, the potential of three different data driven methods, artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for forecasting river flow in the semiarid mountain region, northwestern China. The models analyzed different combinations of antecedent river flow values and the appropriate input vector has been selected based on the analysis of residuals. The performance of the ANN, ANFIS and SVM models in training and validation sets are compared with the observed data. The model which consists of three antecedent values of flow has been selected as the best fit model for river flow forecasting. To get more accurate evaluation of the results of ANN, ANFIS and SVM models, the four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency coefficient (NS) and mean absolute relative error (MARE), were employed to evaluate the performances of various models developed. The results indicate that the performance obtained by ANN, ANFIS and SVM in terms of different evaluation criteria during the training and validation period does not vary substantially; the performance of the ANN, ANFIS and SVM models in river flow forecasting was satisfactory. A detailed comparison of the overall performance indicated that the SVM model performed better than ANN and ANFIS in river flow forecasting for the validation data sets. The results also suggest that ANN, ANFIS and SVM method can be successfully applied to establish river flow with complicated topography forecasting models in the semiarid mountain regions.

He, Zhibin; Wen, Xiaohu; Liu, Hu; Du, Jun

2014-02-01

34

NASA Astrophysics Data System (ADS)

This paper aims to study the relationship between climatic large-scale synoptic patterns and rainfall in Khorasan Razavi Province. The adaptive neural-fuzzy inference system was used in this study to predict rainfall in the period between Jan and March in Khorasan Razavi Province. We first analyzed the relationship between average regional rainfall and the changes in synoptic patterns including sea-level pressure, sea-level pressure difference, sea-level temperature, temperature difference between sea level and 1000-mb level, the temperature of 700-mb level, the thickness between 500 and 1000-mb levels, the relative humidity of 300-mb level, Outgoing Long Wave Radiation (OLR), zonal wind and meridional wind. In the selection of these regions, which include a number of locations in the Persian Gulf, the Oman Sea, the Black Sea, the Caspian, the Mediterranean, the Adriatic, the Red Sea, the Eden Gulf, the Atlantic, the Indian Ocean, and Siberia, we have examined the effect of synoptic patterns in these regions on the rainfall in the northeast region of Iran. Then, the adaptive neural-fuzzy inference system in the period 1970 -1997 has been taught. Finally, the rainfall in the period 1998-2007 has been predicted. The results show that the adaptive neural-fuzzy inference system can predict the rainfall with reasonable accuracy

Fallah Ghalhary, G. A.; Khoshhal, J.; Habibi Nokhandan, M.

2009-09-01

35

Adaptive neuro-fuzzy estimation of optimal lens system parameters

NASA Astrophysics Data System (ADS)

Due to the popularization of digital technology, the demand for high-quality digital products has become critical. The quantitative assessment of image quality is an important consideration in any type of imaging system. Therefore, developing a design that combines the requirements of good image quality is desirable. Lens system design represents a crucial factor for good image quality. Optimization procedure is the main part of the lens system design methodology. Lens system optimization is a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. Therefore lens system design provides ideal problems for intelligent optimization algorithms. There are many tools which can be used to measure optical performance. One very useful tool is the spot diagram. The spot diagram gives an indication of the image of a point object. In this paper, one optimization criterion for lens system, the spot size radius, is considered. This paper presents new lens optimization methods based on adaptive neuro-fuzzy inference strategy (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated.

Petkovi?, Dalibor; Pavlovi?, Nenad T.; Shamshirband, Shahaboddin; Mat Kiah, Miss Laiha; Badrul Anuar, Nor; Idna Idris, Mohd Yamani

2014-04-01

36

Neuro-fuzzy systems for intelligent robot navigation and control under uncertainty

This paper describes neuro-fuzzy systems for intelligent robot navigation and control under uncertainty. First, we present a new neuro-fuzzy system architecture for behavior navigation of a mobile robot in unknown environments. In this neuro-fuzzy system, a neural network is used to process range information for understanding distribution of obstacles in local regions; while fuzzy sets and a rule base are

Wei Li

1995-01-01

37

A Hierarchical Neuro-Fuzzy System to Minimum-Time Trajectory Planning of Redundant Manipulators

In this paper, the problem of minimum-time trajectory planning is studied for a three degrees-of- freedom (3-DOF) planar manipulator using a hierarchical hybrid neuro-fuzzy system1. A first pre- processing step involves two components. The first component is NeFIK (for Neuro-Fuzzy Inverse Kinematics), a neuro-fuzzy network designed to learn and solve the inverse kinematics problem. The second one is an optimal

Amar KHOUKHI; Luc BARON; Marek BALAZINSKI Kudret DEMIRLI

38

Ration power plants, to generate power, have become common worldwide. One such one is the steam power plant. In such plants, various moving parts of heavy machines generate a lot of noise. Operators are subjected to high levels of noise. High noise level exposure leads to psychological as well physiological problems; different kinds of ill effects. It results in deteriorated work efficiency, although the exact nature of work performance is still unknown. To predict work efficiency deterioration, neuro-fuzzy tools are being used in research. It has been established that a neuro-fuzzy computing system helps in identification and analysis of fuzzy models. The last decade has seen substantial growth in development of various neuro-fuzzy systems. Among them, adaptive neuro-fuzzy inference system provides a systematic and directed approach for model building and gives the best possible design parameters in minimum possible time. This study aims to develop a neuro-fuzzy model to predict the effects of noise pollution on human work efficiency as a function of noise level, exposure time, and age of the operators doing complex type of task. PMID:19805930

Ahmed, Hameed Kaleel; Zulquernain, Mallick

2009-01-01

39

Using acceleration measurements and neuro-fuzzy systems for monitoring and diagnosis of bearings

NASA Astrophysics Data System (ADS)

Ball bearing is an important type of bearings. The radial acceleration of ball bearings has been measured for monitoring and diagnosis. Feature extraction is used to extract essential features from the experimental data. Three features, including peak amplitude of the frequency domain, percent power, and peak RMS, have been extracted from the radial acceleration of ball bearings. Then Sequential Forward Search Algorithm (SFS? was utilized for feature selection in order to effectively obtain the best vibration features. Adaptive Neuro Fuzzy Inference Systems (ANFIS) have been used. The selected features were the inputs to the neuro-fuzzy system. Whether there is a defect or not and what types of defects were the outputs of this system. Although there is no analytical relationship between the input and the output of the neuro-fuzzy system, this system still can establish the input/output relationship. In other words, this approach can most accurately, most quickly, and most reliably determine whether there is a defect or not and what types of defects, which is very important for preventive monitoring, diagnosis, and maintenance of ball bearings.

Liu, Tien-I.; Lee, Junyi; Singh, Palvinder; Liu, George

2013-10-01

40

Neuro-fuzzy modeling and control

Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called adaptive-network-based fuzzy inference system (ANFIS), which possess certain advantages over neural networks. We introduce

JYH-SHING ROGER JANG; Chuen-Tsai Sun

1995-01-01

41

A Neuro-Fuzzy System for Characterization of Arm Movements

The myoelectric signal reflects the electrical activity of skeletal muscles and contains information about the structure and function of the muscles which make different parts of the body move. Advances in engineering have extended electromyography beyond the traditional diagnostic applications to also include applications in diverse areas such as rehabilitation, movement analysis and myoelectric control of prosthesis. This paper aims to study and develop a system that uses myoelectric signals, acquired by surface electrodes, to characterize certain movements of the human arm. To recognize certain hand-arm segment movements, was developed an algorithm for pattern recognition technique based on neuro-fuzzy, representing the core of this research. This algorithm has as input the preprocessed myoelectric signal, to disclosed specific characteristics of the signal, and as output the performed movement. The average accuracy obtained was 86% to 7 distinct movements in tests of long duration (about three hours). PMID:23429579

Balbinot, Alexandre; Favieiro, Gabriela

2013-01-01

42

Self-organizing neuro-fuzzy system for control of unknown plants

A cluster-based self-organizing neuro-fuzzy system (SO-NFS) is proposed for control of unknown plants. The neuro-fuzzy system can learn its knowledge base from input-output training data. A plant model is not required for training, that is, the plant is unknown to the SO-NFS. Using new data types, the vectors and matrices, a construction theory is developed for the organization process and

Chunshien Li; Chun-Yi Lee

2003-01-01

43

System identification of smart structures using a wavelet neuro-fuzzy model

NASA Astrophysics Data System (ADS)

This paper proposes a complex model of smart structures equipped with magnetorheological (MR) dampers. Nonlinear behavior of the structure-MR damper systems is represented by the use of a wavelet-based adaptive neuro-fuzzy inference system (WANFIS). The WANFIS is developed through the integration of wavelet transforms, artificial neural networks, and fuzzy logic theory. To evaluate the effectiveness of the WANFIS model, a three-story building employing an MR damper under a variety of natural hazards is investigated. An artificial earthquake is used for training the input-output mapping of the WANFIS model. The artificial earthquake is generated such that the characteristics of a variety of real recorded earthquakes are included. It is demonstrated that this new WANFIS approach is effective in modeling nonlinear behavior of the structure-MR damper system subjected to a variety of disturbances while resulting in shorter training times in comparison with an adaptive neuro-fuzzy inference system (ANFIS) model. Comparison with high fidelity data proves the viability of the proposed approach in a structural health monitoring setting, and it is validated using known earthquake signals such as El-Centro, Kobe, Northridge, and Hachinohe.

Mitchell, Ryan; Kim, Yeesock; El-Korchi, Tahar

2012-11-01

44

NASA Astrophysics Data System (ADS)

The aim of this study is to compare the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network (ANN) to estimate the cross-talk contamination of 99 m Tc / 201 Tl image acquisition in the 201 Tl energy window (77 ± 15% keV). GATE (Geant4 Application in Emission and Tomography) is employed due to its ability to simulate multiple radioactive sources concurrently. Two kinds of phantoms, including two digital and one physical phantom, are used. In the real and the simulation studies, data acquisition is carried out using eight energy windows. The ANN and the ANFIS are prepared in MATLAB, and the GATE results are used as a training data set. Three indications are evaluated and compared. The ANFIS method yields better outcomes for two indications (Spearman's rank correlation coefficient and contrast) and the two phantom results in each category. The maximum image biasing, which is the third indication, is found to be 6% more than that for the ANN.

Heidary, Saeed; Setayeshi, Saeed; Ghannadi-Maragheh, Mohammad

2014-09-01

45

The dynamics of robot manipulators are highly nonlinear with strong couplings existing between joints and are frequently subjected to structured and unstructured uncertainties. Fuzzy Logic Controller can very well describe the desired system behavior with simple “if-then ” relations owing the designer to derive “if-then ” rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). This paper presents the control of six degrees of freedom robot arm (PUMA Robot) using Adaptive Neuro Fuzzy Inference System (ANFIS) based PD plus I controller. Numerical simulation using the dynamic model of six DOF robot arm shows the effectiveness of the approach in trajectory tracking problems. Comparative evaluation with respect to PID, Fuzzy PD+I controls are presented to validate the controller design. The results presented emphasize that a satisfactory tracking precision could be achieved using ANFIS controller than PID and Fuzzy PD+I controllers

unknown authors

2008-01-01

46

Neuro-fuzzy Learning of Strategies for Optimal Control Problems Kaivan Kamali1

systems such as adaptive network-based fuzzy inference system (ANFIS) [3], can be used to learn fuzzy ifNeuro-fuzzy Learning of Strategies for Optimal Control Problems Kaivan Kamali1 , Lijun Jiang2 of neuro-fuzzy systems which yields reusable knowledge in the form of fuzzy if-then rules. Ex- perimental

47

Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro-Fuzzy

-fuzzy "ANFIS" control. The tracking algorithm integrated with a solar PV system has been simulated with boostMaximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro- Fuzzy "ANFIS availability and vast potential, world has turned to solar photovoltaic energy to meet out its ever increasing

Paris-Sud XI, UniversitÃ© de

48

Modulation transfer function estimation of optical lens system by adaptive neuro-fuzzy methodology

NASA Astrophysics Data System (ADS)

The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to estimate MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

Petkovi?, Dalibor; Shamshirband, Shahaboddin; Pavlovi?, Nenad T.; Anuar, Nor Badrul; Kiah, Miss Laiha Mat

2014-07-01

49

Adaptive neuro-fuzzy prediction of modulation transfer function of optical lens system

NASA Astrophysics Data System (ADS)

The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the adaptive neuro-fuzzy (ANFIS) estimator is designed and adapted to predict MTF value of the actual optical system. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system. The back propagation learning algorithm is used for training this network. This intelligent estimator is implemented using MATLAB/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.

Petkovi?, Dalibor; Shamshirband, Shahaboddin; Anuar, Nor Badrul; Md Nasir, Mohd Hairul Nizam; Pavlovi?, Nenad T.; Akib, Shatirah

2014-07-01

50

Real-time neuro-fuzzy systems for adaptive control of musical processes

NASA Astrophysics Data System (ADS)

We have added a real-time interactive fuzzy reasoning system and neural network simulator to the MAX real-time music programming language. This environment allows us to quickly prototype and experiment with Neural, Fuzzy, and Neuro-Fuzzy systems for control of real- time musical processes. In this paper we introduce our tools and discuss musical contexts that call for the adaptive and generalization capabilities of these systems.

Lee, Michael; Wessel, David

1993-12-01

51

Simulation of elastic tissues in virtual medicine using neuro-fuzzy systems

NASA Astrophysics Data System (ADS)

To improve the benefit of surgical simulators for education and research a visual convincing modeling of the operation scenario and the involved tissues is not sufficient. It is also necessary to simulate the deformation and resulting inner forces of tissue under influence of external forces caused by, for example, medical instruments or gravity. In this paper, we present a hybrid neuro-fuzzy system, which was designed for the description and simulation of tissues. The neuro-fuzzy system can be used to simulate the physical behavior like stiffness, viscosity and inertia of deformable or elastic tissues in surgical simulation. The parameters of a physical model or prior expert knowledge in the form of linguistic terms can be used to initialize the network parameters. Using a neural network structure, local changes to the system like cuts or ruptures can be performed during simulation. As an application example, some simulation results in the area of gynecological laparoscopy are given.

Radetzky, Arne; Nuernberger, Andreas; Pretschner, Dietrich P.

1998-06-01

52

The purpose of this study was to estimate the torque from high-density surface electromyography signals of biceps brachii, brachioradialis, and the medial and lateral heads of triceps brachii muscles during moderate-to-high isometric elbow flexion-extension. The elbow torque was estimated in two following steps: First, surface electromyography (EMG) amplitudes were estimated using principal component analysis, and then a fuzzy model was proposed to illustrate the relationship between the EMG amplitudes and the measured torque signal. A neuro-fuzzy method, with which the optimum number of rules could be estimated, was used to identify the model with suitable complexity. Utilizing the proposed neuro-fuzzy model, the clinical interpretability was introduced; contrary to the previous linear and nonlinear black-box system identification models. It also reduced the estimation error compared with that of the most recent and accurate nonlinear dynamic model introduced in the literature. The optimum number of the rules for all trials was 4 ± 1, that might be related to motor control strategies and the % variance accounted for criterion was 96.40 ± 3.38 which in fact showed considerable improvement compared with the previous methods. The proposed method is thus a promising new tool for EMG-Torque modeling in clinical applications. PMID:25426427

Jafari, Zohreh; Edrisi, Mehdi; Marateb, Hamid Reza

2014-10-01

53

Route Selection in a Neuro-Fuzzy Vehicle Navigation System

time). Such a system would be particularly useful when accidents or roadworks occurred in the traffic exchange and real-time control as new functions in road traffic. The information system must also support and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong. Email: gpang@eee.hku.hk 1

Pang, Grantham

54

A Neuro-Fuzzy System for Extracting Environment Features Based on Ultrasonic Sensors

In this paper, a method to extract features of the environment based on ultrasonic sensors is presented. A 3D model of a set of sonar systems and a workplace has been developed. The target of this approach is to extract in a short time, while the vehicle is moving, features of the environment. Particularly, the approach shown in this paper has been focused on determining walls and corners, which are very common environment features. In order to prove the viability of the devised approach, a 3D simulated environment has been built. A Neuro-Fuzzy strategy has been used in order to extract environment features from this simulated model. Several trials have been carried out, obtaining satisfactory results in this context. After that, some experimental tests have been conducted using a real vehicle with a set of sonar systems. The obtained results reveal the satisfactory generalization properties of the approach in this case. PMID:22303160

Marichal, Graciliano Nicolás; Hernández, Angela; Acosta, Leopoldo; González, Evelio José

2009-01-01

55

Multi Groups Cooperation based Symbiotic Evolution for TSK-type Neuro-Fuzzy Systems Design.

In this paper, a TSK-type neuro-fuzzy system with multi groups cooperation based symbiotic evolution method (TNFS-MGCSE) is proposed. The TNFS-MGCSE is developed from symbiotic evolution. The symbiotic evolution is different from traditional GAs (genetic algorithms) that each chromosome in symbiotic evolution represents a rule of fuzzy model. The MGCSE is different from the traditional symbiotic evolution; with a population in MGCSE is divided to several groups. Each group formed by a set of chromosomes represents a fuzzy rule and cooperate with other groups to generate the better chromosomes by using the proposed cooperation based crossover strategy (CCS). In this paper, the proposed TNFS-MGCSE is used to evaluate by numerical examples (Mackey-Glass chaotic time series and sunspot number forecasting). The performance of the TNFS-MGCSE achieves excellently with other existing models in the simulations. PMID:21709856

Cheng, Yi-Chang; Hsu, Yung-Chi; Lin, Sheng-Fuu

2010-07-01

56

Experimental study of a novel neuro-fuzzy system for on-line handwritten UNIPEN digit recognition

Abstract: This paper presents an on-line hand-printed character recognition system, tested on datasets produced by the UNIPENproject, thus ensuring sufficient dataset size, author-independence and a capacity for objective benchmarking. Newpreprocessing and segmentation methods are proposed in order to derive a sequence of strokes for each character, followingZ suggestions of biological models for handwriting. Variants of a novel neuro-fuzzy system, FasArt

Eduardo Gómez-sánchez; J. A. Gago González; Yannis A. Dimitriadis; J. Manuel Cano Izquierdo; Juan López Coronado

1998-01-01

57

Experimental study of a novel neuro-fuzzy system for on-line handwritten UNIPEN digit recognition

This paper presents an on-line hand-printed character recognition system, tested on datasets produced by the UNIPEN project, thus ensuring sufficient dataset size, author-independence and a capacity for objective benchmarking. New preprocessing and segmentation methods are proposed in order to derive a sequence of strokes for each character, following suggestions of biological models for handwriting. Variants of a novel neuro-fuzzy system,

E. Gomez Sanchez; J. A. Gago Gonzalez; Y. A. Dimitriadis

58

NASA Astrophysics Data System (ADS)

This paper presents a new algorithm for building an adaptive neuro-fuzzy inference system (ANFIS) from a training data set called B-ANFIS. In order to increase accuracy of the model, the following issues are executed. Firstly, a data merging rule is proposed to build and perform a data-clustering strategy. Subsequently, a combination of clustering processes in the input data space and in the joint input-output data space is presented. Crucial reason of this task is to overcome problems related to initialization and contradictory fuzzy rules, which usually happen when building ANFIS. The clustering process in the input data space is accomplished based on a proposed merging-possibilistic clustering (MPC) algorithm. The effectiveness of this process is evaluated to resume a clustering process in the joint input-output data space. The optimal parameters obtained after completion of the clustering process are used to build ANFIS. Simulations based on a numerical data, ‘Daily Data of Stock A', and measured data sets of a smart damper are performed to analyze and estimate accuracy. In addition, convergence and robustness of the proposed algorithm are investigated based on both theoretical and testing approaches.

Nguyen, Sy Dzung; Nguyen, Quoc Hung; Choi, Seung-Bok

2015-01-01

59

A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system

This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three multi-layered feed forwarded Artificial Neural Networks (ANN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated ANN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and nonlinear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network and the Perturb and Observe (P and O) algorithm dispositive. (author)

Chaouachi, Aymen; Kamel, Rashad M.; Nagasaka, Ken [Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, Nakamachi (Japan)

2010-12-15

60

Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

NASA Technical Reports Server (NTRS)

Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

Mitra, Sunanda; Pemmaraju, Surya

1992-01-01

61

Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production. PMID:22991575

Shahinfar, Saleh; Mehrabani-Yeganeh, Hassan; Lucas, Caro; Kalhor, Ahmad; Kazemian, Majid; Weigel, Kent A.

2012-01-01

62

Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production. PMID:22991575

Shahinfar, Saleh; Mehrabani-Yeganeh, Hassan; Lucas, Caro; Kalhor, Ahmad; Kazemian, Majid; Weigel, Kent A

2012-01-01

63

A Neuro-Fuzzy Based Software Reusability Evaluation System with Optimized Rule Selection

There are metrics for identifying the quality of reusable components but the function that makes use of these metrics to find reusability of software components is still not clear. We critically analyzed the CK metrics, tried to remove the inconsistencies and devised neuro-fuzzy framework that gets input in form of tuned WMC, DIT, NOC, CBO, LCOM values of a software

Parvinder Singh Sandhu; Hardeep Singh

2006-01-01

64

Prediction of autistic disorder using neuro fuzzy system by applying ANN technique.

The major challenge in medical field is to diagnose disorder rather than a disease. In this paper, a neuro fuzzy based model is designed for identification or diagnosis of autism. The problematic areas are gathered from every individual and the related linguistic inputs are converted into fuzzy input values which are in turn given as input to feed forward multilayer neural network. The network is trained using back propagation training algorithm and tested for its performance with the expertise. PMID:18706991

Arthi, K; Tamilarasi, A

2008-11-01

65

A neuro-fuzzy control system for intelligent overlock sewing machines

A neuro-fuzzy control model has been devised for the next generation of so-called “intelligent sewing machines”. The model incorporates discrimination of material characteristics to be stitched by automatic determination of their properties. The fabric\\/machine interactions at different speeds have been computed in the form of linguistic rules of a fuzzy model and implemented in a neural network to allow for

George Stylios; J. O. Sotomi

1995-01-01

66

Control of a pneumatic gantry robot for grinding: a neuro-fuzzy approach to PID tuning

This paper addresses an application that involves the grinding of the edges of steel blanks with a pneumatic gantry robot. It presents a PID tuning method that uses an adaptive neuro-fuzzy inference system (ANFIS) to model the relationship between the controller gains and the target output response, with the response specification set by desired percent overshoot and settling time. The

Murad Samhouri; Asghar Raoufi; Brian Surgenor

2005-01-01

67

Neuro-fuzzy control of a steam boiler-turbine unit

Conceptually, fuzzy logic possesses the quality of simplicity. However, its early applications relied on trial and error in selecting either the fuzzy membership functions or the fuzzy rules. This made it depend rather too heavily on expert knowledge which may not always be available. Hence, a self-tuning or an adaptive fuzzy logic controller (FLC) such as Adaptive Neuro-Fuzzy Inference System

Fahd A. Alturki; Adel Ben Abdennour

1999-01-01

68

An adaptive neuro-fuzzy inference system (ANFIS) with a supervisory control system (SCS) was used to predict the occurrence of gait events using the electromyographic (EMG) activity of lower extremity muscles in the child with cerebral palsy (CP). This is anticipated to form the basis of a control algorithm for the application of electrical stimulation (ES) to leg or ankle muscles

Richard T. Lauer; Brian T. Smith; Randal R. Betz

2005-01-01

69

In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) model have been successfully used for the evaluation of relationships between concrete compressive strength and ultrasonic pulse velocity (UPV) values using the experimental data obtained from many cores taken from different reinforced concrete structures having different ages and unknown ratios of concrete mixtures. A comparative study is

Mahmut Bilgehan

2011-01-01

70

Development of an intelligent neuro-fuzzy maneuver identification system for autonomous aircraft

NASA Astrophysics Data System (ADS)

This dissertation reports an investigation of the design of intelligent systems for the high-level control of autonomous aircraft. In a departure from recent work in this field, an attempt has been made to synthesize a high-level control architecture that emulates a human pilot's reasoning capabilities. The system architecture uses pilot-type classifications of aircraft modes (the various maneuvers that pilots are trained to execute) within all decision-making and reasoning processes. A flight control system structured in terms of these modes offers scope for efficient combination of concepts from artificial intelligence, control theory and aviation practice. A critical component of this intelligent flight controller is an automated mode inference system. This innovative system extracts high-level knowledge of the current maneuver (or segment of the overall mission) from sensed measurements of dynamic state variables. Using a blend of soft computing approaches, this inference engine consistently identifies the correct maneuver being flown, even in the presence of moderate sensor noise and data ambiguities. In the process of creating this inference engine, a novel scheme to generate training data sets for neural networks has been developed. This data generation scheme permits complete coverage of the aircraft's capability envelope; this coverage is achieved without recourse to the voluminous flight data (actual or simulated) normally required to train neural networks. The data generation scheme thus significantly reduces developmental effort. Apart from this innovation, pilot-like techniques to cope with the phenomenon of chatter (where identification rapidly switches back-and-forth between modes) have been developed and implemented within the inference system. This dissertation also discusses the development of logic to interpret and implement commands from remote operators, using high-level knowledge of the current mission segment. This knowledge is used to contextually understand such commands, and to decide on a future course of action in terms of a sequence of maneuvers. A family of optimal controllers to track such maneuvers has also been formulated. The formulation permits pilot-like combinations of regulation and tracking functions. A few such maneuver-based controllers are presented, and used to investigate the development of logic to monitor the accuracy of mathematical models of the aircraft.

Krishnamurthy, Karthik

2000-10-01

71

Only a few studies in the literature have focused on the effects of age on virtual environment (VE) sickness susceptibility and even less research was carried out focusing on the elderly. In general, the elderly usually browse VEs on a thin film transistor liquid crystal display (TFT-LCD) at home or somewhere, not a head-mounted display (HMD). While the TFT-LCD is used to present VEs, this set-up does not physically enclose the user. Therefore, this study investigated the factors that contribute to cybersickness among the elderly when immersed into a VE on TFT-LCD, including exposure durations, navigation rotating speeds and angle of inclination. Participants were elderly, with an average age of 69.5 years. The results of the first experiment showed that the rate of simulator sickness questionnaire (SSQ) scores increases significantly with navigational rotating speed and duration of exposure. However, the experimental data also showed that the rate of SSQ scores does not increase with the increase in angle of inclination. In applying these findings, the neuro-fuzzy technology was used to develop a neuro-fuzzy cybersickness-warning system integrating fuzzy logic reasoning and neural network learning. The contributing factors were navigational rotating speed and duration of exposure. The results of the second experiment showed that the proposed system can efficiently determine the level of cybersickness based on the associated subjective sickness estimates and combat cybersickness due to long exposure to a VE. PMID:19144322

Liu, Cheng-Li

2009-05-01

72

A hybrid neuro-fuzzy power system stabilizer for multimachine power systems

A fuzzy basis function network (FBFN) based power system stabilizer (PSS) is presented in this paper to improve power system dynamic stability. The proposed FBFN based PSS provides a natural framework for combining numerical and linguistic information in a uniform fashion. The proposed FBFN is trained over a wide range of operating conditions in order to re-tune the PSS parameters

M. A. Abido; Y. L. Abdel-Magid

1998-01-01

73

The aim of the current study was to evaluate the potential of the dynamic lipolysis model to simulate the absorption of a poorly soluble model drug compound, probucol, from three lipid-based formulations and to predict the in vitro-in vivo correlation (IVIVC) using neuro-fuzzy networks. An oil solution and two self-micro and nano-emulsifying drug delivery systems were tested in the lipolysis model. The release of probucol to the aqueous (micellar) phase was monitored during the progress of lipolysis. These release profiles compared with plasma profiles obtained in a previous bioavailability study conducted in mini-pigs at the same conditions. The release rate and extent of release from the oil formulation were found to be significantly lower than from SMEDDS and SNEDDS. The rank order of probucol released (SMEDDS approximately SNEDDS > oil formulation) was similar to the rank order of bioavailability from the in vivo study. The employed neuro-fuzzy model (AFM-IVIVC) achieved significantly high prediction ability for different data formations (correlation greater than 0.91 and prediction error close to zero), without employing complex configurations. These preliminary results suggest that the dynamic lipolysis model combined with the AFM-IVIVC can be a useful tool in the prediction of the in vivo behavior of lipid-based formulations. PMID:18367386

Fatouros, Dimitrios G; Nielsen, Flemming Seier; Douroumis, Dionysios; Hadjileontiadis, Leontios J; Mullertz, Anette

2008-08-01

74

Selecting an optimum advanced technology system for an organization is one of the most crucial issues in any industry. Any technology system which makes business process more efficient and business management more simplified is one of the important Information System (IS) to the organization. The comprehensive framework is a three-phase approach which introduces two main ideas, one is the adopting

D. R. Kalbande; Nilesh Deotale; Priyank Singhal; Sumiran Shah; G. T. Thampi

2011-01-01

75

NASA Astrophysics Data System (ADS)

In this research work, two areas hydro-thermal power system connected through tie-lines is considered. The perturbation of frequencies at the areas and resulting tie line power flows arise due to unpredictable load variations that cause mismatch between the generated and demanded powers. Due to rising and falling power demand, the real and reactive power balance is harmed; hence frequency and voltage get deviated from nominal value. This necessitates designing of an accurate and fast controller to maintain the system parameters at nominal value. The main purpose of system generation control is to balance the system generation against the load and losses so that the desired frequency and power interchange between neighboring systems are maintained. The intelligent controllers like fuzzy logic, artificial neural network (ANN) and hybrid fuzzy neural network approaches are used for automatic generation control for the two area interconnected power systems. Area 1 consists of thermal reheat power plant whereas area 2 consists of hydro power plant with electric governor. Performance evaluation is carried out by using intelligent (ANFIS, ANN and fuzzy) control and conventional PI and PID control approaches. To enhance the performance of controller sliding surface i.e. variable structure control is included. The model of interconnected power system has been developed with all five types of said controllers and simulated using MATLAB/SIMULINK package. The performance of the intelligent controllers has been compared with the conventional PI and PID controllers for the interconnected power system. A comparison of ANFIS, ANN, Fuzzy and PI, PID based approaches shows the superiority of proposed ANFIS over ANN, fuzzy and PI, PID. Thus the hybrid fuzzy neural network controller has better dynamic response i.e., quick in operation, reduced error magnitude and minimized frequency transients.

Prakash, S.; Sinha, S. K.

2014-08-01

76

Prediction of Conductivity by Adaptive Neuro-Fuzzy Model

Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity. PMID:24658582

Akbarzadeh, S.; Arof, A. K.; Ramesh, S.; Khanmirzaei, M. H.; Nor, R. M.

2014-01-01

77

Neuro-fuzzy algorithm implemented in Altera's FPGA for mobile robot's obstacle avoidance mission

This paper presents the designed obstacle avoidance program for mobile robot that incorporates a neuro-fuzzy algorithm using Altera¿ Field Programmable Gate Array (FPGA) development DE2 board. The neuro-fuzzy-based-obstacle avoidance program is simulated and implemented on the hardware system using Altera Quartus® II design software, System-on-programmable-chip (SOPC) Builder, Nios® II Integrated Design Environment (IDE) software, and FPGA development and education board

Muhammad Nasiruddin Mahyuddin; Chan Zhi Wei; Mohd Rizal Arshad

2009-01-01

78

Neuro-fuzzy in six DOF tele-robotic control

A force-force bilateral scheme based on neuro-fuzzy control was designed for a six DOF tele-robotic system. An open architecture controller for the six DOF tele-robotic system has been successfully implemented. Increased system bandwidth system can be achieved with the new embedded PUMA 760 and PUMA 260 controllers. Both the slave and master robot controllers comprise a PC running a real-time

W. Po-ngaen; R. Choomuang; J. Bhuripanyo

2008-01-01

79

A New Neuro-Fuzzy Adaptive Genetic Algorithm

Novel neuro-fuzzy techniques are used to dynamically control parameter settings of genetic algorithms (GAs). The benchmark routine is an adaptive genetic algorithm (AGA) that uses a fuzzy knowledge-based system to control GA parameters. The self-learning ability of the cerebellar model ariculation controller(CMAC) neural network makes it possible for on-line learning the knowledge on GAs throughout the run. Automatically designing and

ZHU Lili ZHANG

2003-01-01

80

Daily soil temperature modeling using neuro-fuzzy approach

NASA Astrophysics Data System (ADS)

Soil temperature is an important meteorological parameter which influences a number of processes in agriculture, hydrology, and environment. However, soil temperature records are not routinely available from meteorological stations. This work aimed to estimate daily soil temperature using the coactive neuro-fuzzy inference system (CANFIS) in arid and semiarid regions. For this purpose, daily soil temperatures were recorded at six depths of 5, 10, 20, 30, 50, and 100 cm below the surface at two synoptic stations in Iran. According to correlation analysis, mean, maximum, and minimum air temperatures, relative humidity, sunshine hours, and solar radiation were selected as the inputs of the CANFIS models. It was concluded that, in most cases, the best soil temperature estimates with a CANFIS model can be provided with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Comparison of the models' performances at arid and semiarid locations showed that the CANFIS models' performances in arid site were slightly better than those in semiarid site. Overall, the obtained results indicated the capabilities of the CANFIS model in estimating soil temperature in arid and semiarid regions.

Hosseinzadeh Talaee, P.

2014-11-01

81

The strength of neuro-fuzzy systems involves two contradictory requirements in neuro-fuzzy modeling: interpretability versus accuracy. The Yager-inference-scheme-based fuzzy CMAC (FCMAC-Yager) architecture shows advantages such as it exhibits learning and memory capabilities of the human cerebellum through the CMAC (cerebellar model articulation controller) structure and the human way of reasoning through the Yager inference scheme. However, it suffered from an exponential

Ngoc Nam Nguyen; Chai Quek

2009-01-01

82

Recognition of Handwritten Arabic words using a neuro-fuzzy network

We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descent learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system.The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database.

Boukharouba, Abdelhak [Departement de Genie electrique, Universite 08 Mai 45 de Guelma (Algeria); Bennia, Abdelhak [Departement d'Electronique, Universite Mentouri de Constantine (Algeria)

2008-06-12

83

Recognition of Handwritten Arabic words using a neuro-fuzzy network

NASA Astrophysics Data System (ADS)

We present a new method for the recognition of handwritten Arabic words based on neuro-fuzzy hybrid network. As a first step, connected components (CCs) of black pixels are detected. Then the system determines which CCs are sub-words and which are stress marks. The stress marks are then isolated and identified separately and the sub-words are segmented into graphemes. Each grapheme is described by topological and statistical features. Fuzzy rules are extracted from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data using a fuzzy c-means, and rule parameter tuning phase using gradient descent learning. After learning, the network encodes in its topology the essential design parameters of a fuzzy inference system. The contribution of this technique is shown through the significant tests performed on a handwritten Arabic words database.

Boukharouba, Abdelhak; Bennia, Abdelhak

2008-06-01

84

Neuro-Fuzzy Control of a Robotic Manipulator

NASA Astrophysics Data System (ADS)

In this paper, to solve the problem of control of a robotic manipulator's movement with holonomical constraints, an intelligent control system was used. This system is understood as a hybrid controller, being a combination of fuzzy logic and an artificial neural network. The purpose of the neuro-fuzzy system is the approximation of the nonlinearity of the robotic manipulator's dynamic to generate a compensatory control. The control system is designed in such a way as to permit modification of its properties under different operating conditions of the two-link manipulator

Gierlak, P.; Muszy?ska, M.; ?ylski, W.

2014-08-01

85

Adaptive Neuro-Fuzzy Inference System Based Autonomous Flight Control of Unmanned Air Vehicles

This paper proposes ANFIS logic based autonomous flight controller for UAVs (unmanned aerial vehicles). Three fuzzy logic\\u000a modules are developed for the control of the altitude, the speed, and the roll angle, through which the altitude and the latitude-longitude\\u000a of the air vehicle is controlled. The implementation framework utilizes MATLAB’s standard configuration and the Aerosim Aeronautical\\u000a Simulation Block Set which

Sefer Kurnaz; Okyay Kaynak; Ekrem Konakoglu

2007-01-01

86

Electricity demand forecasting is known as one of the most important challenges in managing supply and demand of electricity and has been studied from different views. Electrical load forecast might be performed over different time intervals of short, medium and long term. Various techniques have been proposed for short term, medium term or long term load forecasting. In this study

Arash Ghanbari; S. Farid Ghaderi; M. A. Azadeh

2010-01-01

87

A Neuro-fuzzy Inference System for the Evaluation of New Product Development Projects

\\u000a As a vital activity for companies, new product development is also a very risky process due to the high uncertainty degree\\u000a encountered at every development stage and the inevitable dependence on how previous steps are successfully accomplished.\\u000a Hence, there is an apparent need to evaluate new product initiatives systematically and make accurate decisions under uncertainty.\\u000a Another major concern is the

Orhan Feyzioglu; Gülçin Büyüközkan

2006-01-01

88

Modeling of thrust force in drilling of CFRP composites using adaptive neuro fuzzy inference system

Carbon fiber reinforced plastic (CFRP) material is identified as an emerging material for solving critical problems such as light weight, corrosion resistance and environmental durability. CFRP suits these properties in various engineering applications that have structural variations. In order to join such structures, drilling is an essential operation. Several problems are encountered in drilling of composites which delamination poses a

A. Krishnamoorthy; R. V. Sarathy; S. R. Boopathy; K. Palanikumar

2010-01-01

89

Modeling tunnel boring machine performance by neuro-fuzzy methods

This paper presents the results of a study into the application of neuro-fuzzy methods to model the performance of tunnel boring machines. A database consisting of over 640 TBM projects in rock has been used. It is shown that neuro-fuzzy methods give better results than other, more conventional, modeling approaches. Fuzzy set theory, fuzzy logic and neural networks techniques seem

M. Alvarez Grima; P. A. Bruines; P. N. W. Verhoef

2000-01-01

90

Hybrid neuro-fuzzy control approach of robot manipulators

Recently, robot manipulators are expected to perform more sophisticated tasks. Thus, these manipulators have to be intelligent enough to work in an unknown and constrained environment. In this paper, we are interested by the uses of a hybrid neuro-fuzzy control approaches for robots manipulators moving in such environment. For this purpose, two adaptive neuro-fuzzy control structures are presented, an external

Y. Touati; Y. Amirat

2003-01-01

91

Tuning of a neuro-fuzzy controller by genetic algorithm.

Due to their powerful optimization property, genetic algorithms (GAs) are currently being investigated for the development of adaptive or self-tuning fuzzy logic control systems. This paper presents a neuro-fuzzy logic controller (NFLC) where all of its parameters can be tuned simultaneously by GA. The structure of the controller is based on the radial basis function neural network (RBF) with Gaussian membership functions. The NFLC tuned by GA can somewhat eliminate laborious design steps such as manual tuning of the membership functions and selection of the fuzzy rules. The GA implementation incorporates dynamic crossover and mutation probabilistic rates for faster convergence. A flexible position coding strategy of the NFLC parameters is also implemented to obtain near optimal solutions. The performance of the proposed controller is compared with a conventional fuzzy controller and a PID controller tuned by GA. Simulation results show that the proposed controller offers encouraging advantages and has better performance. PMID:18252294

Seng, T L; Bin Khalid, M; Yusof, R

1999-01-01

92

Terrorism Event Classification Using Fuzzy Inference Systems

Terrorism has led to many problems in Thai societies, not only property damage but also civilian casualties. Predicting terrorism activities in advance can help prepare and manage risk from sabotage by these activities. This paper proposes a framework focusing on event classification in terrorism domain using fuzzy inference systems (FISs). Each FIS is a decision-making model combining fuzzy logic and approximate reasoning. It is generated in five main parts: the input interface, the fuzzification interface, knowledge base unit, decision making unit and output defuzzification interface. Adaptive neuro-fuzzy inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic and neural network. The ANFIS utilizes automatic identification of fuzzy logic rules and adjustment of membership function (MF). Moreover, neural network can directly learn from data set to construct fuzzy logic rules and MF implemented in various applications. FIS settings are evaluated based on two comparisons. The first evaluat...

Inyaem, Uraiwan; Meesad, Phayung; Tran, Dat

2010-01-01

93

Modeling Belt-Servomechanism by Chebyshev Functional Recurrent Neuro-Fuzzy Network

NASA Astrophysics Data System (ADS)

A novel Chebyshev functional recurrent neuro-fuzzy (CFRNF) network is developed from a combination of the Takagi-Sugeno-Kang (TSK) fuzzy model and the Chebyshev recurrent neural network (CRNN). The CFRNF network can emulate the nonlinear dynamics of a servomechanism system. The system nonlinearity is addressed by enhancing the input dimensions of the consequent parts in the fuzzy rules due to functional expansion of a Chebyshev polynomial. The back propagation algorithm is used to adjust the parameters of the antecedent membership functions as well as those of consequent functions. To verify the performance of the proposed CFRNF, the experiment of the belt servomechanism is presented in this paper. Both of identification methods of adaptive neural fuzzy inference system (ANFIS) and recurrent neural network (RNN) are also studied for modeling of the belt servomechanism. The analysis and comparison results indicate that CFRNF makes identification of complex nonlinear dynamic systems easier. It is verified that the accuracy and convergence of the CFRNF are superior to those of ANFIS and RNN by the identification results of a belt servomechanism.

Huang, Yuan-Ruey; Kang, Yuan; Chu, Ming-Hui; Chang, Yeon-Pun

94

A neuro-fuzzy controller for axial power distribution an nuclear reactors

A neuro-fuzzy control algorithm is applied for the core power distribution in a pressurized water reactor. The inputs of the neural fuzzy system are composed of data from each region of the reactor core. Rule outputs consist of linear combinations of their inputs (first-order Sugeno-Takagi type). The consequent and antecedent parameters of the fuzzy rules are updated by the backpropagation

Man Gyun Na; B. R. Upadhyaya

1998-01-01

95

Adaptive Neuro-Fuzzy Methodology for Noise Assessment of Wind Turbine

Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. PMID:25075621

Shamshirband, Shahaboddin; Petkovi?, Dalibor; Hashim, Roslan; Motamedi, Shervin

2014-01-01

96

Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.

Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method. PMID:25075621

Shamshirband, Shahaboddin; Petkovi?, Dalibor; Hashim, Roslan; Motamedi, Shervin

2014-01-01

97

In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an

Masoud Sadeghian; Alireza Fatehi

2009-01-01

98

A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling

Intelligent computing tools based on fuzzy logic and Artificial Neural Networks (ANN) have been successfully applied in various problems with superior performances. A new approach of combining these two powerful AI tools, known as neuro-fuzzy systems, has increasingly attracted scientists in different fields. Although many studies have been carried out using this approach in pattern recognition and signal processing, few

Amin Talei; Lloyd Hock Chye Chua; Chai Quek

2010-01-01

99

Prediction of Solar Activity Based on Neuro-Fuzzy Modeling

NASA Astrophysics Data System (ADS)

This paper presents an application of the neuro-fuzzy modeling to analyze the time series of solar activity, as measured through the relative Wolf number. The neuro-fuzzy structure is optimized based on the linear adapted genetic algorithm with controlling population size (LAGA-POP). Initially, the dimension of the time series characteristic attractor is obtained based on the smallest regularity criterion (RC) and the neuro-fuzzy model. Then the performance of the proposed approach, in forecasting yearly sunspot numbers, is favorably compared to that of other published methods. Finally, a comparison predictions for the remaining part of the 22nd and the whole 23rd cycle of the solar activity are presented.

Attia, Abdel-Fattah; Abdel-Hamid, Rabab; Quassim, Maha

2005-03-01

100

A novel approach to neuro-fuzzy classification.

A new model for neuro-fuzzy (NF) classification systems is proposed. The motivation is to utilize the feature-wise degree of belonging of patterns to all classes that are obtained through a fuzzification process. A fuzzification process generates a membership matrix having total number of elements equal to the product of the number of features and classes present in the data set. These matrix elements are the input to neural networks. The effectiveness of the proposed model is established with four benchmark data sets (completely labeled) and two remote sensing images (partially labeled). Different performance measures such as misclassification, classification accuracy and kappa index of agreement for completely labeled data sets, and beta index of homogeneity and Davies-Bouldin (DB) index of compactness for remotely sensed images are used for quantitative analysis of results. All these measures supported the superiority of the proposed NF classification model. The proposed model learns well even with a lower percentage of training data that makes the system fast. PMID:19004614

Ghosh, Ashish; Shankar, B Uma; Meher, Saroj K

2009-01-01

101

The main purpose of the most research, especially the economic one is to access a good estimate as well as the prediction for the future. The last objective is to explore the future by which the economic plan is adopted, and the strategic policy is development. The success or failure of these plans and strategies depends on the credibility of

Wang XiangJun; Muzahem M. Y. Al-Hashimi

2012-01-01

102

Background Intelligent management of wearable applications in rehabilitation requires an understanding of the current context, which is constantly changing over the rehabilitation process because of changes in the person's status and environment. This paper presents a dynamic recurrent neuro-fuzzy system that implements expert-and evidence-based reasoning. It is intended to provide context-awareness for wearable intelligent agents/assistants (WIAs). Methods The model structure includes the following types of signals: inputs, states, outputs and outcomes. Inputs are facts or events which have effects on patients' physiological and rehabilitative states; different classes of inputs (e.g., facts, context, medication, therapy) have different nonlinear mappings to a fuzzy "effect." States are dimensionless linguistic fuzzy variables that change based on causal rules, as implemented by a fuzzy inference system (FIS). The FIS, with rules based on expertise and evidence, essentially defines the nonlinear state equations that are implemented by nuclei of dynamic neurons. Outputs, a function of weighing of states and effective inputs using conventional or fuzzy mapping, can perform actions, predict performance, or assist with decision-making. Outcomes are scalars to be extremized that are a function of outputs and states. Results The first example demonstrates setup and use for a large-scale stroke neurorehabilitation application (with 16 inputs, 12 states, 5 outputs and 3 outcomes), showing how this modelling tool can successfully capture causal dynamic change in context-relevant states (e.g., impairments, pain) as a function of input event patterns (e.g., medications). The second example demonstrates use of scientific evidence to develop rule-based dynamic models, here for predicting changes in muscle strength with short-term fatigue and long-term strength-training. Conclusion A neuro-fuzzy modelling framework is developed for estimating rehabilitative change that can be applied in any field of rehabilitation if sufficient evidence and/or expert knowledge are available. It is intended to provide context-awareness of changing status through state estimation, which is critical information for WIA's to be effective. PMID:15985181

Wang, Yu; Winters, Jack M

2005-01-01

103

BACKGROUND: Intelligent management of wearable applications in rehabilitation requires an understanding of the current context, which is constantly changing over the rehabilitation process because of changes in the person's status and environment. This paper presents a dynamic recurrent neuro-fuzzy system that implements expert-and evidence-based reasoning. It is intended to provide context-awareness for wearable intelligent agents\\/assistants (WIAs). METHODS: The model structure

Yu Wang; Jack M Winters

2005-01-01

104

Combining classifiers of pesticides toxicity through a neuro-fuzzy approach

Combining classifiers of pesticides toxicity through a neuro-fuzzy approach Emilio Benfenati1 to integrate various approaches. The goal of this research is to apply neuro-fuzzy networks to provide an improvement in combining the results of five classifiers applied in toxicity of pesticides. Nevertheless

Gini, Giuseppina

105

of statistics-based attempts to predict AF occurrence may be overcome using a hybrid neuro-fuzzy predictionClassification of Atrial Fibrillation prone Patients using Electrocardiographic Parameters in Neuro-Fuzzy postoperative AF often lead to longer hospital stays and higher heath care costs. The literature showed that AF

Simon, Dan

106

WAVELET PACKET TRANSFORM AND NEURO-FUZZY APPROACH TO HANDWRITTEN CHARACTER RECOGNITION

WAVELET PACKET TRANSFORM AND NEURO-FUZZY APPROACH TO HANDWRITTEN CHARACTER RECOGNITION SREELA SASI character recognition by combining wavelet packet transform with neuro- fuzzy approach. The time automatically adapts the transform to best match the characteristics of the signal, minimizing the additive cost

Schwiebert, Loren

107

Backpropagation through time training of a neuro-fuzzy controller.

The paper considers gradient training of fuzzy logic controller (FLC) presented in the form of neural network structure. The proposed neuro-fuzzy structure allows keeping linguistic meaning of fuzzy rule base. Its main adjustable parameters are shape determining parameters of the linguistic variables fuzzy values as well as that of the used as intersection operator parameterized T-norm. The backpropagation through time method was applied to train neuro-FLC for a highly non-linear plant (a biotechnological process). The obtained results are discussed with respect to adjustable parameters rationality. Conclusions are made with respect to the appropriate intersection operations too. PMID:20945520

Koprinkova-Hristova, Petia

2010-10-01

108

Recurrent neuro-fuzzy networks for nonlinear process modeling.

A type of recurrent neuro-fuzzy network is proposed in this paper to build long-term prediction models for nonlinear processes. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification which is essentially the interpolation of local model outputs. This modeling strategy utilizes both process knowledge and process input output data. Process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights. Process input output data are used to train the network. Network weights are such trained so that the long-term prediction errors are minimized. Through training, membership functions of fuzzy operating regions are refined and local models are learn. Based on the recurrent neuro-fuzzy network model, a novel type of nonlinear model-based long range predictive controller can be developed and it consists of several local linear model-based predictive controllers. Local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions. This control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model-based predictive control. The techniques have been successfully applied to the modeling and control of a neutralization process. PMID:18252529

Zhang, J; Morris, A J

1999-01-01

109

An intelligent load shedding scheme using neural networks and neuro-fuzzy.

Load shedding is some of the essential requirement for maintaining security of modern power systems, particularly in competitive energy markets. This paper proposes an intelligent scheme for fast and accurate load shedding using neural networks for predicting the possible loss of load at the early stage and neuro-fuzzy for determining the amount of load shed in order to avoid a cascading outage. A large scale electrical power system has been considered to validate the performance of the proposed technique in determining the amount of load shed. The proposed techniques can provide tools for improving the reliability and continuity of power supply. This was confirmed by the results obtained in this research of which sample results are given in this paper. PMID:20039470

Haidar, Ahmed M A; Mohamed, Azah; Al-Dabbagh, Majid; Hussain, Aini; Masoum, Mohammad

2009-12-01

110

1 INTRODUCTION In Nuclear Power Plant (NPP) systems, effective

to anticipate, diag- nose and control abnormal events in a timely man- ner, and to prevent high economic losses-driven approaches for condition monitoring of engineering systems. In par- ticular, Support Vector Regression (SVR modeling combined with the Industrial Source Complex (ISC) model and an Adaptive Neuro-Fuzzy Inference Sys

Paris-Sud XI, UniversitÃ© de

111

A genetic-based neuro-fuzzy approach for prediction of solar activity

NASA Astrophysics Data System (ADS)

This paper presents an application of the neuro-fuzzy modeling to analyze the time series of solar activity, as measured through the relative Wolf number. The neuro-fuzzy structure will be optimized based on the linear adapted genetic algorithm with controlling population size (LAGA-POP). First, the dimension of the time series characteristic attractor is obtained based on the smallest Regularity Criterion (RC) and the neuro-fuzzy modeling. Second, after describing the neuro-fuzzy structure and optimizing its parameters based on LAGA-POP, the performance of the present approach in forecasting yearly sunspot numbers is favorably compared to that of other published methods. Finally, the comparison predictions for the remaining part of the 22nd and the whole 23rd cycle of solar activity are presented.

Attia, Abdel-Fattah A.; Abdel-Hamid, Rabab H.; Quassim, Maha

2004-09-01

112

Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. In this paper, some new hybrid adaptive neuro-fuzzy control algorithms (ANFIS) have been proposed for manipulator control with uncertainties. These hybrid controllers consist of adaptive neuro-fuzzy controllers and conventional controllers. The outputs of these controllers are applied to produce the final actuation signal based on current position and velocity errors. Numerical simulation using the dynamic model of six DOF puma robot arm with uncertainties shows the effectiveness of the approach in trajectory tracking problems. Performance indices of RMS error, maximum error are used for comparison. It is observed that the hybrid adaptive neuro-fuzzy controllers perform better than only conventional/adaptive controllers and in particular hybrid controller structure consisting of adaptive neuro-fuzzy controller and critically damped inverse dynamics controller. PMID:19523623

Alavandar, Srinivasan; Nigam, M J

2009-10-01

113

On neurobiological, neuro-fuzzy, machine learning, and statistical pattern recognition techniques

In this paper, we propose two new neuro-fuzzy schemes, one for classification and one for clustering problems. The classification scheme is based on Simpson's fuzzy min-max method (1992, 1993) and relaxes some assumptions he makes. This enables our scheme to handle mutually nonexclusive classes. The neuro-fuzzy clustering scheme is a multiresolution algorithm that is modeled after the mechanics of human

Anupam Joshi; Narendran Ramakrishman; Elias N. Houstis; John R. Rice

1997-01-01

114

NASA Astrophysics Data System (ADS)

SummaryModeling of rainfall-runoff dynamics is one of the most studied topics in hydrology due to its essential application to water resources management. Recently, artificial intelligence has gained much popularity for calibrating the nonlinear relationships inherent in the rainfall-runoff process. In this study, the advantages of artificial neural networks and neuro-fuzzy system in continuous modeling of the daily and hourly behaviour of runoff were examined. Three different adaptive techniques were constructed and examined namely, Levenberg-Marquardt feed forward neural network, Bayesian regularization feed forward neural network, and neuro-fuzzy. In addition, the effects of data transformation on model performance were also investigated. This was done by examining the performance of the three network architectures and training algorithms using both raw and transformed data. Through inspection of the results it was found that although the model built on transformed data outperforms the model built on raw data, no significant differences were found between the forecast accuracies of the three examined models. A detailed comparison of the overall performance indicated that the neuro-fuzzy model performed better than both the Levenberg-Marquardt-FFNN and the Bayesian regularization-FFNN. In order to enable users to process the data easily, a graphic user interface (GUI) was developed. This program allows users to process the rainfall-runoff data, to train/test the model using various input options and to visualize results.

Aqil, Muhammad; Kita, Ichiro; Yano, Akira; Nishiyama, Soichi

2007-04-01

115

A Neuro-Fuzzy Approach in the Classification of Students' Academic Performance

Classifying the student academic performance with high accuracy facilitates admission decisions and enhances educational services at educational institutions. The purpose of this paper is to present a neuro-fuzzy approach for classifying students into different groups. The neuro-fuzzy classifier used previous exam results and other related factors as input variables and labeled students based on their expected academic performance. The results showed that the proposed approach achieved a high accuracy. The results were also compared with those obtained from other well-known classification approaches, including support vector machine, Naive Bayes, neural network, and decision tree approaches. The comparative analysis indicated that the neuro-fuzzy approach performed better than the others. It is expected that this work may be used to support student admission procedures and to strengthen the services of educational institutions. PMID:24302928

2013-01-01

116

This research examines the precision of an adaptive neuro-fuzzy computing technique in estimating the anti-obesity property of a potent medicinal plant in a clinical dietary intervention. Even though a number of mathematical functions such as SPSS analysis have been proposed for modeling the anti-obesity properties estimation in terms of reduction in body mass index (BMI), body fat percentage, and body weight loss, there are still disadvantages of the models like very demanding in terms of calculation time. Since it is a very crucial problem, in this paper a process was constructed which simulates the anti-obesity activities of caraway (Carum carvi) a traditional medicine on obese women with adaptive neuro-fuzzy inference (ANFIS) method. The ANFIS results are compared with the support vector regression (SVR) results using root-mean-square error (RMSE) and coefficient of determination (R(2)). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The following statistical characteristics are obtained for BMI loss estimation: RMSE=0.032118 and R(2)=0.9964 in ANFIS testing and RMSE=0.47287 and R(2)=0.361 in SVR testing. For fat loss estimation: RMSE=0.23787 and R(2)=0.8599 in ANFIS testing and RMSE=0.32822 and R(2)=0.7814 in SVR testing. For weight loss estimation: RMSE=0.00000035601 and R(2)=1 in ANFIS testing and RMSE=0.17192 and R(2)=0.6607 in SVR testing. Because of that, it can be applied for practical purposes. PMID:25453384

Kazemipoor, Mahnaz; Hajifaraji, Majid; Radzi, Che Wan Jasimah Bt Wan Mohamed; Shamshirband, Shahaboddin; Petkovi?, Dalibor; Mat Kiah, Miss Laiha

2015-01-01

117

Evolutionary Local Search of Fuzzy Rules through a novel Neuro-Fuzzy encoding method.

This paper proposes a new approach for constructing fuzzy knowledge bases using evolutionary methods. We have designed a genetic algorithm that automatically builds neuro-fuzzy architectures based on a new indirect encoding method. The neuro-fuzzy architecture represents the fuzzy knowledge base that solves a given problem; the search for this architecture takes advantage of a local search procedure that improves the chromosomes at each generation. Experiments conducted both on artificially generated and real world problems confirm the effectiveness of the proposed approach. PMID:14629866

Carrascal, A; Manrique, D; Ríos, J; Rossi, C

2003-01-01

118

Trends and Issues in Fuzzy Control and Neuro-Fuzzy Modeling

NASA Technical Reports Server (NTRS)

Everyday experience in building and repairing things around the home have taught us the importance of using the right tool for the right job. Although we tend to think of a 'job' in broad terms, such as 'build a bookcase,' we understand well that the 'right job' associated with each 'right tool' is typically a narrowly bounded subtask, such as 'tighten the screws.' Unfortunately, we often lose sight of this principle when solving engineering problems; we treat a broadly defined problem, such as controlling or modeling a system, as a narrow one that has a single 'right tool' (e.g., linear analysis, fuzzy logic, neural network). We need to recognize that a typical real-world problem contains a number of different sub-problems, and that a truly optimal solution (the best combination of cost, performance and feature) is obtained by applying the right tool to the right sub-problem. Here I share some of my perspectives on what constitutes the 'right job' for fuzzy control and describe recent advances in neuro-fuzzy modeling to illustrate and to motivate the synergistic use of different tools.

Chiu, Stephen

1996-01-01

119

Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning

NASA Astrophysics Data System (ADS)

SummaryA study using local learning Neuro-Fuzzy System (NFS) was undertaken for a rainfall-runoff modeling application. The local learning model was first tested on three different catchments: an outdoor experimental catchment measuring 25 m2 (Catchment 1), a small urban catchment 5.6 km2 in size (Catchment 2), and a large rural watershed with area of 241.3 km2 (Catchment 3). The results obtained from the local learning model were comparable or better than results obtained from physically-based, i.e. Kinematic Wave Model (KWM), Storm Water Management Model (SWMM), and Hydrologiska Byråns Vattenbalansavdelning (HBV) model. The local learning algorithm also required a shorter training time compared to a global learning NFS model. The local learning model was next tested in real-time mode, where the model was continuously adapted when presented with current information in real time. The real-time implementation of the local learning model gave better results, without the need for retraining, when compared to a batch NFS model, where it was found that the batch model had to be retrained periodically in order to achieve similar results.

Talei, Amin; Chua, Lloyd Hock Chye; Quek, Chai; Jansson, Per-Erik

2013-04-01

120

NASA Astrophysics Data System (ADS)

The correct observation/estimation of surface incoming solar radiation (RS) is very important for many agricultural, meteorological and hydrological related applications. While most weather stations are provided with sensors for air temperature detection, the presence of sensors necessary for the detection of solar radiation is not so habitual and the data quality provided by them is sometimes poor. In these cases it is necessary to estimate this variable. Temperature based modeling procedures are reported in this study for estimating daily incoming solar radiation by using Gene Expression Programming (GEP) for the first time, and other artificial intelligence models such as Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Traditional temperature based solar radiation equations were also included in this study and compared with artificial intelligence based approaches. Root mean square error (RMSE), mean absolute error (MAE) RMSE-based skill score (SSRMSE), MAE-based skill score (SSMAE) and r2 criterion of Nash and Sutcliffe criteria were used to assess the models' performances. An ANN (a four-input multilayer perceptron with ten neurons in the hidden layer) presented the best performance among the studied models (2.93 MJ m-2 d-1 of RMSE). A four-input ANFIS model revealed as an interesting alternative to ANNs (3.14 MJ m-2 d-1 of RMSE). Very limited number of studies has been done on estimation of solar radiation based on ANFIS, and the present one demonstrated the ability of ANFIS to model solar radiation based on temperatures and extraterrestrial radiation. By the way this study demonstrated, for the first time, the ability of GEP models to model solar radiation based on daily atmospheric variables. Despite the accuracy of GEP models was slightly lower than the ANFIS and ANN models the genetic programming models (i.e., GEP) are superior to other artificial intelligence models in giving a simple explicit equation for the phenomenon which shows the relationship between the input and output parameters. This study provided new alternatives for solar radiation estimation based on temperatures.

Landeras, G.; López, J. J.; Kisi, O.; Shiri, J.

2012-04-01

121

In the present study, activated carbon (AC) simply derived from Pistacia khinjuk and characterized using different techniques such as SEM and BET analysis. This new adsorbent was used for methylene blue (MB) adsorption. Fitting the experimental equilibrium data to various isotherm models shows the suitability and applicability of the Langmuir model. The adsorption mechanism and rate of processes was investigated by analyzing time dependency data to conventional kinetic models and it was found that adsorption follow the pseudo-second-order kinetic model. Principle component analysis (PCA) has been used for preprocessing of input data and genetic algorithm optimization have been used for prediction of adsorption of methylene blue using activated carbon derived from P. khinjuk. In our laboratory various activated carbon as sole adsorbent or loaded with various nanoparticles was used for removal of many pollutants (Ghaedi et al., 2012). These results indicate that the small amount of proposed adsorbent (1.0g) is applicable for successful removal of MB (RE>98%) in short time (45min) with high adsorption capacity (48-185mgg(-1)). PMID:23849465

Ghaedi, M; Ghaedi, A M; Abdi, F; Roosta, M; Vafaei, A; Asghari, A

2013-10-01

122

CLASSIFICATION OF ATRIAL FIBRILLATION PRONE PATIENTS USING ELECTROCARDIOGRAPHIC PARAMETERS IN NEURO-FUZZY hospital stays and higher heath care costs. The literature showed that AF may be preceded by changes in electrocardiogram (ECG) characteristics such as premature atrial activity, heart rate variability (HRV), and P- wave

Simon, Dan

123

This research illustrates semi-active structural control of a three-story nonlinear building using magnetorheological dampers and a neuro-fuzzy algorithm. The structure being studied was developed for a third generation benchmark problem...

Likhitruangsilp, Visit

2002-01-01

124

New direct torque neuro-fuzzy control based SVM-three level inverter-fed induction motor

In this paper, a novel direct torque neuro-fuzzy control (DTCNF) scheme combining with space voltage modulation (SVM) technique\\u000a of a three levels inverter is presented. Using neuro-fuzzy technique, the reference space voltage vector can be obtained dynamically\\u000a in terms of torque error, stator flux error and the angle of stator flux. Compared with conventional direct torque control\\u000a (C_DTC), in this

Toufouti Riad; Benalla Hocine; Meziane Salima

2010-01-01

125

A hybrid neuro-fuzzy filter for edge preserving restoration of images corrupted by impulse noise.

A new operator for restoring digital images corrupted by impulse noise is presented. The proposed operator is a hybrid filter obtained by appropriately combining a median filter, an edge detector, and a neuro-fuzzy network. The internal parameters of the neuro-fuzzy network are adaptively optimized by training. The training is easily accomplished by using simple artificial images that can be generated in a computer. The most distinctive feature of the proposed operator over most other operators is that it offers excellent line, edge, detail, and texture preservation performance while, at the same time, effectively removing noise from the input image. Extensive simulation experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image. PMID:16579379

Yüksel, M Emin

2006-04-01

126

NASA Astrophysics Data System (ADS)

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

127

NASA Astrophysics Data System (ADS)

The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e.g., DT, SVM and ANFIS) is viable. As far as the performance of the models are concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative susceptibility.

Pradhan, Biswajeet

2013-02-01

128

Experimental Validation of a Neuro-Fuzzy Approach to Phasing the SIBOA Segmented Mirror Testbed

NASA Astrophysics Data System (ADS)

NASA is preparing to launch the Next Generation Space Telescope (NGST). This telescope will be larger than the Hubble Space Telescope, be launched on an Atlas missile rather than the Space Shuttle, have a segmented primary mirror, and be placed in a higher orbit. All these differences pose significant challenges. This effort addresses the challenge of aligning the segments of the primary mirror during the initial deployment. The segments need to piston values aligned to within one tenth of a wavelength. The present study considers using a neuro-fuzzy model of the Fraunhofer diffraction theory. The intention of the current study was to experimentally verify the algorithm derived earlier. The experimental study was to be performed on the SIBOA (Systematic Image Based Optical Alignment) test bed. Unfortunately the hardware/software for SIBOA was not ready by the end of the study period. We did succeed in capturing several images of two stacked segments with various relative phases. These images can be used to calibrate the algorithm for future implementation. This effort is a continuation of prior work. The basic effort involves developing a closed loop control algorithm to phase a segmented mirror test bed (SIBOA). The control algorithm is based on a neuro-fuzzy model of SIBOA and incorporates nonlinear observers built from observer banks. This effort involves implementing the algorithm on the SIBOA test bed.

Olivier, Philip D.

2002-07-01

129

Neuro-fuzzy quantification of personal perceptions of facial images based on a limited data set.

Artificial neural networks are nonlinear techniques which typically provide one of the most accurate predictive models perceiving faces in terms of the social impressions they make on people. However, they are often not suitable to be used in many practical application domains because of their lack of transparency and comprehensibility. This paper proposes a new neuro-fuzzy method to investigate the characteristics of the facial images perceived as Iyashi by one hundred and fourteen subjects. Iyashi is a Japanese word used to describe a peculiar phenomenon that is mentally soothing, but is yet to be clearly defined. In order to gain a clear insight into the reasoning made by the nonlinear prediction models such as holographic neural networks (HNN) in the classification of Iyashi expressions, the interpretability of the proposed fuzzy-quantized HNN (FQHNN) is improved by reducing the number of input parameters, creating membership functions and extracting fuzzy rules from the responses provided by the subjects about a limited dataset of 20 facial images. The experimental results show that the proposed FQHNN achieves 2-8% increase in the prediction accuracy compared with traditional neuro-fuzzy classifiers while it extracts 35 fuzzy rules explaining what characteristics a facial image should have in order to be classified as Iyashi-stimulus for 87 subjects. PMID:22128002

Diago, Luis; Kitaoka, Tetsuko; Hagiwara, Ichiro; Kambayashi, Toshiki

2011-12-01

130

Verifying Stability of Dynamic Soft-Computing Systems

NASA Technical Reports Server (NTRS)

Soft computing is a general term for algorithms that learn from human knowledge and mimic human skills. Example of such algorithms are fuzzy inference systems and neural networks. Many applications, especially in control engineering, have demonstrated their appropriateness in building intelligent systems that are flexible and robust. Although recent research have shown that certain class of neuro-fuzzy controllers can be proven bounded and stable, they are implementation dependent and difficult to apply to the design and validation process. Many practitioners adopt the trial and error approach for system validation or resort to exhaustive testing using prototypes. In this paper, we describe our on-going research towards establishing necessary theoretic foundation as well as building practical tools for the verification and validation of soft-computing systems. A unified model for general neuro-fuzzy system is adopted. Classic non-linear system control theory and recent results of its applications to neuro-fuzzy systems are incorporated and applied to the unified model. It is hoped that general tools can be developed to help the designer to visualize and manipulate the regions of stability and boundedness, much the same way Bode plots and Root locus plots have helped conventional control design and validation.

Wen, Wu; Napolitano, Marcello; Callahan, John

1997-01-01

131

Characterizing root distribution with adaptive neuro-fuzzy analysis

Technology Transfer Automated Retrieval System (TEKTRAN)

Root-soil relationships are pivotal to understanding crop growth and function in a changing environment. Plant root systems are difficult to measure and remain understudied relative to above ground responses. High variation among field samples often leads to non-significance when standard statistics...

132

Neuro-fuzzy Control of an Intelligent Mobile Robot

This paper describes the reactive controlling of a mobile robotic system using a hybrid approach by adopting both neural network and fuzzy logic, so that, an autonomous robot should move in a crowded unknown environment to reach at a decided goal. A fuzzy logic controller with a set of certain rules is used to obtain a goal reaching task. While

Dinesh Kumar; Kapil Dhama

2012-01-01

133

A neuro-fuzzy architecture for real-time applications

NASA Technical Reports Server (NTRS)

Neural networks and fuzzy expert systems perform the same task of functional mapping using entirely different approaches. Each approach has certain unique features. The ability to learn specific input-output mappings from large input/output data possibly corrupted by noise and the ability to adapt or continue learning are some important features of neural networks. Fuzzy expert systems are known for their ability to deal with fuzzy information and incomplete/imprecise data in a structured, logical way. Since both of these techniques implement the same task (that of functional mapping--we regard 'inferencing' as one specific category under this class), a fusion of the two concepts that retains their unique features while overcoming their individual drawbacks will have excellent applications in the real world. In this paper, we arrive at a new architecture by fusing the two concepts. The architecture has the trainability/adaptibility (based on input/output observations) property of the neural networks and the architectural features that are unique to fuzzy expert systems. It also does not require specific information such as fuzzy rules, defuzzification procedure used, etc., though any such information can be integrated into the architecture. We show that this architecture can provide better performance than is possible from a single two or three layer feedforward neural network. Further, we show that this new architecture can be used as an efficient vehicle for hardware implementation of complex fuzzy expert systems for real-time applications. A numerical example is provided to show the potential of this approach.

Ramamoorthy, P. A.; Huang, Song

1992-01-01

134

Adaptive neuro-fuzzy fusion of sensor data

NASA Astrophysics Data System (ADS)

A framework is proposed, which consolidates the benefits of a fuzzy rationale and a neural system. The framework joins together Kalman separating and delicate processing guideline i.e. ANFIS to structure an effective information combination strategy for the target following framework. A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory. Fuzzy versatile combination calculation is a compelling device to make the genuine quality of the leftover covariance steady with its hypothetical worth. ANFIS indicates great taking in and forecast proficiencies, which makes it a productive device to manage experienced vulnerabilities in any framework. A neural system is presented, which can concentrate the measurable properties of the samples throughout the preparation sessions. Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data. This sagacious estimator is actualized utilizing Matlab/Simulink and the exhibitions are explored.

Petkovi?, Dalibor

2014-11-01

135

NASA Astrophysics Data System (ADS)

Shear wave velocity is a critical physical property of rock, which provides significant data for geomechanical and geophysical studies. This study proposes a multi-step strategy to construct a model estimating shear wave velocity from conventional well log data. During the first stage, three correlation structures, including power law, exponential, and trigonometric were designed to formulate conventional well log data into shear wave velocity. Then, a Genetic Algorithm-Pattern Search tool was used to find the optimal coefficients of these correlations. Due to the different natures of these correlations, they might overestimate/underestimate in some regions relative to each other. Therefore, a neuro-fuzzy algorithm is employed to combine results of intelligently derived formulas. Neuro-fuzzy technique can compensate the effect of overestimation/underestimation to some extent, through the use of fuzzy rules. One set of data points was used for constructing the model and another set of unseen data points was employed to assess the reliability of the propounded model. Results have shown that the hybrid genetic algorithm-pattern search technique is a robust tool for finding the most appropriate form of correlations, which are meant to estimate shear wave velocity. Furthermore, neuro-fuzzy combination of derived correlations was capable of improving the accuracy of the final prediction significantly.

Asoodeh, Mojtaba; Bagheripour, Parisa

2013-06-01

136

Abstract The use of wearable recorders for long-term monitoring of physiological parameters has increased in the last few years. The ambulatory electrocardiogram (A-ECG) signals of five healthy subjects with four body movements or physical activities (PA)-left arm up down, right arm up down, waist twisting and walking-have been recorded using a wearable ECG recorder. The classification of these four PAs has been performed using neuro-fuzzy classifier (NFC) and support vector machines (SVM). The PA classification is based on the distinct, time-frequency features of the extracted motion artifacts contained in recorded A-ECG signals. The motion artifacts in A-ECG signals have been separated first by the discrete wavelet transform (DWT) and the time-frequency features of these motion artifacts have then been extracted using the Gabor transform. The Gabor energy feature vectors have been fed to the NFC and SVM classifiers. Both the classifiers have achieved a PA classification accuracy of over 95% for all subjects. PMID:25641014

Kher, Rahul; Pawar, Tanmay; Thakar, Vishvjit; Shah, Hitesh

2015-02-01

137

NASA Astrophysics Data System (ADS)

Image alignment is considered a key problem in visual inspection applications. The main concerns for such tasks are fast image alignment with subpixel accuracy. About this, neural network-based approaches are very popular in visual inspection because of their high accuracy and efficiency of aligning images. However, such methods are difficult to identify the structure and parameters of neural network. In this study, a Takagi-Sugeno-Kang-type neuro-fuzzy network (NFN) with data-mining-based evolutionary learning algorithm (DMELA) is proposed. Compared with traditional learning algorithms, DMELA combines the self-organization algorithm (SOA), data-mining selection method (DMSM), and regularized least square (RLS) method to not only determine a suitable number of fuzzy rules, but also automatically tune the parameters of NFN. Experimental results are shown to demonstrate superior performance of the DMELA constructed image alignment system over other typical learning algorithms and existing alignment systems. Such system is useful to develop accurate and efficient image alignment systems.

Hsu, Chi-Yao; Cheng, Yi-Chang; Lin, Sheng-Fuu

2011-12-01

138

NASA Astrophysics Data System (ADS)

Functional electrical stimulation (FES) is used to improve motor function after injury to the central nervous system. Some FES systems use artificial sensors to switch between finite control states. To optimize FES control of the complex behavior of the musculo-skeletal system in activities of daily life, it is highly desirable to implement feedback control. In theory, sensory neural signals could provide the required control signals. Recent studies have demonstrated the feasibility of deriving limb-state estimates from the firing rates of primary afferent neurons recorded in dorsal root ganglia (DRG). These studies used multiple linear regression (MLR) methods to generate estimates of limb position and velocity based on a weighted sum of firing rates in an ensemble of simultaneously recorded DRG neurons. The aim of this study was to test whether the use of a neuro-fuzzy (NF) algorithm (the generalized dynamic fuzzy neural networks (GD-FNN)) could improve the performance, robustness and ability to generalize from training to test sets compared to the MLR technique. NF and MLR decoding methods were applied to ensemble DRG recordings obtained during passive and active limb movements in anesthetized and freely moving cats. The GD-FNN model provided more accurate estimates of limb state and generalized better to novel movement patterns. Future efforts will focus on implementing these neural recording and decoding methods in real time to provide closed-loop control of FES using the information extracted from sensory neurons.

Rigosa, J.; Weber, D. J.; Prochazka, A.; Stein, R. B.; Micera, S.

2011-08-01

139

NASA Astrophysics Data System (ADS)

Gravity measurements are utilized at active volcanoes to detect mass changes linked to magma transfer processes and thus to recognize forerunners to paroxysmal volcanic events. Continuous gravity measurements are now increasingly performed at sites very close to active craters, where there is the greatest chance to detect meaningful gravity changes. Unfortunately, especially when used against the adverse environmental conditions usually encountered at such places, gravimeters have been proved to be affected by meteorological parameters, mainly by changes in the atmospheric temperature. The pseudo-signal generated by these perturbations is often stronger than the signal generated by actual changes in the gravity field. Thus, the implementation of well-performing algorithms for reducing the gravity signal for the effect of meteorological parameters is vital to obtain sequences useful from the volcano surveillance standpoint. In the present paper, a Neuro-Fuzzy algorithm, which was already proved to accomplish the required task satisfactorily, is tested over a data set from three gravimeters which worked continuously for about 50 days at a site far away from active zones, where changes due to actual fluctuation of the gravity field are expected to be within a few microgal. After accomplishing the reduction of the gravity series, residuals are within about 15 ?Gal peak-to-peak, thus confirming the capabilities of the Neuro-Fuzzy algorithm under test of performing the required task satisfactorily.

Andò, Bruno; Carbone, Daniele

2004-05-01

140

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. XX, NO. X, MONTH 2013 1 A Neuro-Fuzzy Approach multiscale geometric analysis of non-subsampled contourlet transform and fuzzy-adaptive reduced pulse them as the fuzzy membership values, representing their significance in the corresponding source image

Kundu, Malay Kumar

141

The purpose of this paper is to analyze the electroencephalogram (EEG) signals of imaginary left and right hand movements, an application of Brain-Computer Interface (BCI). We propose here to use an Adaptive Neuron-Fuzzy Inference System (ANFIS) as the classification algorithm. ANFIS has an advantage over many classification algorithms in that it provides a set of parameters and linguistic rules that can be useful in interpreting the relationship between extracted features. The continuous wavelet transform will be used to extract highly representative features from selected scales. The performance of ANFIS will be compared with the well-known support vector machine classifier. PMID:18002681

Darvishi, Sam; Al-Ani, Ahmed

2007-01-01

142

. The aim of this study was to employ neuro-fuzzy logic and regression calculations to determine the accuracy of prediction\\u000a of the power output (P) of the maximal lactate steady-state (MLSS) on a cycle ergometer calculated from the results of incremental tests. A group\\u000a of 17 male and 17 female sports students underwent two incremental tests (a 1 min test T1: initial

Gerhard Smekal; Arno Scharl; Serge P. von Duvillard; Rochus Pokan; Arnold Baca; Ramon Baron; Harald Tschan; Peter Hofmann; Norbert Bachl

2002-01-01

143

In the present research, two neuro-fuzzy models and a neural network model are presented for predictions of material removal\\u000a rate (MRR), tool wear rate (TWR), and radial overcut (G) in die sinking electrical discharge machining (EDM) process for American Iron and Steel Institute D2 tool steel with copper\\u000a electrode. The discharge current (I\\u000a p), pulse duration (T\\u000a on), duty cycle

Mohan Kumar Pradhan; Chandan Kumar Biswas

2010-01-01

144

For modern metals industries using thermomechanical processing, off-line modelling and on-line control based on physical knowledge are highly desirable in order to improve the quality of existing materials, the time and cost efficiency, and to develop new materials. Neural network and neuro-fuzzy models are the most popular tools, but they do not embed physical knowledge. On the other hand, current

Q. Zhu; M. F. Abbod; J. Talamantes-Silva; C. M. Sellars; D. A. Linkens; J. H. Beynon

2003-01-01

145

Bearing fault diagnosis based on wavelet transform and fuzzy inference

This paper deals with a new scheme for the diagnosis of localised defects in ball bearings based on the wavelet transform and neuro-fuzzy classification. Vibration signals for normal bearings, bearings with inner race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform was used to process the accelerometer signals and to generate feature vectors. An

Xinsheng Lou; Kenneth A Loparo

2004-01-01

146

Strong Inference for Systems Biology

Platt's essay on strong inference (Platt, J.R., 1964. Science 146, 347-353) illuminates a rational approach to scientific inquiry that integrates seamlessly with current investigations on the operation of complex biological systems. Yet in reexamining the 1964 essay in light of current trends, it is apparent that the groundbreaking approach has failed to become universal. Here it is argued that both

Daniel A. Beard; Martin J. Kushmerick

2009-01-01

147

In this study, we introduce general frame of MAny Connected Intelligent Particles Systems (MACIPS). Connections and interconnections between particles get a complex behavior of such merely simple system (system in system).Contribution of natural computing, under information granulation theory, are the main topic of this spacious skeleton. Upon this clue, we organize different algorithms involved a few prominent intelligent computing and approximate reasoning methods such as self organizing feature map (SOM)[9], Neuro- Fuzzy Inference System[10], Rough Set Theory (RST)[11], collaborative clustering, Genetic Algorithm and Ant Colony System. Upon this, we have employed our algorithms on the several engineering systems, especially emerged systems in Civil and Mineral processing. In other process, we investigated how our algorithms can be taken as a linkage of government-society interaction, where government catches various fashions of behavior: solid (absolute) or flexible. So, transition of such society, by chan...

Owladeghaffari, Hamed

2008-01-01

148

Simulation of torsional shear test results with neuro-fuzzy control system

In the first part of this study, a series of stress-controlled hollow cylinder cyclic torsional triaxial shear tests were conducted on loose to medium dense saturated samples of clean Toyoura sand to investigate its liquefaction behavior. A uniform cyclic sinusoidal loading at a 0.1Hz frequency was applied to air-pluviated samples where confining pressure and relative density was varied. Cyclic shear

S. Altun; A. B. Göktepe; A. M. Ansal; C. Akgüner

2009-01-01

149

A Stable NeuroFuzzy Controller for Output Tracking in Composite Nonlinear Systems \\Lambda

Â008 and NSC 84Â2212ÂEÂ001Â003. Parts of this work have been presented in 1996 IEEE InternaÂ tional Conference in response to plant outputs and external commands are related to or resulted from experience, that is process control [4], [5], robot control [6] and automobile transmission control [7]. However, the majority

Chen, Sheng-Wei

150

Inference Concerning Physical Systems

NASA Astrophysics Data System (ADS)

The question of whether the universe "is" just an information- processing system has been extensively studied in physics. To address this issue, the canonical forms of information processing in physical systems - observation, prediction, control and memory - were analyzed in [24]. Those forms of information processing are all inherently epistemological; they transfer information concerning the universe as a whole into a scientist's mind. Accordingly, [24] formalized the logical relationship that must hold between the state of a scientist's mind and the state of the universe containing the scientist whenever one of those processes is successful. This formalization has close analogs in the analysis of Turing machines. In particular, it can be used to define an "informational analog" of algorithmic information complexity. In addition, this formalization allows us to establish existence and impossibility results concerning observation, prediction, control and memory. The impossibility results establish that Laplace was wrong to claim that even in a classical, non-chaotic universe the future can be unerringly predicted, given sufficient knowledge of the present. Alternatively, the impossibility results can be viewed as a non-quantum mechanical "uncertainty principle". Here I present a novel motivation of the formalization introduced in [24] and extend some of the associated impossibility results.

Wolpert, David H.

151

A Comparison of ANFIS, MLP and SVM in Identification of Chemical Mehmet Onder Efe

Abstract-- This paper presents a comparison of Adaptive Neuro Fuzzy Inference Systems (ANFIS), Multilayer of the well known strategies is Adaptive Neuro Fuzzy Inference Systems exploiting the power of verbal the achievement of optimum regression functions. This paper is organized as follows. The second section introduces

Efe, Mehmet Ã?nder

152

Flood Forecasting in River System Using ANFIS

The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.

Ullah, Nazrin; Choudhury, P. [Dept. of Civil Eng., NIT, Silchar (India)

2010-10-26

153

Flood Forecasting in River System Using ANFIS

NASA Astrophysics Data System (ADS)

The aim of the present study is to investigate applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood flow in a river system. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. The technique is applied to forecast discharge at a downstream station using flow information at various upstream stations. A total of three years data has been selected for the implementation of this model. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate efficiency of the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and Coefficient of Efficiency (CE) are used to evaluate performance of the ANFIS models in forecasting river flood. The values of the indices show that ANFIS model can accurately and reliably be used to forecast flood in a river system.

Ullah, Nazrin; Choudhury, P.

2010-10-01

154

Exploiting expert systems in cardiology: a comparative study.

An improved Adaptive Neuro-Fuzzy Inference System (ANFIS) in the field of critical cardiovascular diseases is presented. The system stems from an earlier application based only on a Sugeno-type Fuzzy Expert System (FES) with the addition of an Artificial Neural Network (ANN) computational structure. Thus, inherent characteristics of ANNs, along with the human-like knowledge representation of fuzzy systems are integrated. The ANFIS has been utilized into building five different sub-systems, distinctly covering Coronary Disease, Hypertension, Atrial Fibrillation, Heart Failure, and Diabetes, hence aiding doctors of medicine (MDs), guide trainees, and encourage medical experts in their diagnoses centering a wide range of Cardiology. The Fuzzy Rules have been trimmed down and the ANNs have been optimized in order to focus into each particular disease and produce results ready-to-be applied to real-world patients. PMID:25417018

Economou, George-Peter K; Sourla, Efrosini; Stamatopoulou, Konstantina-Maria; Syrimpeis, Vasileios; Sioutas, Spyros; Tsakalidis, Athanasios; Tzimas, Giannis

2015-01-01

155

A wind energy generator for smart grid applications using wireless coding neuro-fuzzy power control

The wind energy generation is the huge driver behind the push for supergrids and cross-border infrastructure for renewable energy systems into smart grids. To provide balance supply, demand, and storage of energy over a region in a much more efficient manner than it is done today, smart grids will need to use an advanced communication infrastructure into a robust control

J. L. Azcue; A. J. Sguarezi Filho; C. E. Capovilla; Ivan R. S. Casella; E. Ruppert

2012-01-01

156

During the last decade, intraspinal microstimulation (ISMS) has been proposed as a potential technique for restoring motor function in paralyzed limbs. A major challenge to restoration of a desired functional limb movement through the use of ISMS is the development of a robust control strategy for determining the stimulation patterns. Accurate and stable control of limbs by functional intraspinal microstimulation is a very difficult task because neuromusculoskeletal systems have significant nonlinearity, time variability, large latency and time constant, and muscle fatigue. Furthermore, the controller must be able to compensate the effect of the dynamic interaction between motor neuron pools and electrode sites during ISMS. In this paper, we present a robust strategy for multi-joint control through ISMS in which the system parameters are adapted online and the controller requires no offline training phase. The method is based on the combination of sliding mode control with fuzzy logic and neural control. Extensive experiments on six rats are provided to demonstrate the robustness, stability, and tracking accuracy of the proposed method. Despite the complexity of the spinal neuronal networks, our results show that the proposed strategy could provide accurate tracking control with fast convergence and could generate control signals to compensate for the effects of muscle fatigue. PMID:22711783

Asadi, Ali-Reza; Erfanian, Abbas

2012-07-01

157

An inference engine for embedded diagnostic systems

NASA Technical Reports Server (NTRS)

The implementation of an inference engine for embedded diagnostic systems is described. The system consists of two distinct parts. The first is an off-line compiler which accepts a propositional logical statement of the relationship between facts and conclusions and produces data structures required by the on-line inference engine. The second part consists of the inference engine and interface routines which accept assertions of fact and return the conclusions which necessarily follow. Given a set of assertions, it will generate exactly the conclusions which logically follow. At the same time, it will detect any inconsistencies which may propagate from an inconsistent set of assertions or a poorly formulated set of rules. The memory requirements are fixed and the worst case execution times are bounded at compile time. The data structures and inference algorithms are very simple and well understood. The data structures and algorithms are described in detail. The system has been implemented on Lisp, Pascal, and Modula-2.

Fox, Barry R.; Brewster, Larry T.

1987-01-01

158

Inference System Integration Via Logic Morphisms

NASA Technical Reports Server (NTRS)

This is a final report on the accomplishments during the period of the NASA grant. The work on inference servers accomplished the integration of the SLANG logic (Specware's default specification logic) with a number of inference servers in order to make their complementary strengths available. These inverence servers are (1) SNARK. (2) Gandalf, Setheo, and Spass, (3) the Prototype Verification System (PVS) from SRI. (4) HOL98. We designed and implemented MetaSlang, an ML-like language, which we are using to specify and implement all our logic morphisms.

Bjorner, Nikolaj S.; Espinosa, David

2000-01-01

159

In industrial production processes, materials and different forms of energy are provided, converted, stored and transported. Environmental impacts can be identified at any stage of the energy and material flow process. Due to the fact that production units and processes are interconnected with energy and material flows, it is of special interest to develop production control mechanisms, which control the

Axel Tuma; Hans-Dietrich Haasis; Otto Rentz

1996-01-01

160

In industrial production processes, materials and different forms of energy are provided, transformed respectively converted, stored and transported. With this process joint products in different states of aggregation are emitted. Environmental impacts can be identified at any stage of the energy and material flow process. Due to the fact that production units and processes are interconnected with energy and material

A. Tuma; H.-D. Haasis; O. Rentz

1996-01-01

161

The wide and growing usage of cellular phones has raised questions about the possible health risks associated with radio frequency (RF) electromagnetic fields. Since it is very difficult to accurately measure and quantify the RF exposure level for all individuals, it would be helpful for epidemiologists and cellular phone users to obtain a time estimate of specific radiation exposure generating

T. N. Kapetanakis; A. Kaprana; I. O. Vardiambasis; M. P. Ioannidou

2011-01-01

162

Inference problems in multilevel secure database management systems

An inference channel in a database is a means by which one can infer data classified at a high level from data classified at a low level. The in-ference problem is the problem of detecting and removing inference chan-nels. It is clear that inference problems are of vital interest to the designers and users of secure databases. Database management sys-tems

S. Jajodia; C. Meadows

1995-01-01

163

Collision avoidance is currently one of the main research areas in road intelligent transportation systems. Among the different possibilities available in the literature, the prediction of abrupt maneuvers has been shown to be useful in reducing the possibility of collisions. A supervised version of dynamic Fuzzy Adaptive System ART-based (dFasArt), which is a neuronal-architecture-based method that employs dynamic activation functions

Rafael Toledo-Moreo; Miguel Pinzolas-Prado; Jose Manuel Cano-Izquierdo

2010-01-01

164

In this paper a novel sensorless adaptive neurofuzzy speed controller for induction motor derives is formulated. An artificial neural network (ANN) is adopted to estimate the motor speed and thus provide a sensorless speed estimator system. The performance of the proposed adaptive neurofuzzy speed controller is evaluated for a wide range of operating conditions for induction motor. These include startup,

Farzan Rashidi

2004-01-01

165

One of the most important parts of a cement factory is the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not

Masoud Sadeghian; Alireza Fatehi

2009-01-01

166

An Ada inference engine for expert systems

NASA Technical Reports Server (NTRS)

The purpose is to investigate the feasibility of using Ada for rule-based expert systems with real-time performance requirements. This includes exploring the Ada features which give improved performance to expert systems as well as optimizing the tradeoffs or workarounds that the use of Ada may require. A prototype inference engine was built using Ada, and rule firing rates in excess of 500 per second were demonstrated on a single MC68000 processor. The knowledge base uses a directed acyclic graph to represent production lines. The graph allows the use of AND, OR, and NOT logical operators. The inference engine uses a combination of both forward and backward chaining in order to reach goals as quickly as possible. Future efforts will include additional investigation of multiprocessing to improve performance and creating a user interface allowing rule input in an Ada-like syntax. Investigation of multitasking and alternate knowledge base representations will help to analyze some of the performance issues as they relate to larger problems.

Lavallee, David B.

1986-01-01

167

NASA Astrophysics Data System (ADS)

The terrorist attack of 9/11 has revealed how vulnerable the civil aviation industry is from both security and safety points of view. Dealing with several aircrafts cruising in the sky of a specific region requires decision makers to have an automated system that can raise their situational awareness of how much a threat an aircraft presents. In this research, an in-flight array of sensors has been deployed in a simulated aircraft to extract knowledge-base information of how passengers and equipment behave in normal flighttime which has been used to train artificial neural networks to provide real-time streams of normal behaviours. Finally, a cascading of fuzzy logic networks is designed to measure the deviation of real-time data from the predicted ones. The results suggest that Neural-Fuzzy networks have a promising future to raise the awareness of decision makers about certain aviation situations.

Ali, Ali H.; Tarter, Alex

2009-05-01

168

ANFIS: adaptive-network-based fuzzy inference system

The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation,

Jyh-Shing Roger Jang

1993-01-01

169

LOWER LEVEL INFERENCE CONTROL IN STATISTICAL DATABASE SYSTEMS

An inference is the process of transforming unclassified data values into confidential data values. Most previous research in inference control has studied the use of statistical aggregates to deduce individual records. However, several other types of inference are also possible. Unknown functional dependencies may be apparent to users who have 'expert' knowledge about the characteristics of a population. Some correlations between attributes may be concluded from 'commonly-known' facts about the world. To counter these threats, security managers should use random sampling of databases of similar populations, as well as expert systems. 'Expert' users of the DATABASE SYSTEM may form inferences from the variable performance of the user interface. Users may observe on-line turn-around time, accounting statistics. the error message received, and the point at which an interactive protocol sequence fails. One may obtain information about the frequency distributions of attribute values, and the validity of data object names from this information. At the back-end of a database system, improved software engineering practices will reduce opportunities to bypass functional units of the database system. The term 'DATA OBJECT' should be expanded to incorporate these data object types which generate new classes of threats. The security of DATABASES and DATABASE SySTEMS must be recognized as separate but related problems. Thus, by increased awareness of lower level inferences, system security managers may effectively nullify the threat posed by lower level inferences.

Lipton, D.L.; Wong, H.K.T.

1984-02-01

170

An alternative respiratory sounds classification system utilizing artificial neural networks.

Background: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. Methods: This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification. Results: The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. Conclusions: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques. PMID:25179722

Oweis, Rami J; Abdulhay, Enas W; Khayal, Amer; Awad, Areen

2014-09-01

171

Connectionist Inference Systems Hans Werner Gusgen

this observation is surprising as the main building block of the human nervous system, the neuron, is quite slow conclusion. Massive parallelism must take place in the human nervous system. Though the human nervous system with which neurons excite or inhibit each other. But our nervous system has more remarkable features

Hoelldobler, Steffen

172

A Combined sEMG and Accelerometer System for Monitoring Functional Activity in Stroke

Remote monitoring of physical activity using body-worn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data (eight channels each) were recorded from 10 hemiparetic patients while they carried out a sequence of 11 activities of daily living (identification tasks), and 10 activities used to evaluate misclassification errors (nonidentification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the nonidentification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of four ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0%, and a mean specificity of 99.7% for the identification tasks, and a mean misclassification error of <10% for the nonidentification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke. PMID:20051332

Roy, Serge H.; Cheng, M. Samuel; Chang, Shey-Sheen; Moore, John; De Luca, Gianluca; Nawab, S. Hamid; De Luca, Carlo J.

2010-01-01

173

Hybrid soft computing systems for reservoir PVT properties prediction

NASA Astrophysics Data System (ADS)

In reservoir engineering, the knowledge of Pressure-Volume-Temperature (PVT) properties is of great importance for many uses, such as well test analyses, reserve estimation, material balance calculations, inflow performance calculations, fluid flow in porous media and the evaluation of new formations for the potential development and enhancement oil recovery projects. The determination of these properties is a complex problem because laboratory-measured properties of rock samples ("cores") are only available from limited and isolated well locations and/or intervals. Several correlation models have been developed to relate these properties to other measures which are relatively abundant. These models include empirical correlations, statistical regression and artificial neural networks (ANNs). In this paper, a comprehensive study is conducted on the prediction of the bubble point pressure and oil formation volume factor using two hybrid of soft computing techniques; a genetically optimised neural network and a genetically enhanced subtractive clustering for the parameter identification of an adaptive neuro-fuzzy inference system. Simulation experiments are provided, showing the performance of the proposed techniques as compared with commonly used regression correlations, including standard artificial neural networks.

Khoukhi, Amar

2012-07-01

174

Predictions not commands: active inference in the motor system.

The descending projections from motor cortex share many features with top-down or backward connections in visual cortex; for example, corticospinal projections originate in infragranular layers, are highly divergent and (along with descending cortico-cortical projections) target cells expressing NMDA receptors. This is somewhat paradoxical because backward modulatory characteristics would not be expected of driving motor command signals. We resolve this apparent paradox using a functional characterisation of the motor system based on Helmholtz's ideas about perception; namely, that perception is inference on the causes of visual sensations. We explain behaviour in terms of inference on the causes of proprioceptive sensations. This explanation appeals to active inference, in which higher cortical levels send descending proprioceptive predictions, rather than motor commands. This process mirrors perceptual inference in sensory cortex, where descending connections convey predictions, while ascending connections convey prediction errors. The anatomical substrate of this recurrent message passing is a hierarchical system consisting of functionally asymmetric driving (ascending) and modulatory (descending) connections: an arrangement that we show is almost exactly recapitulated in the motor system, in terms of its laminar, topographic and physiological characteristics. This perspective casts classical motor reflexes as minimising prediction errors and may provide a principled explanation for why motor cortex is agranular. PMID:23129312

Adams, Rick A; Shipp, Stewart; Friston, Karl J

2013-05-01

175

NASA Astrophysics Data System (ADS)

This paper gives a technical solution to improve the efficiency in multi-sensor wireless network based estimation for distributed parameter systems. A complex structure based on some estimation algorithms, with regression and autoregression, implemented using linear estimators, neural estimators and ANFIS estimators, is developed for this purpose. The three kinds of estimators are working with precision on different parts of the phenomenon characteristic. A comparative study of three methods - linear and nonlinear based on neural networks and adaptive neuro-fuzzy inference system - to implement these algorithms is made. The intelligent wireless sensor networks are taken in consideration as an efficient tool for measurement, data acquisition and communication. They are seen as a "distributed sensor", placed in the desired positions in the measuring field. The algorithms are based on regression using values from adjacent and also on auto-regression using past values from the same sensor. A modelling and simulation for a case study is presented. The quality of estimation is validated using a quadratic criterion. A practical implementation is made using virtual instrumentation. Applications of this complex estimation system are in fault detection and diagnosis of distributed parameter systems and discovery of malicious nodes in wireless sensor networks.

Volosencu, Constantin; Curiac, Daniel-Ioan

2013-12-01

176

Evaluation of fuzzy inference systems using fuzzy least squares

NASA Technical Reports Server (NTRS)

Efforts to develop evaluation methods for fuzzy inference systems which are not based on crisp, quantitative data or processes (i.e., where the phenomenon the system is built to describe or control is inherently fuzzy) are just beginning. This paper suggests that the method of fuzzy least squares can be used to perform such evaluations. Regressing the desired outputs onto the inferred outputs can provide both global and local measures of success. The global measures have some value in an absolute sense, but they are particularly useful when competing solutions (e.g., different numbers of rules, different fuzzy input partitions) are being compared. The local measure described here can be used to identify specific areas of poor fit where special measures (e.g., the use of emphatic or suppressive rules) can be applied. Several examples are discussed which illustrate the applicability of the method as an evaluation tool.

Barone, Joseph M.

1992-01-01

177

Improving real time flood forecasting using fuzzy inference system

NASA Astrophysics Data System (ADS)

In order to improve the real time forecasting of foods, this paper proposes a modified Takagi Sugeno (T-S) fuzzy inference system termed as threshold subtractive clustering based Takagi Sugeno (TSC-T-S) fuzzy inference system by introducing the concept of rare and frequent hydrological situations in fuzzy modeling system. The proposed modified fuzzy inference systems provide an option of analyzing and computing cluster centers and membership functions for two different hydrological situations, i.e. low to medium flows (frequent events) as well as high to very high flows (rare events) generally encountered in real time flood forecasting. The methodology has been applied for flood forecasting using the hourly rainfall and river flow data of upper Narmada basin, Central India. The available rainfall-runoff data has been classified in frequent and rare events and suitable TSC-T-S fuzzy model structures have been suggested for better forecasting of river flows. The performance of the model during calibration and validation is evaluated by performance indices such as root mean square error (RMSE), model efficiency and coefficient of correlation (R). In flood forecasting, it is very important to know the performance of flow forecasting model in predicting higher magnitude flows. The above described performance criteria do not express the prediction ability of the model precisely from higher to low flow region. Therefore, a new model performance criterion termed as peak percent threshold statistics (PPTS) is proposed to evaluate the performance of a flood forecasting model. The developed model has been tested for different lead periods using hourly rainfall and discharge data. Further, the proposed fuzzy model results have been compared with artificial neural networks (ANN), ANN models for different classes identified by Self Organizing Map (SOM) and subtractive clustering based Takagi Sugeno fuzzy model (SC-T-S fuzzy model). It has been concluded from the study that the TSC-T-S fuzzy model provide reasonably accurate forecast with sufficient lead-time.

Lohani, Anil Kumar; Goel, N. K.; Bhatia, K. K. S.

2014-02-01

178

An expert system shell for inferring vegetation characteristics

NASA Technical Reports Server (NTRS)

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. VEG is described in detail in several references. The first generation version of VEG was extended. In the first year of this contract, an interface to a file of unknown cover type data was constructed. An interface that allowed the results of VEG to be written to a file was also implemented. A learning system that learned class descriptions from a data base of historical cover type data and then used the learned class descriptions to classify an unknown sample was built. This system had an interface that integrated it into the rest of VEG. The VEG subgoal PROPORTION.GROUND.COVER was completed and a number of additional techniques that inferred the proportion ground cover of a sample were implemented. This work was previously described. The work carried out in the second year of the contract is described. The historical cover type database was removed from VEG and stored as a series of flat files that are external to VEG. An interface to the files was provided. The framework and interface for two new VEG subgoals that estimate the atmospheric effect on reflectance data were built. A new interface that allows the scientist to add techniques to VEG without assistance from the developer was designed and implemented. A prototype Help System that allows the user to get more information about each screen in the VEG interface was also added to VEG.

Harrison, P. Ann; Harrison, Patrick R.

1993-01-01

179

Power system dynamic load modeling using adaptive-network-based fuzzy inference system

The representation of the dynamic characteristics of power system loads is widely used for obtaining power system operations, controls and stability limits and becomes a critical factor in power system dynamic performance. In this paper, the performance of power system dynamic load modeling using adaptive-network-base fuzzy inference system (ANFIS) is compared with traditional architectures. The ANFIS models can represent nonlinear

A. Oonsivilai; M. E. El-Hawary

1999-01-01

180

Implementation of an intelligent SINS navigator based on ANFIS

In this work an intelligent navigator developed to overcome the limitations of existing strapdown inertial navigation systems (SINS) algorithm. This system is based on adaptive neuro-fuzzy inference system (ANFIS). As in previous work, which is based on artificial neural network, the window based weight updating strategy was used, and the intelligent navigator evaluated using several SINS hypothetical field tests data.

Karim M. Ahjebory; Salam A. Ismaeel; Ahmed M. Alqaissi

2009-01-01

181

Erratum: Erratum to Central European Journal of Engineering, Volume 4, Issue 1

NASA Astrophysics Data System (ADS)

Paper by M. Ajay Kumar, N. V. Srikanth, et al. "An adaptive neuro fuzzy inference system controlled space cector pulse width modulation based HVDC light transmission system under AC fault conditions" in Volume 4, Issue 1, 27-38/March 2014 doi: 10.2478/s13531-013-0143-4 contains an error in the title. The correct title is presented below

Kumar, M. Ajay; Srikanth, N. V.

2014-06-01

182

Data-Driven MCMC for Learning and Inference in Switching Linear Dynamic Systems

Abstract Switching Linear Dynamic System (SLDS) models are a popular technique for modeling complex nonlinear dy - namic systems An SLDS has significantly more descriptive power than an HMM, but inference in SLDS models is computationally intractable This paper describes a novel inference algorithm for SLDS models based on the Data - Driven MCMC paradigm We describe a new proposal

Sang Min Oh; James M. Rehg; Tucker R. Balch; Frank Dellaert

2005-01-01

183

Inferring Meta-Models for Runtime System Data from the Clients of Management APIs

Inferring Meta-Models for Runtime System Data from the Clients of Management APIs Hui Song1 , Gang. To do this, users have to understand the different management APIs provided by different systems to inferring such meta-models by analyzing client code that accesses man- agement APIs. A set of experiments

Paris-Sud XI, UniversitÃ© de

184

A Self-Tuning Kalman Filter for Autonomous Navigation Using the Global Positioning System (GPS)

NASA Technical Reports Server (NTRS)

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS (Global Positioning Systems) data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.

Truong, Son H.

1999-01-01

185

Predicting Foaming Slag Quality in Electric Arc Furnace Using Power Quality Indices and Fuzzy Method

In this paper, a new method based on adaptive neuro fuzzy inference system (ANFIS) and fuzzy logic is presented to de- termine the slag quality in electric arc furnace using power quality indices. To train ANFIS, all electrical power quality parameters are measured for 13 meltings using a power quality analyzer. Twelve different sets of power quality parameters are examined

Behzad Mirzaeian Dehkordi; Mehdi Moallem; Amir Parsapour

2011-01-01

186

Artificial Intelligence Techniques for Steam Generator Modelling

This paper investigates the use of different Artificial Intelligence methods to predict the values of several continuous variables from a Steam Generator. The objective was to determine how the different artificial intelligence methods performed in making predictions on the given dataset. The artificial intelligence methods evaluated were Neural Networks, Support Vector Machines, and Adaptive Neuro-Fuzzy Inference Systems. The types of

Sarah Wright; Tshilidzi Marwala

2008-01-01

187

Perturbation Biology: Inferring Signaling Networks in Cellular Systems

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245

Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

2013-01-01

188

A Self-Tuning Kalman Filter for Autonomous Navigation using the Global Positioning System (GPS)

NASA Technical Reports Server (NTRS)

Most navigation systems currently operated by NASA are ground-based, and require extensive support to produce accurate results. Recently developed systems that use Kalman filter and GPS data for orbit determination greatly reduce dependency on ground support, and have potential to provide significant economies for NASA spacecraft navigation. These systems, however, still rely on manual tuning from analysts. A sophisticated neuro-fuzzy component fully integrated with the flight navigation system can perform the self-tuning capability for the Kalman filter and help the navigation system recover from estimation errors in real time.

Truong, S. H.

1999-01-01

189

A Modular Artificial Intelligence Inference Engine System (MAIS) for support of on orbit experiments

NASA Technical Reports Server (NTRS)

This paper describes a Modular Artificial Intelligence Inference Engine System (MAIS) support tool that would provide health and status monitoring, cognitive replanning, analysis and support of on-orbit Space Station, Spacelab experiments and systems.

Hancock, Thomas M., III

1994-01-01

190

Automatic Road Gap Detection Using Fuzzy Inference System

NASA Astrophysics Data System (ADS)

Automatic feature extraction from aerial and satellite images is a high-level data processing which is still one of the most important research topics of the field. In this area, most of the researches are focused on the early step of road detection, where road tracking methods, morphological analysis, dynamic programming and snakes, multi-scale and multi-resolution methods, stereoscopic and multi-temporal analysis, hyper spectral experiments, are some of the mature methods in this field. Although most researches are focused on detection algorithms, none of them can extract road network perfectly. On the other hand, post processing algorithms accentuated on the refining of road detection results, are not developed as well. In this article, the main is to design an intelligent method to detect and compensate road gaps remained on the early result of road detection algorithms. The proposed algorithm consists of five main steps as follow: 1) Short gap coverage: In this step, a multi-scale morphological is designed that covers short gaps in a hierarchical scheme. 2) Long gap detection: In this step, the long gaps, could not be covered in the previous stage, are detected using a fuzzy inference system. for this reason, a knowledge base consisting of some expert rules are designed which are fired on some gap candidates of the road detection results. 3) Long gap coverage: In this stage, detected long gaps are compensated by two strategies of linear and polynomials for this reason, shorter gaps are filled by line fitting while longer ones are compensated by polynomials.4) Accuracy assessment: In order to evaluate the obtained results, some accuracy assessment criteria are proposed. These criteria are obtained by comparing the obtained results with truly compensated ones produced by a human expert. The complete evaluation of the obtained results whit their technical discussions are the materials of the full paper.

Hashemi, S.; Valadan Zoej, M. J.; Mokhtarzadeh, M.

2011-09-01

191

DYNAMICAL INFERENCE FROM A KINEMATIC SNAPSHOT: THE FORCE LAW IN THE SOLAR SYSTEM

If a dynamical system is long-lived and non-resonant (that is, if there is a set of tracers that have evolved independently through many orbital times), and if the system is observed at any non-special time, it is possible to infer the dynamical properties of the system (such as the gravitational force or acceleration law) from a snapshot of the positions and velocities of the tracer population at a single moment in time. In this paper, we describe a general inference technique that solves this problem while allowing (1) the unknown distribution function of the tracer population to be simultaneously inferred and marginalized over, and (2) prior information about the gravitational field and distribution function to be taken into account. As an example, we consider the simplest problem of this kind: we infer the force law in the solar system using only an instantaneous kinematic snapshot (valid at 2009 April 1.0) for the eight major planets. We consider purely radial acceleration laws of the form a{sub r} = -A [r/r{sub 0}]{sup -a}lpha, where r is the distance from the Sun. Using a probabilistic inference technique, we infer 1.989 < alpha < 2.052 (95% interval), largely independent of any assumptions about the distribution of energies and eccentricities in the system beyond the assumption that the system is phase-mixed. Generalizations of the methods used here will permit, among other things, inference of Milky Way dynamics from Gaia-like observations.

Bovy, Jo; Hogg, David W. [Center for Cosmology and Particle Physics, Department of Physics, New York University, 4 Washington Place, New York, NY 10003 (United States); Murray, Iain, E-mail: jo.bovy@nyu.ed [Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4 (Canada)

2010-03-10

192

Comparison of soft computing systems for the post-calibration of weather radar

NASA Astrophysics Data System (ADS)

The most usual tools to monitor rainfall events are raingauges and weather radar. Networks of raingauges provide accurate point estimates of rainfall, when appropriately set, but their usual low density restricts considerably the spatial resolution of the gathered information. Such networks, with rain gauges at distinct points, do not reflect the spatial distribution of rainfall. The quality of raingauge observations is also susceptible to some error sources, for example wind effects around the raingauges and poor raingauge reports due to hardware problems. Radar systems offer high spatial and temporal resolution observation which is much more efficient at providing the space-time evolution of a rainfall event in comparison with raingauge networks. However the radar measurements are not free of errors due to a variety of factors including ground clutter, bright bands, anomalous propagation, beam blockages, and attenuation. The effectiveness of weather radar operation is strongly linked to rigorous calibration. Various methods have been proposed to calibrate radar data. They can be classified into two main categories: deterministic and statistical. The deterministic approach involves the calibration of radar rainfall estimations against raingauge observations. The statistical approach includes multivariate analysis and cokriging. Geostatistical approaches are known as the best methods for radar-raingauge data integration but they are usually inefficient in real time, especially when dealing with the sampling rates of one hour or less necessary for urban and small watershed applications. Such methods also rely on a strong human expertise which can lead to user-dependent results. The objectives of this research are to introduce and to investigate the feasibility of soft computing systems for the post-calibration of weather radar in comparison with the best existing method based on geostatistics. In this work, the soft computing systems include artificial neural networks and Adaptive Neuro-Fuzzy Inference System (ANFIS) and the geostatistical approach includes residual kriging. The residual kriging calibration results are satisfying however this method is based on stationary hypotheses and requires variogram modeling, making it difficult in an operational context. This method has the advantage of providing a mean squared errors map based on variogram modeling for the estimations. For the artificial neural network, thirteen variants of the multilayer feedforward networks and two variants of radial basis functions are tested in this work. The neural calibration results showed that the Levenberg-Marquardt algorithm using Bayesian regularization is robust and reliable for radar-raingauge data integration. The ANFIS offers the precision and learning capability of artificial neural networks combined with the advantages of fuzzy logic. This method based on the Jackknife approach allows the use of all the available data for training and checking the neuro-fuzzy inference system, and provides a degree of reliability of the post-calibration. The training and the interpolation results of proposed methods can be obtained within just a few seconds using an ordinary personal computer, which is incomparably faster than geostatistical approaches. The proposed algorithms would be very efficient for real time post-calibration.

Hessami Kermani, Masoud Reza

193

Bugs as Deviant Behavior: A General Approach to Inferring Errors in Systems Code

Bugs as Deviant Behavior: A General Approach to Inferring Errors in Systems Code Dawson Engler: contradictions and common behavior. How can we de- tect a lie? We can cross-check statements from many witnesses

Brown, Angela Demke

194

Bayesian inference for dynamic transcriptional regulation; the Hes1 system as a case study.

to the time taken for transcription, translation, post-translational modification, transportation etc. ThisBayesian inference for dynamic transcriptional regulation; the Hes1 system as a case study such as binding of the transcription factors, the assembly and in

Rand, David

195

From free energy measurements to free energy inference in small systems Felix Ritort

From free energy measurements to free energy inference in small systems the knowledge of the free energy of nucleic acid and protein structures the free energy of pure equilibrium states, FTs have been extended

Potsdam, UniversitÃ¤t

196

FPGA-Based Fuzzy Inference System for Real-time Embedded Applications

changing, and unknown environmental parameters[1]. A successful design for real-time systems shouldFPGA-Based Fuzzy Inference System for Real-time Embedded Applications Dr. Kasim M. Al:- The traditional way of implementing algorithms in software limits the performance of real-time systems, since

197

Fuzzy inference system for the characterization of SRM drives under normal and fault conditions

A fuzzy inference system is used to characterize switched reluctance motor, SRM, drive systems under normal and fault operating conditions. The Fuzzy Logic (FL) is applied for its ability to be very suitable for problems with large uncertainty. Knowledge about the system is accumulated using coupled finite-element (FE) magnetic field and state space (SS) models. The validity of the FL

M. Bouji; A. A. Arkadan; T. Ericsen

2001-01-01

198

Inferring the Gibbs state of a small quantum system

Gibbs states are familiar from statistical mechanics, yet their use is not limited to that domain. For instance, they also feature in the maximum entropy reconstruction of quantum states from incomplete measurement data. Outside the macroscopic realm, however, estimating a Gibbs state is a nontrivial inference task, due to two complicating factors: the proper set of relevant observables might not be evident a priori; and whenever data are gathered from a small sample only, the best estimate for the Lagrange parameters is invariably affected by the experimenter's prior bias. I show how the two issues can be tackled with the help of Bayesian model selection and Bayesian interpolation, respectively, and illustrate the use of these Bayesian techniques with a number of simple examples.

Rau, Jochen [Institut fuer Theoretische Physik, Johann Wolfgang Goethe-Universitaet, Max-von-Laue-Strasse 1, D-60438 Frankfurt am Main (Germany)

2011-07-15

199

Applications of fuzzy inference mechanisms to power system relaying

Most transmission line protective schemes are based on deterministic computations on a well defined model of the system to be protected. This results in difficulty because of the complexity of the system model, the lack of knowledge of its parameters, the great number of information to be processed, and the difficulty in taking into consideration any system variation as the

OMAR A. S. YOUSSEF

2004-01-01

200

An expert system shell for inferring vegetation characteristics: The learning system (tasks C and D)

NASA Technical Reports Server (NTRS)

This report describes the implementation of a learning system that uses a data base of historical cover type reflectance data taken at different solar zenith angles and wavelengths to learn class descriptions of classes of cover types. It has been integrated with the VEG system and requires that the VEG system be loaded to operate. VEG is the NASA VEGetation workbench - an expert system for inferring vegetation characteristics from reflectance data. The learning system provides three basic options. Using option one, the system learns class descriptions of one or more classes. Using option two, the system learns class descriptions of one or more classes and then uses the learned classes to classify an unknown sample. Using option three, the user can test the system's classification performance. The learning system can also be run in an automatic mode. In this mode, options two and three are executed on each sample from an input file. The system was developed using KEE. It is menu driven and contains a sophisticated window and mouse driven interface which guides the user through various computations. Input and output file management and data formatting facilities are also provided.

Harrison, P. Ann; Harrison, Patrick R.

1992-01-01

201

Learning and Inference in Parametric Switching Linear Dynamical Systems

We introduce parametric switching linear dynamic systems (P-SLDS) for learning and interpretation of parametrized motion, i. e., motion that exhibits systematic temporal and spatial variations. Our motivating example is the honeybee dance: bees communicate the orientation and distance to food sources through the dance angles and waggle lengths of their stylized dances. Switching linear dy-namic systems (SLDS) are a compelling

Sang Min Oh; James M. Rehg; Tucker R. Balch; Frank Dellaert

2005-01-01

202

Bugs as Deviant Behavior: A General Approach to Inferring Errors in Systems Code

Bugs as Deviant Behavior: A General Approach to Inferring Errors in Systems Code Dawson Engler is correct. This problem has two well-known solutions: contradictions and common behavior. How can we detect is wrong without knowing the truth. Sim- ilarly, how can we divine accepted behavior? We can look

Yang, Junfeng

203

Bugs as Deviant Behavior: A General Approach to Inferring Errors in Systems Code

Bugs as Deviant Behavior: A General Approach to Inferring Errors in Systems Code Dawson Engler: contradictions and common behavior. How can we de- tect a lie? We can cross-check statements from many witnesses accepted behavior? We can look at examples. If one person acts in a given way, it may be correct behav- ior

Engler, Dawson

204

Functional equivalence between radial basis function networks and fuzzy inference systems

It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent. This functional equivalence makes it possible to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is of interest to observe

J.-S. R. Jang; C.-T. Sun

1993-01-01

205

Inferring Likelihoods and Climate System Characteristics from Climate Models and Multiple

Inferring Likelihoods and Climate System Characteristics from Climate Models and Multiple Tracers K of anthropogenic climate change poses considerable sta- tistical challenges. A key problem is how to combine ocean conveyor belt circulation and transfers heat between low and high latitudes in the Atlantic basin

Haran, Murali

206

FUZZY INFERENCE SYSTEM FOR PIOPED-COMPLIANT DIAGNOSIS OF PULMONARY EMBOLISM

using ventilation-perfusion scans and correlated chest x-rays. The diagnosis achieved needed and correlating chest x-rays to facilitate the computer-aided diagnosis process. The proposed inference system has/perfusion mismatch, (d) Chest x-ray abnormality, and (e) Pleural effusion. Two ventilation-perfusion scan features

Serpen, Gursel

207

A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems

We introduce a method for approximate smoothed inference in a class of switching linear dynamical systems, based on a novel form of Gaussian Sum smoother. This class includes the switching Kalman Filter and the more general case of switch transitions dependent on the continuous latent state. The method improves on the standard Kim smoothing approach by dispensing with one of

David Barber; Bertrand Mesot

2006-01-01

208

FINDS: A fault inferring nonlinear detection system. User's guide

NASA Technical Reports Server (NTRS)

The computer program FINDS is written in FORTRAN-77, and is intended for operation on a VAX 11-780 or 11-750 super minicomputer, using the VMS operating system. The program detects, isolates, and compensates for failures in navigation aid instruments and onboard flight control and navigation sensors of a Terminal Configured Vehicle aircraft in a Microwave Landing System environment. In addition, FINDS provides sensor fault tolerant estimates for the aircraft states which are then used by an automatic guidance and control system to land the aircraft along a prescribed path. FINDS monitors for failures by evaluating all sensor outputs simultaneously using the nonlinear analytic relationships between the various sensor outputs arising from the aircraft point mass equations of motion. Hence, FINDS is an integrated sensor failure detection and isolation system.

Lancraft, R. E.; Caglayan, A. K.

1983-01-01

209

NASA Technical Reports Server (NTRS)

Techniques to identify sources of electric current systems and their channels of flow in solar active regions are explored. Measured photospheric vector magnetic fields together with high-resolution white-light and H-alpha filtergrams provide the data base to derive the current systems in the photosphere and chromosphere. As an example, the techniques are then applied to infer current systems in AR 2372 in early April 1980.

Ding, Y. J.; Hong, Q. F.; Hagyard, M. J.; Deloach, A. C.; Liu, X. P.

1987-01-01

210

Asymptotic inference in system identification for the atom maser

System identification is an integrant part of control theory and plays an increasing role in quantum engineering. In the quantum set-up, system identification is usually equated to process tomography, i.e. estimating a channel by probing it repeatedly with different input states. However for quantum dynamical systems like quantum Markov processes, it is more natural to consider the estimation based on continuous measurements of the output, with a given input which may be stationary. We address this problem using asymptotic statistics tools, for the specific example of estimating the Rabi frequency of an atom maser. We compute the Fisher information of different measurement processes as well as the quantum Fisher information of the atom maser, and establish the local asymptotic normality of these statistical models. The statistical notions can be expressed in terms of spectral properties of certain deformed Markov generators and the connection to large deviations is briefly discussed.

Catalin Catana; Merlijn van Horssen; Madalin Guta

2011-12-09

211

It is well known that fuzzy inference system and fuzzy comprehensive evaluation, as important member of computational intelligence, are consistent in the target layer. The paper analyzes the mapping relations of fuzzy comprehensive evaluation, and also proves that nonlinear approximation ability of fuzzy comprehensive evaluation is as good as fuzzy inference system's. In order to acquire better weight, The paper

Jianxin Zhang; Qihua Xiao; Dongmei Huang; Jianwei Zhang

2009-01-01

212

Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis

Motor fault detection and diagnosis involves processing a large amount of information of the motor system. With the combined synergy of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault detection\\/diagnosis process and successful fault detection\\/diagnosis schemes can be achieved. This paper presents two neural fuzzy (NN\\/FZ) inference systems, namely, fuzzy adaptive learning control\\/decision

Sinan Altug; Mo-Yuen Chen; H. Joel Trussell

1999-01-01

213

Background Reaction-diffusion systems are frequently used in systems biology to model developmental and signalling processes. In many applications, count numbers of the diffusing molecular species are very low, leading to the need to explicitly model the inherent variability using stochastic methods. Despite their importance and frequent use, parameter estimation for both deterministic and stochastic reaction-diffusion systems is still a challenging problem. Results We present a Bayesian inference approach to solve both the parameter and state estimation problem for stochastic reaction-diffusion systems. This allows a determination of the full posterior distribution of the parameters (expected values and uncertainty). We benchmark the method by illustrating it on a simple synthetic experiment. We then test the method on real data about the diffusion of the morphogen Bicoid in Drosophila melanogaster. The results show how the precision with which parameters can be inferred varies dramatically, indicating that the ability to infer full posterior distributions on the parameters can have important experimental design consequences. Conclusions The results obtained demonstrate the feasibility and potential advantages of applying a Bayesian approach to parameter estimation in stochastic reaction-diffusion systems. In particular, the ability to estimate credibility intervals associated with parameter estimates can be precious for experimental design. Further work, however, will be needed to ensure the method can scale up to larger problems. PMID:20219114

2010-01-01

214

Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology

Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named “posterior parameter ensemble”. We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor. PMID:25089832

Murakami, Yohei

2014-01-01

215

SOANFIS assisted GPS\\/MEMS-INS integrated positioning errors prediction

To solve the problem that GPS\\/MEMS-INS (micro-electro-mechanical system - inertial navigation system) integrated positioning errors accumulate rapidly with time during GPS outages, a kind of SOANFIS (self-organizing adaptive neuro-fuzzy inference system) is proposed to predict the positioning errors of GPS\\/MEMS-INS integrated navigation system. When GPS is available, not only the parameters of SOANFIS are tuned, but also its structure is

Li Cong; Honglei Qin; Juhong Xing

2010-01-01

216

Design of a Software Sensor for Feedwater Flow Measurement Using a Fuzzy Inference System

Venturi meters are used to measure the feedwater flow rate in most current pressurized water reactors. These meters can decrease the thermal performance of nuclear power plants because the feedwater flow rate can be overmeasured due to their fouling phenomena that make corrosion products caused by long-term operation accumulate in the feedwater flow meters. Therefore, in this paper, a software sensor using a fuzzy inference system is developed in order to increase the thermal efficiency by accurately estimating online the feedwater flow rate. The fuzzy inference system to be used for black-box modeling of the feedwater system is equipped with an automatic design algorithm that automates the selection of the input signals to the fuzzy inference system and its fuzzy rule generation including parameter optimization. The proposed algorithm was verified by using the numerical simulation data of the MARS code for Kori Nuclear Power Plant Unit 1 and also the real plant data of Yonggwang Nuclear Power Plant Unit 3. In the simulations using numerical simulation data and real plant data, the relative 2{sigma} errors and the relative maximum error are small enough. The proposed method can be applied successfully to validate and monitor the existing feedwater flow meters.

Na, Man Gyun; Shin, Sun Ho; Jung, Dong Won [Chosun University (Korea, Republic of)

2005-06-15

217

Assessing water quality in rivers with fuzzy inference systems: a case study.

In recent years, fuzzy-logic-based methods have demonstrated to be appropriated to address uncertainty and subjectivity in environmental problems. In the present study, a methodology based on fuzzy inference systems (FIS) to assess water quality is proposed. A water quality index calculated with fuzzy reasoning has been developed. The relative importance of water quality indicators involved in the fuzzy inference process has been dealt with a multi-attribute decision-aiding method. The potential application of the fuzzy index has been tested with a case study. A data set collected from the Ebro River (Spain) by two different environmental protection agencies has been used. The current findings, managed within a geographic information system, clearly agree with official reports and expert opinions about the pollution problems in the studied area. Therefore, this methodology emerges as a suitable and alternative tool to be used in developing effective water management plans. PMID:16678900

Ocampo-Duque, William; Ferré-Huguet, Núria; Domingo, José L; Schuhmacher, Marta

2006-08-01

218

On the Use of Fuzzy Inference Systems for Assessment and Decision Making Problems

\\u000a The Fuzzy Inference System (FIS) is a popular paradigm for undertaking assessment\\/measurement and decision problems. In practical\\u000a applications, it is important to ensure the monotonicity property between the attributes (inputs) and the measuring index\\u000a (output) of an FIS-based assessment\\/measurement model. In this chapter, the sufficient conditions for an FIS-based model to\\u000a satisfy the monotonicity property are first investigated. Then, an

Kai Meng Tay; Chee Peng Lim

219

NASA Astrophysics Data System (ADS)

This paper aims to study the relationship between climatic large-scale synoptic patterns and rainfall in Khorasan Razavi Province. The adaptive neural-fuzzy inference system was used in this study to predict rainfall in the period between April and June in Khorasan Razavi Province. We first analyzed the relationship between average regional rainfall and the changes in synoptic patterns including sea-level pressure, sea-level pressure difference, sea-level temperature, temperature difference between sea level and 1000-mb level, the temperature of 700-mb level, the thickness between 500 and 1000-mb levels, the relative humidity of 300-mb level and precipitable water. In the selection of these regions, which include a number of locations in the Persian Gulf, the Oman Sea, the Black Sea, the Caspian, the Mediterranean, the Adriatic, the Red Sea, the Eden Gulf, the Atlantic, the Indian Ocean, and Siberia, we have examined the effect of synoptic patterns in these regions on the rainfall in the northeast region of Iran. Then, the adaptive neural-fuzzy inference system in the period 1970 -1997 has been taught. Finally, the rainfall in the period 1998-2007 has been predicted. The results show that the adaptive neural-fuzzy inference system can predict the rainfall with reasonable accuracy in 90 percent of the years

Fallah-Ghalhary, G.-A.; Habibi Nokhandan, M.; Mousavi Baygi, M.

2009-04-01

220

Inference of biological S-system using the separable estimation method and the genetic algorithm.

Reconstruction of a biological system from its experimental time series data is a challenging task in systems biology. The S-system which consists of a group of nonlinear ordinary differential equations (ODEs) is an effective model to characterize molecular biological systems and analyze the system dynamics. However, inference of S-systems without the knowledge of system structure is not a trivial task due to its nonlinearity and complexity. In this paper, a pruning separable parameter estimation algorithm (PSPEA) is proposed for inferring S-systems. This novel algorithm combines the separable parameter estimation method (SPEM) and a pruning strategy, which includes adding an l? regularization term to the objective function and pruning the solution with a threshold value. Then, this algorithm is combined with the continuous genetic algorithm (CGA) to form a hybrid algorithm that owns the properties of these two combined algorithms. The performance of the pruning strategy in the proposed algorithm is evaluated from two aspects: the parameter estimation error and structure identification accuracy. The results show that the proposed algorithm with the pruning strategy has much lower estimation error and much higher identification accuracy than the existing method. PMID:21968962

Liu, Li-Zhi; Wu, Fang-Xiang; Zhang, W J

2012-01-01

221

Network inference of AP pattern formation system in D.melanogaster by structural equation modeling

NASA Astrophysics Data System (ADS)

Within the field of systems biology, revealing the control systems functioning during embryogenesis is an important task. To clarify the mechanisms controlling sequential events, the relationships between various factors and the expression of specific genes should be determined. In this study, we applied a method based on Structural Equation Modeling (SEM), combined with factor analysis. SEM can include the latent variables within the constructed model and infer the relationships among the latent and observed variables, as a network model. We improved a method for the construction of initial models for the SEM calculation, and applied our approach to estimate the regulatory network for Antero-Posterior (AP) pattern formation in D. melanogaster embryogenesis. In this new approach, we combined cross-correlation and partial correlation to summarize the temporal information and to extract the direct interactions from the gene expression profiles. In the inferred model, 18 transcription factor genes were regulated by not only the expression of other genes, but also the estimated factors. Since each factor regulated the same type of genes, these factors were considered to be involved in maternal effects or spatial morphogen distributions. The interpretation of the inferred network model allowed us to reveal the regulatory mechanism for the patterning along the head to tail axis in D. melanogaster.

Aburatani, S.; Toh, H.

2014-03-01

222

Evaluation of probabilistic and logical inference for a SNP annotation system.

Genome wide association studies (GWAS) are an important approach to understanding the genetic mechanisms behind human diseases. Single nucleotide polymorphisms (SNPs) are the predominant markers used in genome wide association studies, and the ability to predict which SNPs are likely to be functional is important for both a priori and a posteriori analyses of GWA studies. This article describes the design, implementation and evaluation of a family of systems for the purpose of identifying SNPs that may cause a change in phenotypic outcomes. The methods described in this article characterize the feasibility of combinations of logical and probabilistic inference with federated data integration for both point and regional SNP annotation and analysis. Evaluations of the methods demonstrate the overall strong predictive value of logical, and logical with probabilistic, inference applied to the domain of SNP annotation. PMID:20015478

Shen, Terry H; Tarczy-Hornoch, Peter; Detwiler, Landon T; Cadag, Eithon; Carlson, Christopher S

2010-06-01

223

(cont.) by presenting two case studies both in the context of the Chicago Transit Authority. One study proposes an enhanced method of inferring the rail trip OD matrix from an origin-only AFC system to replace the routine ...

Zhao, Jinhua, 1977-

2004-01-01

224

Welding Penetration Control of Fixed Pipe in TIG Welding Using Fuzzy Inference System

NASA Astrophysics Data System (ADS)

This paper presents a study on welding penetration control of fixed pipe in Tungsten Inert Gas (TIG) welding using fuzzy inference system. The welding penetration control is essential to the production quality welds with a specified geometry. For pipe welding using constant arc current and welding speed, the bead width becomes wider as the circumferential welding of small diameter pipes progresses. Having welded pipe in fixed position, obviously, the excessive arc current yields burn through of metals; in contrary, insufficient arc current produces imperfect welding. In order to avoid these errors and to obtain the uniform weld bead over the entire circumference of the pipe, the welding conditions should be controlled as the welding proceeds. This research studies the intelligent welding process of aluminum alloy pipe 6063S-T5 in fixed position using the AC welding machine. The monitoring system used a charge-coupled device (CCD) camera to monitor backside image of molten pool. The captured image was processed to recognize the edge of molten pool by image processing algorithm. Simulation of welding control using fuzzy inference system was constructed to simulate the welding control process. The simulation result shows that fuzzy controller was suitable for controlling the welding speed and appropriate to be implemented into the welding system. A series of experiments was conducted to evaluate the performance of the fuzzy controller. The experimental results show the effectiveness of the control system that is confirmed by sound welds.

Baskoro, Ario Sunar; Kabutomori, Masashi; Suga, Yasuo

225

Free-energy inference from partial work measurements in small systems.

Fluctuation relations (FRs) are among the few existing general results in nonequilibrium systems. Their verification requires the measurement of the total work performed on a system. Nevertheless in many cases only a partial measurement of the work is possible. Here we consider FRs in dual-trap optical tweezers where two different forces (one per trap) are measured. With this setup we perform pulling experiments on single molecules by moving one trap relative to the other. We demonstrate that work should be measured using the force exerted by the trap that is moved. The force that is measured in the trap at rest fails to provide the full dissipation in the system, leading to a (incorrect) work definition that does not satisfy the FR. The implications to single-molecule experiments and free-energy measurements are discussed. In the case of symmetric setups a second work definition, based on differential force measurements, is introduced. This definition is best suited to measure free energies as it shows faster convergence of estimators. We discuss measurements using the (incorrect) work definition as an example of partial work measurement. We show how to infer the full work distribution from the partial one via the FR. The inference process does also yield quantitative information, e.g., the hydrodynamic drag on the dumbbell. Results are also obtained for asymmetric dual-trap setups. We suggest that this kind of inference could represent a previously unidentified and general application of FRs to extract information about irreversible processes in small systems. PMID:25099353

Ribezzi-Crivellari, Marco; Ritort, Felix

2014-08-19

226

Intelligent Adaptive Mobile Robot Navigation

This paper deals with the application of a neuro-fuzzy inference system to a mobile robot navigation in an unknown, or partially unknown environment. The final aim of the robot is to reach some pre-defined goal. For this purpose, a sort of a co-operation between three main sub-modules is performed. These sub-modules consist in three elementary robot tasks: following a wall,

Samia Nefti; Mourad Oussalah; K. Djouani; J. Pontnau

2001-01-01

227

WiFi indoor location determination via ANFIS with PCA methods

This paper proposes the WiFi indoor location determination method based on adaptive neuro-fuzzy inference system (ANFIS) with principal component analysis (PCA). It reduces the WiFi signal vectors dimensions and saves the storage cost and simplifies the fuzzy rules generated by subtractive clustering method for ANFIS training. In the off-line phase, the received signal strength (RSS) or signal to noise ratio

Yubin Xu; Mu Zhou; Lin Ma

2009-01-01

228

Use of fuzzy inference system for condition monitoring of induction motor

NASA Astrophysics Data System (ADS)

Three phase induction motors are commonly used in industry due to its robustness, simplicity of its construction and high reliability. The tasks performed by these motors grow increasingly complex because of modern industries hence there is a need to determine the faults. Early detection of faults will reduce an unscheduled machine downtime that can upset production deadlines and may cause heavy financial losses. This paper is focused in developing a computer based system using Fuzzy Inference system's membership function. An unusual increase in vibration of the motor could be an indicator of faulty condition hence the vibration of the motor of an induction motor was used as an input, whereas the output is the motor condition. An inference system of the Fuzzy Logic was created to classify the vibration characteristics of the motor which is called vibration analysis. The system classified the motor of the gas distribution pump condition as from 'acceptable' to 'monitor closely'. The early detection of unusual increase in vibration of the induction motor is an important part of a predictive maintenance for motor driven machinery.

Janier, Josefina B.; Zaim Zaharia, M. F.; Karim, Samsul Ariffin Abd.

2012-09-01

229

Strapdown fiber optic gyrocompass using adaptive network-based fuzzy inference system

NASA Astrophysics Data System (ADS)

This paper aims to propose a new strapdown fiber optic gyrocompass (strapdown FOGC) using adaptive network-based fuzzy inference system (ANFIS). The strapdown FOGC is based on the principle of strapdown inertial navigation system and utilizes electromagnetic log (EM log) and damping equalizer to bound oscillatory errors existing in attitude and heading. As the introduction of damping technique, extra errors are aroused by EM log errors. To decrease the extra errors, ANFIS is utilized to adjust the damping ratio automatically in terms of the ship maneuver conditions. The simulation and trial results indicate that, compared with the conventional gyrocompass scheme, the proposed one can reduce the attitude and heading errors and improve the system performance effectively.

Li, Qian; Ben, Yueyang; Sun, Feng

2014-01-01

230

Neuro-fuzzy extraction of interpretable fuzzy rules from data

The paper addresses extraction of linguistic fuzzy rules from data, paying specific attention to such properties of the resulting fuzzy model as interpretability and generalization ability. A modeling technique, combining some previously known heuristic modeling approaches, is developed. Experiments of controller identification based on the truck backer-upper application demonstrate that the proposed technique is able to capture the relevant information

Andri Riid; Ennu Riistern

2004-01-01

231

Application of Soft Computing in Coherent Communications Phase Synchronization

NASA Technical Reports Server (NTRS)

The use of soft computing techniques in coherent communications phase synchronization provides an alternative to analytical or hard computing methods. This paper discusses a novel use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for phase synchronization in coherent communications systems utilizing Multiple Phase Shift Keying (MPSK) modulation. A brief overview of the M-PSK digital communications bandpass modulation technique is presented and it's requisite need for phase synchronization is discussed. We briefly describe the hybrid platform developed by Jang that incorporates fuzzy/neural structures namely the, Adaptive Neuro-Fuzzy Interference Systems (ANFIS). We then discuss application of ANFIS to phase estimation for M-PSK. The modeling of both explicit, and implicit phase estimation schemes for M-PSK symbols with unknown structure are discussed. Performance results from simulation of the above scheme is presented.

Drake, Jeffrey T.; Prasad, Nadipuram R.

2000-01-01

232

NASA Technical Reports Server (NTRS)

This paper summarizes the modifications made to the design of a fault inferring nonlinear detection system (FINDS) algorithm to accommodate flight computer constraints and the resulting impact on the algorithm performance. An overview of the flight data-driven FINDS algorithm is presented. This is followed by a brief analysis of the effects of modifications to the algorithm on program size and execution speed. Significant improvements in estimation performance for the aircraft states and normal operating sensor biases, which have resulted from improved noise design parameters and a new steady-state wind model, are documented. The aircraft state and sensor bias estimation performances of the algorithm's extended Kalman filter are presented as a function of update frequency of the piecewise constant filter gains. The results of a new detection system strategy and failure detection performance, as a function of an update frequency, are also presented.

Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.

1986-01-01

233

NASA Technical Reports Server (NTRS)

The modifications to the design of a fault inferring nonlinear detection system (FINDS) algorithm to accommodate flight computer constraints and the resulting impact on the algorithm performance are summarized. An overview of the flight data-driven FINDS algorithm is presented. This is followed by a brief analysis of the effects of modifications to the algorithm on program size and execution speed. Significant improvements in estimation performance for the aircraft states and normal operating sensor biases, which have resulted from improved noise design parameters and a new steady-state wind model, are documented. The aircraft state and sensor bias estimation performances of the algorithm's extended Kalman filter are presented as a function of update frequency of the piecewise constant filter gains. The results of a new detection system strategy and failure detection performance, as a function of gain update frequency, are also presented.

Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.

1986-01-01

234

Another expert system rule inference based on DNA molecule logic gates

NASA Astrophysics Data System (ADS)

With the help of silicon industry microfluidic processors were invented utilizing nano membrane valves, pumps and microreactors. These so called lab-on-a-chips combined together with molecular computing create molecular-systems-ona- chips. This work presents a new approach to implementation of molecular inference systems. It requires the unique representation of signals by DNA molecules. The main part of this work includes the concept of logic gates based on typical genetic engineering reactions. The presented method allows for constructing logic gates with many inputs and for executing them at the same quantity of elementary operations, regardless of a number of input signals. Every microreactor of the lab-on-a-chip performs one unique operation on input molecules and can be connected by dataflow output-input connections to other ones.

WÄ siewicz, Piotr

2013-10-01

235

An integrated fault detection and diagnostic system with a capability of providing extremely early detection of disturbances in a process through the analysis of the stochastic content of dynamic signals is described. The sequential statistical analysis of the signal noise (a pattern-recognition technique) that is employed has been shown to provide the theoretically shortest sampling time to detect disturbances and thus has the potential of providing incipient fault detection information to operators sufficiently early to avoid forced process shutdowns. This system also provides a diagnosis of the cause of the initiating fault(s) by a physical-model-derived rule-based expert system in which system and subsystem state uncertainties are handled using fuzzy inference techniques. This system has been initially applied to the monitoring of the operational state of the primary coolant pumping system on the EBR-II nuclear reactor. Early validation studies have shown that a rapidly developing incipient fault on centrifugal pumps can be detected well in advance of any changes in the nominal process signals. 17 refs., 6 figs.

Singer, R.M.; Gross, K.C. (Argonne National Lab., IL (USA)); Humenik, K.E. (Maryland Univ., Baltimore, MD (USA). Dept. of Computer Science)

1991-01-01

236

Tribal particle swarm optimization for neurofuzzy inference systems and its prediction applications

NASA Astrophysics Data System (ADS)

This study presents tribal particle swarm optimization (TPSO) to optimize the parameters of the functional-link-based neurofuzzy inference system (FLNIS) for prediction applications. The proposed TPSO uses particle swarm optimization (PSO) as evolution strategies of the tribes optimization algorithm (TOA) to balance local and global exploration of the search space. The proposed TPSO uses a self-clustering algorithm to divide the particle swarm into multiple tribes, and selects suitable evolution strategies to update each particle. The TPSO also uses a tribal adaptation mechanism to remove and generate particles and reconstruct tribal links. The tribal adaptation mechanism can improve the qualities of the tribe and the tribe adaptation. Finally, the FLNIS model with the proposed TPSO (FLNIS-TPSO) was used in several predictive applications. Experimental results demonstrated that the proposed TPSO method converges quickly and yields a lower RMS error than other current methods.

Chen, Cheng-Hung; Liao, Yen-Yun

2014-04-01

237

Languages with a solvable implication problem but without complete and consistent systems of inference rules (`poor` languages) are considered. The problem of existence of finite complete and consistent inference rule system for a ``poor`` language is stated independently of the language or rules syntax. Several properties of the problem arc proved. An application of results to the language of join dependencies is given.

Petrov, S.

1996-10-01

238

NASA Astrophysics Data System (ADS)

Arsenic is a prominent trace element in the Gulf Coastal Aquifer System (GCAS) in Texas, particularly in the southwestern portion where 29% of wells exceed the USEPA maximum contaminant level of 10 ?g/L for drinking water. While the dominant source is generally thought to be geogenic rather than anthropogenic, little is known about the hydrologic/geochemical mechanisms affecting occurrence in groundwater. The aim of this study was to assess spatial trends in hydrochemistry on a regional scale to help infer relevant processes. The investigation included geostatistical analysis of water quality results from the Texas Water Development Board groundwater database (n>1000) and chemical/isotopic analysis of a transect (17 wells) in the unconfined portion of the Jasper Aquifer, where some of the highest arsenic concentrations in the GCAS are found. Across the GCAS, arsenic and other oxyanion-forming elements (vanadium, molybdenum etc) are most common in the Miocene-age Jasper Aquifer, and tend to decrease with decreasing aquifer age. Principal Component Analysis suggests that spatial variations in arsenic in the GCAS as a whole are related to both total mineralization (TDS), and a second orthogonal component comprised of several trace elements (most prominently vanadium and silicon). A similar relationship is apparent for the Jasper Aquifer, but without a strong correspondence to TDS. The Jasper Aquifer transect also reflects these patterns. Near-neutral pH and slightly-oxidizing conditions observed in the transect are not likely to promote reductive dissolution or desorption from mineral oxides, and no relationship with pH or Eh is present. Rather, maximum arsenic values in the transect (120 ?g/L) coincide with the boundary of the underlying Catahoula Formation which is a known source of saline fluids. Mixing of upward leakage with meteoric recharge is therefore considered to be a likely mechanism controlling arsenic concentrations. This inference is consistent with other geochemical and hydrogeologic considerations and will help to target further research.

Gates, J. B.; Nicot, J.; Scanlon, B. R.

2008-12-01

239

A multi-step predictor with a variable input pattern for system state forecasting

NASA Astrophysics Data System (ADS)

A reliable predictor is very useful to a wide array of industries to forecast the behaviour of dynamic systems. In this paper, an adaptive multi-step predictor is developed based on a novel weighted recurrent neuro-fuzzy paradigm for system state forecasting. A variable input pattern is proposed to improve the forecasting performance. A hybrid training algorithm, based on the recursive Levenberg-Marquardt algorithm and recursive least square estimate, is suggested to enhance forecasting convergence and to accommodate time-varying system conditions. The viability of the developed predictor is evaluated by simulations on both benchmark data sets and experimental data sets corresponding to machinery condition monitoring. The investigation results show that the developed adaptive predictor is a reliable and robust multi-step forecasting tool. It can capture and track system's response quickly and accurately. It outperforms other related classical forecasting schemes.

Liu, Jie; Wang, Wilson; Golnaraghi, Farid

2009-07-01

240

Common Sense Inference is an increasingly attractive technique to make computer interfaces more in touch with how human users think. However, the results of the inference process are often hard to interpret and evaluate. ...

Henke, Joseph D

2014-01-01

241

Bayesian Model Comparison and Parameter Inference in Systems Biology Using Nested Sampling

Inferring parameters for models of biological processes is a current challenge in systems biology, as is the related problem of comparing competing models that explain the data. In this work we apply Skilling's nested sampling to address both of these problems. Nested sampling is a Bayesian method for exploring parameter space that transforms a multi-dimensional integral to a 1D integration over likelihood space. This approach focusses on the computation of the marginal likelihood or evidence. The ratio of evidences of different models leads to the Bayes factor, which can be used for model comparison. We demonstrate how nested sampling can be used to reverse-engineer a system's behaviour whilst accounting for the uncertainty in the results. The effect of missing initial conditions of the variables as well as unknown parameters is investigated. We show how the evidence and the model ranking can change as a function of the available data. Furthermore, the addition of data from extra variables of the system can deliver more information for model comparison than increasing the data from one variable, thus providing a basis for experimental design. PMID:24523891

Pullen, Nick; Morris, Richard J.

2014-01-01

242

A new fuzzy controller is presented based on the single input rule modules (SIRMs) dynamically connected fuzzy inference model for upswing and stabilization control of inverted pendulum system. The fuzzy controller takes the angle and angular velocity of the pendulum and the position and velocity of the cart as its input items, and the driving force as its output item.

Jianqiang Yi; Naoyoshi Yubazaki; Kaoru Hirota

2001-01-01

243

The application of fuzzy Delphi and fuzzy inference system in supplier ranking and selection

NASA Astrophysics Data System (ADS)

In today's highly rival market, an effective supplier selection process is vital to the success of any manufacturing system. Selecting the appropriate supplier is always a difficult task because suppliers posses varied strengths and weaknesses that necessitate careful evaluations prior to suppliers' ranking. This is a complex process with many subjective and objective factors to consider before the benefits of supplier selection are achieved. This paper identifies six extremely critical criteria and thirteen sub-criteria based on the literature. A new methodology employing those criteria and sub-criteria is proposed for the assessment and ranking of a given set of suppliers. To handle the subjectivity of the decision maker's assessment, an integration of fuzzy Delphi with fuzzy inference system has been applied and a new ranking method is proposed for supplier selection problem. This supplier selection model enables decision makers to rank the suppliers based on three classifications including "extremely preferred", "moderately preferred", and "weakly preferred". In addition, in each classification, suppliers are put in order from highest final score to the lowest. Finally, the methodology is verified and validated through an example of a numerical test bed.

Tahriri, Farzad; Mousavi, Maryam; Hozhabri Haghighi, Siamak; Zawiah Md Dawal, Siti

2014-06-01

244

Crop parameters estimation by fuzzy inference system using X-band scatterometer data

NASA Astrophysics Data System (ADS)

Learning fuzzy rule based systems with microwave remote sensing can lead to very useful applications in solving several problems in the field of agriculture. Fuzzy logic provides a simple way to arrive at a definite conclusion based upon imprecise, ambiguous, vague, noisy or missing input information. In the present paper, a subtractive based fuzzy inference system is introduced to estimate the potato crop parameters like biomass, leaf area index, plant height and soil moisture. Scattering coefficient for HH- and VV-polarizations were used as an input in the Fuzzy network. The plant height, biomass, and leaf area index of potato crop and soil moisture measured at its various growth stages were used as the target variables during the training and validation of the network. The estimated values of crop/soil parameters by this methodology are much closer to the experimental values. The present work confirms the estimation abilities of fuzzy subtractive clustering in potato crop parameters estimation. This technique may be useful for the other crops cultivated over regional or continental level.

Pandey, Abhishek; Prasad, R.; Singh, V. P.; Jha, S. K.; Shukla, K. K.

2013-03-01

245

Parameter estimation in dynamic systems finds applications in various disciplines, including system biology. The well-known expectation-maximization (EM) algorithm is a popular method and has been widely used to solve system identification and parameter estimation problems. However, the conventional EM algorithm cannot exploit the sparsity. On the other hand, in gene regulatory network inference problems, the parameters to be estimated often exhibit sparse structure. In this paper, a regularized expectation-maximization (rEM) algorithm for sparse parameter estimation in nonlinear dynamic systems is proposed that is based on the maximum a posteriori (MAP) estimation and can incorporate the sparse prior. The expectation step involves the forward Gaussian approximation filtering and the backward Gaussian approximation smoothing. The maximization step employs a re-weighted iterative thresholding method. The proposed algorithm is then applied to gene regulatory network inference. Results based on both synthetic and real data show the effectiveness of the proposed algorithm. PMID:24708632

2014-01-01

246

Parameter estimation in dynamic systems finds applications in various disciplines, including system biology. The well-known expectation-maximization (EM) algorithm is a popular method and has been widely used to solve system identification and parameter estimation problems. However, the conventional EM algorithm cannot exploit the sparsity. On the other hand, in gene regulatory network inference problems, the parameters to be estimated often exhibit sparse structure. In this paper, a regularized expectation-maximization (rEM) algorithm for sparse parameter estimation in nonlinear dynamic systems is proposed that is based on the maximum a posteriori (MAP) estimation and can incorporate the sparse prior. The expectation step involves the forward Gaussian approximation filtering and the backward Gaussian approximation smoothing. The maximization step employs a re-weighted iterative thresholding method. The proposed algorithm is then applied to gene regulatory network inference. Results based on both synthetic and real data show the effectiveness of the proposed algorithm. PMID:24708632

Jia, Bin; Wang, Xiaodong

2014-01-01

247

User's guide to the Fault Inferring Nonlinear Detection System (FINDS) computer program

NASA Technical Reports Server (NTRS)

Described are the operation and internal structure of the computer program FINDS (Fault Inferring Nonlinear Detection System). The FINDS algorithm is designed to provide reliable estimates for aircraft position, velocity, attitude, and horizontal winds to be used for guidance and control laws in the presence of possible failures in the avionics sensors. The FINDS algorithm was developed with the use of a digital simulation of a commercial transport aircraft and tested with flight recorded data. The algorithm was then modified to meet the size constraints and real-time execution requirements on a flight computer. For the real-time operation, a multi-rate implementation of the FINDS algorithm has been partitioned to execute on a dual parallel processor configuration: one based on the translational dynamics and the other on the rotational kinematics. The report presents an overview of the FINDS algorithm, the implemented equations, the flow charts for the key subprograms, the input and output files, program variable indexing convention, subprogram descriptions, and the common block descriptions used in the program.

Caglayan, A. K.; Godiwala, P. M.; Satz, H. S.

1988-01-01

248

Asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS).

This paper presents an asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS) that directly extends the SuPFuNIS model by permitting signal and weight fuzzy sets to be modeled by asymmetric Gaussian membership functions. The asymmetric subsethood-product network admits both numeric as well as linguistic inputs. Input nodes, which act as tunable feature fuzzifiers, fuzzify numeric inputs with asymmetric Gaussian fuzzy sets; and linguistic inputs are presented as is. The antecedent and consequent labels of standard fuzzy if-then rules are represented as asymmetric Gaussian fuzzy connection weights of the network. The model uses mutual subsethood based activation spread and a product aggregation operator that works in conjunction with volume defuzzification in a gradient descent learning framework. Despite the increase in the number of free parameters, the proposed model performs better than SuPFuNIS, on various benchmarking problems, both in terms of the performance accuracy and architectural economy and compares excellently with other various existing models with a performance better than most of them. PMID:15732396

Velayutham, C Shunmuga; Kumar, Satish

2005-01-01

249

Hydrological data are often missing due to natural disasters, improper operation, limited equipment life, and other factors, which limit hydrological analysis. Therefore, missing data recovery is an essential process in hydrology. This paper investigates the accuracy of artificial neural networks (ANN) in estimating missing flow records. The purpose is to develop and apply neural networks models to estimate missing flow records in a station when data from adjacent stations is available. Multilayer perceptron neural networks model (MLP) and coactive neurofuzzy inference system model (CANFISM) are used to estimate daily flow records for Li-Lin station using daily flow data for the period 1997 to 2009 from three adjacent stations (Nan-Feng, Lao-Nung and San-Lin) in southern Taiwan. The performance of MLP is slightly better than CANFISM, having R2 of 0.98 and 0.97, respectively. We conclude that accurate estimations of missing flow records under the complex hydrological conditions of Taiwan could be attained by intelligent methods such as MLP and CANFISM. PMID:24453876

Tfwala, Samkele S.; Wang, Yu-Min; Lin, Yu-Chieh

2013-01-01

250

The proposed mechanism for jamming attack detection for wireless sensor networks is novel in three respects: firstly, it upgrades the jammer to include versatile military jammers; secondly, it graduates from the existing node-centric detection system to the network-centric system making it robust and economical at the nodes, and thirdly, it tackles the problem through fuzzy inference system, as the decision regarding intensity of jamming is seldom crisp. The system with its high robustness, ability to grade nodes with jamming indices, and its true-detection rate as high as 99.8%, is worthy of consideration for information warfare defense purposes. PMID:22319307

Misra, Sudip; Singh, Ranjit; Rohith Mohan, S V

2010-01-01

251

The proposed mechanism for jamming attack detection for wireless sensor networks is novel in three respects: firstly, it upgrades the jammer to include versatile military jammers; secondly, it graduates from the existing node-centric detection system to the network-centric system making it robust and economical at the nodes, and thirdly, it tackles the problem through fuzzy inference system, as the decision regarding intensity of jamming is seldom crisp. The system with its high robustness, ability to grade nodes with jamming indices, and its true-detection rate as high as 99.8%, is worthy of consideration for information warfare defense purposes. PMID:22319307

Misra, Sudip; Singh, Ranjit; Rohith Mohan, S. V.

2010-01-01

252

NASA Astrophysics Data System (ADS)

Ocean color images acquired from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) from 1998 to 2006 were used to examine the patterns of physical connectivity between land and reefs, and among reefs in the Mesoamerican Barrier Reef System (MBRS) in the northwestern Caribbean Sea. Connectivity was inferred by tracking surface water features in weekly climatologies and a time series of weekly mean chlorophyll- a concentrations derived from satellite imagery. Frequency of spatial connections between 17 pre-defined, geomorphological domains that include the major reefs in the MBRS and river deltas in Honduras and Nicaragua were recorded and tabulated as percentage of connections. The 9-year time series of 466 weekly mean images portrays clearly the seasonal patterns of connectivity, including river plumes and transitions in the aftermath of perturbations such as hurricanes. River plumes extended offshore from the Honduras coast to the Bay Islands (Utila, Cayo Cochinos, Guanaja, and Roatán) in 70% of the weekly mean images. Belizean reefs, especially those in the southern section of the barrier reef and Glovers Atoll, were also affected by riverine discharges in every one of the 9 years. Glovers Atoll was exposed to river plumes originating in Honduras 104/466 times (22%) during this period. Plumes from eastern Honduras went as far as Banco Chinchorro and Cozumel in Mexico. Chinchorro appeared to be more frequently connected to Turneffe Atoll and Honduran rivers than with Glovers and Lighthouse Atolls, despite their geographic proximity. This new satellite data analysis provides long-term, quantitative assessments of the main pathways of connectivity in the region. The percentage of connections can be used to validate predictions made using other approaches such as numerical modeling, and provides valuable information to ecosystem-based management in coral reef provinces.

Soto, I.; Andréfouët, S.; Hu, C.; Muller-Karger, F. E.; Wall, C. C.; Sheng, J.; Hatcher, B. G.

2009-06-01

253

The possibility is discussed of inferring or simulating some aspects of quantum dynamics by adding classical statistical fluctuations to classical mechanics. We introduce a general principle of mechanical stability and derive a necessary condition for classical chaotic fluctuations to affect confined dynamical systems, on any scale, ranging from microscopic to macroscopic domains. As a consequence we obtain, both for microscopic and macroscopic aggregates, dimensional relations defining the minimum unit of action of individual constituents, yielding in all cases Planck action constant.

Salvatore De Martino; Silvio De Siena; Fabrizio Illuminati

1999-01-15

254

A Cloud Model Inference System Based Alpha-Beta Filter for Tracking of Maneuvering Target

An adaptive alpha-beta filter based on cloud model inference is presented for maneuvering target tracking. The proposed tracker incorporates cloud model in a conventional alpha-beta filter by using the rule bank based on cloud model, which utilizes the residue error and the change of residue error in the last prediction to determine the values of alpha and beta, then track

Jianjun Huang; Jiali Zhong; Pengfei Li

2010-01-01

255

NASA Astrophysics Data System (ADS)

Integration of process planning with scheduling by considering the manufacturing system's capacity, cost and capacity in its workshop is a critical issue. The concurrency between them can also eliminate the redundant process and optimize the entire production cycle, but most integrated process planning and scheduling methods only consider the time aspects of the alternative machines when constructing schedules. In this paper, a fuzzy inference system (FIS) in choosing alternative machines for integrated process planning and scheduling of a job shop manufacturing system is presented. Instead of choosing alternative machines randomly, machines are being selected based on the machines reliability. The mean time to failure (MTF) values is input in a fuzzy inference mechanism, which outputs the machine reliability. The machine is then being penalized based on the fuzzy output. The most reliable machine will have the higher priority to be chosen. In order to overcome the problem of un-utilization machines, sometimes faced by unreliable machine, the particle swarm optimization (PSO) have been used to balance the load for all the machines. Simulation study shows that the system can be used as an alternative way of choosing machines in integrated process planning and scheduling.

Yang, Yahong; Zhao, Fuqing; Hong, Yi; Yu, Dongmei

2005-12-01

256

Inductive Inference: Theory and Methods

There has been a great deal of theoretical and experimental work in computer science on inductive inference systems, that is, systems that try to infer general rules from examples. However, a complete and applicable theory of such systems is still a distant goal. This survey highlights and explains the main ideas that have been developed in the study of inductive

Dana Angluin; Carl H. Smith

1983-01-01

257

NASA Astrophysics Data System (ADS)

Across diverse fields ranging from physics to biology, sociology, and economics, the technological advances of the past decade have engendered an unprecedented explosion of data on highly complex systems with thousands, if not millions of interacting components. These systems exist at many scales of size and complexity, and it is becoming ever-more apparent that they are, in fact, universal, arising in every field of study. Moreover, they share fundamental properties---chief among these, that the individual interactions of their constituent parts may be well-understood, but the characteristic behaviour produced by the confluence of these interactions---by these complex networks---is unpredictable; in a nutshell, the whole is more than the sum of its parts. There is, perhaps, no better illustration of this concept than the discoveries being made regarding complex networks in the biological sciences. In particular, though the sequencing of the human genome in 2003 was a remarkable feat, scientists understand that the "cellular-level blueprints" for the human being are cellular-level parts lists, but they say nothing (explicitly) about cellular-level processes. The challenge of modern molecular biology is to understand these processes in terms of the networks of parts---in terms of the interactions among proteins, enzymes, genes, and metabolites---as it is these processes that ultimately differentiate animate from inanimate, giving rise to life! It is the goal of systems biology---an umbrella field encapsulating everything from molecular biology to epidemiology in social systems---to understand processes in terms of fundamental networks of core biological parts, be they proteins or people. By virtue of the fact that there are literally countless complex systems, not to mention tools and techniques used to infer, simulate, analyze, and model these systems, it is impossible to give a truly comprehensive account of the history and study of complex systems. The author's own publications have contributed network inference, simulation, modeling, and analysis methods to the much larger body of work in systems biology, and indeed, in network science. The aim of this thesis is therefore twofold: to present this original work in the historical context of network science, but also to provide sufficient review and reference regarding complex systems (with an emphasis on complex networks in systems biology) and tools and techniques for their inference, simulation, analysis, and modeling, such that the reader will be comfortable in seeking out further information on the subject. The review-like Chapters 1, 2, and 4 are intended to convey the co-evolution of network science and the slow but noticeable breakdown of boundaries between disciplines in academia as research and comparison of diverse systems has brought to light the shared properties of these systems. It is the author's hope that theses chapters impart some sense of the remarkable and rapid progress in complex systems research that has led to this unprecedented academic synergy. Chapters 3 and 5 detail the author's original work in the context of complex systems research. Chapter 3 presents the methods and results of a two-stage modeling process that generates candidate gene-regulatory networks of the bacterium B.subtilis from experimentally obtained, yet mathematically underdetermined microchip array data. These networks are then analyzed from a graph theoretical perspective, and their biological viability is critiqued by comparing the networks' graph theoretical properties to those of other biological systems. The results of topological perturbation analyses revealing commonalities in behavior at multiple levels of complexity are also presented, and are shown to be an invaluable means by which to ascertain the level of complexity to which the network inference process is robust to noise. Chapter 5 outlines a learning algorithm for the development of a realistic, evolving social network (a city) into which a disease is introduced. The results of simulations in populat

Christensen, Claire Petra

258

NASA Technical Reports Server (NTRS)

The Visible Imaging System (VIS) on the polar spacecraft provided time sequences of auroral images at multiple wavelengths that yield information of auroral dynamics on a global scale with a spatial resolution of - 20 km and temporal resolution of approx. 1 minute. Time sequences of VIS images in which the aurora was highly dynamic are used to infer global maps for the electron precipitation parameters, energy flux and characteristic energies, and ionospheric conductances. The maps are inferred from the corresponding VIS images using an auroral model (Lumerzheim et al., 1987). The temporal and spatial resolution of the VIS inferred patterns are unprecedented. The inferred patterns are highly structured and vary significantly on a time scale of less than 5 minutes. These patterns can be very beneficial for global physics-based numerical models for the high-latitude ionosphere which previously had to rely on statistical models for the electron precipitation and ionospheric conductance.

Sigwarth, John B.; Bekerat, Hamed A.

2008-01-01

259

RegPredict web server is designed to provide comparative genomics tools for reconstruction and analysis of microbial regulons using comparative genomics approach. The server allows the user to rapidly generate reference sets of regulons and regulatory motif profiles in a group of prokaryotic genomes. The new concept of a cluster of co-regulated orthologous operons allows the user to distribute the analysis of large regulons and to perform the comparative analysis of multiple clusters independently. Two major workflows currently implemented in RegPredict are: (i) regulon reconstruction for a known regulatory motif and (ii) ab initio inference of a novel regulon using several scenarios for the generation of starting gene sets. RegPredict provides a comprehensive collection of manually curated positional weight matrices of regulatory motifs. It is based on genomic sequences, ortholog and operon predictions from the MicrobesOnline. An interactive web interface of RegPredict integrates and presents diverse genomic and functional information about the candidate regulon members from several web resources. RegPredict is freely accessible at http://regpredict.lbl.gov.

Novichkov, Pavel S.; Rodionov, Dmitry A.; Stavrovskaya, Elena D.; Novichkova, Elena S.; Kazakov, Alexey E.; Gelfand, Mikhail S.; Arkin, Adam P.; Mironov, Andrey A.; Dubchak, Inna

2010-05-26

260

In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) are investigated to predict the thickness of the compensator filter in radiation therapy. In the proposed models, the input parameters are field size (S), off-axis distance, and relative dose (D/D0), and the output is the thickness of the compensator. The obtained results show that the proposed ANN and ANFIS models are useful, reliable, and cheap tools to predict the thickness of the compensator filter in intensity-modulated radiation therapy. PMID:25498836

Dehlaghi, Vahab; Taghipour, Mostafa; Haghparast, Abbas; Roshani, Gholam Hossein; Rezaei, Abbas; Shayesteh, Sajjad Pashootan; Adineh-Vand, Ayoub; Karimi, Gholam Reza

2015-01-01

261

Decremental Learning of Evolving Fuzzy Inference Systems Using a Sliding Window

that decremental learning is necessary to maintain the system learning capacity over time, making decremental. The aim of decremental learning is twofold. First, to maintain the system learning capacity, and second of this work is the use of on-line handwriting gesture classifiers to facilitate interactions with computers

Boyer, Edmond

262

Bugs as deviant behavior: a general approach to inferring errors in systems code

A major obstacle to finding program errors in a real system is knowing what correctness rules the system must obey. These rules are often undocumented or specified in an ad hoc manner. This paper demonstrates techniques that automatically extract such checking information from the source code itself, rather than the programmer, thereby avoiding the need for a priori knowledge of

Dawson Engler; David Yu Chen; Seth Hallem; Andy Chou; Benjamin Chelf

2001-01-01

263

In this paper we generalize a methodology [T. E. Ouldridge, A. A. Louis, and J. P. K. Doye, J. Phys.: Condens. Matter {\\bf 22}, 104102 (2010)] for dealing with the inference of bulk properties from small simulations of self-assembling systems of characteristic finite size. In particular, schemes for extrapolating the results of simulations of a single self-assembling object to the bulk limit are established in three cases: for assembly involving multiple particle species, for systems with one species localized in space and for simulations in the grand canonical ensemble. Furthermore, methodologies are introduced for evaluating the accuracy of these extrapolations. Example systems demonstrate that differences in cluster concentrations between simulations of a single self-assembling structure and bulk studies of the same model under identical conditions can be large, and that convergence on bulk results as system size is increased can be slow and non-trivial.

Thomas E. Ouldridge

2012-10-02

264

NASA Astrophysics Data System (ADS)

In this paper, we generalize a methodology [T. E. Ouldridge, A. A. Louis, and J. P. K. Doye, J. Phys.: Condens. Matter 22, 104102 (2010)] for dealing with the inference of bulk properties from small simulations of self-assembling systems of characteristic finite size. In particular, schemes for extrapolating the results of simulations of a single self-assembling object to the bulk limit are established in three cases: for assembly involving multiple particle species, for systems with one species localized in space and for simulations in the grand canonical ensemble. Furthermore, methodologies are introduced for evaluating the accuracy of these extrapolations. Example systems demonstrate that differences in cluster concentrations between simulations of a single self-assembling structure and bulk studies of the same model under identical conditions can be large, and that convergence on bulk results as system size is increased can be slow and non-trivial.

Ouldridge, Thomas E.

2012-10-01

265

NASA Astrophysics Data System (ADS)

An extensive self-potential survey was carried out in the central volcanic complex of Tenerife Island (Canary Islands, Spain). A total amount of ~237 km of profiles with 20 m spacing between measurements was completed, including radial profiles extending from the summits of Teide and Pico Viejo, and circular profiles inside and around Las Cañadas caldera and the northern slopes of Teide and Pico Viejo. One of the main results of this mapping is the detection of well-developed hydrothermal systems within the edifices of Teide and Pico Viejo, and also associated with the flank satellite M. Blanca and M. Rajada volcanoes. A strong structural control of the surface manifestation of these hydrothermal systems is deduced from the data, pointing to the subdivision of Teide and Pico Viejo hydrothermal systems in three zones: summit crater, upper and lower hydrothermal systems. Self-potential maxima related to hydrothermal activity are absent from the proximal parts of the NE and NW rift zones as well as from at least two of the mafic historical eruptions (Chinyero and Siete Fuentes), indicating that long-lived hydrothermal systems have developed exclusively over relatively shallow felsic magma reservoirs. Towards Las Cañadas caldera floor and walls, the influence of the central hydrothermal systems disappears and the self-potential signal is controlled by the topography, the distance to the water table of Las Cañadas aquifer and its geometry. Nevertheless, fossil or remanent hydrothermal activity at some points along the Caldera wall, especially around the Roques de García area, is also suggested by the data. Self-potential data indicate the existence of independent groundwater systems in the three calderas of Ucanca, Guajara and Diego Hernández, with a funnel shaped negative anomaly in the Diego Hernández caldera floor related to the subsurface topography of the caldera bottom. Two other important self-potential features are detected: positive values towards the northwestern Santiago rift, possibly due to the relatively high altitude of the water-table in this area; and a linear set of minima to the west of Pico Viejo, aligned with the northwestern rift and related to meteoric water infiltration along its fracture system.

Villasante-Marcos, Víctor; Finizola, Anthony; Barde-Cabusson, Stéphanie; López, Carmen; Di Gangi, Fabio; Levieux, Guillaume; Morin, Julie; Ricci, Tullio; Schütze, Claudia; Suski-Ricci, Barbara

2014-05-01

266

NASA Astrophysics Data System (ADS)

An extensive self-potential survey was carried out in the central volcanic complex of Tenerife Island (Canary Islands, Spain). A total amount of ~ 237 km of profiles with 20 m spacing between measurements was completed, including radial profiles extending from the summits of Teide and Pico Viejo, and circular profiles inside and around Las Cañadas caldera and the northern slopes of Teide and Pico Viejo. One of the main results of this mapping is the detection of well-developed hydrothermal systems within the edifices of Teide and Pico Viejo, and also associated with the flank satellite M. Blanca and M. Rajada volcanoes. A strong structural control of the surface manifestation of these hydrothermal systems is deduced from the data, pointing to the subdivision of Teide and Pico Viejo hydrothermal systems in three zones: summit crater, upper and lower hydrothermal systems. Self-potential maxima related to hydrothermal activity are absent from the proximal parts of the NE and NW rift zones as well as from at least two of the mafic historical eruptions (Chinyero and Siete Fuentes), indicating that long-lived hydrothermal systems have developed exclusively over relatively shallow felsic magma reservoirs. Towards Las Cañadas caldera floor and walls, the influence of the central hydrothermal systems disappears and the self-potential signal is controlled by the topography, the distance to the water table of Las Cañadas aquifer and its geometry. Nevertheless, fossil or remanent hydrothermal activity at some points along the Caldera wall, especially around the Roques de García area, is also suggested by the data. Self-potential data indicate the existence of independent groundwater systems in the three calderas of Ucanca, Guajara and Diego Hernández, with a funnel shaped negative anomaly in the Diego Hernández caldera floor related to the subsurface topography of the caldera bottom. Two other important self-potential features are detected: positive values towards the northwestern Santiago rift, possibly due to the relatively high altitude of the water-table in this area; and a linear set of minima to the west of Pico Viejo, aligned with the northwestern rift and related to meteoric water infiltration along its fracture system.

Villasante-Marcos, Víctor; Finizola, Anthony; Abella, Rafael; Barde-Cabusson, Stéphanie; Blanco, María José; Brenes, Beatriz; Cabrera, Víctor; Casas, Benito; De Agustín, Pablo; Di Gangi, Fabio; Domínguez, Itahiza; García, Olaya; Gomis, Almudena; Guzmán, Juan; Iribarren, Ilazkiñe; Levieux, Guillaume; López, Carmen; Luengo-Oroz, Natividad; Martín, Isidoro; Moreno, Manuel; Meletlidis, Stavros; Morin, Julie; Moure, David; Pereda, Jorge; Ricci, Tullio; Romero, Enrique; Schütze, Claudia; Suski-Ricci, Barbara; Torres, Pedro; Trigo, Patricia

2014-02-01

267

A neural network mode inference engine for the advisory system for training and safety

for different phases of the flight. In order to perform this task, the ASTRAS system is endowed with an artificial intelligence engine or Situation Recognizer (SR) which is able to discern the flight mode from sensor readings. The current SR is based on fuzzy...

Nguyen, Thinh Xuan

2012-06-07

268

We present a novel automated strategy (PISTACHIO) for the probabilistic assignment of backbone and sidechain chemical shifts in proteins. The algorithm uses peak lists derived from various NMR experiments as input and provides as output ranked lists of assignments for all signals recognized in the input data as constituting spin systems. PISTACHIO was evaluate00000000d by comparing its performance with raw

Hamid R. Eghbalnia; Arash Bahrami; Liya Wang; Amir Assadi; John L. Markley

2005-01-01

269

DESIGN OF A PREDICTION MODEL FOR CEMENT ROTARY KILN USING WAVELET PROJECTION FUZZY INFERENCE SYSTEM

In a cement factory, a rotary kiln is the most complex component and it plays a key role in the quality and quantity of the final product. This system involves complex nonlinear dynamic equations that have not been completely worked out yet. In conventional modeling procedures, a large number of the involved parameters are crossed out and an approximation model

A. Sharifi; M. Aliyari Shoorehdeli; M. Teshnehlab

2012-01-01

270

Identification of cement rotary kiln using hierarchical wavelet fuzzy inference system

Rotary kiln is the central and the most complex component of cement production process. It is used to convert calcineous raw meal into cement clinkers, which plays a key role in quality and quantity of the final produced cement. This system has complex nonlinear dynamic equations that have not been completely worked out yet. In conventional modeling procedure, a large

A. Sharifi; M. Aliyari Shoorehdeli; M. Teshnehlab

271

The Ba isotopic composition of primitive meteorites is one of the more useful tracers to study nucleosynthetic processes in the early solar system, because the seven stable Ba isotopes consist of p-, r-, and s-process nucleosynthetic components and possible decay products from presently extinct 135Cs. Ba isotopic analyses were performed on acid leachates from five carbonaceous chondrites, Murchison (CM2), Sayama

Hiroshi Hidaka; Yohei Ohta; Shigekazu Yoneda

2003-01-01

272

NASA Astrophysics Data System (ADS)

Methodology development was conducted to incorporate a modular knowledge-base representation into an expert system engineering design application. The objective for using multidisciplinary methodologies in defining a design system was to develop a system framework that would be applicable to a wide range of engineering applications. The technique of "knowledge clustering" was used to construct a general decision tree for all factual information relating to the design application. This construction combined the design process surface knowledge and specific application depth knowledge. Utilization of both levels of knowledge created a system capable of processing multiple controlling tasks including; organizing factual information relative to the cognitive levels of the design process, building finite element models for depth knowledge analysis, developing a standardized finite element code for parallel processing, and determining a best solution generated by design optimization procedures. Proof of concept for the methodology developed here is shown in the implementation of an application defining the analysis and optimization of a composite aircraft canard subjected to a general compound loading condition. This application contained a wide range of factual information and heuristic rules. The analysis tools used included a finite element (FE) processor and numerical optimizer. An advisory knowledge-base was also developed to provide a standard for conversion of serial FE code for parallel processing. All knowledge-bases developed operated as either an advisory, selection, or classification systems. Laminate properties are limited to even-numbered, quasi-isotropic ply stacking sequences. This retained full influence of the coupled in-plane and bending effects of the structures behavior. The canard is modeled as a constant thickness plate and discretized into a varying number of four or nine-noded, quadrilateral, shear-deformable plate elements. The benefit gained by a designer from using this design methodology is presented by examining the capability of the system to satisfy the different levels of engineering design cognitive abilities. Numerical results of design iterations are provided to detail the expert system's advise in feasible region identification, and multiple iteration outcomes are used to justify solution assessment rules used in controlling the optimization process.

Winter, Steven John

273

Adaptive fuzzy system for 3-D vision

NASA Technical Reports Server (NTRS)

An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.

Mitra, Sunanda

1993-01-01

274

Early history of Earth's crust-mantle system inferred from hafnium isotopes in chondrites

The 176Lu to 176Hf decay series has been widely used to understand the nature of Earth's early crust-mantle system. The interpretation, however, of Lu-Hf isotope data requires accurate knowledge of the radioactive decay constant of 176Lu (lambda176Lu), as well as bulk-Earth reference parameters. A recent calibration of the lambda176Lu value calls for the presence of highly unradiogenic hafnium in terrestrial

Martin Bizzarro; Joel A. Baker; Henning Haack; David Ulfbeck; Minik Rosing

2003-01-01

275

We perform a joint inversion of Quaternary strain rates and 238 Global Positioning System (GPS) velocities in Asia for a self-consistent velocity field. The reference frames for all geodetic velocity observations are determined in our inversion procedure. India (IN) moves relative to Eurasia (EU) about a pole of rotation at (29.78°N, 7.51°E, 0.353° Myr-1), which yields a velocity along the

W. E. Holt; N. Chamot-Rooke; X. Le Pichon; A. J. Haines; B. Shen-Tu; J. Ren

2000-01-01

276

Belief systems and action inferences as a source of violence in the name of Islam

I draw on the belief system literature and use a cognitive mapping methodology to compare Islamists from the nonviolent Muslim Brotherhood and from the formerly violent groups al-Jihad and al-Jama'a al-Islamiyya in Egypt. Using data from in-depth interviews conducted in Egypt, I identify seven combinations of beliefs antecedent to decisions for and against violence and make three main claims. First,

Stephanie Dornschneider

2010-01-01

277

NASA Astrophysics Data System (ADS)

Wideband magnetotelluric (MT) soundings were carried out on Mt. Fuji volcano along a northeast to southwest axis. It was found by two-dimensional inversion using the highest quality data (in the frequency range 1-300 Hz) that a good conductor (resistivity of approximately a few ohm m) was located beneath the summit with a lateral extent of approximately 4 km. It begins approximately 1 km below the ground surface; however, its depth cannot be resolved. In our previous study, an intense positive self-potential (SP) anomaly (approximately 2000 mV), was found around a summit crater having a diameter of approximately 3 km. We interpreted the presence of the good conductor and positive SP anomaly as a strong indication of an active hydrothermal system. Subsequently, we searched for conduction current sources to explain the SP distribution on the surface by using the resistivity structure determined by the MT inversion. The results obtained were that a positive conduction current source of the order of 1000 A should be located at the top of the conductor. From these results, we deduced that the conductor represents a hydrothermal system in which single-phase (liquid) convection is taking place. Since the resistivity at a distance from the good conductor can be explained by the effect of cold groundwater, the hydrothermal system does not seem to extend throughout the entire body of the volcano, but seems to be confined to the area beneath the summit crater. Finally, an estimate of the order of magnitude of the subsurface hydrothermal flow was performed using a relation between the fluid volume flux and electric current density in the capillary model. The result suggested that there exists fairly low permeability within the shallow part of Mt. Fuji. We speculate that the low permeability in the volcano has a correlation with the confinement of the hydrothermal system and quiescence of volcanic activities, such as low seismicity, no gas emanations, and no natural hot springs.

Aizawa, K.; Yoshimura, R.; Oshiman, N.; Yamazaki, K.; Uto, T.; Ogawa, Y.; Tank, S. B.; Kanda, W.; Sakanaka, S.; Furukawa, Y.; Hashimoto, T.; Uyeshima, M.; Ogawa, T.; Shiozaki, I.; Hurst, A. W.

2005-06-01

278

Background: In recent years, phylogeographic studies have produced detailed knowledge on the worldwide distribution of mitochondrial DNA (mtDNA) variants, linking specific clades of the mtDNA phylogeny with certain geographic areas. However, a multiplex genotyping system for the detection of the mtDNA haplogroups of major continental distribution that would be desirable for efficient DNA-based bio-geographic ancestry testing in various applications is

Mannis van Oven; Mark Vermeulen; Manfred Kayser

2011-01-01

279

Turnera provides a useful system for exploring two significant evolutionary phenomena-shifts in breeding system (distyly vs. homostyly) and the evolution of polyploids. To explore these, the first molecular phylogeny of Turnera was constructed using sequences of the internal transcribed spacer region (ITS) of nuclear ribosomal DNA for 37 taxa. We attempted to resolve the origins of allopolyploid species using single-strand conformation polymorphism and sequencing of homeologous copies of ITS. Two allohexaploid species possessed putative ITS homeologues (T. velutina and T. orientalis). A phylogenetic analysis to identify progenitors contributing to the origins of these polyploids was unsuccessful, possibly as a result of concerted evolution of ITS. Breeding system evolution was mapped onto the phylogeny assuming distyly to be ancestral in Turnera. Self-compatible homostyly appears to have arisen independently at least three times in Turnera; however, we were not able to determine whether there have been independent origins of homostyly among hexaploid species in series Turnera. Our phylogenetic analyses suggest that series Turnera is monophyletic. Neither series Microphyllae nor Anomalae, however, appear to be monophyletic. Future taxonomic revisions may require new circumscriptions of these latter series. PMID:21646092

Truyens, Simon; Arbo, Maria M; Shore, Joel S

2005-10-01

280

NASA Astrophysics Data System (ADS)

On volcanoes which display hydrothermal/magmatic unrests, Electromagnetic (EM) methods can be combined with geochemical (GC) and thermal methods. The integration of these methods allows to image in detail hydrothermal systems, to find out possible scenarios of volcanic unrest, and to monitor the on-going activity with knowledge on the sources of heat, gas and fluid transfers. Since the 1990's the volcano shows recurrent periods of seismic activity, ground deformation, hydrothermal activity, and surface activity (geysers). Combined EM and GC methods noticeably contribute to map in detail the hydrothermal system and to analyse the sources of the activity: - Total magnetic field mapping evidences demagnetised zones over the two main areas forming the hydrothermal system (in the northern part of Main crater (MC)). These low magnetized areas are ascribed to thermal sources located at some hundreds metres of depth, - Self-potential surveys, delineate the contours of the fluids-heat transfer, and the northern and southern structural discontinuities enclosing the hydrothermal system, - Ground temperature gradient measurements evidence the distinctive heat transfer modes, from low fluxes related to soil temperature dominated by solar input to extremely high temperature gradients of 1200 °C m-1 or to more related to magmatic fluids. - Ground temperature and surface temperature of central acidic lake calculated by Thermal Aster imaging highlight the location of the most active ground fissures, outcrops and diffuse areas. Higher and larger anomalies are observed in the northern part of MC. A rough estimation of the thermal discharge in the northern part of the volcano gives 17 MW. - CO2 concentrations and fluxes from soil supply inform on fluids origin and on local processes operating along active fractures. Much higher carbon dioxide fluxes at MC sites confirm that the source of Taal activity is presently located in the northern part of the crater. - Heat and fluids release from the hydrothermal system delineate a general NW-SE ellipsoid in the northern part of MC and may be related to a suspected NW-SE fault along which seismicity takes place and dikes are believed to intrude triggering volcanic crises. The northern flank of the volcano is mechanically and hydro thermally reactivated during seismic crises and this sector could be subjected to a flank failure.

Zlotnicki, Jacques; Toutain, Jean Paul; Sasai, Yoichi; Villacorte, Egardo; Bernard, Alain; Fauquet, Frederic; Nagao, Toshiyatsu

2010-05-01

281

Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents—an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments. PMID:24068902

Yildiz, Izzet B.; von Kriegstein, Katharina; Kiebel, Stefan J.

2013-01-01

282

NASA Astrophysics Data System (ADS)

An expert system for diagnosing sickness and suggesting treatment based on Islamic Medication (IM) was constructed using Rule Based (RB) and Bayes' theorem (BT) algorithms independently as its inference engine. Comparative assessment on the quality of diagnosing based on symptoms provided by users for certain type of sickness using RB and BT reasoning that lead to the suggested treatment (based on IM) are discussed. Both approaches are found to be useful, each has its own advantages and disadvantages. Major difference of the two algorithms is the selection of symptoms during the diagnosing process. For BT, likely combinations of symptoms need to be classified for each sickness before the diagnosing process. This eliminates any irrelevant sickness based on the combination of symptoms provided by user and combination of symptoms that is unlikely. This is not the case for RB, it will diagnose the sickness as long as one the symptoms is related to the sickness regardless of unlikely combination. Few tests have been carried out using combinations of symptoms for same sickness to investigate their diagnosing accuracy in percentage. BT gives more promising diagnosing results compared to RB for each sickness that comes with common symptoms.

Daud, H.; Razali, R.; Low, T. J.; Sabdin, M.; Zafrul, S. Z. Mohd

2014-06-01

283

Technical and economic processes are getting constantly more complex. Analytical models are often insufficient to describe and handle this complexity. Data accumulations, on the other hand, are growing at ever increasing speeds, providing a potential source of knowledge and experience to understand and manage these processes. This work is therefore concerned with the general problem of creating comprehensible computational models

Mario Drobics

284

Crustal growth of oceanic island arc inferred from seismic structure of Mariana arc-backarc system

NASA Astrophysics Data System (ADS)

The Izu-Ogasawara-Marina arc (IBM arc) is one of the typical oceanic island arcs and it has developed repeating magmatic arc volcanisms and backarc spreading since Eocene. Because tectonics of the IBM arc is relatively simple and does not include collisions between the arc and a continent, it is one of best targets to research crustal growth. In 2003, wide-angle seismic survey using 106 ocean bottom seismographs had been carried out as a part of Margin program in collaboration between US and Japan in Mariana region. The seismic line runs from a serpentine diaper near the trench to Parece Vela basin through the Mariana arc, the Marina trough and the West Mariana ridge. We present the characteristics of the seismic structure of the Mariana arc-backarc system and discuss the crustal growth process by comparison with a structure of the northern Izu-Ogasawara arc. Main structural characteristics of the Mariana arc-backarc system are (1) variation of the crustal thickness (Mariana arc: 20 km, West Mariana ridge: 17 km, Mariana trough and Parece Vela basin: 6 km), (2) distribution of an andesitic middle crust with about P-wave velocity of 6 km/s, (3) variation of P-wave velocity in the middle crust (4) velocity anomalies of the lower crust in transition area between the arc and the backarc, (5) thickening of the lower crust under the Mariana trough axis and (6) slow mantle velocities under the Mariana arc, Mariana trough axis and the West Mariana ridge. Above characteristics from (1) to (4) are common to the seismic structure of the northern Izu-Ogasawara arc. In particular, the vertical P-wave velocity gradients of the middle crust under the forearc in both regions tend to become large rather than those under the arc. Main differences of seismic structures between both regions are the velocity gradients and an existence of a thin transition layer between the middle and lower crust. These differences and similarities of the velocity gradient might originate the age and indicate a difference of a crustal differentiation relating each tectonic stage.

Takahashi, N.; Kodaira, S.; Ito, A.; Klemperer, S. L.; Kaneda, Y.; Suyehiro, K.

2004-12-01

285

NASA Technical Reports Server (NTRS)

All the NASA VEGetation Workbench (VEG) goals except the Learning System provide the scientist with several different techniques. When VEG is run, rules assist the scientist in selecting the best of the available techniques to apply to the sample of cover type data being studied. The techniques are stored in the VEG knowledge base. The design and implementation of an interface that allows the scientist to add new techniques to VEG without assistance from the developer were completed. A new interface that enables the scientist to add techniques to VEG without assistance from the developer was designed and implemented. This interface does not require the scientist to have a thorough knowledge of Knowledge Engineering Environment (KEE) by Intellicorp or a detailed knowledge of the structure of VEG. The interface prompts the scientist to enter the required information about the new technique. It prompts the scientist to enter the required Common Lisp functions for executing the technique and the left hand side of the rule that causes the technique to be selected. A template for each function and rule and detailed instructions about the arguments of the functions, the values they should return, and the format of the rule are displayed. Checks are made to ensure that the required data were entered, the functions compiled correctly, and the rule parsed correctly before the new technique is stored. The additional techniques are stored separately from the VEG knowledge base. When the VEG knowledge base is loaded, the additional techniques are not normally loaded. The interface allows the scientist the option of adding all the previously defined new techniques before running VEG. When the techniques are added, the required units to store the additional techniques are created automatically in the correct places in the VEG knowledge base. The methods file containing the functions required by the additional techniques is loaded. New rule units are created to store the new rules. The interface that allow the scientist to select which techniques to use is updated automatically to include the new techniques. Task H was completed. The interface that allows the scientist to add techniques to VEG was implemented and comprehensively tested. The Common Lisp code for the Add Techniques system is listed in Appendix A.

Harrison, P. Ann

1993-01-01

286

Asteroseismic inference on rotation, gyrochronology and planetary system dynamics of 16 Cygni

NASA Astrophysics Data System (ADS)

The solar analogues 16 Cyg A and B are excellent asteroseismic targets in the Kepler field of view and together with a red dwarf and a Jovian planet form an interesting system. For these more evolved Sun-like stars we cannot detect surface rotation with the current Kepler data but instead use the technique of asteroseimology to determine rotational properties of both 16 Cyg A and B. We find the rotation periods to be 23.8^{+1.5}_{-1.8} and 23.2^{+11.5}_{-3.2} d, and the angles of inclination to be 56^{+6}_{-5}° and 36^{+17}_{-7}°, for A and B, respectively. Together with these results we use the published mass and age to suggest that, under the assumption of a solar-like rotation profile, 16 Cyg A could be used when calibrating gyrochronology relations. In addition, we discuss the known 16 Cyg B star-planet eccentricity and measured low obliquity which is consistent with Kozai cycling and tidal theory.

Davies, G. R.; Chaplin, W. J.; Farr, W. M.; García, R. A.; Lund, M. N.; Mathis, S.; Metcalfe, T. S.; Appourchaux, T.; Basu, S.; Benomar, O.; Campante, T. L.; Ceillier, T.; Elsworth, Y.; Handberg, R.; Salabert, D.; Stello, D.

2015-01-01

287

Final Report: Large-Scale Optimization for Bayesian Inference in Complex Systems

The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimiza- tion) Project focuses on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimiza- tion and inversion methods. Our research is directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. Our efforts are integrated in the context of a challenging testbed problem that considers subsurface reacting flow and transport. The MIT component of the SAGUARO Project addresses the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas-Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to- observation map) to implicitly extract lower dimensional information on the posterior distribution; this greatly speeds up sampling methods, so that fewer sampling points are needed. We can think of these two approaches as "reduce then sample" and "sample then reduce." In fact, these two approaches are complementary, and can be used in conjunction with each other. Moreover, they both exploit deterministic inverse problem structure, in the form of adjoint-based gradient and Hessian information of the underlying parameter-to-observation map, to achieve their speedups.

Ghattas, Omar [The University of Texas at Austin] [The University of Texas at Austin

2013-10-15

288

This paper presents an adaptive-network-based fuzzy inference system (ANFIS)-fuzzy data envelopment analysis (FDEA)) for long-term natural gas (NG) consumption forecasting and analysis. Six models are proposed to forecast annual NG demand. 104 ANFIS have been constructed and tested in order to find the best ANFIS for natural gas (NG) consumption. Two parameters have been considered in construction and examination of

A. Behrouznia; M. Saberi; A. Azadeh; S. M. Asadzadeh; P. Pazhoheshfar

2010-01-01

289

Background Studies of speciation mode based on phylogenies usually test the predicted effect on diversification patterns or on geographical distribution of closely related species. Here we outline an approach to infer the prevalent speciation mode in Iberian Hymenoplia chafers through the comparison of the evolutionary rates of morphological character systems likely to be related to sexual or ecological selection. Assuming that mitochondrial evolution is neutral and not related to measured phenotypic differences among the species, we contrast hypothetic outcomes of three speciation modes: 1) geographic isolation with subsequent random morphological divergence, resulting in overall change proportional to the mtDNA rate; 2) sexual selection on size and shape of the male intromittent organs, resulting in an evolutionary rate decoupled to that of the mtDNA; and 3) ecological segregation, reflected in character systems presumably related to ecological or biological adaptations, with rates decoupled from that of the mtDNA. Results The evolutionary rate of qualitative external body characters was significantly correlated to that of the mtDNA both for the overall root-to-tip patristic distances and the individual inter-node branches, as measured with standard statistics and the randomization of a global comparison metric (the z-score). The rate of the body morphospace was significantly correlated to that of the mtDNA only for the individual branches, but not for the patristic distances, while that of the paramere outline was significantly correlated with mtDNA rates only for the patristic distances but not for the individual branches. Conclusion Structural morphological characters, often used for species recognition, have evolved at a rate proportional to that of the mtDNA, with no evidence of directional or stabilising selection according to our measures. The change in body morphospace seems to have evolved randomly at short term, but the overall change is different from that expected under a pure random drift or randomly fluctuating selection, reflecting either directional or stabilising selection or developmental constraints. Short term changes in paramere shape possibly reflect sexual selection, but their overall amount of change was unconstrained, possibly reflecting their lack of functionality. Our approach may be useful to provide indirect insights into the prevalence of different speciation modes in entire lineages when direct evidence is lacking. PMID:19754949

Ahrens, Dirk; Ribera, Ignacio

2009-01-01

290

Daily water level forecasting using wavelet decomposition and artificial intelligence techniques

NASA Astrophysics Data System (ADS)

Reliable water level forecasting for reservoir inflow is essential for reservoir operation. The objective of this paper is to develop and apply two hybrid models for daily water level forecasting and investigate their accuracy. These two hybrid models are wavelet-based artificial neural network (WANN) and wavelet-based adaptive neuro-fuzzy inference system (WANFIS). Wavelet decomposition is employed to decompose an input time series into approximation and detail components. The decomposed time series are used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) for WANN and WANFIS models, respectively. Based on statistical performance indexes, the WANN and WANFIS models are found to produce better efficiency than the ANN and ANFIS models. WANFIS7-sym10 yields the best performance among all other models. It is found that wavelet decomposition improves the accuracy of ANN and ANFIS. This study evaluates the accuracy of the WANN and WANFIS models for different mother wavelets, including Daubechies, Symmlet and Coiflet wavelets. It is found that the model performance is dependent on input sets and mother wavelets, and the wavelet decomposition using mother wavelet, db10, can further improve the efficiency of ANN and ANFIS models. Results obtained from this study indicate that the conjunction of wavelet decomposition and artificial intelligence models can be a useful tool for accurate forecasting daily water level and can yield better efficiency than the conventional forecasting models.

Seo, Youngmin; Kim, Sungwon; Kisi, Ozgur; Singh, Vijay P.

2015-01-01

291

Artificial Intelligent Control for a Novel Advanced Microwave Biodiesel Reactor

NASA Astrophysics Data System (ADS)

Biodiesel, an alternative diesel fuel made from a renewable source, is produced by the transesterification of vegetable oil or fat with methanol or ethanol. In order to control and monitor the progress of this chemical reaction with complex and highly nonlinear dynamics, the controller must be able to overcome the challenges due to the difficulty in obtaining a mathematical model, as there are many uncertain factors and disturbances during the actual operation of biodiesel reactors. Classical controllers show significant difficulties when trying to control the system automatically. In this paper we propose a comparison of artificial intelligent controllers, Fuzzy logic and Adaptive Neuro-Fuzzy Inference System(ANFIS) for real time control of a novel advanced biodiesel microwave reactor for biodiesel production from waste cooking oil. Fuzzy logic can incorporate expert human judgment to define the system variables and their relationships which cannot be defined by mathematical relationships. The Neuro-fuzzy system consists of components of a fuzzy system except that computations at each stage are performed by a layer of hidden neurons and the neural network's learning capability is provided to enhance the system knowledge. The controllers are used to automatically and continuously adjust the applied power supplied to the microwave reactor under different perturbations. A Labview based software tool will be presented that is used for measurement and control of the full system, with real time monitoring.

Wali, W. A.; Hassan, K. H.; Cullen, J. D.; Al-Shamma'a, A. I.; Shaw, A.; Wylie, S. R.

2011-08-01

292

Artificial Intelligence in Public Health Prevention of Legionelosis in Drinking Water Systems

Good quality water supplies and safe sanitation in urban areas are a big challenge for governments throughout the world. Providing adequate water quality is a basic requirement for our lives. The colony forming units of the bacterium Legionella pneumophila in potable water represent a big problem which cannot be overlooked for health protection reasons. We analysed several methods to program a virtual hot water tank with AI (artificial intelligence) tools including neuro-fuzzy systems as a precaution against legionelosis. The main goal of this paper is to present research which simulates the temperature profile in the water tank. This research presents a tool for a water management system to simulate conditions which are able to prevent legionelosis outbreaks in a water system. The challenge is to create a virtual water tank simulator including the water environment which can simulate a situation which is common in building water distribution systems. The key feature of the presented system is its adaptation to any hot water tank. While respecting the basic parameters of hot water, a water supplier and building maintainer are required to ensure the predefined quality and water temperature at each sampling site and avoid the growth of Legionella. The presented system is one small contribution how to overcome a situation when legionelosis could find good conditions to spread and jeopardize human lives. PMID:25153475

Sin?ak, Peter; Ondo, Jaroslav; Kaposztasova, Daniela; Vir?ikova, Maria; Vranayova, Zuzana; Sabol, Jakub

2014-01-01

293

Artificial intelligence in public health prevention of legionelosis in drinking water systems.

Good quality water supplies and safe sanitation in urban areas are a big challenge for governments throughout the world. Providing adequate water quality is a basic requirement for our lives. The colony forming units of the bacterium Legionella pneumophila in potable water represent a big problem which cannot be overlooked for health protection reasons. We analysed several methods to program a virtual hot water tank with AI (artificial intelligence) tools including neuro-fuzzy systems as a precaution against legionelosis. The main goal of this paper is to present research which simulates the temperature profile in the water tank. This research presents a tool for a water management system to simulate conditions which are able to prevent legionelosis outbreaks in a water system. The challenge is to create a virtual water tank simulator including the water environment which can simulate a situation which is common in building water distribution systems. The key feature of the presented system is its adaptation to any hot water tank. While respecting the basic parameters of hot water, a water supplier and building maintainer are required to ensure the predefined quality and water temperature at each sampling site and avoid the growth of Legionella. The presented system is one small contribution how to overcome a situation when legionelosis could find good conditions to spread and jeopardize human lives. PMID:25153475

Sin?ak, Peter; Ondo, Jaroslav; Kaposztasova, Daniela; Vir?ikova, Maria; Vranayova, Zuzana; Sabol, Jakub

2014-08-01

294

NASA Astrophysics Data System (ADS)

In this paper we propose a new method for predicting rock porosity based on a combination of several artificial intelligence systems. The method focuses on one of the Iranian carbonate fields in the Persian Gulf. Because there is strong heterogeneity in carbonate formations, estimation of rock properties experiences more challenge than sandstone. For this purpose, seismic colored inversion (SCI) and a new approach of committee machine are used in order to improve porosity estimation. The study comprises three major steps. First, a series of sample-based attributes is calculated from 3D seismic volume. Acoustic impedance is an important attribute that is obtained by the SCI method in this study. Second, porosity log is predicted from seismic attributes using common intelligent computation systems including: probabilistic neural network (PNN), radial basis function network (RBFN), multi-layer feed forward network (MLFN), ?-support vector regression (?-SVR) and adaptive neuro-fuzzy inference system (ANFIS). Finally, a power law committee machine (PLCM) is constructed based on imperial competitive algorithm (ICA) to combine the results of all previous predictions in a single solution. This technique is called PLCM-ICA in this paper. The results show that PLCM-ICA model improved the results of neural networks, support vector machine and neuro-fuzzy system.

Ansari, Hamid Reza

2014-09-01

295

NASA Astrophysics Data System (ADS)

Rock mass classification systems are one of the most common ways of determining rock mass excavatability and related equipment assessment. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This causes particular ambiguities, leading to the misuse of such classifications in practical applications. Recently, intelligence system approaches such as artificial neural networks (ANNs) and neuro-fuzzy methods, along with multiple regression models, have been used successfully to overcome such uncertainties. The purpose of the present study is the construction of several models by using an adaptive neuro-fuzzy inference system (ANFIS) method with two data clustering approaches, including fuzzy c-means (FCM) clustering and subtractive clustering, an ANN and non-linear multiple regression to estimate the basic rock mass diggability index. A set of data from several case studies was used to obtain the real rock mass diggability index and compared to the predicted values by the constructed models. In conclusion, it was observed that ANFIS based on the FCM model shows higher accuracy and correlation with actual data compared to that of the ANN and multiple regression. As a result, one can use the assimilation of ANNs with fuzzy clustering-based models to construct such rigorous predictor tools.

Saeidi, Omid; Torabi, Seyed Rahman; Ataei, Mohammad

2014-03-01

296

Neuro-Fuzzy Controller of a Sensorless PM Motor Drive For Washing Machines

, and cycle time, while delivering added benefits such as better cleaning, more productive spin cycles, appliance design engineers are working hard to reduce the machine's energy consumption, water use, weight that minimize the hot water that the washing cycle consumes[2]. While clutch and gearbox assemblies in washing

297

Neuro-fuzzy model of superelastic shape memory alloys with application to seismic engineering

...................................................................................................... 1 2. OVERVIEW OF SHAPE MEMORY ALLOYS............................................. 4 2.1. General Characteristics of Shape Memory Alloys............................. 4 2.1.1 Shape Memory Effect.................................................................... 2.1.2 Superelastic Effect...................................................................... 6 2.2. Commonly Used Shape Memory Alloys............................................. 7 2.2.1. Shape Memory Materials...

Ozbulut, Osman Eser

2009-05-15

298

Neuro-fuzzy networks for short-term wind power forecasting

This paper presents a statistical model based on a hybrid computational intelligence technique that merging neural networks and fuzzy logic for wind power forecasting. A mesoscale NWP model is used to forecast meteorological variables at a reference point of a wind farm for the next 36 hours at half-hour intervals. The output of the NWP model, together with measured data

Junrong Xia; Pan Zhao; Yiping Dai

2010-01-01

299

A systematic neuro-fuzzy modeling framework with application to material property prediction

A systematic neural-fuzzy modeling framework that includes the initial fuzzy model self-generation, significant input selection, partition validation, parameter optimization, and rule-base simplification is proposed in this paper. In this framework, the structure identification and parameter optimization are carried out automatically and efficiently by the combined use of a sell-organization network, fuzzy clustering, adaptive back-propagation learning, and similarity analysis-based model simplification.

Min-you Chen; Derek A. Linkens

2001-01-01

300

A Neuro-Fuzzy Approach as Medical Diagnostic R. Brause, F. Friedrich

and habits of physi- cians and other medically trained people. As an example, a liver disease diagnosis applications, using the notation and habits of physicians and other medically trained people. In Fig. 1 this concept is visu- alized. Data parameters rules, training data terms physician learning algorithm Screen

Brause, R.

301

Intelligent Speed Adaptation Using a Self-Organizing Neuro-Fuzzy Controller

reaches a curve, which can cause the vehicle to go off the road and create an accident. Though the vehicle speed on roads [5] [6]. They have been tested and proved efficient in several countries Pasquier Anne Spalanzani Abstract-- The need to increase road safety is a major concern, with millions

Paris-Sud XI, UniversitÃ© de

302

Neuro-Fuzzy Knowledge Representation for Toxicity Prediction of Organic Compounds

that artificial intelligence (AI) tools could play in the problem of toxicity prediction and QSAR modeling of chemicals to human health and the environment. The huge number of compounds to be studied makes-chemical properties of the molecules, the computational algorithm to produce the statistical relationship

Gini, Giuseppina

303

Inferring Hierarchical Pronunciation Rules from a Phonetic Dictionary

Inferring Hierarchical Pronunciation Rules from a Phonetic Dictionary Erika Pigliapoco, Valerio Freschi, and Alessandro Bogliolo Abstract--This work presents a new phonetic transcription system based. The tree is automatically inferred from a phonetic dictionary by incrementally analyzing deeper context

Bogliolo, Alessandro

304

Estimation and optimization of thermal performance of evacuated tube solar collector system

NASA Astrophysics Data System (ADS)

In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy (ANFIS) in order to predict the thermal performance of evacuated tube solar collector system have been used. The experimental data for the training and testing of the networks were used. The results of ANN are compared with ANFIS in which the same data sets are used. The R2-value for the thermal performance values of collector is 0.811914 which can be considered as satisfactory. The results obtained when unknown data were presented to the networks are satisfactory and indicate that the proposed method can successfully be used for the prediction of the thermal performance of evacuated tube solar collectors. In addition, new formulations obtained from ANN are presented for the calculation of the thermal performance. The advantages of this approaches compared to the conventional methods are speed, simplicity, and the capacity of the network to learn from examples. In addition, genetic algorithm (GA) was used to maximize the thermal performance of the system. The optimum working conditions of the system were determined by the GA.

Dikmen, Erkan; Ayaz, Mahir; Ezen, H. Hüseyin; Küçüksille, Ecir U.; ?ahin, Arzu ?encan

2014-05-01

305

The importance of a detection technique to prevent process deterioration is increasing. For the fast detection of this disturbance, a diagnostic algorithm was developed to determine types of equipment faults by using on-line ORP and DO profile in sequencing batch reactors (SBRs). To develop the rule base for fault diagnosis, the sensor profiles were obtained from a pilot-scale SBR when blower, influent pump and mixer were broken. The rules were generated based on the calculated error between an abnormal profile and a normal profile, e(ORP)(t) and e(DO)(t). To provide intermediate diagnostic results between "normal" and "fault", a fuzzy inference algorithm was incorporated to the rules. Fuzzified rules could present the diagnosis result "need to be checked". The diagnosis showed good performance in detecting and diagnosing various faults. The developed algorithm showed its applicability to detect faults and make possible fast action to correct them. PMID:16722090

Kim, Y J; Bae, H; Poo, K M; Ko, J H; Kim, B G; Park, T J; Kim, C W

2006-01-01

306

Nonmonotonic inference rules for multiple inheritance with exceptions

The semantics of inheritance ''hierarchies'' with multiple inheritance and exceptions is discussed, and a partial semantics in terms of a number of structure types is defined. Previously proposed inference systems for inheritance with exceptions are discussed. A new and improved inference system is proposed, using a fixed number of nonmonotonic inference rules. The hierarchy is viewed as a set of atomic propositions using the two relations isa (subsumption) and nisa (nonsubsumption). General results concerning systems of nonmonotonic inference rules can immediately be applied to the proposed inference system.

Sandewall, E.

1986-10-01

307

I greatly enjoyed Seth's compelling synthesis of sensorimotor contingencies and active inference. I would also like to thank Jim Hopkins for sending me the quote (below)-which speaks directly to the embodied nature of perceptual inference that underlies the perspectives reconciled in Seth (this issue). These perspectives include perception as hypothesis testing, affordance, and sensorimotor contingencies. This commentary briefly rehearses the fundaments of active inference and offers a formal basis for Seth's key argument. PMID:24702520

Friston, Karl

2014-01-01

308

Efficient ECG signal analysis using wavelet technique for arrhythmia detection: an ANFIS approach

NASA Astrophysics Data System (ADS)

This paper deals with improved ECG signal analysis using Wavelet Transform Techniques and employing subsequent modified feature extraction for Arrhythmia detection based on Neuro-Fuzzy technique. This improvement is based on suitable choice of features in evaluating and predicting life threatening Ventricular Arrhythmia . Analyzing electrocardiographic signals (ECG) includes not only inspection of P, QRS and T waves, but also the causal relations they have and the temporal sequences they build within long observation periods. Wavelet-transform is used for effective feature extraction and Adaptive Neuro-Fuzzy Inference System (ANFIS) is considered for the classifier model. In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia and CSE databases, developed for validation purposes. Features based on the ECG waveform shape and heart beat intervals are used as inputs to the classifiers. The performance of the ANFIS model is evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. Cross validation is used to measure the classifier performance. A testing classification accuracy of 95.13% is achieved which is a significant improvement.

Khandait, P. D.; Bawane, N. G.; Limaye, S. S.

2010-02-01

309

Risk prediction procedures can be quite useful for the patient’s treatment selection, prevention strategy, or disease management in evidence-based medicine. Often, potentially important new predictors are available in addition to the conventional markers. The question is how to quantify the improvement from the new markers for prediction of the patient’s risk in order to aid cost–benefit decisions. The standard method, using the area under the receiver operating characteristic curve, to measure the added value may not be sensitive enough to capture incremental improvements from the new markers. Recently, some novel alternatives to area under the receiver operating characteristic curve, such as integrated discrimination improvement and net reclassification improvement, were proposed. In this paper, we consider a class of measures for evaluating the incremental values of new markers, which includes the preceding two as special cases. We present a unified procedure for making inferences about measures in the class with censored event time data. The large sample properties of our procedures are theoretically justified. We illustrate the new proposal with data from a cancer study to evaluate a new gene score for prediction of the patient’s survival. PMID:23037800

Uno, Hajime; Tian, Lu; Cai, Tianxi; Kohane, Isaac S.; Wei, L. J.

2013-01-01

310

NASA Astrophysics Data System (ADS)

magnetotelluric (MT) measurements were conducted in 2010 and 2011 in the vicinity of Shinmoe-dake Volcano in the Kirishima volcano group, Japan, where sub-Plinian eruptions took place 3 times during 26-27 January 2011. By combining the new observations with previous MT data, it is found that an anomalous phase in excess of 90° is commonly observed in the northern sector of the Kirishima volcano group. Because the anomalous phase is not explained by 1-D or 2-D structure with isotropic resistivity media, 3-D inversions were performed. By applying small errors to the anomalous phase, we successfully estimated a 3-D resistivity structure that explains not only the normal data but also the anomalous phase data. The final model shows a vertical conductor that is located between a deep-seated conductive body (at a depth greater than 10 km) and a shallow conductive layer. By applying the findings of geophysical and petrological studies of the 2011 sub-Plinian eruptions, we infer that the subvertical conductor represents a zone of hydrothermal aqueous fluids at temperatures over 400°C, in which a magma pathway (interconnected melt) is partially and occasionally formed before magmatic eruptions. To the north of the deep conductor, earthquake swarms occurred from 1968 to 1969, suggesting that these earthquakes were caused by volcanic fluids.

Aizawa, Koki; Koyama, Takao; Hase, Hideaki; Uyeshima, Makoto; Kanda, Wataru; Utsugi, Mitsuru; Yoshimura, Ryokei; Yamaya, Yusuke; Hashimoto, Takeshi; Yamazaki, Ken'ichi; Komatsu, Shintaro; Watanabe, Atsushi; Miyakawa, Koji; Ogawa, Yasuo

2014-01-01

311

Prediction on carbon dioxide emissions based on fuzzy rules

NASA Astrophysics Data System (ADS)

There are several ways to predict air quality, varying from simple regression to models based on artificial intelligence. Most of the conventional methods are not sufficiently able to provide good forecasting performances due to the problems with non-linearity uncertainty and complexity of the data. Artificial intelligence techniques are successfully used in modeling air quality in order to cope with the problems. This paper describes fuzzy inference system (FIS) to predict CO2 emissions in Malaysia. Furthermore, adaptive neuro-fuzzy inference system (ANFIS) is used to compare the prediction performance. Data of five variables: energy use, gross domestic product per capita, population density, combustible renewable and waste and CO2 intensity are employed in this comparative study. The results from the two model proposed are compared and it is clearly shown that the ANFIS outperforms FIS in CO2 prediction.

Pauzi, Herrini; Abdullah, Lazim

2014-06-01

312

Within the deuterostomes, the similarity of the dipleu- rula-type larvae of echinoderms (auricularia, bipinnaria) and hemichordates (tornaria) is striking. Here we describe the serotonergic system of the auricularia larvae of the apodid sea cucumber Chiridota gigas to broaden the com- parison of the dipleurula-type larval nervous system in the Holothuroidea. This larva has a simple serotonergic ner- vous system largely

MARIA BYRNE; MARY A. SEWELL; PAULINA SELVAKUMARASWAMY

2006-01-01

313

NASA Technical Reports Server (NTRS)

A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.

Aires, Filipe; Rossow, William B.; Hansen, James E. (Technical Monitor)

2001-01-01

314

BACKGROUND: The core enzymes of the DNA replication systems show striking diversity among cellular life forms and more so among viruses. In particular, and counter-intuitively, given the central role of DNA in all cells and the mechanistic uniformity of replication, the core enzymes of the replication systems of bacteria and archaea (as well as eukaryotes) are unrelated or extremely distantly

Eugene V Koonin

2006-01-01

315

NASA Astrophysics Data System (ADS)

Total organic carbon (TOC) content present in reservoir rocks is one of the important parameters, which could be used for evaluation of residual production potential and geochemical characterization of hydrocarbon-bearing units. In general, organic-rich rocks are characterized by higher porosity, higher sonic transit time, lower density, higher ?-ray, and higher resistivity than other rocks. Current study suggests an improved and optimal model for TOC estimation by integration of intelligent systems and the concept of committee machine with an example from Kangan and Dalan Formations, in South Pars Gas Field, Iran. This committee machine with intelligent systems (CMIS) combines the results of TOC predicted from intelligent systems including fuzzy logic (FL), neuro-fuzzy (NF), and neural network (NN), each of them has a weight factor showing its contribution in overall prediction. The optimal combination of weights is derived by a genetic algorithm (GA). This method is illustrated using a case study. One hundred twenty-four data points including petrophysical data and measured TOC from three wells of South Pars Gas Field were divided into 87 training sets to build the CMIS model and 37 testing sets to evaluate the reliability of the developed model. The results show that the CMIS performs better than any one of the individual intelligent systems acting alone for predicting TOC.

Kadkhodaie-Ilkhchi, Ali; Rahimpour-Bonab, Hossain; Rezaee, Mohammadreza

2009-03-01

316

Social Inference Through Technology

NASA Astrophysics Data System (ADS)

Awareness cues are computer-mediated, real-time indicators of people’s undertakings, whereabouts, and intentions. Already in the mid-1970 s, UNIX users could use commands such as “finger” and “talk” to find out who was online and to chat. The small icons in instant messaging (IM) applications that indicate coconversants’ presence in the discussion space are the successors of “finger” output. Similar indicators can be found in online communities, media-sharing services, Internet relay chat (IRC), and location-based messaging applications. But presence and availability indicators are only the tip of the iceberg. Technological progress has enabled richer, more accurate, and more intimate indicators. For example, there are mobile services that allow friends to query and follow each other’s locations. Remote monitoring systems developed for health care allow relatives and doctors to assess the wellbeing of homebound patients (see, e.g., Tang and Venables 2000). But users also utilize cues that have not been deliberately designed for this purpose. For example, online gamers pay attention to other characters’ behavior to infer what the other players are like “in real life.” There is a common denominator underlying these examples: shared activities rely on the technology’s representation of the remote person. The other human being is not physically present but present only through a narrow technological channel.

Oulasvirta, Antti

317

NASA Astrophysics Data System (ADS)

creep, depending on its rate and spatial extent, is thought to reduce earthquake hazard by releasing tectonic strain aseismically. We use Bayesian inversion and a newly expanded GPS data set to infer the deep slip rates below assigned locking depths on the San Andreas, Maacama, and Bartlett Springs Faults of Northern California and, for the latter two, the spatially variable interseismic creep rate above the locking depth. We estimate deep slip rates of 21.5 ± 0.5, 13.1 ± 0.8, and 7.5 ± 0.7 mm/yr below 16 km, 9 km, and 13 km on the San Andreas, Maacama, and Bartlett Springs Faults, respectively. We infer that on average the Bartlett Springs fault creeps from the Earth's surface to 13 km depth, and below 5 km the creep rate approaches the deep slip rate. This implies that microseismicity may extend below the locking depth; however, we cannot rule out the presence of locked patches in the seismogenic zone that could generate moderate earthquakes. Our estimated Maacama creep rate, while comparable to the inferred deep slip rate at the Earth's surface, decreases with depth, implying a slip deficit exists. The Maacama deep slip rate estimate, 13.1 mm/yr, exceeds long-term geologic slip rate estimates, perhaps due to distributed off-fault strain or the presence of multiple active fault strands. While our creep rate estimates are relatively insensitive to choice of model locking depth, insufficient independent information regarding locking depths is a source of epistemic uncertainty that impacts deep slip rate estimates.

Murray, Jessica R.; Minson, Sarah E.; Svarc, Jerry L.

2014-07-01

318

Background The core enzymes of the DNA replication systems show striking diversity among cellular life forms and more so among viruses. In particular, and counter-intuitively, given the central role of DNA in all cells and the mechanistic uniformity of replication, the core enzymes of the replication systems of bacteria and archaea (as well as eukaryotes) are unrelated or extremely distantly related. Viruses and plasmids, in addition, possess at least two unique DNA replication systems, namely, the protein-primed and rolling circle modalities of replication. This unexpected diversity makes the origin and evolution of DNA replication systems a particularly challenging and intriguing problem in evolutionary biology. Results I propose a specific succession for the emergence of different DNA replication systems, drawing argument from the differences in their representation among viruses and other selfish replicating elements. In a striking pattern, the DNA replication systems of viruses infecting bacteria and eukaryotes are dominated by the archaeal-type B-family DNA polymerase (PolB) whereas the bacterial replicative DNA polymerase (PolC) is present only in a handful of bacteriophage genomes. There is no apparent mechanistic impediment to the involvement of the bacterial-type replication machinery in viral DNA replication. Therefore, I hypothesize that the observed, markedly unequal distribution of the replicative DNA polymerases among the known cellular and viral replication systems has a historical explanation. I propose that, among the two types of DNA replication machineries that are found in extant life forms, the archaeal-type, PolB-based system evolved first and had already given rise to a variety of diverse viruses and other selfish elements before the advent of the bacterial, PolC-based machinery. Conceivably, at that stage of evolution, the niches for DNA-viral reproduction have been already filled with viruses replicating with the help of the archaeal system, and viruses with the bacterial system never took off. I further suggest that the two other systems of DNA replication, the rolling circle mechanism and the protein-primed mechanism, which are represented in diverse selfish elements, also evolved prior to the emergence of the bacterial replication system. This hypothesis is compatible with the distinct structural affinities of PolB, which has the palm-domain fold shared with reverse transcriptases and RNA-dependent RNA polymerases, and PolC that has a distinct, unrelated nucleotidyltransferase fold. I propose that PolB is a descendant of polymerases that were involved in the replication of genetic elements in the RNA-protein world, prior to the emergence of DNA replication. By contrast, PolC might have evolved from an ancient non-templated polymerase, e.g., polyA polymerase. The proposed temporal succession of the evolving DNA replication systems does not depend on the specific scenario adopted for the evolution of cells and viruses, i.e., whether viruses are derived from cells or virus-like elements are thought to originate from a primordial gene pool. However, arguments are presented in favor of the latter scenario as the most parsimonious explanation of the evolution of DNA replication systems. Conclusion Comparative analysis of the diversity of genomic strategies and organizations of viruses and cellular life forms has the potential to open windows into the deep past of life's evolution, especially, with the regard to the origin of genome replication systems. When complemented with information on the evolution of the relevant protein folds, this comparative approach can yield credible scenarios for very early steps of evolution that otherwise appear to be out of reach. Reviewers Eric Bapteste, Patrick Forterre, and Mark Ragan. PMID:17176463

Koonin, Eugene V

2006-01-01

319

CUE PROBABILISM AND INFERENCE BEHAVIOR

This is an investigation of how Ss use probabilistic cues to make inferences about objects' (Os') class memberships. Os having specified probabilistic cue properties were presented and Ss' inferences were compared to the theoretical predictions. It was concluded that: (a) for unfamiliar Os cue probabilism is important in class inferences; (b) for familiar Os inferences are based upon recognition; (c)

Lee Roy Beach

1964-01-01

320

Distributed generation system using wind/photovoltaic/fuel cell

NASA Astrophysics Data System (ADS)

This dissertation investigates the performance and the operation of a distributed generation (DG) power system using wind/photovoltaic/fuel cell (W/PV/FC). The power system consists of a 2500 W photovoltaic array subsystem, a 500 W proton exchange membrane fuel cell (PEMFC) stack subsystem, 300 W wind turbine, 500 W wind turbine, and 1500 W wind energy conversion subsystems. To extract maximum power from the PV, a maximum power point tracker was designed and fabricated. A 4 kW single phase inverter was used to convert the DC voltage to AC voltage; also a 44 kWh battery bank was used to store energy and prevent fluctuation of the power output of the DG system. To connect the fuel cell to the batteries, a DC/DC controller was designed and fabricated. To monitor and study the performance of the DG system under variable conditions, a data acquisition system was designed and installed. The fuel cell subsystem performance was evaluated under standalone operation using a variable resistance and under interactive mode, connected to the batteries. The manufacturing data and the experimental data were used to develop an electrical circuit model to the fuel cell. Furthermore, harmonic analysis of the DG system was investigated. For an inverter, the AC voltage delivered to the grid changed depending on the time, load, and electronic equipment that was connected. The quality of the DG system was evaluated by investigating the harmonics generated by the power electronics converters. Finally, each individual subsystem of the DG system was modeled using the neuro-fuzzy approach. The model was used to predict the performance of the DG system under variable conditions, such as passing clouds and wind gust conditions. The steady-state behaviors of the model were validated by the experimental results under different operating conditions.

Buasri, Panhathai

321

Hydrol. Earth Syst. Sci., 12, 123139, 2008 www.hydrol-earth-syst-sci.net/12/123/2008/

, applicability of Adaptive Neuro Fuzzy Inference Sys- tem (ANFIS) and Artificial Neural Network (ANN) methods models for both training and test- ing are evaluated and the best fit input structures and methods for both stations are determined according to criteria of per- formance evaluation. Moreover the best fit

Boyer, Edmond

322

Cytogenetic studies in Neotropical electric knifefish of genus Gymnotus have shown a remarkable interspecific variability, including distinct sex chromosome systems. In this study, we present the first chromosomal data in Gymnotus bahianus from Contas River basin, northeastern South America. Based on extensive analyses, the modal diploid values were 2n = 36 (30m/sm + 6st) for females and 2n = 37 (32m/sm + 5st) for males. Therefore, a novel XX/XY1Y2 sex chromosome system is described for the genus. Single nucleolar organizer regions (NORs) interspersed to GC-rich sites were detected on a subtelocentric pair (7th) for both sexes and confirmed by ?uorescent in situ hybridization with 18S rDNA probes. Heterochromatin was detected at pericentromeric regions of all chromosomes and interspersed to NORs on pair 7 and 5S rDNA cistrons on pair 9. The highly differentiated karyotype of Gymnoytus bahianus, with low diploid numbers and a unique XX/XY1Y2 system, reinforces the independent origin of sex chromosomes in Gymnotiformes and seems to reflect the particular evolutionary history of this species in a small and isolated drainage system. Moreover, in spite of morphological similarities, the present results indicate a remarkable chromosomal divergence in relation to closely related species such as G. sylvius and G. carapo. PMID:25596613

Almeida, Josivanda S; Migues, Vitor H; Diniz, Débora; Affonso, Paulo Roberto A M

2015-01-01

323

Static Inference of Universe Types Ana Milanova

Static Inference of Universe Types Ana Milanova Rensselaer Polytechnic Institute milanova@cs.rpi.edu Abstract The Universe type system is an ownership type system which en- forces the owners the same owner), and any which does not give any information. The Universe type system en- forces

Milanova, Ana

324

Transitive inference in rats (Rattus norvegicus).

Although Piagetian theory proposes that the ability to make transitive inferences is confined to humans above age 7, recent evidence has suggested that this logical ability may be more broad based. In nonverbal tests, transitive inference has been demonstrated in preschool children and 2 species of nonhuman primates. In these experiments, I demonstrate evidence of transitive inference in rats (Rattus norvegicus). I used an ordered series of 5 olfactory stimuli (A < B < C < D < E) from which correct inferences were made about the novel B versus D pair. Control procedures indicated that performance did not depend on the recency with which the correct answer was rewarded during training and may be disrupted by the addition of logically inconsistent premises (F > E and A > F). The possibility that logical transitivity may reflect a form of spatial paralogic rather than formal deductions from a syllogistic-verbal system is discussed. PMID:1451416

Davis, H

1992-12-01

325

This book addresses two questions of great importance to Artificial Intelligence: - how can search be controlled in domains with a large search space. -how can this control information be learned. It is argued that both problems can be tackled with the aid of a technique called meta-level inference. In programs that use meta-level inference the control information is separated from the factual information. The control information is expressed declaratively, i.e. it is represented as explicit rules. These rules are axioms in the meta-theory of the domain. This gives rise to a two-level program: the factual information forms the object-level and the control information forms the meta-level. Inference is performed at the meta-level, and this induces inference at the object-level. Search at the object level is replaced by search at the meta-level. Two Prolog programs, which use this technique, are presented in the book to demonstrate the utility of meta-level inference.

Silver, B.

1986-01-01

326

"Groundwater ages" of the Lake Chad multi-layer aquifers system inferred from 14C and 36Cl data

NASA Astrophysics Data System (ADS)

Assessment of recharge, paleo-recharge and groundwater residence time of aquifer systems of the Sahel is pivotal for a sustainable management of this vulnerable resource. Due to its stratified aquifer system, the Lake Chad Basin (LCB) offers the opportunity to assess recharge processes over time and to link climate and hydrology in the Sahel. Located in north-central Africa at the fringe between the Sahel and the Sahara, the lake Chad basin (LCB) is an endorheic basin of 2,5.106 km2. With a monsoon climate, the majority of the rainfall occurs in the southern one third of the basin, the Chari/Logone River system transporting about 90% of the runoff generated within the drainage basin. A complex multi-layer aquifer system is located in the central part of the LCB. The Quaternary unconfined aquifer, covering 500 000 km2, is characterized by the occurrence of poorly understood piezometric depressions. Artesian groundwaters are found in the Plio-Pleistocene lacustrine and deltaic sedimentary aquifers (early Pliocene and Continental Terminal). The present-day lake is in hydraulic contact with the Quaternary Aquifer, but during past megalake phases, most of the Quaternary aquifer was submerged and may experience major recharge events. To identify active recharge area and assess groundwater dynamics, one hundred surface and groundwater samples of all layers have been collected over the southern part of the LCB. Major and trace elements have been analyzed. Measurements of 36Cl have been carried out at CEREGE, on the French 5 MV AMS National Facility ASTER and 14C activities have been analyzed for 17 samples on the French AMS ARTEMIS. Additionally, the stable isotopic composition was measured on the artesian aquifer samples. In the Quaternary aquifer, results show a large scatter with waters having very different isotopic and geochemical signature. In its southern part and in the vicinity of the surface waters, groundwaters are predominantly Ca-Mg-HCO3 type waters with very high 36Cl/Cl ratio (>1000.10-15 at/at) very likely linked to the bomb pulse. These high 36Cl/Cl ratios are in the same order than the 36Cl/Cl signature of surface waters active modern recharge in this area. In the other part of the Quaternary Aquifer, waters are Na-HCO3-SO4-Cl type and are characterized by lower 36Cl/Cl ratios (around 200.10-15 at/at), suggesting longer residence time of the groundwaters. The 14C contents of the unconfined aquifer waters are all above 50 pmc, suggesting recent or Holocene recharge of this system. In contrast, the confined aquifer has a more homogeneous geochemical signature. The 14C contents are below all 0.5 pmc and mainly below detection level. 36Cl/Cl ratios are

Bouchez, Camille; Deschamps, Pierre; Goncalves, Julio; Hamelin, Bruno; Seidel, Jean-Luc; Doumnang, Jean-Claude

2014-05-01

327

NASA Astrophysics Data System (ADS)

We investigate the utility of satellite measurements of chlorophyll fluorescence (Fs) in constraining gross primary productivity (GPP). We ingest Fs measurements into the Carbon-Cycle Data Assimilation System (CCDAS) which has been augmented by the fluorescence component of the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. CCDAS simulates well the patterns of Fs suggesting the combined model is capable of ingesting these measurements. However simulated Fs is insensitive to the key parameter controlling GPP, the carboxylation capacity (Vcmax). Simulated Fs is sensitive to both the incoming absorbed photosynthetically active radiation (aPAR) and leaf chlorophyll concentration both of which are treated as perfectly known in previous CCDAS versions. Proper use of Fs measurements therefore requires enhancement of CCDAS to include and expose these variables.

Koffi, E. N.; Rayner, P. J.; Norton, A. J.; Frankenberg, C.; Scholze, M.

2015-01-01

328

The authors are developing a computer application, called the Bayes Inference Engine, to provide the means to make inferences about models of physical reality within a Bayesian framework. The construction of complex nonlinear models is achieved by a fully object-oriented design. The models are represented by a data-flow diagram that may be manipulated by the analyst through a graphical programming environment. Maximum a posteriori solutions are achieved using a general, gradient-based optimization algorithm. The application incorporates a new technique of estimating and visualizing the uncertainties in specific aspects of the model.

Hanson, K.M.; Cunningham, G.S.

1996-04-01

329

We present and analyze a chemical dataset that includes the concentrations and fluxes of HCO3-, SO42-, Cl-, and F- in the major rivers draining Yellowstone National Park (YNP) for the 2002-2004 water years (1 October 2001 - 30 September 2004). The total (molar) flux in all rivers decreases in the following order, HCO3- > Cl- > SO42- > F-, but each river is characterized by a distinct chemical composition, implying large-scale spatial heterogeneity in the inputs of the various solutes. The data also display non-uniform temporal trends; whereas solute concentrations and fluxes are nearly constant during base-flow conditions, concentrations decrease, solute fluxes increase, and HCO3-/Cl-, and SO42-/Cl- increase during the late-spring high-flow period. HCO3-/SO42- decreases with increasing discharge in the Madison and Falls Rivers, but increases with discharge in the Yellowstone and Snake Rivers. The non-linear relations between solute concentrations and river discharge and the change in anion ratios associated with spring runoff are explained by mixing between two components: (1) a component that is discharged during base-flow conditions and (2) a component associated with snow-melt runoff characterized by higher HCO3-/Cl- and SO42-/Cl-. The fraction of the second component is greater in the Yellowstone and Snake Rivers, which host lakes in their drainage basins and where a large fraction of the solute flux follows thaw of ice cover in the spring months. Although the total river HCO3- flux is larger than the flux of other solutes (HCO3-/Cl- ??? 3), the CO2 equivalent flux is only ??? 1% of the estimated emission of magmatic CO2 soil emissions from Yellowstone. No anomalous solute flux in response to perturbations in the hydrothermal system was observed, possibly because gage locations are too distant from areas of disturbance, or because of the relatively low sampling frequency. In order to detect changes in river hydrothermal solute fluxes, sampling at higher frequencies with better spatial coverage would be required. Our analysis also suggests that it might be more feasible to detect large-scale heating or cooling of the hydrothermal system by tracking changes in gas and steam flux than by tracking changes in river solute flux.

Hurwitz, S.; Lowenstern, J.B.; Heasler, H.

2007-01-01

330

NASA Astrophysics Data System (ADS)

Magmatic volatiles, specifically water, fluorine, chlorine and sulfur, play important and diverse roles in silicate melts by controlling many physiochemical processes such as thermal stabilities of minerals and melts, melt density and viscosity, magma eruptive processes, and the formation of hydrothermal fluids that transport economically important metals. Some of these volatiles, perhaps most notably water, likely play a crucial role in the origin of life. Although the terrestrial magmatic volatile budget is well constrained, much remains uncertain about the martian volatile budget. Mars has commonly been referred to as a "volatile-rich" planet, and there is little doubt about the presence of frozen water-ice at the martian poles and abundant Cl and S in rocks, soils and dust. Yet, contradictory information abounds, particularly regarding magmatic water contents and the accepted mantle water budget for Mars. This body of work provides the first studies focused on assessing the volatile budget of martian magmas and exploring the implications of these volatiles on ancient martian igneous and hydrothermal systems. We report, through textural analysis and electron probe microanalysis (EPMA) of minerals in martian meteorites, strong evidence for water, F, and Cl-bearing magmas and strong evidence for both water-rich and chlorine-rich hydrothermal fluids in martian magmatic systems. We collected new secondary ion mass spectrometry (SIMS) data on kaersutite from the Chassigny meteorite, which we use to show that at least some magma source regions on Mars likely have water contents similar to terrestrial values. In order to show that low-OH F-Cl apatite analyses obtained from the Chassigny meteorite are viable compositions (these compositions are rare in terrestrial rocks), low-OH F-Cl apatite was synthesized and characterized by EPMA, single-crystal X-ray diffraction and various nuclear magnetic resonance (NMR) techniques. Finally, the effect of water on the compositional diversity of magmas that can be produced from fractionation of a martian liquid at the base of a thick crust was investigated experimentally. Using a synthetic powder modeled after Humphrey (a picrobasalt analyzed in Gusev Crater, Mars), we verified the possibility of igneous crustal stratification, which does not require large-scale lithologic diversity among rocks on the martian surface.

McCubbin, Francis Michael

331

ANFIS-based approach for predicting sediment transport in clean sewer

The necessity of sewers to carry sediment has been recognized for many years. Typically, old sewage systems were designated based on self-cleansing concept where there is no deposition in sewer. These codes were applicable to non-cohesive sediments (typically storm sewers). This study presents adaptive neuro-fuzzy inference system (ANFIS), which is a combination of neural network and fuzzy logic, as an alternative approach to predict the functional relationships of sediment transport in sewer pipe systems. The proposed relationship can be applied to different boundaries with partially full flow. The present ANFIS approach gives satisfactory results (r2 = 0.98 and RMSE = 0.002431) compared to the existing predictor. PMID:22389640

Azamathulla, H. Md.; Ab. Ghani, Aminuddin; Fei, Seow Yen

2012-01-01

332

NASA Technical Reports Server (NTRS)

All the options in the NASA VEGetation Workbench (VEG) make use of a database of historical cover types. This database contains results from experiments by scientists on a wide variety of different cover types. The learning system uses the database to provide positive and negative training examples of classes that enable it to learn distinguishing features between classes of vegetation. All the other VEG options use the database to estimate the error bounds involved in the results obtained when various analysis techniques are applied to the sample of cover type data that is being studied. In the previous version of VEG, the historical cover type database was stored as part of the VEG knowledge base. This database was removed from the knowledge base. It is now stored as a series of flat files that are external to VEG. An interface between VEG and these files was provided. The interface allows the user to select which files of historical data to use. The files are then read, and the data are stored in Knowledge Engineering Environment (KEE) units using the same organization of units as in the previous version of VEG. The interface also allows the user to delete some or all of the historical database units from VEG and load new historical data from a file. This report summarizes the use of the historical cover type database in VEG. It then describes the new interface to the files containing the historical data. It describes minor changes that were made to VEG to enable the externally stored database to be used. Test runs to test the operation of the new interface and also to test the operation of VEG using historical data loaded from external files are described. Task F was completed. A Sun cartridge tape containing the KEE and Common Lisp code for the new interface and the modified version of the VEG knowledge base was delivered to the NASA GSFC technical representative.

1993-01-01

333

NASA Astrophysics Data System (ADS)

The flux of methane from gas hydrate bearing seeps in the marine environment is partially mitigated by the anaerobic oxidation of methane coupled with sulfate reduction. Sedimentary porewater sulfate profiles above gas hydrate deposits are frequently used to estimate the efficacy of this important microbial biofilter. However, to differentiate how other processes (e.g., sulfate reduction coupled to organic matter oxidation, sulfide re-oxidation and sulfur disproportionation) affect sulfate profiles, a complete accounting of the sulfur cycle is necessary. To this end, we have obtained the first ever measurements of minor sulfur isotopic ratios (33S/32S, 36S/32S), in conjunction with the more commonly measured 34S -32S ratio, from porewater sulfate above a gas hydrate-bearing seep. Characteristic minor isotopic fractionations, even when major isotopic fractionations are similar in magnitude, help to quantify the contributions of different microbial processes to the overall sulfur cycling in the system. Down to sediment depths of 1.5 to 4 meters, the ?34S values of porewater sulfate generally increased in association with a decrease in sulfate concentrations as would be expected for active sulfate reduction. Of greater interest, covariance between the ?34S values and measured minor isotopic fractionation suggests sulfide reoxidation and sulfur disproportionation are important components of the local sulfur cycle. We hypothesize that sulfide reoxidation is coupled to redox processes involving Fe(III) and Mn(IV) reduction and that the reoxidized forms of sulfur are available for additional methane oxidation. Recognizing that sulfate reduction is only one of several microbial processes controlling sulfate profiles challenges current paradigms for interpreting sulfate profiles and may alter our understanding of methane oxidation at gas hydrate-bearing seeps.

Bui, T.; Pohlman, J.; Lapham, L.; Riedel, M.; Wing, B. A.

2010-12-01

334

Perception as Unconscious Inference

5 Perception as Unconscious Inference GARY HATFIELD Department of Philosophy, University perception to which I've drawn your attention are objects of study in contemporary perceptual psychology, which considers the perception of size, shape, distance, motion, and color. These phenomenal aspects

Hatfield, Gary

335

NSDL National Science Digital Library

Different approaches to teaching the reading comprehension strategy of inferring in K-5 classrooms are identified in this article. The article appears in the free, online magazine Beyond Weather and the Water Cycle, which is structured around the essential principles of climate science.

Fries-Gaither, Jessica

2011-05-01

336

Sampling in Statistical Inference

NSDL National Science Digital Library

This site, presented by the Department of Statistics at Yale University, gives an explanation, a definition and an example of sampling in statistical inference. Topics include parameters, statistics, sampling distributions, bias, and variability. Overall, this is a great resource for any mathematics classroom studying statistics.

Lacey, Michelle

2008-12-23

337

Decision generation tools and Bayesian inference

NASA Astrophysics Data System (ADS)

Digital Decision Generation (DDG) tools are important software sub-systems of Command and Control (C2) systems and technologies. In this paper, we present a special type of DDGs based on Bayesian Inference, related to adverse (hostile) networks, including such important applications as terrorism-related networks and organized crime ones.

Jannson, Tomasz; Wang, Wenjian; Forrester, Thomas; Kostrzewski, Andrew; Veeris, Christian; Nielsen, Thomas

2014-05-01

338

A wavelet transform based feature extraction and classification of cardiac disorder.

This paper approaches an intellectual diagnosis system using hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals. This method is based on using Symlet Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias. In these particular parameters were used as input of ANFIS classifier, five most important types of ECG signals they are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Pre-Ventricular Contraction (PVC), Ventricular Fibrillation (VF), and Ventricular Flutter (VFLU) Myocardial Ischemia. The inclusion of ANFIS in the complex investigating algorithms yields very interesting recognition and classification capabilities across a broad spectrum of biomedical engineering. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies. The results give importance to that the proposed ANFIS model illustrates potential advantage in classifying the ECG signals. The classification accuracy of 98.24 % is achieved. PMID:25023652

Sumathi, S; Beaulah, H Lilly; Vanithamani, R

2014-09-01

339

;Specification inference A specification language Mining with patterns What's next The problem formal system The problem formal system specifications are useful for testing, verification, maintenance, understanding formal system specifications are useful for testing, verification, maintenance, understanding

Rydeheard, David

340

NASA Astrophysics Data System (ADS)

The potential of using three different data-driven techniques namely, multilayer perceptron with backpropagation artificial neural network (MLP), M5 decision tree model, and Takagi-Sugeno (TS) inference system for mimic stage-discharge relationship at Gharraf River system, southern Iraq has been investigated and discussed in this study. The study used the available stage and discharge data for predicting discharge using different combinations of stage, antecedent stages, and antecedent discharge values. The models' results were compared using root mean squared error (RMSE) and coefficient of determination (R 2) error statistics. The results of the comparison in testing stage reveal that M5 and Takagi-Sugeno techniques have certain advantages for setting up stage-discharge than multilayer perceptron artificial neural network. Although the performance of TS inference system was very close to that for M5 model in terms of R 2, the M5 method has the lowest RMSE (8.10 m3/s). The study implies that both M5 and TS inference systems are promising tool for identifying stage-discharge relationship in the study area.

Al-Abadi, Alaa M.

2014-12-01

341

Multimodel inference and adaptive management

Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study's inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference. ?? 2010 Elsevier Ltd.

Rehme, S.E.; Powell, L.A.; Allen, C.R.

2011-01-01

342

A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

NASA Astrophysics Data System (ADS)

The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called “Coactive Neuro-Fuzzy Inference System” (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.

Tahmasebi, Pejman; Hezarkhani, Ardeshir

2012-05-01

343

A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called “Coactive Neuro-Fuzzy Inference System” (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) – as a well-known technique to solve the complex optimization problems – is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS–GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS–GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.

Tahmasebi, Pejman; Hezarkhani, Ardeshir

2012-01-01

344

NASA Astrophysics Data System (ADS)

The Muglad rift basin of Sudan, is a good example of polyphase rifting, with at least three major phases of basin development. Each phase has resulted in the generation of source rock, reservoir and seal geology with structural traps often closely linked to basement highs. In this paper we investigate on a regional scale the tectonic processes that have contributed to rift basin development. On a regional scale, the evolution of the Africa-wide Mesozoic rift system is intimately linked to relative movements of African sub-plates and to global plate tectonic processes and plate interactions. Changes in plate interactions are observed in the oceanic crust as azimuth changes of fracture zone geometries and by inference have caused significant modifications to both the orientation and magnitude of the motions of the African sub-plates. Such plate motion processes have controlled the polyphase development of the West and Central African Rift System. On the basinal scale, changes of sub-plate motions have resulted in changes in the stress field which have had a clear impact on the deformation and fault geometries of rift basins and on the resulting stratigraphy. The construction of the first unified stratigraphic chart for the West and Central African Rift System shows a close correlation in the timing of the major unconformities with the timing of changes in relative plate motion as observed in the changes of the azimuthal geometry of the oceanic fracture zones in the Central Atlantic. Since similarly timed unconformities exist along the continental margins of Africa and South America, we propose that the causative mechanism is change in relative plate motion which leads to an increase or decrease in the tension on the plate and thus controls the strength or effective elastic thickness, Te, of the crust/plate beneath the margins. This results in a focused change in isostatic response of the margin during short-period changes in relative plate motion; i.e. more tension will mean that loads are not compensated locally resulting in local uplift of the margin.

Fairhead, J. D.; Green, C. M.; Masterton, S. M.; Guiraud, R.

2013-05-01

345

NASA Astrophysics Data System (ADS)

Estimating sediment volume carried by a river is an important issue in water resources engineering. This paper compares the accuracy of three different soft computing methods, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gene Expression Programming (GEP), in estimating daily suspended sediment concentration on rivers by using hydro-meteorological data. The daily rainfall, streamflow and suspended sediment concentration data from Eel River near Dos Rios, at California, USA are used as a case study. The comparison results indicate that the GEP model performs better than the other models in daily suspended sediment concentration estimation for the particular data sets used in this study. Levenberg-Marquardt, conjugate gradient and gradient descent training algorithms were used for the ANN models. Out of three algorithms, the Conjugate gradient algorithm was found to be better than the others.

Kisi, Ozgur; Shiri, Jalal

2012-06-01

346

Approximately more than 90% of all coal production in Iranian underground mines is derived directly longwall mining method. Out of seam dilution is one of the essential problems in these mines. Therefore the dilution can impose the additional cost of mining and milling. As a result, recognition of the effective parameters on the dilution has a remarkable role in industry. In this way, this paper has analyzed the influence of 13 parameters (attributed variables) versus the decision attribute (dilution value), so that using two approximate reasoning methods, namely Rough Set Theory (RST) and Self Organizing Neuro- Fuzzy Inference System (SONFIS) the best rules on our collected data sets has been extracted. The other benefit of later methods is to predict new unknown cases. So, the reduced sets (reducts) by RST have been obtained. Therefore the emerged results by utilizing mentioned methods shows that the high sensitive variables are thickness of layer, length of stope, rate of advance, number of miners, type of...

Owladeghaffari, H; Saeedi, G H R

2008-01-01

347

NASA Astrophysics Data System (ADS)

SummaryEvapotranspiration, as a major component of the hydrological cycle, is of importance for water resources management and development, as well as for estimating the water budget of irrigation schemes. This study presents a Gene Expression Programming (GEP) approach, for estimating daily reference evapotranspiration ( ET0) in four weather stations in Basque Country (Northern Spain), for a 5-year period (1999-2003). The data set comprising air temperature, relative humidity, wind speed and solar radiation was employed for modeling ET0 using FAO-56 Penman Monteith equation as the reference. The GEP results were compared with the Adaptive Neuro-Fuzzy Inference System (ANFIS), Priestley-Taylor and Hargreaves-Samani models. Based on the comparisons, the GEP was found to perform better than the ANFIS, Priestley-Taylor and Hargreaves-Samani models. The ANFIS model is ranked as the second best model.

Shiri, Jalal; Ki?i, Özgur; Landeras, Gorka; López, José Javier; Nazemi, Amir Hossein; Stuyt, Louis C. P. M.

2012-01-01

348

NASA Astrophysics Data System (ADS)

This study presents Artificial Intelligence (AI)-based modeling of total bed material load through developing the accuracy level of the predictions of traditional models. Gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS)-based models were developed and validated for estimations. Sediment data from Qotur River (Northwestern Iran) were used for developing and validation of the applied techniques. In order to assess the applied techniques in relation to traditional models, stream power-based and shear stress-based physical models were also applied in the studied case. The obtained results reveal that developed AI-based models using minimum number of dominant factors, give more accurate results than the other applied models. Nonetheless, it was revealed that k-fold test is a practical but high-cost technique for complete scanning of applied data and avoiding the over-fitting.

Roushangar, Kiyoumars; Mehrabani, Fatemeh Vojoudi; Shiri, Jalal

2014-06-01

349

Functional association networks as priors for gene regulatory network inference

Motivation: Gene regulatory network (GRN) inference reveals the influences genes have on one another in cellular regulatory systems. If the experimental data are inadequate for reliable inference of the network, informative priors have been shown to improve the accuracy of inferences. Results: This study explores the potential of undirected, confidence-weighted networks, such as those in functional association databases, as a prior source for GRN inference. Such networks often erroneously indicate symmetric interaction between genes and may contain mostly correlation-based interaction information. Despite these drawbacks, our testing on synthetic datasets indicates that even noisy priors reflect some causal information that can improve GRN inference accuracy. Our analysis on yeast data indicates that using the functional association databases FunCoup and STRING as priors can give a small improvement in GRN inference accuracy with biological data. Contact: matthew.studham@scilifelab.se Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24931976

Studham, Matthew E.; Nordling, Torbjörn E.M.; Nelander, Sven; Sonnhammer, Erik L. L.

2014-01-01

350

ERIC Educational Resources Information Center

The use of large-scale assessments for making high stakes inferences about students and the schools in which they are situated is premised on the assumption that tests are sensitive to good instruction. An increase in the quality of classroom instruction should cause, on the average, an increase in test scores. In work with a number of colleagues…

Briggs, Derek C.

2010-01-01

351

Reliability of the Granger causality inference

NASA Astrophysics Data System (ADS)

How to characterize information flows in physical, biological, and social systems remains a major theoretical challenge. Granger causality (GC) analysis has been widely used to investigate information flow through causal interactions. We address one of the central questions in GC analysis, that is, the reliability of the GC evaluation and its implications for the causal structures extracted by this analysis. Our work reveals that the manner in which a continuous dynamical process is projected or coarse-grained to a discrete process has a profound impact on the reliability of the GC inference, and different sampling may potentially yield completely opposite inferences. This inference hazard is present for both linear and nonlinear processes. We emphasize that there is a hazard of reaching incorrect conclusions about network topologies, even including statistical (such as small-world or scale-free) properties of the networks, when GC analysis is blindly applied to infer the network topology. We demonstrate this using a small-world network for which a drastic loss of small-world attributes occurs in the reconstructed network using the standard GC approach. We further show how to resolve the paradox that the GC analysis seemingly becomes less reliable when more information is incorporated using finer and finer sampling. Finally, we present strategies to overcome these inference artifacts in order to obtain a reliable GC result.

Zhou, Douglas; Zhang, Yaoyu; Xiao, Yanyang; Cai, David

2014-04-01

352

Continuity of the Maximum-Entropy Inference

NASA Astrophysics Data System (ADS)

We study the inverse problem of inferring the state of a finite-level quantum system from expected values of a fixed set of observables, by maximizing a continuous ranking function. We have proved earlier that the maximum-entropy inference can be a discontinuous map from the convex set of expected values to the convex set of states because the image contains states of reduced support, while this map restricts to a smooth parametrization of a Gibbsian family of fully supported states. Here we prove for arbitrary ranking functions that the inference is continuous up to boundary points. This follows from a continuity condition in terms of the openness of the restricted linear map from states to their expected values. The openness condition shows also that ranking functions with a discontinuous inference are typical. Moreover it shows that the inference is continuous in the restriction to any polytope which implies that a discontinuity belongs to the quantum domain of non-commutative observables and that a geodesic closure of a Gibbsian family equals the set of maximum-entropy states. We discuss eight descriptions of the set of maximum-entropy states with proofs of accuracy and an analysis of deviations.

Stephan, Weis

2014-09-01

353

Continuity of the Maximum-Entropy Inference

We study the inverse problem of inferring the state of a finite-level quantum system from expected values of a fixed set of observables, by maximizing a continuous ranking function. We have proved earlier that the maximum-entropy inference can be a discontinuous map from the convex set of expected values to the convex set of states because the image contains states of reduced support, while this map restricts to a smooth parametrization of a Gibbsian family of fully supported states. Here we prove for arbitrary ranking functions that the inference is continuous up to boundary points. This follows from a continuity condition in terms of the openness of the restricted linear map from states to their expected values. The openness condition shows also that ranking functions with a discontinuous inference are typical. Moreover it shows that the inference is continuous in the restriction to any polytope which implies that a discontinuity belongs to the quantum domain of non-commutative observables and that a geodesic closure of a Gibbsian family equals the set of maximum-entropy states. We discuss eight descriptions of the set of maximum-entropy states with proofs of accuracy and an analysis of deviations.

Stephan Weis

2014-04-21

354

Robust Textual Inference via Graph Matching

We present a system for deciding whether a given sentence can be inferred from text. Each sentence is represented as a directed graph (extracted from a depen- dency parser) in which the nodes repre- sent words or phrases, and the links repre- sent syntactic and semantic relationships. We develop a learned graph matching ap- proach to approximate entailment using the

Aria Haghighi; Andrew Y. Ng; Christopher D. Manning

2005-01-01

355

Evaluation of an inference network-based retrieval model

The use of inference networks to support document retrieval is introduced. A network-based retrieval model is described and compared to conventional probabilistic and Boolean models. The performance of a retrieval system based on the inference network model is evaluated and compared to performance with conventional retrieval models.

Howard R. Turtle

1991-01-01

356

NASA Technical Reports Server (NTRS)

The goal of visual inference programming is to develop a software framework data analysis and to provide machine learning algorithms for inter-active data exploration and visualization. The topics include: 1) Intelligent Data Understanding (IDU) framework; 2) Challenge problems; 3) What's new here; 4) Framework features; 5) Wiring diagram; 6) Generated script; 7) Results of script; 8) Initial algorithms; 9) Independent Component Analysis for instrument diagnosis; 10) Output sensory mapping virtual joystick; 11) Output sensory mapping typing; 12) Closed-loop feedback mu-rhythm control; 13) Closed-loop training; 14) Data sources; and 15) Algorithms. This paper is in viewgraph form.

Wheeler, Kevin; Timucin, Dogan; Rabbette, Maura; Curry, Charles; Allan, Mark; Lvov, Nikolay; Clanton, Sam; Pilewskie, Peter

2002-01-01

357

Causal network inference using biochemical kinetics

Motivation: Networks are widely used as structural summaries of biochemical systems. Statistical estimation of networks is usually based on linear or discrete models. However, the dynamics of biochemical systems are generally non-linear, suggesting that suitable non-linear formulations may offer gains with respect to causal network inference and aid in associated prediction problems. Results: We present a general framework for network inference and dynamical prediction using time course data that is rooted in non-linear biochemical kinetics. This is achieved by considering a dynamical system based on a chemical reaction graph with associated kinetic parameters. Both the graph and kinetic parameters are treated as unknown; inference is carried out within a Bayesian framework. This allows prediction of dynamical behavior even when the underlying reaction graph itself is unknown or uncertain. Results, based on (i) data simulated from a mechanistic model of mitogen-activated protein kinase signaling and (ii) phosphoproteomic data from cancer cell lines, demonstrate that non-linear formulations can yield gains in causal network inference and permit dynamical prediction and uncertainty quantification in the challenging setting where the reaction graph is unknown. Availability and implementation: MATLAB R2014a software is available to download from warwick.ac.uk/chrisoates. Contact: c.oates@warwick.ac.uk or sach@mrc-bsu.cam.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25161235

Oates, Chris J.; Dondelinger, Frank; Bayani, Nora; Korkola, James; Gray, Joe W.; Mukherjee, Sach

2014-01-01

358

Moment inference from tomograms

Time-lapse geophysical tomography can provide valuable qualitative insights into hydrologic transport phenomena associated with aquifer dynamics, tracer experiments, and engineered remediation. Increasingly, tomograms are used to infer the spatial and/or temporal moments of solute plumes; these moments provide quantitative information about transport processes (e.g., advection, dispersion, and rate-limited mass transfer) and controlling parameters (e.g., permeability, dispersivity, and rate coefficients). The reliability of moments calculated from tomograms is, however, poorly understood because classic approaches to image appraisal (e.g., the model resolution matrix) are not directly applicable to moment inference. Here, we present a semi-analytical approach to construct a moment resolution matrix based on (1) the classic model resolution matrix and (2) image reconstruction from orthogonal moments. Numerical results for radar and electrical-resistivity imaging of solute plumes demonstrate that moment values calculated from tomograms depend strongly on plume location within the tomogram, survey geometry, regularization criteria, and measurement error. Copyright 2007 by the American Geophysical Union.

Day-Lewis, F. D.; Chen, Y.; Singha, K.

2007-01-01

359

Bayes factors and multimodel inference

Multimodel inference has two main themes: model selection, and model averaging. Model averaging is a means of making inference conditional on a model set, rather than on a selected model, allowing formal recognition of the uncertainty associated with model choice. The Bayesian paradigm provides a natural framework for model averaging, and provides a context for evaluation of the commonly used AIC weights. We review Bayesian multimodel inference, noting the importance of Bayes factors. Noting the sensitivity of Bayes factors to the choice of priors on parameters, we define and propose nonpreferential priors as offering a reasonable standard for objective multimodel inference.

Link, W.A.; Barker, R.J.

2009-01-01

360

Inferring attitudes from mindwandering.

Self-perception theory posits that people understand their own attitudes and preferences much as they understand others', by interpreting the meaning of their behavior in light of the context in which it occurs. Four studies tested whether people also rely on unobservable "behavior," their mindwandering, when making such inferences. It is proposed here that people rely on the content of their mindwandering to decide whether it reflects boredom with an ongoing task or a reverie's irresistible pull. Having the mind wander to positive events, to concurrent as opposed to past activities, and to many events rather than just one tends to be attributed to boredom and therefore leads to perceived dissatisfaction with an ongoing task. Participants appeared to rely spontaneously on the content of their wandering minds as a cue to their attitudes, but not when an alternative cause for their mindwandering was made salient. PMID:20625177

Critcher, Clayton R; Gilovich, Thomas

2010-09-01

361

Feature Inference Learning and Eyetracking

ERIC Educational Resources Information Center

Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…

Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.

2009-01-01

362

ERACER: A Database Approach for Statistical Inference and Data Cleaning

SQL and user defined functions. The system performs the inference and cleansing tasks in an integrated cleaning for the purpose of maintaining quality in relational databases. Data cleaning (or cleansing it possible to automate the cleansing proc

Neville, Jennifer

363

Double jeopardy in inferring cognitive processes

Inferences we make about underlying cognitive processes can be jeopardized in two ways due to problematic forms of aggregation. First, averaging across individuals is typically considered a very useful tool for removing random variability. The threat is that averaging across subjects leads to averaging across different cognitive strategies, thus harming our inferences. The second threat comes from the construction of inadequate research designs possessing a low diagnostic accuracy of cognitive processes. For that reason we introduced the systems factorial technology (SFT), which has primarily been designed to make inferences about underlying processing order (serial, parallel, coactive), stopping rule (terminating, exhaustive), and process dependency. SFT proposes that the minimal research design complexity to learn about n number of cognitive processes should be equal to 2n. In addition, SFT proposes that (a) each cognitive process should be controlled by a separate experimental factor, and (b) The saliency levels of all factors should be combined in a full factorial design. In the current study, the author cross combined the levels of jeopardies in a 2 × 2 analysis, leading to four different analysis conditions. The results indicate a decline in the diagnostic accuracy of inferences made about cognitive processes due to the presence of each jeopardy in isolation and when combined. The results warrant the development of more individual subject analyses and the utilization of full-factorial (SFT) experimental designs. PMID:25374545

Fific, Mario

2014-01-01

364

Inverse Ising inference with correlated samples

NASA Astrophysics Data System (ADS)

Correlations between two variables of a high-dimensional system can be indicative of an underlying interaction, but can also result from indirect effects. Inverse Ising inference is a method to distinguish one from the other. Essentially, the parameters of the least constrained statistical model are learned from the observed correlations such that direct interactions can be separated from indirect correlations. Among many other applications, this approach has been helpful for protein structure prediction, because residues which interact in the 3D structure often show correlated substitutions in a multiple sequence alignment. In this context, samples used for inference are not independent but share an evolutionary history on a phylogenetic tree. Here, we discuss the effects of correlations between samples on global inference. Such correlations could arise due to phylogeny but also via other slow dynamical processes. We present a simple analytical model to address the resulting inference biases, and develop an exact method accounting for background correlations in alignment data by combining phylogenetic modeling with an adaptive cluster expansion algorithm. We find that popular reweighting schemes are only marginally effective at removing phylogenetic bias, suggest a rescaling strategy that yields better results, and provide evidence that our conclusions carry over to the frequently used mean-field approach to the inverse Ising problem.

Obermayer, Benedikt; Levine, Erel

2014-12-01

365

Double jeopardy in inferring cognitive processes.

Inferences we make about underlying cognitive processes can be jeopardized in two ways due to problematic forms of aggregation. First, averaging across individuals is typically considered a very useful tool for removing random variability. The threat is that averaging across subjects leads to averaging across different cognitive strategies, thus harming our inferences. The second threat comes from the construction of inadequate research designs possessing a low diagnostic accuracy of cognitive processes. For that reason we introduced the systems factorial technology (SFT), which has primarily been designed to make inferences about underlying processing order (serial, parallel, coactive), stopping rule (terminating, exhaustive), and process dependency. SFT proposes that the minimal research design complexity to learn about n number of cognitive processes should be equal to 2 (n) . In addition, SFT proposes that (a) each cognitive process should be controlled by a separate experimental factor, and (b) The saliency levels of all factors should be combined in a full factorial design. In the current study, the author cross combined the levels of jeopardies in a 2 × 2 analysis, leading to four different analysis conditions. The results indicate a decline in the diagnostic accuracy of inferences made about cognitive processes due to the presence of each jeopardy in isolation and when combined. The results warrant the development of more individual subject analyses and the utilization of full-factorial (SFT) experimental designs. PMID:25374545

Fific, Mario

2014-01-01

366

Emotional temporal difference learning based intelligent controller

In this paper an objective orientation is presented for controlling multiobjective systems. The principles of this method is based an emotional temporal difference learning, and has a neuro-fuzzy structure. The proposal method, regarding the present conditions, the system action in the part and the controlling aims, can control the system in a way that these objectives are attained in the

F. Rashidi; Mehran Rashidi; A. Hashemi-Hosseini

2003-01-01

367

Inference in {open_quotes}poor{close_quotes} languages

Languages with a solvable implication problem but without complete and consistent systems of inference rules ({open_quote}poor{close_quote} languages) are considered. The problem of existence of a finite, complete, and consistent inference rule system for a {open_quotes}poor{close_quotes} language is stated independently of the language or the rule syntax. Several properties of the problem are proved. An application of the results to the language of join dependencies is given.

Petrov, S. [Oak Ridge National Lab., TN (United States)

1996-12-31

368

NASA Astrophysics Data System (ADS)

Capillary pressure curves are important data for reservoir rock typing, analyzing pore throat distribution, determining height above free water level, and reservoir simulation. Laboratory experiments provide accurate data, however they are expensive, time-consuming and discontinuous through the reservoir intervals. The current study focuses on synthesizing artificial capillary pressure (Pc) curves from seismic attributes with the use of artificial intelligent systems including Artificial Neural Networks (ANNs), Fuzzy logic (FL) and Adaptive Neuro-Fuzzy Inference Systems (ANFISs). The synthetic capillary pressure curves were achieved by estimating pressure values at six mercury saturation points. These points correspond to mercury filled pore volumes of core samples (Hg-saturation) at 5%, 20%, 35%, 65%, 80%, and 90% saturations. To predict the synthetic Pc curve at each saturation point, various FL, ANFIS and ANN models were constructed. The varying neural network models differ in their training algorithm. Based on the performance function, the most accurately functioning models were selected as the final solvers to do the prediction process at each of the above-mentioned mercury saturation points. The constructed models were then tested at six depth points of the studied well which were already unforeseen by the models. The results show that the Fuzzy logic and neuro-fuzzy models were not capable of making reliable estimations, while the predictions from the ANN models were satisfyingly trustworthy. The obtained results showed a good agreement between the laboratory derived and synthetic capillary pressure curves. Finally, a 3D seismic cube was captured for which the required attributes were extracted and the capillary pressure cube was estimated by using the developed models. In the next step, the synthesized Pc cube was compared with the seismic cube and an acceptable correspondence was observed.

Golsanami, Naser; Kadkhodaie-Ilkhchi, Ali; Erfani, Amir

2015-01-01

369

The empirical accuracy of uncertain inference models

NASA Technical Reports Server (NTRS)

Uncertainty is a pervasive feature of the domains in which expert systems are designed to function. Research design to test uncertain inference methods for accuracy and robustness, in accordance with standard engineering practice is reviewed. Several studies were conducted to assess how well various methods perform on problems constructed so that correct answers are known, and to find out what underlying features of a problem cause strong or weak performance. For each method studied, situations were identified in which performance deteriorates dramatically. Over a broad range of problems, some well known methods do only about as well as a simple linear regression model, and often much worse than a simple independence probability model. The results indicate that some commercially available expert system shells should be used with caution, because the uncertain inference models that they implement can yield rather inaccurate results.

Vaughan, David S.; Yadrick, Robert M.; Perrin, Bruce M.; Wise, Ben P.

1987-01-01

370

Inference from aging information.

For many learning tasks the duration of the data collection can be greater than the time scale for changes of the underlying data distribution. The question we ask is how to include the information that data are aging. Ad hoc methods to achieve this include the use of validity windows that prevent the learning machine from making inferences based on old data. This introduces the problem of how to define the size of validity windows. In this brief, a new adaptive Bayesian inspired algorithm is presented for learning drifting concepts. It uses the analogy of validity windows in an adaptive Bayesian way to incorporate changes in the data distribution over time. We apply a theoretical approach based on information geometry to the classification problem and measure its performance in simulations. The uncertainty about the appropriate size of the memory windows is dealt with in a Bayesian manner by integrating over the distribution of the adaptive window size. Thus, the posterior distribution of the weights may develop algebraic tails. The learning algorithm results from tracking the mean and variance of the posterior distribution of the weights. It was found that the algebraic tails of this posterior distribution give the learning algorithm the ability to cope with an evolving environment by permitting the escape from local traps. PMID:20421181

de Oliveira, Evaldo Araujo; Caticha, Nestor

2010-06-01

371

Computational statistics using the Bayesian Inference Engine

NASA Astrophysics Data System (ADS)

This paper introduces the Bayesian Inference Engine (BIE), a general parallel, optimized software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organize and reuse expensive derived data. The BIE is the first platform for computational statistics designed explicitly to enable Bayesian update and model comparison for astronomical problems. Bayesian update is based on the representation of high-dimensional posterior distributions using metric-ball-tree based kernel density estimation. Among its algorithmic offerings, the BIE emphasizes hybrid tempered Markov chain Monte Carlo schemes that robustly sample multimodal posterior distributions in high-dimensional parameter spaces. Moreover, the BIE implements a full persistence or serialization system that stores the full byte-level image of the running inference and previously characterized posterior distributions for later use. Two new algorithms to compute the marginal likelihood from the posterior distribution, developed for and implemented in the BIE, enable model comparison for complex models and data sets. Finally, the BIE was designed to be a collaborative platform for applying Bayesian methodology to astronomy. It includes an extensible object-oriented and easily extended framework that implements every aspect of the Bayesian inference. By providing a variety of statistical algorithms for all phases of the inference problem, a scientist may explore a variety of approaches with a single model and data implementation. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU General Public License.

Weinberg, Martin D.

2013-09-01

372

Ontological inference for image and video analysis

This paper presents an approach to designing and implementing extensible computational models for perceiving systems based on a knowledge-driven joint inference approach. These models can integrate different sources of information both horizontally (multi-modal and temporal fusion) and vertically (bottom–up, top–down) by incorporating prior hierarchical knowledge expressed as an extensible ontology.Two implementations of this approach are presented. The first consists of

Christopher Town

2006-01-01

373

BUILDING DESIGN SUPPORT BY HIERARCHICAL EXPERT NETWORKS

For building design, computational intelligence systems use a knowledge base formed by means of neural network and fuzzy logic (neuro-fuzzy) techniques, from a building design database. The application of such a system to a building design task was preliminarily demonstrated earlier. The present research describes a systematic neural fuzzy modelling of data that forms a knowledge base in a hierarchical

Özer Ciftcioglu; Sanja Durmisevic; Sevil Sariyildiz

2000-01-01

374

Soft Computing and its Application B. M. `Dan' Wilamowski

networks Learning Algorithms Advanced Neural Network Architectures Pulse Coded Neural Networks Fuzzy Systems Genetic Algorithms Hardware implementation of neuro-fuzzy systems Conclusion nn.uidaho.edu wialm) (b) Typical activation functions: (a) hard threshold unipolar, (b) hard threshold bipolar, (c

Wilamowski, Bogdan Maciej

375

Ensemble Inference and Inferability of Gene Regulatory Networks

The inference of gene regulatory network (GRN) from gene expression data is an unsolved problem of great importance. This inference has been stated, though not proven, to be underdetermined implying that there could be many equivalent (indistinguishable) solutions. Motivated by this fundamental limitation, we have developed new framework and algorithm, called TRaCE, for the ensemble inference of GRNs. The ensemble corresponds to the inherent uncertainty associated with discriminating direct and indirect gene regulations from steady-state data of gene knock-out (KO) experiments. We applied TRaCE to analyze the inferability of random GRNs and the GRNs of E. coli and yeast from single- and double-gene KO experiments. The results showed that, with the exception of networks with very few edges, GRNs are typically not inferable even when the data are ideal (unbiased and noise-free). Finally, we compared the performance of TRaCE with top performing methods of DREAM4 in silico network inference challenge. PMID:25093509

Ud-Dean, S. M. Minhaz; Gunawan, Rudiyanto

2014-01-01

376

Bayesian inference of substrate properties from film behavior

NASA Astrophysics Data System (ADS)

We demonstrate that by observing the behavior of a film deposited on a substrate, certain features of the substrate may be inferred with quantified uncertainty using Bayesian methods. We carry out this demonstration on an illustrative film/substrate model where the substrate is a Gaussian random field and the film is a two-component mixture that obeys the Cahn–Hilliard equation. We construct a stochastic reduced order model to describe the film/substrate interaction and use it to infer substrate properties from film behavior. This quantitative inference strategy may be adapted to other film/substrate systems.

Aggarwal, R.; Demkowicz, M. J.; Marzouk, Y. M.

2015-01-01

377

Bayesian Modeling, Inference and Prediction

, 1984). #12;ii David Draper #12;Bayesian Modeling, Inference and Prediction iii To Andrea, from whom I 4.2 Bayesian model choice . . . . . . . . . . . . . . . . . . . . . . 226 4.2.1 Data-analytic model

Draper, David

378

Bayesian Inference: with ecological applications

This text provides a mathematically rigorous yet accessible and engaging introduction to Bayesian inference with relevant examples that will be of interest to biologists working in the fields of ecology, wildlife management and environmental studies as well as students in advanced undergraduate statistics.. This text opens the door to Bayesian inference, taking advantage of modern computational efficiencies and easily accessible software to evaluate complex hierarchical models.

Link, William A.; Barker, Richard J.

2010-01-01

379

Abstract - C networks, fuzzy networks and usefulness and applications. V technological re capable, includi Takagi-Sugano building blocks of fuzzy and ne] several applica concluded with chip. Fascination started in Kohonen uns backpropagatior staredrapid dew Neuro-fuzzy Systems and Their Applications Bogdan M

Wilamowski, Bogdan Maciej

380

ACEEE Int. J. on Electrical and Power Engineering, Vol. 03, No. 01, Feb2012 DOI:01.IJEPE.03.01. 93_5

presents an investigation of five-Level Cascaded H-bridge(CHB) inverter as Active Power Filter in Power.03.01. 93_5 Neuro-Fuzzy Five-levelCascaded Multilevel Inverter forActive Power Filter G.Nageswara Rao connected power electronic converters to improve power quality in power distribution systems represents

Paris-Sud XI, UniversitÃ© de

381

GenSoFNN: a generic self-organizing fuzzy neural network

Existing neural fuzzy (neuro-fuzzy) networks proposed in the literature can be broadly classified into two groups. The first group is essentially fuzzy systems with self-tuning capabilities and requires an initial rule base to be specified prior to training. The second group of neural fuzzy networks, on the other hand, is able to automatically formulate the fuzzy rules from the numerical

W. L. Tung; C. Quek

2002-01-01

382

Causal inference in biology networks with integrated belief propagation.

Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot. PMID:25592596

Chang, Rui; Karr, Jonathan R; Schadt, Eric E

2015-01-01

383

Optimal inference with suboptimal models: Addiction and active Bayesian inference

When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment – as opposed to the agent’s beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less ‘optimally’ than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject’s generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described ‘limited offer’ task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321

Schwartenbeck, Philipp; FitzGerald, Thomas H.B.; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl

2015-01-01

384

Children's Category-Based Inferences Affect Classification

ERIC Educational Resources Information Center

Children learn many new categories and make inferences about these categories. Much work has examined how children make inferences on the basis of category knowledge. However, inferences may also affect what is learned about a category. Four experiments examine whether category-based inferences during category learning influence category knowledge…

Ross, Brian H.; Gelman, Susan A.; Rosengren, Karl S.

2005-01-01

385

Inferring Epidemic Network Topology from Surveillance Data

The transmission of infectious diseases can be affected by many or even hidden factors, making it difficult to accurately predict when and where outbreaks may emerge. One approach at the moment is to develop and deploy surveillance systems in an effort to detect outbreaks as timely as possible. This enables policy makers to modify and implement strategies for the control of the transmission. The accumulated surveillance data including temporal, spatial, clinical, and demographic information, can provide valuable information with which to infer the underlying epidemic networks. Such networks can be quite informative and insightful as they characterize how infectious diseases transmit from one location to another. The aim of this work is to develop a computational model that allows inferences to be made regarding epidemic network topology in heterogeneous populations. We apply our model on the surveillance data from the 2009 H1N1 pandemic in Hong Kong. The inferred epidemic network displays significant effect on the propagation of infectious diseases. PMID:24979215

Wan, Xiang; Liu, Jiming; Cheung, William K.; Tong, Tiejun

2014-01-01

386

Introduction to Statistical Inference Introduction to Statistical Inference

for Statistical methods. Data collection. Data presentation Data analysis. We focus on the third and final step Inference Some important concepts Statistical methods There are two main problems of statistical analysis methods There are two main problems of statistical analysis. Estimation Testing of hypothesis. We

387

In this study, we present application of an artificial intelligence (AI) technique model called dynamic evolving neural-fuzzy inference system (DENFIS) based on an evolving clustering method (ECM), for modelling dissolved oxygen concentration in a river. To demonstrate the forecasting capability of DENFIS, a one year period from 1 January 2009 to 30 December 2009, of hourly experimental water quality data collected by the United States Geological Survey (USGS Station No: 420853121505500) station at Klamath River at Miller Island Boat Ramp, OR, USA, were used for model development. Two DENFIS-based models are presented and compared. The two DENFIS systems are: (1) offline-based system named DENFIS-OF, and (2) online-based system, named DENFIS-ON. The input variables used for the two models are water pH, temperature, specific conductance, and sensor depth. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE), Willmott index of agreement (d) and correlation coefficient (CC) statistics. The lowest root mean square error and highest correlation coefficient values were obtained with the DENFIS-ON method. The results obtained with DENFIS models are compared with linear (multiple linear regression, MLR) and nonlinear (multi-layer perceptron neural networks, MLPNN) methods. This study demonstrates that DENFIS-ON investigated herein outperforms all the proposed techniques for DO modelling. PMID:24705953

Heddam, Salim

2014-08-01

388

Smalltalk is an object-oriented language designed andimplemented by the Learning Research (Group of the Xerox Palo AltoResearch Center [2, 5, 14]. Some features of this language are:abstract data classes, information inheritance by asuperclass-subclass mechanism, message passing semantics, extremelylate binding no type declarations, and automatic storagemanagement. Experience has shown that large complex systems can bewritten in Smalltalk in quite a short

Norihisa Suzuki

1981-01-01

389

An algebra-based method for inferring gene regulatory networks

Background The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. Results This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network. Conclusions Boolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html. PMID:24669835

2014-01-01

390

How Forgetting Aids Heuristic Inference

ERIC Educational Resources Information Center

Some theorists, ranging from W. James (1890) to contemporary psychologists, have argued that forgetting is the key to proper functioning of memory. The authors elaborate on the notion of beneficial forgetting by proposing that loss of information aids inference heuristics that exploit mnemonic information. To this end, the authors bring together 2…

Schooler, Lael J.; Hertwig, Ralph

2005-01-01

391

EMPIRICAL INFERENCE SCIENCE Vladimir Vapnik

description: technical (rational) and holistic (irrational). These lead to the convergence of the exact) distribution P(x, y). (2) the quality of the obtained rule is defined by the expectation of predictive error. Q is to discover rules which allow one to predict outcomes of events. V. Vapnik Empirical Inference Science #12

Jebara, Tony

392

Inference networks for document retrieval

Abstract The use of inference networks,to support,document,retrieval is introduced.,A network-basead retrieval model,is described and compared,to conventional,probabilis- tic and Boolean models. 1,Introduction Network,representations,have,been,used,in information,retrieval since at least the early

H. Turtle; W. B. Croft

1989-01-01

393

The mechanisms of temporal inference

NASA Technical Reports Server (NTRS)

The properties of a temporal language are determined by its constituent elements: the temporal objects which it can represent, the attributes of those objects, the relationships between them, the axioms which define the default relationships, and the rules which define the statements that can be formulated. The methods of inference which can be applied to a temporal language are derived in part from a small number of axioms which define the meaning of equality and order and how those relationships can be propagated. More complex inferences involve detailed analysis of the stated relationships. Perhaps the most challenging area of temporal inference is reasoning over disjunctive temporal constraints. Simple forms of disjunction do not sufficiently increase the expressive power of a language while unrestricted use of disjunction makes the analysis NP-hard. In many cases a set of disjunctive constraints can be converted to disjunctive normal form and familiar methods of inference can be applied to the conjunctive sub-expressions. This process itself is NP-hard but it is made more tractable by careful expansion of a tree-structured search space.

Fox, B. R.; Green, S. R.

1987-01-01

394

Sample Size and Correlational Inference

ERIC Educational Resources Information Center

In 4 studies, the authors examined the hypothesis that the structure of the informational environment makes small samples more informative than large ones for drawing inferences about population correlations. The specific purpose of the studies was to test predictions arising from the signal detection simulations of R. B. Anderson, M. E. Doherty,…

Anderson, Richard B.; Doherty, Michael E.; Friedrich, Jeff C.

2008-01-01

395

Phylogenomics Inference from Integrated Information

Recent progress in molecular biology has provided unprecedented opportunities and challenges for phylogenomics inference. Methods based on evolutionary information such as gene content and gene order have been widely used in this field. Nevertheless, each kind of individual information can only characterize specific evolutionary event of complete genome, not all the evolutionary information. In this study, a new method based

Shu-Bo Zhang; Jian-Huang Lai

2010-01-01

396

NONPARAMETRIC INFERENCE UNDER DEPENDENT TRUNCATION

in scientific investigations. So far, sta- tistical models and inferences are mostly based on the assumption-data problems, where the time of onset of a disease, and the time of death, are observable if and only if the onset time falls to the left of a time-point t, and the time of death lies to the right of t. Models

Cheng, Ming-Yen

397

Bayesian-inference based recommendation in online social networks

In this paper, we propose a Bayesian-inference based recommendation system for online social networks. In our system, users share their movie ratings with friends. The rating similarity between a pair of friends is measured by a set of conditional probabilities derived from their mutual rating history. A user propagates a movie rating query along the social network to his direct

Xiwang Yang; Yang Guo; Yong Liu

2011-01-01

398

Inferring heuristic classification hierarchies from natural language input

NASA Technical Reports Server (NTRS)

A methodology for inferring hierarchies representing heuristic knowledge about the check out, control, and monitoring sub-system (CCMS) of the space shuttle launch processing system from natural language input is explained. Our method identifies failures explicitly and implicitly described in natural language by domain experts and uses those descriptions to recommend classifications for inclusion in the experts' heuristic hierarchies.

Hull, Richard; Gomez, Fernando

1993-01-01

399

Inference of expressive declassification policies Jeffrey A. Vaughan

. Security-type systems can enforce expressive information- security policies, but can require enormous to reason about the information security of these systems. Recent work on language-based information on inference of expressive yet intuitive information-security policies from programs with few programmer

Chong, Stephen

400

Logic Machine Architecture inference mechanisms: Layer 2 user reference manual

Logic Machine Architecture (LMA) is a package of software tools for the construction of inference-based systems. This is the reference manual for layer 2 of LMA. It contains the information necessary to write LMA-based systems at the level of layer 3. Such systems would include theorem provers, expert system reasoning components, and customized deduction components for a variety of application systems.

Lusk, E.L.; Overbeek, R.A.

1982-12-01

401

Active inference, eye movements and oculomotor delays.

This paper considers the problem of sensorimotor delays in the optimal control of (smooth) eye movements under uncertainty. Specifically, we consider delays in the visuo-oculomotor loop and their implications for active inference. Active inference uses a generalisation of Kalman filtering to provide Bayes optimal estimates of hidden states and action in generalised coordinates of motion. Representing hidden states in generalised coordinates provides a simple way of compensating for both sensory and oculomotor delays. The efficacy of this scheme is illustrated using neuronal simulations of pursuit initiation responses, with and without compensation. We then consider an extension of the generative model to simulate smooth pursuit eye movements-in which the visuo-oculomotor system believes both the target and its centre of gaze are attracted to a (hidden) point moving in the visual field. Finally, the generative model is equipped with a hierarchical structure, so that it can recognise and remember unseen (occluded) trajectories and emit anticipatory responses. These simulations speak to a straightforward and neurobiologically plausible solution to the generic problem of integrating information from different sources with different temporal delays and the particular difficulties encountered when a system-like the oculomotor system-tries to control its environment with delayed signals. PMID:25128318

Perrinet, Laurent U; Adams, Rick A; Friston, Karl J

2014-12-01

402

F-OWL: An Inference Engine for Semantic Web

NASA Technical Reports Server (NTRS)

Understanding and using the data and knowledge encoded in semantic web documents requires an inference engine. F-OWL is an inference engine for the semantic web language OWL language based on F-logic, an approach to defining frame-based systems in logic. F-OWL is implemented using XSB and Flora-2 and takes full advantage of their features. We describe how F-OWL computes ontology entailment and compare it with other description logic based approaches. We also describe TAGA, a trading agent environment that we have used as a test bed for F-OWL and to explore how multiagent systems can use semantic web concepts and technology.

Zou, Youyong; Finin, Tim; Chen, Harry

2004-01-01

403

Inference and Uncertainty from Models and Multiple Observations (Invited)

NASA Astrophysics Data System (ADS)

Model-supported inference with uncertainty generally involves examples of observations from some system of interest and a model of that system, for example, Earth-system observations and climate models. Comparison between the model and the observations allow inferences to be made about various aspects of the model and system. Observation error is always a source of uncertainty, and additional uncertainty comes from two sources related to the model: unknown settings of parameters controlling the model behavior, and an unknown discrepancy between the model and the system. There are several sorts of inference that can be targeted in this framework, but from the viewpoint of uncertainty quantification they involve construction of a statistical model that relates these three sources of uncertainty to produce an estimate involving each. These can then be used directly, such as in examining the parameter uncertainty or model discrepancy, or employed in further inferences such as sensitivity analysis or analysis of uncertainty in model-based predictions of the system. This talk examines the combination of evidence in the common case that the system observations move beyond assumptions of simple error models. This is typically the case when comparing varied observations to domains of model response that have distinct structural error. The talk motivates a hierarchical model on parameter settings corresponding to observation domains, and the implications to projected uncertainty. This is discussed in two cases of model analysis: one in model parameter estimation, and another in the assessment of climate sensitivity.

Gattiker, J.

2013-12-01

404

Bayesian time series models and scalable inference

With large and growing datasets and complex models, there is an increasing need for scalable Bayesian inference. We describe two lines of work to address this need. In the first part, we develop new algorithms for inference ...

Johnson, Matthew James, Ph. D. Massachusetts Institute of Technology

2014-01-01

405

The Laws of Natural Deduction in Inference by DNA Computer

We present a DNA-based implementation of reaction system with molecules encoding elements of the propositional logic, that is, propositions and formulas. The protocol can perform inference steps using, for example, modus ponens and modus tollens rules and de Morgan's laws. The set of the implemented operations allows for inference of formulas using the laws of natural deduction. The system can also detect whether a certain proposition a can be deduced from the basic facts and given rules. The whole protocol is fully autonomous; that is, after introducing the initial set of molecules, no human assistance is needed. Only one restriction enzyme is used throughout the inference process. Unlike some other similar implementations, our improved design allows representing simultaneously a fact a and its negation ~a, including special reactions to detect the inconsistency, that is, a simultaneous occurrence of a fact and its negation. An analysis of correctness, completeness, and complexity is included. PMID:25133261

Sosík, Petr

2014-01-01

406

The complex morphology of large sand dunes of the world's great deserts have significant importance on conservation and climate change and hence are of interest to a wide variety of scientific and environmental applications including studies on aeolian processes, paleoclimate, civilian infrastructure management, and design of blown?sand control systems. Scientific studies on dune formation and dynamics have been limited to

L. V. Potts; O. Akyilmaz; A. Braun; C. K. Shum

2008-01-01

407

Transitive and Pseudo-Transitive Inferences

ERIC Educational Resources Information Center

Given that A is longer than B, and that B is longer than C, even 5-year-old children can infer that A is longer than C. Theories of reasoning based on formal rules of inference invoke simple axioms ("meaning postulates") to capture such transitive inferences. An alternative theory proposes instead that reasoners construct mental models of the…

Goodwin, Geoffrey P.; Johnson-Laird, P. N.

2008-01-01

408

Inferring Cell-Scale Signalling Networks via Compressive Sensing

Signalling network inference is a central problem in system biology. Previous studies investigate this problem by independently inferring local signalling networks and then linking them together via crosstalk. Since a cellular signalling system is in fact indivisible, this reductionistic approach may have an impact on the accuracy of the inference results. Preferably, a cell-scale signalling network should be inferred as a whole. However, the holistic approach suffers from three practical issues: scalability, measurement and overfitting. Here we make this approach feasible based on two key observations: 1) variations of concentrations are sparse due to separations of timescales; 2) several species can be measured together using cross-reactivity. We propose a method, CCELL, for cell-scale signalling network inference from time series generated by immunoprecipitation using Bayesian compressive sensing. A set of benchmark networks with varying numbers of time-variant species is used to demonstrate the effectiveness of our method. Instead of exhaustively measuring all individual species, high accuracy is achieved from relatively few measurements. PMID:24748057

Nie, Lei; Yang, Xian; Adcock, Ian; Xu, Zhiwei; Guo, Yike

2014-01-01

409

Inferring cell-scale signalling networks via compressive sensing.

Signalling network inference is a central problem in system biology. Previous studies investigate this problem by independently inferring local signalling networks and then linking them together via crosstalk. Since a cellular signalling system is in fact indivisible, this reductionistic approach may have an impact on the accuracy of the inference results. Preferably, a cell-scale signalling network should be inferred as a whole. However, the holistic approach suffers from three practical issues: scalability, measurement and overfitting. Here we make this approach feasible based on two key observations: 1) variations of concentrations are sparse due to separations of timescales; 2) several species can be measured together using cross-reactivity. We propose a method, CCELL, for cell-scale signalling network inference from time series generated by immunoprecipitation using Bayesian compressive sensing. A set of benchmark networks with varying numbers of time-variant species is used to demonstrate the effectiveness of our method. Instead of exhaustively measuring all individual species, high accuracy is achieved from relatively few measurements. PMID:24748057

Nie, Lei; Yang, Xian; Adcock, Ian; Xu, Zhiwei; Guo, Yike

2014-01-01

410

Quantum inference on Bayesian networks

NASA Astrophysics Data System (ADS)

Performing exact inference on Bayesian networks is known to be #P-hard. Typically approximate inference techniques are used instead to sample from the distribution on query variables given the values e of evidence variables. Classically, a single unbiased sample is obtained from a Bayesian network on n variables with at most m parents per node in time O (nmP(e)-1), depending critically on P (e), the probability that the evidence might occur in the first place. By implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking O (n2mP(e)-1/2) time per sample. We exploit the Bayesian network's graph structure to efficiently construct a quantum state, a q-sample, representing the intended classical distribution, and also to efficiently apply amplitude amplification, the source of our speedup. Thus, our speedup is notable as it is unrelativized—we count primitive operations and require no blackbox oracle queries.

Low, Guang Hao; Yoder, Theodore J.; Chuang, Isaac L.

2014-06-01

411

Dynamic Modeling in Inductive Inference

Introduced is a new inductive inference paradigm, Dynamic Modeling. Within this learning paradigm, for example, function h\\u000a learns function g iff, in the i-th iteration, h and g both produce output, h gets the sequence of all outputs from g in prior iterations as input, g gets all the outputs from h in prior iterations as input, and, from some

John Case; Timo Kötzing

2008-01-01

412

Inference Networks for Document Retrieval

The use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilis- tic and Boolean models. Network representations have been used in information retrieval since at least the early 1960's. Networks have been used to support diverse retrieval functions, including browsing (TC89), document clustering (CroSO), spreading activation search (CK87), support

Howard R. Turtle; W. Bruce Croft

1990-01-01

413

Sexual dimorphism in body size and weaponry was examined in two Cinetorhynchus shrimp species in order to formulate hypotheses on their sexual and mating systems. Collections of Cinetorhynchus sp. A and Cinetorhynchus sp. B were made in March, 2011 on Coconut Island, Hawaii, by hand dipnetting and minnow traps in coral rubble bottom in shallow water. Although there is overlap in male and female size, some males are much larger than females. The major (pereopod 1) chelipeds of males are significantly larger and longer than those of females. In these two Cinetorhynchus species, males and females have third maxillipeds of similar relative size, i.e., those of males are not hypertrophied and probably not used as spear-like weapons as in some other rhynchocinetid (Rhynchocinetes) species. Major chelae of males vary with size, changing from typical female-like chelae tipped with black corneous stout setae to subchelate or prehensile appendages in larger males. Puncture wounds or regenerating major chelipeds were observed in 26.1 % of males examined (N = 38 including both species). We interpret this evidence on sexual dimorphism as an indication of a temporary male mate guarding or "neighborhoods of dominance" mating system, in which larger dominant robustus males defend females and have greater mating success than smaller males. Fecundity of females increased with female size, as in most caridean species (500-800 in Cinetorhynchus sp. A; 300-3800 in Cinetorhynchus sp. B). Based on the sample examined, we conclude that these two species have a gonochoric sexual system (separate sexes) like most but not all other rhynchocinetid species in which the sexual system has been investigated. PMID:25561837

Bauer, Raymond T; Okuno, Junji; Thiel, Martin

2014-01-01

414

Abstract Sexual dimorphism in body size and weaponry was examined in two Cinetorhynchus shrimp species in order to formulate hypotheses on their sexual and mating systems. Collections of Cinetorhynchus sp. A and Cinetorhynchus sp. B were made in March, 2011 on Coconut Island, Hawaii, by hand dipnetting and minnow traps in coral rubble bottom in shallow water. Although there is overlap in male and female size, some males are much larger than females. The major (pereopod 1) chelipeds of males are significantly larger and longer than those of females. In these two Cinetorhynchus species, males and females have third maxillipeds of similar relative size, i.e., those of males are not hypertrophied and probably not used as spear-like weapons as in some other rhynchocinetid (Rhynchocinetes) species. Major chelae of males vary with size, changing from typical female-like chelae tipped with black corneous stout setae to subchelate or prehensile appendages in larger males. Puncture wounds or regenerating major chelipeds were observed in 26.1 % of males examined (N = 38 including both species). We interpret this evidence on sexual dimorphism as an indication of a temporary male mate guarding or “neighborhoods of dominance” mating system, in which larger dominant robustus males defend females and have greater mating success than smaller males. Fecundity of females increased with female size, as in most caridean species (500–800 in Cinetorhynchus sp. A; 300–3800 in Cinetorhynchus sp. B). Based on the sample examined, we conclude that these two species have a gonochoric sexual system (separate sexes) like most but not all other rhynchocinetid species in which the sexual system has been investigated. PMID:25561837

Bauer, Raymond T.; Okuno, Junji; Thiel, Martin

2014-01-01

415

We present a detailed description of temporal variations in the complex frequencies of long-period (LP) events observed at Kusatsu-Shirane Volcano. Using the Sompi method, we analyze 35 LP events that occurred during the period from August 1992 through January 1993. The observed temporal variations in the complex frequencies can be divided into three periods. During the first period the dominant frequency rapidly decreases from 5 to 1 Hz, and Q of the dominant spectral peak remains roughly constant with an average value near 100. During the second period the dominant frequency gradually increases up to 3 Hz, and Q gradually decreases from 160 to 30. During the third period the dominant frequency increases more rapidly from 3 to 5 Hz, and Q shows an abrupt increase at the beginning of this period and then remains roughly constant with an average value near 100. Such temporal variations can be consistently explained by the dynamic response of a hydrothermal crack to a magmatic heat pulse. During the first period, crack growth occurs in response to the overall pressure increase in the hydrothermal system caused by the heat pulse. Once crack formation is complete, heat gradually changes the fluid in the crack from a wet misty gas to a dry gas during the second period. As heating of the hydrothermal system gradually subsides, the overall pressure in this system starts to decrease, causing the collapse of the crack during the third period.

Kumagai, H.; Chouet, B.A.; Nakano, M.

2002-01-01

416

NASA Technical Reports Server (NTRS)

The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters, and the single scattering albedo. After this climatological calibration, the modeling system can provide L-band brightness temperatures with a global mean absolute bias of less than 10K against SMOS observations, across multiple incidence angles and for horizontal and vertical polarization. Third, seasonal and regional variations in the residual biases are addressed by estimating the vegetation optical depth through state augmentation during the assimilation of the L-band brightness temperatures. This strategy, tested here with SMOS data, is part of the baseline approach for the Level 4 Surface and Root Zone Soil Moisture data product from the planned Soil Moisture Active Passive (SMAP) satellite mission.

Reichle, Rolf H.; De Lannoy, Gabrielle J. M.

2012-01-01

417

Pathway network inference from gene expression data

Background The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules. Results We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example. Conclusions PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network's topology obtained for the yeast cell cycle data. PMID:25032889

2014-01-01

418

NASA Astrophysics Data System (ADS)

The temporal variation of magmatic fluid release at Campi Flegrei caldera is investigated using numerical simulations of the hydrothermal system constrained by diffuse CO2 emission data and by the chemical composition of fumarolic vents. The main aim is to understand the recent dynamics of Campi Flegrei, where hundreds of thousands of people live in an area subjected since the middle of the 20th century to a long term crisis characterized by several episodes of ground uplift and correspondent seismic swarms (bradyseism), the most significant of which occurred in A.D. 1950-1953, 1970-1972, and 1982-1984 (maximum total ground uplift ~4 m). In 1998, the first measurements of diffuse degassing from the Solfatara crater, the most active zone of Campi Flegrei, revealed the very intense release of hydrothermal- magmatic CO2 (~1500 t/d) and of thermal energy (~100 W) highlighting that the expulsion of deep fluids is the main form of energy loss from the entire caldera and suggesting an important role of magma degassing during the crisis. The hydrothermal system of Solfatara recently underwent large changes, including compositional variations of fumarolic effluents, compositional homogenization of the fluid released at different vents, changes in the pattern of diffuse degassing, increases in the pressures of the system, and increases in the temperature and in the flow rate of the fumaroles. Furthermore, after 20 yr of subsidence, an uplift period started in 2005. Comparing long-term series of geochemical signals with ground deformation and seismicity, we show that these changes are caused by repeated injections of magmatic fluid into the hydrothermal system. The frequency of the degassing episodes has increased in the last years, causing the almost continuous increase of the magmatic component of the fumaroles, pulsed uplift episodes and swarms of low magnitude earthquakes. Physical simulations of the process show that total injected fluid masses in each episode of magma degassing are the same order of magnitude as those emitted during small to medium size volcanic eruptions, and their cumulative curve highlights a current period of increasing activity.

Chiodini, G.; Caliro, S.; Cardellini, C.; De Martino, P.; Petrillo, Z.

2012-12-01

419

NASA Astrophysics Data System (ADS)

Quaternary volcanic unrest has provided heat for episodic hydrothermal circulation in the Long Valley caldera, including the present-day hydrothermal system, which has been active over the past 40 kyr. The most recent period of crustal unrest in this region of east-central California began around 1980 and has included periods of intense seismicity and ground deformation. Uplift totaling more than 0.7 m has been centered on the caldera's resurgent dome, and is best modeled by a near-vertical ellipsoidal source centered at depths of 6-7 km. Modeling of both deformation and microgravity data now suggests that (1) there are two inflation sources beneath the caldera, a shallower source 7-10 km beneath the resurgent dome and a deeper source ˜15 km beneath the caldera's south moat and (2) the shallower source may contain components of magmatic brine and gas. The Long Valley Exploration Well (LVEW), completed in 1998 on the resurgent dome, penetrates to a depth of 3 km directly above this shallower source, but bottoms in a zone of 100°C fluid with zero vertical thermal gradient. Although these results preclude extrapolations of temperatures at depths below 3 km, other information obtained from flow tests and fluid sampling at this well indicates the presence of magmatic volatiles and fault-related permeability within the metamorphic basement rocks underlying the volcanic fill. In this paper, we present recently acquired data from LVEW and compare them with information from other drill holes and thermal springs in Long Valley to delineate the likely flow paths and fluid system properties under the resurgent dome. Additional information from mineralogical assemblages in core obtained from fracture zones in LVEW documents a previous period of more vigorous and energetic fluid circulation beneath the resurgent dome. Although this system apparently died off as a result of mineral deposition and cooling (and/or deepening) of magmatic heat sources, flow testing and tidal analyses of LVEW water level data show that relatively high permeability and strain sensitivity still exist in the steeply dipping principal fracture zone penetrated at a depth of 2.6 km. The hydraulic properties of this zone would allow a pressure change induced at distances of several kilometers below the well to be observable within a matter of days. This indicates that continuous fluid pressure monitoring in the well could provide direct evidence of future intrusions of magma or high-temperature fluids at depths of 5-7 km.

Farrar, Christopher D.; Sorey, Michael L.; Roeloffs, Evelyn; Galloway, Devin L.; Howle, James F.; Jacobson, Ronald

2003-10-01

420

Larval dispersal is a crucial factor for fish recruitment. For fishes with relatively small-bodied larvae, drift has the potential to play a more important role than active habitat selection in determining larval dispersal; therefore, we expect small-bodied fish larvae to be poorly associated with habitat characteristics. To test this hypothesis, we used as model yellow perch (Perca flavescens), whose larvae are among the smallest among freshwater temperate fishes. Thus, we analysed the habitat association of yellow perch larvae at multiple spatial scales in a large shallow fluvial lake by explicitly modelling directional (e.g. due to water currents) and non-directional (e.g. due to aggregation) spatial patterns. This allowed us to indirectly assess the relative roles of drift (directional process) and potential habitat choice on larval dispersal. Our results give weak support to the drift hypothesis, whereas yellow perch show a strong habitat association at unexpectedly small sizes, when compared to other systems. We found consistent non-directional patterns in larvae distributions at both broad and medium spatial scales but only few significant directional components. The environmental variables alone (e.g. vegetation) generally explained a significant and biologically relevant fraction of the variation in fish larvae distribution data. These results suggest that (i) drift plays a minor role in this shallow system, (ii) larvae display spatial patterns that only partially covary with environmental variables, and (iii) larvae are associated to specific habitats. By suggesting that habitat association potentially includes an active choice component for yellow perch larvae, our results shed new light on the ecology of freshwater fish larvae and should help in building more realistic recruitment models. PMID:23185585

Bertolo, Andrea; Blanchet, F. Guillaume; Magnan, Pierre; Brodeur, Philippe; Mingelbier, Marc; Legendre, Pierre

2012-01-01

421

Larval dispersal is a crucial factor for fish recruitment. For fishes with relatively small-bodied larvae, drift has the potential to play a more important role than active habitat selection in determining larval dispersal; therefore, we expect small-bodied fish larvae to be poorly associated with habitat characteristics. To test this hypothesis, we used as model yellow perch (Perca flavescens), whose larvae are among the smallest among freshwater temperate fishes. Thus, we analysed the habitat association of yellow perch larvae at multiple spatial scales in a large shallow fluvial lake by explicitly modelling directional (e.g. due to water currents) and non-directional (e.g. due to aggregation) spatial patterns. This allowed us to indirectly assess the relative roles of drift (directional process) and potential habitat choice on larval dispersal. Our results give weak support to the drift hypothesis, whereas yellow perch show a strong habitat association at unexpectedly small sizes, when compared to other systems. We found consistent non-directional patterns in larvae distributions at both broad and medium spatial scales but only few significant directional components. The environmental variables alone (e.g. vegetation) generally explained a significant and biologically relevant fraction of the variation in fish larvae distribution data. These results suggest that (i) drift plays a minor role in this shallow system, (ii) larvae display spatial patterns that only partially covary with environmental variables, and (iii) larvae are associated to specific habitats. By suggesting that habitat association potentially includes an active choice component for yellow perch larvae, our results shed new light on the ecology of freshwater fish larvae and should help in building more realistic recruitment models. PMID:23185585

Bertolo, Andrea; Blanchet, F Guillaume; Magnan, Pierre; Brodeur, Philippe; Mingelbier, Marc; Legendre, Pierre

2012-01-01

422

Efficient inference for hybrid dynamic Bayesian networks

NASA Astrophysics Data System (ADS)

This paper is a revision of a paper presented at the SPIE conference on Signal Processing, Senior Fusion, and Target Recognition XII, Aug. 2004, Orlando, Florida. The paper presented there appears (unrefereed) in SPIE Proceedings Vol. 5429. Bayesian networks for static as well as for dynamic cases have been the subject of a great deal of theoretical analysis and practical inference-algorithm development in the research community of artificial intelligence, machine learning, and pattern recognition. After summarizing the well-known theory of discrete and continuous Bayesian networks, we introduce an efficient reasoning scheme into hybrid Bayesian networks. In addition to illustrating the similarities between the dynamic Bayesian networks and the Kalman filter, we present a computationally efficient approach for the inference problem of hybrid dynamic Bayesian networks (HDBNs). The proposed method is based on the separation of the dynamic and static nodes, and subsequent hypercubic partitions via the decision tree algorithm. Experiments show that with high statistical confidence the novel algorithm used in the HDBN performs favorably in the trade-offs of computational complexity and accuracy performance, compared to other exact and approximate methods for applications with uncertainty in a dynamic system.

Chang, Kuo Chu; Chen, Hongda

2005-07-01

423

Active Inference, Attention, and Motor Preparation

Perception is the foundation of cognition and is fundamental to our beliefs and consequent action planning. The Editorial (this issue) asks: “what mechanisms, if any, mediate between perceptual and cognitive processes?” It has recently been argued that attention might furnish such a mechanism. In this paper, we pursue the idea that action planning (motor preparation) is an attentional phenomenon directed toward kinesthetic signals. This rests on a view of motor control as active inference, where predictions of proprioceptive signals are fulfilled by peripheral motor reflexes. If valid, active inference suggests that attention should not be limited to the optimal biasing of perceptual signals in the exteroceptive (e.g., visual) domain but should also bias proprioceptive signals during movement. Here, we investigate this idea using a classical attention (Posner) paradigm cast in a motor setting. Specially, we looked for decreases in reaction times when movements were preceded by valid relative to invalid cues. Furthermore, we addressed the hierarchical level at which putative attentional effects were expressed by independently cueing the nature of the movement and the hand used to execute it. We found a significant interaction between the validity of movement and effector cues on reaction times. This suggests that attentional bias might be mediated at a low level in the motor hierarchy, in an intrinsic frame of reference. This finding is consistent with attentional enabling of top-down predictions of proprioceptive input and may rely upon the same synaptic mechanisms that mediate directed spatial attention in the visual system. PMID:21960978

Brown, Harriet; Friston, Karl; Bestmann, Sven

2011-01-01

424

Entropic Biological Score: a cell cycle investigation for GRNs inference.

Inference of gene regulatory networks (GRNs) is one of the most challenging research problems of Systems Biology. In this investigation, a new GRNs inference methodology, called Entropic Biological Score (EBS), which linearly combines the mean conditional entropy (MCE) from expression levels and a Biological Score (BS), obtained by integrating different biological data sources, is proposed. The EBS is validated with the Cell Cycle related functional annotation information, available from Munich Information Center for Protein Sequences (MIPS), and compared with some existing methods like MRNET, ARACNE, CLR and MCE for GRNs inference. For real networks, the performance of EBS, which uses the concept of integrating different data sources, is found to be superior to the aforementioned inference methods. The best results for EBS are obtained by considering the weights w1=0.2 and w2=0.8 for MCE and BS values, respectively, where approximately 40% of the inferred connections are found to be correct and significantly better than related methods. The results also indicate that expression profile is able to recover some true connections, that are not present in biological annotations, thus leading to the possibility of discovering new relations between its genes. PMID:24631265

Lopes, Fabrício M; Ray, Shubhra Sankar; Hashimoto, Ronaldo F; Cesar, Roberto M

2014-05-15

425

Inference for reaction networks using the linear noise approximation.

We consider inference for the reaction rates in discretely observed networks such as those found in models for systems biology, population ecology, and epidemics. Most such networks are neither slow enough nor small enough for inference via the true state-dependent Markov jump process to be feasible. Typically, inference is conducted by approximating the dynamics through an ordinary differential equation (ODE) or a stochastic differential equation (SDE). The former ignores the stochasticity in the true model and can lead to inaccurate inferences. The latter is more accurate but is harder to implement as the transition density of the SDE model is generally unknown. The linear noise approximation (LNA) arises from a first-order Taylor expansion of the approximating SDE about a deterministic solution and can be viewed as a compromise between the ODE and SDE models. It is a stochastic model, but discrete time transition probabilities for the LNA are available through the solution of a series of ordinary differential equations. We describe how a restarting LNA can be efficiently used to perform inference for a general class of reaction networks; evaluate the accuracy of such an approach; and show how and when this approach is either statistically or computationally more efficient than ODE or SDE methods. We apply the LNA to analyze Google Flu Trends data from the North and South Islands of New Zealand, and are able to obtain more accurate short-term forecasts of new flu cases than another recently proposed method, although at a greater computational cost. PMID:24467590

Fearnhead, Paul; Giagos, Vasilieos; Sherlock, Chris

2014-06-01

426

KIT REPORT 111 Deriving Inference Rules for Description Logics

be investigated under different perspectives. The aim of this report is to provide the basis for a tighterKIT REPORT 111 Deriving Inference Rules for Description Logics: a Rewriting Approach into Sequent combination of theoretical investigations with issues arising in the actual implementation of DL systems. We

Wichmann, Felix

427

Inference and Information Resources: A design case study

.Fields Nicholas.Merriam g@cs.york.ac.uk Abstract. Much attention has been paid in HCI to techniques for designingInference and Information Resources: A design case study R.E. Fields and N.A. Merriam Human, however, are highly information intensive, and the way in which a human-machine cognitive system makes

Fields, Bob

428

Flexible types: robust type inference for first-class polymorphism

We present HML, a type inference system that supports full first- class polymorphism where few annotations are needed: only func- tion parameters with a polymorphic type need to be annotated. HML is a simplification of MLF where only flexibly quantified types are used. This makes the types easier to work with from a programmers perspective, and simplifies the implementation of

Daan Leijen

2009-01-01

429

Flexible types: robust type inference for first-class polymorphism

We present HML, a type inference system that supports full first-class polymorphism where few annotations are needed: only function parameters with a polymorphic type need to be annotated. HML is a simplification of MLF where only flexibly quantified types are used. This makes the types easier to work with from a programmers perspective, and simplifies the implementation of the type

Daan Leijen

2009-01-01

430

Inferences from the dark sky: Olbers' paradox revisited

NASA Astrophysics Data System (ADS)

The classical formulation of 'Olbers' paradox' consists in looking for an explanation of the fact that the sky at night is dark. We use the experimental datum of the nocturnal darkness in order to put constraints on a Newtonian cosmological model. We infer then that the stellar system in such a model should have had an origin at a finite time in the past.

Arpino, Mauro; Scardigli, Fabio

2003-01-01

431

Cerebellarlike Corrective Model Inference Engine for Manipulation Tasks

This paper presents how a simple cerebellumlike architecture can infer corrective models in the framework of a control task when manipulating objects that significantly affect the dynamics model of the system. The main motivation of this paper is to evaluate a simplified bio-mimetic approach in the framework of a manipulation task. More concretely, the paper focuses on how the model

Niceto Rafael Luque; Jesús Alberto Garrido; Richard Rafael Carrillo; Olivier J.-M. D. Coenen; Eduardo Ros

2011-01-01

432

Social Structure Simulation and Inference using Artificial Intelligence

that relies on social network theory and Artificial Intelligence algorithms. I further propose the creationSocial Structure Simulation and Inference using Artificial Intelligence Techniques Maksim Tsvetovat Foundation, or the U.S. government. #12;Abstract The study of complex social and technological systems

433

Behavioral Resource-Aware Model Inference Tony Ohmann Michael Herzberg Sebastian Fiss Armand Halbert

and comprehension efforts. We describe Perfume, an automated approach for inferring behav- ioral, resource understanding of system behavior and resource use. Perfume improves on the state of the art in model inference by differentiating behaviorally similar executions that differ in resource consumption. For example, Perfume

Brun, Yuriy

434

Inferring Dynamic Credentials for R^ole-based Trust Management Daniele Gorla

Inferring Dynamic Credentials for R^ole-based Trust Management Daniele Gorla Dip. di Informatica is the r^ole-based trust-management language RT0, a formalism inspired by logic programming that handles Terms Languages, Security. Keywords trust-management, r^ole-based access control, infer- ence systems

Gorla, Daniele

435

Bayesian multimodel inference for dose-response studies

Statistical inference in dose?response studies is model-based: The analyst posits a mathematical model of the relation between exposure and response, estimates parameters of the model, and reports conclusions conditional on the model. Such analyses rarely include any accounting for the uncertainties associated with model selection. The Bayesian inferential system provides a convenient framework for model selection and multimodel inference. In this paper we briefly describe the Bayesian paradigm and Bayesian multimodel inference. We then present a family of models for multinomial dose?response data and apply Bayesian multimodel inferential methods to the analysis of data on the reproductive success of American kestrels (Falco sparveriuss) exposed to various sublethal dietary concentrations of methylmercury.

Link, W.A.; Albers, P.H.

2007-01-01

436

A Full Bayesian Approach for Boolean Genetic Network Inference

Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data. PMID:25551820

Han, Shengtong; Wong, Raymond K. W.; Lee, Thomas C. M.; Shen, Linghao; Li, Shuo-Yen R.; Fan, Xiaodan

2014-01-01

437

NASA Astrophysics Data System (ADS)

numerical models are developed for a coupled magmatism-mantle convection system with tectonic plates in a two-dimensional rectangular box to understand the Earth's mantle evolution. The mantle evolves in two stages owing to decaying internal and basal heating, provided that the lithosphere is mechanically strong enough to inhibit spontaneous formation of new subduction zones by ridge push force. On the earlier stage that continues for the first 1-2 Gyr, the deep mantle is strongly heated, and hot materials there frequently ascend to the surface as bursts. The mantle bursts cause vigorous magmatism and make the lithosphere move chaotically. The thermostat effect of the vigorous magmatism keeps the average temperature in the upper mantle below about 1800 K no matter how strongly the mantle is heated. As the heating rate of the mantle declines, however, the mantle evolves into the later stage where mantle bursts subside, rigid tectonic plates emerge to move rather steadily, and subducted basaltic crusts accumulate on the core-mantle boundary to form compositionally dense piles. Hot plumes occasionally ascend from the basaltic piles to cause magmatism. It takes time on the order of one billion years for the slabs that sink into the lower mantle to return back to the upper mantle, and the long overturn time makes the thermal history of the upper mantle, which has been petrologically constrained for the Earth, distinct from that of the whole mantle. The long overturn time also makes water injected into the mantle by slabs distribute heterogeneously.

Ogawa, Masaki

2014-03-01

438

NASA Astrophysics Data System (ADS)

Monogenetic basaltic cinder cones are the most abundant volcanic landform on Earth. While typically short-lived, cinder cone eruptions often display a range of eruption styles, including Strombolian, violent Strombolian, and even sub-Plinian activity. However, the processes driving explosive cinder cone eruptions remain poorly understood. In this study we investigate the volatile (H2O, CO2), major, and trace element chemistry of olivine-hosted melt inclusions from the tephra of 'Cinder Cone,' Lassen Volcanic NP, to better understand basaltic cinder cone eruptions and their underlying plumbing systems. Erupted in 1666 C.E., Cinder Cone is a young, un-vegetated cinder cone with well-preserved lava flows and tephra deposits. We have divided the tephra sequence, previously described by Heiken (1978) as Units 1, 2 and 3, into nine fall samples (LCC-9 through LCC-1). From these nine, we have obtained data from four tephra samples that span the sequence, one each corresponding with Units 1 and 3, and two from Unit 2. Olivine-hosted melt inclusions from the tephra at Cinder Cone trapped some of the most volatile-rich (1.7-3.4 wt% H2O, 530-1375 ppm CO2) and primitive (8.4-9.7 wt% MgO; olivine hosts Fo88-90) melts yet measured in the Cascade Arc (Ruscitto et al., 2010, EPSL). The melt inclusions, however, do not show evidence of the temporal changes in composition seen in whole rock and bulk tephra data that result from crustal contamination. Nearly all of the analyzed melt inclusions have lower SiO2 (50.4 wt.%) and higher TiO2 contents (0.8-0.95 wt%) than the whole-rock compositions (53-60 wt% and 0.5-0.85 wt.%, respectively). The range in H2O and CO2 concentrations likewise remains remarkably constant throughout the tephra deposit, with only the basal-most unit having a few higher H2O values. The differences between the whole-rock and melt inclusion compositions suggest that olivine crystallized from a primitive parental magma as it was rising to the surface, prior to the crustal contamination experienced by the magma near the surface. Additionally, the contamination must have occurred with little cooling of the magma, because cooling would have caused more olivine to form, with melt inclusions showing the effects of contamination and likely lower pressures. At Cinder Cone, we do not see any evidence for formation of a shallow magma reservoir beneath the volcano later in the eruption, as has been found at Jorullo and Parícutin in Mexico. Further detailed investigation of the Cinder Cone tephra sequence will better constrain our understanding of the underlying plumbing system.

Walowski, K. J.; Wallace, P. J.; Cashman, K. V.; Clynne, M. A.

2011-12-01

439

We investigate temporal variations in the complex frequencies (frequency and quality factor Q) of long-period (LP) events that occurred at Kusatsu-Shirane Volcano, central Japan. We analyze LP waveforms observed at this volcano in the period between 1988 and 1995, which covers a seismically active period between 1989 and 1993. Systematic temporal variations in the complex frequencies are observed in October-November 1989, July-October 1991, and September 1992-January 1993. We use acoustic properties of a crack filled with hydrothermal fluids to interpret the observed temporal variations in the complex frequencies. The temporal variations in October-November 1989 can be divided into two periods, which are explained by a gradual decrease and increase of a gas-volume fraction in a water-steam mixture in a crack, respectively. The temporal variations in July-October 1991 can be also divided into two periods. These variations in the first and second periods are similar to those observed in November 1989 and in September-November 1992, respectively, and are interpreted as drying of a water-steam mixture and misty gas in a crack, respectively. The repeated nature of the temporal variations observed in similar seasons between July and November suggests the existence of seasonality in the occurrence of LP events. This may be caused by a seasonally variable meteoritic water supply to a hydrothermal system, which may have been heated by the flux of volcanic gases from magma beneath this volcano. ?? 2005 Elsevier B.V. All rights reserved.

Nakano, M.; Kumagai, H.

2005-01-01

440

NASA Astrophysics Data System (ADS)

The lithospheric, and shallow asthenospheric, mantle in Southern Victoria Land are known to record anomalously high heat flow but the cause remains imperfectly understood. To address this issue plagioclase peridotite xenoliths have been collected from Cenozoic alkalic igneous rocks at three localities along a 150 km transect across the western shoulder of the West Antarctic rift system in Southern Victoria Land, Antarctica. There is a geochemical, thermal and chronological progression across this section of the rift shoulder from relatively hot, young and thick lithosphere in the west to cooler, older and thinner lithosphere in the east. Overprinting this progression are relatively more recent mantle refertilising events. Melt depletion and refertilisation was relatively limited in the lithospheric mantle to the west but has been more extensive in the east. Thermometry obtained from orthopyroxene in these plagioclase peridotites indicates that those samples most recently affected by refertilising melts have attained the highest temperatures, above those predicted from idealised dynamic rift or Northern Victoria Land geotherms and higher than those prevailing in the equivalent East Antarctic mantle. Anomalously high heat flow can thus be attributed to entrapment of syn-rift melts in the lithosphere, probably since regional magmatism commenced at least 24 Myr ago. The chemistry and mineralogy of shallow plagioclase peridotite mantle can be explained by up to 8% melt extraction and a series of refertilisation events. These include: (a) up to 8% refertilisation by a N-MORB melt; (b) metasomatism involving up to 1% addition of a subduction-related component; and (c) addition of ~ 1.5% average calcio-carbonatite. A high MgO group of clinopyroxenes can be modelled by the addition of up to 1% alkalic melt. Melt extraction and refertilisation mainly occurred in the spinel stability field prior to decompression and uplift. In this region mantle plagioclase originates by a combination of subsolidus recrystallisation during decompression within the plagioclase stability field and refertilisation by basaltic melt.

Martin, A. P.; Cooper, A. F.; Price, R. C.

2014-03-01

441

NASA Astrophysics Data System (ADS)

U-Pb data for plagioclase and sulfide are reported from the Burakovka and Olanga layered mafic-ultramafic complexes in Karelia. These 2.44-2.45 Ga complexes differ in the age of their host rocks and in post-emplacement metamorphic history. Acid leaching of optically discrete plagioclase populations revealed three components of Pb present in the Olanga plagioclases: initial, radiogenic, and alteration-related. Leaching in HCl and HNO 3 was found to remove most of U and radiogenic Pb from plagioclase and to fractionate U from Pb. The alteration component, abundant in the Olanga plagioclase, is only partially removed by leaching. The Olanga sulfides also contain a significant radiogenic component. Plagioclase from the unmetamorphosed Burakovka Complex yields more reliable initial Pb ratios. The Pb isotopic compositions of plagioclase from the three intrusions of the Olanga Complex form a single steep linear trend, which shows excess scatter due to residual radiogenic and alteration Pb. Calculated single-stage ? 1? of about 7.9 in the Kivakka intrusion is close to the Archean depleted mantle value, but combined Pb, Sr, and Nd isotopic systematics suggest the presence of an enriched component from either lithospheric or slightly contaminated plume source. Internal variations in initial Pb and Sr isotopic ratios and Pb isotopic systematics of sulfides are explained by late-magmatic fluid exchange with 2.7 Ga country rocks. The Burakovka Complex, emplaced into the pre-3.1 Ga crust, shows large correlated variations in 207Pb/ 204Pb, ? Nd(T) and 87Sr/ 86Sr(T) in plagioclase. Initial Pb, Nd, and Sr isotopic variations in the Burakovka Complex are interpreted as a result of a multistage contamination process, including contamination en route and during emplacement and local assimilation of enclosing gneiss. In addition, the Burakovka mantle source was probably modified with addition of sediment-derived Pb by a subduction-zone fluid. Time-integrated Th/U ? 4.3 in mantle sources and crustal contaminants of the Olanga and Burakovka complexes are uniform and are similar to the Archean mantle value. This implies that U/Pb fractionation in the Archean-Paleoproterozoic crust-mantle system probably occurred by hydrothermal Pb transfer that fractionated Pb from U, but not Th from U.

Amelin, Yuri V.; Neymark, Leonid A.

1998-02-01