Andreasen, Søren Juhl
Methanol Reformer System Modeling and Control using an Adaptive Neuro-Fuzzy Inference System Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Figure 2 shows the general structure of the ANFIS approach. ANFIS is a neuro-fuzzy modeling approach which uses linguistic variables and parameters which
Adaptive Neuro-Fuzzy Inference System for mid term prognostic error stabilization
Paris-Sud XI, Université de
Adaptive Neuro-Fuzzy Inference System for mid term prognostic error stabilization Otilia DRAGOMIR prediction errors appears to be essential. For that purpose a neuro-fuzzy predictor based on the ANFIS model is proposed to perform prognostic. Keywords: prognostic, neuro-fuzzy system, ANFIS, error of prediction. 1
Structure identification of generalized adaptive neuro-fuzzy inference systems
Mohammad Fazle Azeem; Madasu Hanmandlu; Nesar Ahmad
2003-01-01
This paper presents a method to identify the structure of generalized adaptive neuro-fuzzy inference systems (GANFISs). The structure of GANFIS consists of a number of generalized radial basis function (GRBF) units. The radial basis functions are irregularly distributed in the form of hyper-patches in the input-output space. The minimum number of GRBF units is selected based on a heuristic using
Paris-Sud XI, Université de
Proton Exchange Membrane Fuel Cell degradation prediction based on Adaptive Neuro Fuzzy Inference Management, Time-series prediction, Adaptive Neuro-Fuzzy Inference System. [*] Corresponding author, silvasan nominal operating condition of a PEM fuel cell stack. It proposes a methodology based on Adaptive Neuro
Evapotranspiration estimation by two different neuro-fuzzy inference systems
NASA Astrophysics Data System (ADS)
Cobaner, Murat
2011-02-01
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.
A Tabu-Search Based Neuro-Fuzzy Inference System for Fault Diagnosis
Rizvi, Syed Z.
A Tabu-Search Based Neuro-Fuzzy Inference System for Fault Diagnosis Haris M. Khalid S.Z. Rizvi@kfupm.edu.sa). Abstract: This paper presents a novel hybrid Tabu Search (TS) Subtractive Clustering (SC) based Neuro. Keywords: Tabu Search, Subtractive Clustering, Neuro-Fuzzy, Soft Computing, Artificial Neural Network
Pin power reconstruction for CANDU reactors using a neuro-fuzzy inference system
Man Gyun Na; Won Sik Yang; Hangbok Choi
2001-01-01
A neuro-fuzzy inference system has been developed for reconstructing fuel pin powers from Canada deuterium uranium (CANDU) core calculations performed with a coarse-mesh finite difference diffusion approximation and single-assembly lattice calculations. The neuro-fuzzy inference system is trained by a genetic algorithm and a least-squares method using the partial core calculation results of two 6×6 fuel bundle models. Verification tests have
Leszek Rutkowski; Krzysztof Cpalka
2003-01-01
In this paper, we derive new neuro-fuzzy structures called flexible neuro-fuzzy inference systems or FLEXNFIS. Based on the input-output data, we learn not only the parameters of the membership functions but also the type of the systems (Mamdani or logical). Moreover, we introduce: 1) softness to fuzzy implication operators, to aggregation of rules and to connectives of antecedents; 2) certainty
Adaptive neuro-fuzzy inference system for prediction of water level in reservoir
Fi-John Chang; Ya-Ting Chang
2006-01-01
Accurate prediction of the water level in a reservoir is crucial to optimizing the management of water resources. A neuro-fuzzy hybrid approach was used to construct a water level forecasting system during flood periods. In particular, we used the adaptive network-based fuzzy inference system (ANFIS) to build a prediction model for reservoir management. To illustrate the applicability and capability of
Adaptive Neuro-Fuzzy Inference System PID controller for SG water level of nuclear power plant
Xue-Kui Wang; Xu-Hong Yang; Gang Liu; Hong Qian
2009-01-01
In a nuclear power plant, the water level in the steam generator (SG) is one of main causes that shutdown the reactor, this problem has been of great concern for many years as the SG is a highly nonlinear system showing inverse response dynamics. For controlling the SG water level at a certain range, adaptive neuro-fuzzy inference system (ANFIS) PID
Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients
?nan Güler; Elif Derya Übeyli
2005-01-01
This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Five types of EEG signals were used as input patterns of
A new approach to estimate anthropometric measurements by adaptive neuro-fuzzy inference system
M. Dursun Kaya; A. Samet Hasiloglu; Mahmut Bayramoglu; Hakki Yesilyurt; A. Fahri Ozok
2003-01-01
Eighteen anthropometric measurements were taken in standing and sitting positions, from 387 subjects between 15 and 17 years old. “Adaptive Neuro-Fuzzy Inference System (ANFIS)” was used to estimate anthropometric measurements as an alternative to stepwise regression analysis. Six outputs (shoulder width, hip width, knee height, buttock-popliteal height, popliteal height, and height) were selected for estimation purpose. The results showed that
Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles
Sefer Kurnaz; Omer Cetin; Okyay Kaynak
2010-01-01
In this paper, an ANFIS (adaptive neuro-fuzzy inference system) based autonomous flight controller for UAVs (unmanned aerial vehicles) is described. To control the position of the UAV in three dimensional space as altitude and longitude–latitude location, three fuzzy logic modules are developed. These adjust the pitch angle, the roll angle and the throttle position of the UAV so that its
Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival
Aickelin, Uwe
Adaptive Neuro-Fuzzy Inference System (ANFIS) in Modelling Breast Cancer Survival Hazlina Hamdan for breast cancer. I. INTRODUCTION Breast cancer is one of the most common cancers to afflict the female population. It is estimated that one in nine women in the UK will develop breast cancer at some point
Na, Man Gyun [Chosun University (Korea, Republic of); Oh, Seungrohk [Dankook University (Korea, Republic of)
2002-11-15
A neuro-fuzzy inference system combined with the wavelet denoising, principal component analysis (PCA), and sequential probability ratio test (SPRT) methods has been developed to monitor the relevant sensor using the information of other sensors. The parameters of the neuro-fuzzy inference system that estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The wavelet denoising technique was applied to remove noise components in input signals into the neuro-fuzzy system. By reducing the dimension of an input space into the neuro-fuzzy system without losing a significant amount of information, the PCA was used to reduce the time necessary to train the neuro-fuzzy system, simplify the structure of the neuro-fuzzy inference system, and also, make easy the selection of the input signals into the neuro-fuzzy system. By using the residual signals between the estimated signals and the measured signals, the SPRT is applied to detect whether the sensors are degraded or not. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level, the pressurizer pressure, and the hot-leg temperature sensors in pressurized water reactors.
HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems
Jaesoo Kim; Nikola K. Kasabov
1999-01-01
This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input–output fuzzy membership functions can be optimally tuned from training examples by a hybrid
Self-adaptive neuro-fuzzy inference systems for classification applications
Jeen-Shing Wang; C. S. George Lee
2002-01-01
This paper presents a self-adaptive neuro-fuzzy inference system (SANFIS) that is capable of self-adapting and self-organizing its internal structure to acquire a parsimonious rule-base for interpreting the embedded knowledge of a system from the given training data set. A connectionist topology of fuzzy basis functions with their universal approximation capability is served as a fundamental SANFIS architecture that provides an
Kok Chew Lee; Peter Gardner
2006-01-01
This paper describes an adaptive digital predistorter (ADP) for RF power amplifier (PA) linearization using an adaptive neuro-fuzzy inference system (ANFIS). The ANFIS predistorter (PD) employs the advantage of real-time modeling of the PA's responses in determining the PD's functions. The amplitude and phase corrections for the PD are represented in an easy-to-understand fuzzy if-then rule, while the parameters involved
Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer
Elif Derya Übeyli
2009-01-01
This paper intends to an integrated view of implementing adaptive neuro-fuzzy inference system (ANFIS) for breast cancer detection.\\u000a The Wisconsin breast cancer database contained records of patients with known diagnosis. The ANFIS classifiers learned how\\u000a to differentiate a new case in the domain by given a training set of such records. The ANFIS classifier was used to detect\\u000a the breast
NASA Astrophysics Data System (ADS)
Yi, J.; Choi, C.
2014-12-01
Rainfall observation and forecasting using remote sensing such as RADAR(Radio Detection and Ranging) and satellite images are widely used to delineate the increased damage by rapid weather changeslike regional storm and flash flood. The flood runoff was calculated by using adaptive neuro-fuzzy inference system, the data driven models and MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as the input variables.The result of flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated by comparing it with the actual data.The Adaptive Neuro Fuzzy method was applied to the Chungju Reservoir basin in Korea. The six rainfall events during the flood seasons in 2010 and 2011 were used for the input data.The reservoir inflow estimation results were comparedaccording to the rainfall data used for training, checking and testing data in the model setup process. The results of the 15 models with the combination of the input variables were compared and analyzed. Using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation in this study.The model using the MAPLE forecasted precipitation data showed better result for inflow estimation in the Chungju Reservoir.
Era Purwanto; Syamsul Arifin; Bian-Sioe So
2001-01-01
Direct field-oriented induction motor drive system need rotor flux observer and rotor angular speed identifier. ANFIS (adaptive neuro fuzzy inference system) used for identifying parameter dynamics and system variable estimation, linear or nonlinear. ANFIS with backpropagation learning algorithm has applied to estimate flux rotor and identify rotor angular speed of three-phase induction motor
Al-batah, Mohammad Subhi; Isa, Nor Ashidi Mat; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi
2014-01-01
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
Subhi Al-batah, Mohammad; Mat Isa, Nor Ashidi; Klaib, Mohammad Fadel; Al-Betar, Mohammed Azmi
2014-01-01
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
Malik S. Yilmaz; Emine Ayaz
2009-01-01
In this study the features for bearing fault diagnosis is investigated based on the analysis of temperature, vibration and current measurements of a 3 phase, 4 poles, 5 HP induction motors which are chemically, thermally and electrically aged by artificial aging methods. Then three adaptive neuro-fuzzy inference systems which takes the temperature, current and vibration measurements as inputs and the
Heddam, Salim
2014-01-01
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
Training Hybrid Neuro-Fuzzy System to Infer Permeability in Wells on Maracaibo Lake, Venezuela
Hurtado, Nuri; Torres, Julio
2014-01-01
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.
Adaptive neuro-fuzzy inference systems for automatic detection of breast cancer.
Ubeyli, Elif Derya
2009-10-01
This paper intends to an integrated view of implementing adaptive neuro-fuzzy inference system (ANFIS) for breast cancer detection. The Wisconsin breast cancer database contained records of patients with known diagnosis. The ANFIS classifiers learned how to differentiate a new case in the domain by given a training set of such records. The ANFIS classifier was used to detect the breast cancer when nine features defining breast cancer indications were used as inputs. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the impacts of features on the detection of breast cancer were obtained through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performances and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting the breast cancer. PMID:19827261
Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands.
Dzakpasu, Mawuli; Scholz, Miklas; McCarthy, Valerie; Jordan, Siobhán; Sani, Abdulkadir
2015-01-01
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
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 neuro-fuzzy techniques is used for non-linear system identification, output estimation, and fault
Modelling Dissolved Pollutants in Krishna River Using Adaptive Neuro Fuzzy Inference Systems
NASA Astrophysics Data System (ADS)
Matli, C. S.; Umamahesh, N. V.
2014-01-01
Water quality models are used to describe the discharge concentration relationships in the river. Number of models exists to simulate the pollutant loads in a river, of which some of them are based on simple cause effect relationships and others on highly sophisticated physical and mathematical approaches that require extensive data inputs. Fuzzy rule based modeling extensively used in other disciplines, is attempted in the present study for modeling water quality with respect of dissolved pollutants in Krishna river flowing in Southern part of India. Adaptive Neuro Fuzzy Inference Systems (ANFIS), a recent development in the area of neuro-computing, based on the concept of fuzzy sets is used to model highly non-linear relationships and are capable of adaptive learning. This paper presents the results of the application of ANFIS for modeling dissolved pollutants in the Krishna River. The application and validation of the models is carried out using water quality and flow data obtained from the monitoring stations on the river. The results indicate that the models are quite successful in simulating the physical processes of the relationships between discharge and concentrations.
Prediction of Scour Depth around Bridge Piers using Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
NASA Astrophysics Data System (ADS)
Valyrakis, Manousos; Zhang, Hanqing
2014-05-01
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.
NASA Astrophysics Data System (ADS)
Ramesh, K.; Kesarkar, A. P.; Bhate, J.; Venkat Ratnam, M.; Jayaraman, A.
2015-01-01
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.
Wilamowski, Bogdan Maciej
in an Adaptive Neuro Fuzzy Inference System M. Onder Efe Bogazici University, Electrical and Electronic@ieee.org Abstract: Adaptive neuro-fuzzy inference systems exhibit both the numeric power of neural networks et al [4] propose an Adaptive Neuro Fuzzy Inference System (ANFIS), in which polynomials are used
Risk Mapping of Cutaneous Leishmaniasis via a Fuzzy C Means-based Neuro-Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Akhavan, P.; Karimi, M.; Pahlavani, P.
2014-10-01
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.
NASA Astrophysics Data System (ADS)
Sezer, Ebru; Pradhan, Biswajeet; Gokceoglu, Candan
2010-05-01
Landslides are one of the recurrent natural hazard problems throughout most of Malaysia. Recently, the Klang Valley area of Selangor state has faced numerous landslide and mudflow events and much damage occurred in these areas. However, only little effort has been made to assess or predict these events which resulted in serious damages. Through scientific analyses of these landslides, one can assess and predict landslide-susceptible areas and even the events as such, and thus reduce landslide damages through proper preparation and/or mitigation. For this reason , the purpose of the present paper is to produce landslide susceptibility maps of a part of the Klang Valley areas in Malaysia by employing the results of the adaptive neuro-fuzzy inference system (ANFIS) analyses. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. Landsat TM satellite imagery was used to map vegetation index. Maps of topography, lineaments and NDVI were constructed from the spatial datasets. Seven landslide conditioning factors such as altitude, slope angle, plan curvature, distance from drainage, soil type, distance from faults and NDVI were extracted from the spatial database. These factors were analyzed using an ANFIS to construct the landslide susceptibility maps. During the model development works, total 5 landslide susceptibility models were obtained by using ANFIS results. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values was calculated. Landslide locations were used to validate results of the landslide susceptibility map and the verification results showed 98% accuracy for the model 5 employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed sufficient agreement between the obtained susceptibility map and the existing data on landslide areas. Qualitatively, the model yields reasonable results which can be used for preliminary land-use planning purposes. As a final conclusion, the results obtained from the study showed that the ANFIS modeling is a very useful and powerful tool for the regional landslide susceptibility assessments. However, the results to be obtained from the ANFIS modeling should be assessed carefully because the overlearning may cause misleading results. To prevent overlerning, the numbers of membership functions of inputs and the number of training epochs should be selected optimally and carefully.
Kolus, Ahmet; Dubé, Philippe-Antoine; Imbeau, Daniel; Labib, Richard; Dubeau, Denise
2014-11-01
In new approaches based on adaptive neuro-fuzzy systems (ANFIS) and analytical method, heart rate (HR) measurements were used to estimate oxygen consumption (VO2). Thirty-five participants performed Meyer and Flenghi's step-test (eight of which performed regeneration release work), during which heart rate and oxygen consumption were measured. Two individualized models and a General ANFIS model that does not require individual calibration were developed. Results indicated the superior precision achieved with individualized ANFIS modelling (RMSE = 1.0 and 2.8 ml/kg min in laboratory and field, respectively). The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data. The General ANFIS model's estimates of VO2 were not significantly different from actual field VO2 measurements (RMSE = 3.5 ml/kg min). With its ease of use and low implementation cost, the General ANFIS model shows potential to replace any of the traditional individualized methods for VO2 estimation from HR data collected in the field. PMID:24793823
Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning
ERIC Educational Resources Information Center
Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun
2012-01-01
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…
Klaus Dalinghaus Realisierung und Optimierung eines Neuro-Fuzzy Systems
Kallenrode, May-Britt
Klaus Dalinghaus Realisierung und Optimierung eines Neuro-Fuzzy Systems zur Erkennung rhythmischer: Thorsten Hinrichs © Institute of Cognitive Science #12;Realisierung und Optimierung eines Neuro Realisierung und Optimierung eines Neuro-Fuzzy Systems zur Erkennung rhythmischer Muster MAGISTERARBEIT ZUR
Karami, Ali; Keiter, Steffen; Hollert, Henner; Courtenay, Simon C
2013-03-01
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
Neuro-fuzzy systems for function approximation
Detlef Nauck; Rudolf Kruse
1999-01-01
We present a neuro-fuzzy architecture for function approximation based on supervised learning. The learning algorithm is able to determine the structure and the parameters of a fuzzy system. The approach is an extension to our already published NEFCON and NEFCLASS models which are used for control or classification purposes. The proposed extended model, which we call NEFPROX, is more general
Jhin, Changho; Hwang, Keum Taek
2014-01-01
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
Neuro-Fuzzy System for Post-Dialysis Urea Rebound Prediction
A. T. Azar; A. H. Kandil; K. M. Wahba; A. M. Elgarhy; W. A. Massoud
2008-01-01
Measuring post dialysis urea rebound (PDUR) requires a 30- or 60-minute post-dialysis sampling, which is inconvenient. This paper presents a novel technique for predicting equilibrated urea concentration and post dialysis urea rebound in the form of a Takagi-Sugeno-Kang fuzzy inference system. The advantage of this neuro-fuzzy hybrid approach is that it doesn't require 30-60-minute post-dialysis urea sample. Adaptive neuro-fuzzy inference
NASA Astrophysics Data System (ADS)
Heidary, Saeed; Setayeshi, Saeed
2015-01-01
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.
NASA Astrophysics Data System (ADS)
Goodarzi, Mohammad; Olivieri, Alejandro C.; Freitas, Matheus P.
2009-08-01
A spectrophotometric method for the simultaneous determination of Al(III), Co(II) and Ni(II) using Alizarin Red S as a chelating agent was developed. The parameters controlling the behavior of the system were investigated and optimum conditions were selected. The presence of non-linearities was checked using Mallows augmented partial residual plots. To take into account these non-linearities, a principal component analysis-adaptive neuro-fuzzy inference systems (PC-ANFISs) method was used for the analysis of ternary mixtures of Al(III), Co(II) and Ni(II) over the range of 0.05-0.90, 0.05-4.05 and 0.05-0.95 ?g mL -1, respectively. Absorbance data were collected between 370 and 700 nm. The method was applied to accurately and simultaneously determines the content of metal ions in several synthetic mixtures.
Neuro-fuzzy methods for nonlinear system identification
Robert Babuška; Henk Verbruggen
2003-01-01
Most processes in industry are characterized by nonlinear and time-varying behavior. Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve robust fault-tolerant behavior. Among the different nonlinear identification techniques, methods based on neuro-fuzzy models are gradually becoming established not only in the academia but also in industrial applications. Neuro-fuzzy modeling can
A neuro-fuzzy monitoring system. Application to flexible production systems.
Paris-Sud XI, Université de
A neuro-fuzzy monitoring system. Application to flexible production systems. N. Palluat, D system lead to design intelligent monitoring aid systems. Accordingly, the use of neuro- fuzzy technics network detection tool and a neuro-fuzzy diagnosis tool. Learning capabilities due to the neural structure
A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems
Mahdaoui, Rafik; Mouss, Mohamed Djamel; Chouhal, Ouahiba
2011-01-01
Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures....
Differentiating between good credits and bad credits using neuro-fuzzy systems
Rashmi Malhotra; D. K. Malhotra
2002-01-01
To evaluate consumer loan applications, loan officers use many techniques such as judgmental systems, statistical models, or simply intuitive experience. In recent years, fuzzy systems and neural networks have attracted the growing interest of researchers and practitioners. This study compares the performance of artificial neuro-fuzzy inference systems (ANFIS) and multiple discriminant analysis models to screen potential defaulters on consumer loans.
CINTIA: A Neuro-Fuzzy Real Time Controller for Low Power Embedded Systems
Reyneri, Leonardo
CINTIA: A Neuro-Fuzzy Real Time Controller for Low Power Embedded Systems L.M. Reyneri , M@iet.unipi.it e.mail: chiaberge@polito.it Abstract 1 This paper describes CINTIA, a Neuro-Fuzzy real embedded systems. The pro- posed system mixes two di erent approaches, namely Neuro-Fuzzy Controllers
Neuro-Fuzzy Hardware and DSPs: a Promising Marriage for Control of Complex Systems
Reyneri, Leonardo
Neuro-Fuzzy Hardware and DSPs: a Promising Marriage for Control of Complex Systems B. Bona, S intelligent con- trol paradigms mixing Neuro-Fuzzy algorithms with nite state automata and or digital con of control problems. Neuro-Fuzzy systems, specially when com- bined with DSPs can solve e ciently both
Recurrent neuro-fuzzy system for fault detection and isolation in nuclear reactors
Alexandre Evsukoff; Sylviane Gentil
2005-01-01
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
Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro-Fuzzy
Paris-Sud XI, Université de
Maximum Power Point Tracking Control for Photovoltaic System Using Adaptive Neuro- Fuzzy "ANFIS Adaptive Neuro- Fuzzy "ANFIS". The PV array has an optimum operating point to generate maximum power conventional controller like Adaptive Neuro-Fuzzy "ANFIS" and fuzzy logic controller is proposed and simulated
Biogeography-Based Optimization of Neuro-Fuzzy System Parameters for Diagnosis of Cardiac Disease
Simon, Dan
Biogeography-Based Optimization of Neuro-Fuzzy System Parameters for Diagnosis of Cardiac Disease through P wave features. In particular, we design a neuro-fuzzy network trained with a new evolutionary algorithm called biogeography-based optimization (BBO). The neuro-fuzzy network recognizes and classifies P
NASA Astrophysics Data System (ADS)
Fleischer, Christian; Waag, Wladislaw; Bai, Ziou; Sauer, Dirk Uwe
2013-12-01
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.
A neuro-fuzzy decision support system for the diagnosis of heart failure.
Akinyokun, Charles O; Obot, Okure U; Uzoka, Faith-Michael E; Andy, John J
2010-01-01
A neuro-fuzzy decision support system is proposed for the diagnosis of heart failure. The system comprises; knowledge base (database, neural networks and fuzzy logic) of both the quantitative and qualitative knowledge of the diagnosis of heart failure, neuro-fuzzy inference engine and decision support engine. The neural networks employ a multi-layers perception back propagation learning process while the fuzzy logic uses the root sum square inference procedure. The neuro-fuzzy inference engine uses a weighted average of the premise and consequent parameters with the fuzzy rules serving as the nodes and the fuzzy sets representing the weights of the nodes. The decision support engine carries out the cognitive and emotional filtering of the objective and subjective feelings of the medical practitioner. An experimental study of the decision support system was carried out using cases of some patients from three hospitals in Nigeria with the assistance of their medical personnel who collected patients' data over a period of six months. The results of the study show that the neuro-fuzzy system provides a highly reliable diagnosis, while the emotional and cognitive filters further refine the diagnosis results by taking care of the contextual elements of medical diagnosis. PMID:20543357
NASA Astrophysics Data System (ADS)
Islam, Tanvir; Srivastava, Prashant K.; Rico-Ramirez, Miguel A.; Dai, Qiang; Han, Dawei; Gupta, Manika
2014-08-01
The authors have investigated an adaptive neuro fuzzy inference system (ANFIS) for the estimation of hydrometeors from the TRMM microwave imager (TMI). The proposed algorithm, named as Hydro-Rain algorithm, is developed in synergy with the TRMM precipitation radar (PR) observed hydrometeor information. The method retrieves rain rates by exploiting the synergistic relations between the TMI and PR observations in twofold steps. First, the fundamental hydrometeor parameters, liquid water path (LWP) and ice water path (IWP), are estimated from the TMI brightness temperatures. Next, the rain rates are estimated from the retrieved hydrometeor parameters (LWP and IWP). A comparison of the hydrometeor retrievals by the Hydro-Rain algorithm is done with the TRMM PR 2A25 and GPROF 2A12 algorithms. The results reveal that the Hydro-Rain algorithm has good skills in estimating hydrometeor paths LWP and IWP, as well as surface rain rate. An examination of the Hydro-Rain algorithm is also conducted on a super typhoon case, in which the Hydro-Rain has shown very good performance in reproducing the typhoon field. Nevertheless, the passive microwave based estimate of hydrometeors appears to suffer in high rain rate regimes, and as the rain rate increases, the discrepancies with hydrometeor estimates tend to increase as well.
Yolmeh, Mahmoud; Habibi Najafi, Mohammad B; Salehi, Fakhreddin
2014-01-01
Annatto is commonly used as a coloring agent in the food industry and has antimicrobial and antioxidant properties. In this study, genetic algorithm-artificial neural network (GA-ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict the effect of annatto dye on Salmonella enteritidis in mayonnaise. The GA-ANN and ANFIS were fed with 3 inputs of annatto dye concentration (0, 0.1, 0.2 and 0.4%), storage temperature (4 and 25°C) and storage time (1-20 days) for prediction of S. enteritidis population. Both models were trained with experimental data. The results showed that the annatto dye was able to reduce of S. enteritidis and its effect was stronger at 25°C than 4°C. The developed GA-ANN, which included 8 hidden neurons, could predict S. enteritidis population with correlation coefficient of 0.999. The overall agreement between ANFIS predictions and experimental data was also very good (r=0.998). Sensitivity analysis results showed that storage temperature was the most sensitive factor for prediction of S. enteritidis population. PMID:24566279
NASA Astrophysics Data System (ADS)
Woo, Youngkeun; Lee, Juwon; Hwang, Sujin; Hong, Cheol Pyo
2013-03-01
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.
M. B. Djukanovic; M. S. Calovic; B. V. Vesovic; D. J. Sobajic
1997-01-01
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,
Paris-Sud XI, Université de
Prognostics in Switching Systems: Evidential Markovian Classification of Real-Time Neuro on information theory and Choquet Integral, 2) prediction relying on an evolving real-time neuro-fuzzy system real-time neuro-fuzzy system and 3) classification into one of the possible functioning modes using
Reyneri, Leonardo
Hybrid Neuro-Fuzzy System for Control of Complex Plants B. Bona, S. Carabelli, M. Chiaberge, E paradigms mixing Neuro-Fuzzy algorithms with nite state automata and or digital control algorithms. 1 HYBRID CONTROL SYS- TEM Hybrid Neuro-Fuzzy systems have several ad- vantages over both DSP and Neuro-Fuzzy sys
A transductive neuro-fuzzy controller: application to a drilling process
Agustín Gajate; Rodolfo E. Haber; Pastora I. Vega; José. R. Alique
2010-01-01
Recently, new neuro-fuzzy inference algorithms have been developed to deal with the time-varying behavior and uncertainty of many complex systems. This paper presents the design and application of a novel transductive neuro-fuzzy inference method to control force in a high-performance drilling process. The main goal is to study, analyze, and verify the behavior of a transductive neuro-fuzzy inference system for
Authoring Neuro-fuzzy Tutoring Systems for M and E-Learning
Ramon Zatarain-cabada; Ma. Lucia Barrón-estrada; Guillermo Sandoval; J. Moisés Osorio-velásquez; Eduardo Urías; Carlos A. Reyes García
2008-01-01
This paper is about an author tool that can be used to produce neuro-fuzzy tutoring systems for distance and mobile environments.\\u000a These tutoring systems recognize and classify learning characteristics of learners by using a neuro-fuzzy system. The author\\u000a tool has three main components: a content editor for building course structure and learning material; an editor for building\\u000a fuzzy sets for
NASA Astrophysics Data System (ADS)
Heidary, Saeed; Setayeshi, Saeed; Ghannadi-Maragheh, Mohammad
2014-09-01
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.
Chou, Chien-Hsing (Ister)
323 A Self-Generating Neuro-Fuzzy System Through Reinforcements Mu-Chun Su, Chien-Hsing Chou@csie.ncu.edu.tw Abstract: In this paper, a novel self-generating neuro-fuzzy system through reinforcements is proposed reinforcement learning. The proposed neuro-fuzzy system is applied to the inverted pendulum system
A Neuro-Fuzzy Systems for Control Applications F. Berardi, M. Chiaberge, E. Miranda and L.M. Reyneri
Reyneri, Leonardo
A Neuro-Fuzzy Systems for Control Applications F. Berardi, M. Chiaberge, E. Miranda and L, Fuzzy Systems, Control Applications, Embedded Systems. Abstract 1 This paper describes DANIELA a Neuro intelligent control al- gorithms mixing neuro-fuzzy paradigms with nite state automatas and is used to control
Paris-Sud XI, Université de
A Neuro-Fuzzy Self Built System For Prognostics: a Way To Ensure Good Prediction Accuracy of a neuro-fuzzy predictor whose architecture is partially determined thanks to a statistical approach based...). Following that and according to literature, neuro-fuzzy (NF) systems appear to be very promising prognostic
Reyneri, Leonardo
A Walking Hexapod Controlled by a Neuro-Fuzzy System F. Berardi, M. Chiaberge, E. Miranda and L, a Neuro-Fuzzy system for control applications. The sys- tem is based on a custom neural device that can intelligent control al- gorithms mixing neuro-fuzzy algorithms with nite state automata and is used to control
Efficient neuro-fuzzy system and its Memristor Crossbar-based Hardware Implementation
Merrikh-Bayat, Farnood
2011-01-01
In this paper a novel neuro-fuzzy system is proposed where its learning is based on the creation of fuzzy relations by using 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 precisely adjust them. Finally, this structure is hierarchically expandable and can compute operations in real time since it is implemented through analog circuits. Simulation results show 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.
A transductive neuro-fuzzy controller: application to a drilling process.
Gajate, Agustín; Haber, Rodolfo E; Vega, Pastora I; Alique, José R
2010-07-01
Recently, new neuro-fuzzy inference algorithms have been developed to deal with the time-varying behavior and uncertainty of many complex systems. This paper presents the design and application of a novel transductive neuro-fuzzy inference method to control force in a high-performance drilling process. The main goal is to study, analyze, and verify the behavior of a transductive neuro-fuzzy inference system for controlling this complex process, specifically addressing the dynamic modeling, computational efficiency, and viability of the real-time application of this algorithm as well as assessing the topology of the neuro-fuzzy system (e.g., number of clusters, number of rules). A transductive reasoning method is used to create local neuro-fuzzy models for each input/output data set in a case study. The direct and inverse dynamics of a complex process are modeled using this strategy. The synergies among fuzzy, neural, and transductive strategies are then exploited to deal with process complexity and uncertainty through the application of the neuro-fuzzy models within an internal model control (IMC) scheme. A comparative study is made of the adaptive neuro-fuzzy inference system (ANFIS) and the suggested method inspired in a transductive neuro-fuzzy inference strategy. The two neuro-fuzzy strategies are evaluated in a real drilling force control problem. The experimental results demonstrated that the transductive neuro-fuzzy control system provides a good transient response (without overshoot) and better error-based performance indices than the ANFIS-based control system. In particular, the IMC system based on a transductive neuro-fuzzy inference approach reduces the influence of the increase in cutting force that occurs as the drill depth increases, reducing the risk of rapid tool wear and catastrophic tool breakage. PMID:20659865
Andon V. Topalov; Erdal Kayacan; Yesim Oniz; Okyay Kaynak
2009-01-01
A neuro-fuzzy adaptive control approach for nonlinear systems with model uncertainties is proposed. The implemented control scheme consists of a proportional plus derivative controller that is provided both to guarantee global asymptotic stability in compact space and as an inverse reference model of the response of the controlled system. Its output is used as an error signal by an on-line
Memory-based neuro-fuzzy system for interpolation of reflection coefficients of printing inks
Ye. V. Bodyanskiy; N. Ye. Kulishova
2008-01-01
The problem of interpolation of a two-dimensional function on a nonuniform axial rectangular grid is considered. To solve\\u000a the problem, a memory-based neuro-fuzzy system is proposed. This system is computationally simple and provides a high-quality\\u000a interpolation.
CINTIA: A Neuro-Fuzzy Real Time Controller for Low Power Embedded Systems
Reyneri, Leonardo
of learning and intelligent control 11 . This is due mainly to their intrinsic parallelism, their learning partially by the Italian National Project, CNR 93.05234.ST74, Sis- temi Elettronici Avanzati - Reti Neurali, a hybrid Neuro-Fuzzy system devoted to the real-time learning control of non-linear plants. Thanks
MNFS-FPM: A novel memetic neuro-fuzzy system based financial portfolio management
Ernest Lumanpauw; Michel Pasquier; Chai Quek
2007-01-01
Portfolio management consists of deciding what assets to include in a portfolio given the investor's objectives and changing market and economic conditions. The always difficult selection process includes identifying which assets to purchase, how much, and when. This paper presents a novel memetic neuro-fuzzy system for financial portfolio management (MNFS-FPM) which emulates the thinking process of a rational investor and
IMPLEMENTING ADAPTIVE DRIVING SYSTEMS FOR INTELLIGENT VEHICLES BY USING NEURO-FUZZY NETWORKS
Y T Lin; F.-Y. Wang; P B Mirchandani; Long Wu; Z X Wang; Chris Yeo; Michael Do
2001-01-01
The application of supervised learning to train an intelligent vehicle with a neuro-fuzzy controller to mimic the driving behavior of a human driver is discussed. An initial fuzzy control system for vehicle driving was set up on the basis of general human driving experiences, and its control rules were modified to fit the driving behavior of an individual driver. This
Forecasting of natural gas consumption with neural network and neuro fuzzy system
NASA Astrophysics Data System (ADS)
Kaynar, Oguz; Yilmaz, Isik; Demirkoparan, Ferhan
2010-05-01
The prediction of natural gas consumption is crucial for Turkey which follows foreign-dependent policy in point of providing natural gas and whose stock capacity is only 5% of internal total consumption. Prediction accuracy of demand is one of the elements which has an influence on sectored investments and agreements about obtaining natural gas, so on development of sector. In recent years, new techniques, such as artificial neural networks and fuzzy inference systems, have been widely used in natural gas consumption prediction in addition to classical time series analysis. In this study, weekly natural gas consumption of Turkey has been predicted by means of three different approaches. The first one is Autoregressive Integrated Moving Average (ARIMA), which is classical time series analysis method. The second approach is the Artificial Neural Network. Two different ANN models, which are Multi Layer Perceptron (MLP) and Radial Basis Function Network (RBFN), are employed to predict natural gas consumption. The last is Adaptive Neuro Fuzzy Inference System (ANFIS), which combines ANN and Fuzzy Inference System. Different prediction models have been constructed and one model, which has the best forecasting performance, is determined for each method. Then predictions are made by using these models and results are compared. Keywords: ANN, ANFIS, ARIMA, Natural Gas, Forecasting
Neuro-fuzzy control of an MDOF building with a magnetorheological damper using acceleration feedback
Schurter, Kyle Christopher
2000-01-01
Parameter specification of a fuzzy inference system (HS) with the aid of artificial neural networks allows the creation of complex, multi-dimensional models that are computationally efficient and numerically robust. An adaptive neuro-fuzzy inference...
A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning
Shie-Jue Lee; Chen-Sen Ouyang
2003-01-01
We propose an approach for neuro-fuzzy system modeling. A neuro-fuzzy system for a given set of input-output data is obtained in two steps. First, the data set is partitioned automatically into a set of clusters based on input-similarity and output-similarity tests. Membership functions associated with each cluster are defined according to statistical means and variances of the data points included
Cerebral Quotient of Neuro Fuzzy Techniques - Hype or Hallelujah?
Ajith Abraham
Fuzzy inference systems and neural networks are complementary technologies in the design of adaptive intelligent systems. Artificial Neural Network (ANN) learns from scratch by adjusting the interconnections between layers. Fuzzy Inference System (FIS) is a popular computing framework based on the concept of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. A neuro-fuzzy system is simply a fuzzy inference
A fire detection system based on ART2 neuro-fuzzy network
Zhang Qing; Wang Shu
1998-01-01
The ART-2 neural network is a self-organized artificial network that operates according to adaptive resonance theory. A neuro-fuzzy network, which combines ART-2 and the fuzzy system in series, is presented and applied to fire detection. The results of experiments show that this system has a stronger ability to adapt to the environment than the backpropagation (BP) neural network. It can
Li Li; Fuchun Sun
2007-01-01
In this paper, we first present a series of dynamic TS fuzzy subsystems to approximate a nonlinear singularly perturbed system.\\u000a Then the reference model with same fuzzy sets is established. To make the states of the closed-loop system follow those of\\u000a the reference model, a controller including of neuro-fuzzy adaptive and linear feedback term is designed. The linear feedback\\u000a parameters
An adaptive neuro-fuzzy system for automatic image segmentation and edge detection
Victor Boskovitz; Hugo Guterman
2002-01-01
An autoadaptive neuro-fuzzy segmentation and edge detection architecture is presented. The system consists of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is
S. P. Moustakidis; G. A. Rovithakis; J. B. Theocharis
2006-01-01
An adaptive neuro-fuzzy controller is proposed in this paper to deal with the problem of tracking nonlinear affine in the control dynamical systems with unknown nonlinearities. The plant is described by means of a Takagi-Sugeno fuzzy model, including dynamic fuzzy rules of generalized form, where the local submodels are realized through nonlinear input-output mappings. Instead of modelling the plant dynamics
An exTS based Neuro-Fuzzy Algorithm for Prognostics and Tool Condition Monitoring
Paris-Sud XI, Université de
ICARCV2010 An exTS based Neuro-Fuzzy Algorithm for Prognostics and Tool Condition Monitoring OXtended Takagi Sugeno (exTS) based neuro-fuzzy algorithm to predict the tool condition in high-speed machining conditions. The performance of evolving Neuro-Fuzzy model is compared with an Adaptive Neuro-Fuzzy Inference
Jafari, Zohreh; Edrisi, Mehdi; Marateb, Hamid Reza
2014-01-01
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
Naive Bayes Classifiers Using Neuro-Fuzzy Learning1
Borgelt, Christian
Improving Naive Bayes Classifiers Using Neuro-Fuzzy Learning1 A. NÂ¨urnberger, C. Borgelt, and A classification performance. Another promi- nent type of classifiers are neuro-fuzzy classifica- tion systems there are certain structural similarities between a neuro-fuzzy classifier and a naive Bayes classifier, the idea
Some remarks on adaptive neuro-fuzzy systems
Romeo Ortega; Genie Informatique
1995-01-01
Makes three remarks concerning adaptive implementations of neural networks and fuzzy systems. First, the author brings to the readers attention the fact that the potential power of these systems as function approximators is lost when, as done in recently published work, the adjustable parameters are only the linear combination weights of the basis functions. Second, the author shows that the
A simple direct-torque neuro-fuzzy control of PWM-inverter-fed induction motor drive
Pawel Z. Grabowski; Marian P. Kazmierkowski; Bimal K. Bose; Frede Blaabjerg
2000-01-01
In this paper, the concept and implementation of a new simple direct-torque neuro-fuzzy control (DTNFC) scheme for pulsewidth-modulation-inverter-fed induction motor drive are presented. An adaptive neuro-fuzzy inference system is applied to achieve high-performance decoupled flux and torque control. The theoretical principle and tuning procedure of this method are discussed. A 3 kW induction motor experimental system with digital signal processor
NASA Astrophysics Data System (ADS)
Bazzazi, Abbas Aghajani; Esmaeili, Mohammad
2012-12-01
Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (
Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm
NASA Technical Reports Server (NTRS)
Mitra, Sunanda; Pemmaraju, Surya
1992-01-01
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.
Neuro-fuzzy and neural network systems for air quality control
NASA Astrophysics Data System (ADS)
Carnevale, Claudio; Finzi, Giovanna; Pisoni, Enrico; Volta, Marialuisa
In order to define efficient air quality plans, Regional Authorities need suitable tools to evaluate both the impact of emission reduction strategies on pollution indexes and the costs of such emission reductions. The air quality control can be formalized as a two-objective nonlinear mathematical problem, integrating source-receptor models and the estimate of emission reduction costs. Both aspects present several complex elements. In particular the source-receptor models cannot be implemented through deterministic modelling systems, that would bring to a computationally unfeasible mathematical problem. In this paper we suggest to identify source-receptor statistical models (neural network and neuro-fuzzy) processing the simulations of a deterministic multi-phase modelling system (GAMES). The methodology has been applied to ozone and PM10 concentrations in Northern Italy. The results show that, despite a large advantage in terms of computational costs, the selected source-receptor models are able to accurately reproduce the simulation of the 3D modelling system.
Suppression of maternal ECG from fetal ECG using neuro fuzzy logic technique
C. K. S. Vijila; S. Renganathan; S. Johnson
2003-01-01
Soft computing is a new approach to construct intelligent systems. The complex real world problems require intelligent systems that combine knowledge, techniques and methodologies from various sources. Neuro fuzzy is the combination of the neural network and fuzzy logic. Neural networks recognize patterns and adapt themselves to cope with changing environments. Fuzzy inference systems incorporate human knowledge and perform inferencing
Estimation of pile group scour using adaptive neuro-fuzzy approach
S. M. Bateni; D.-S. Jeng
2007-01-01
An accurate estimation of scour depth around piles is important for coastal and ocean engineers involved in the design of marine structures. Owing to the complexity of the problem, most conventional approaches are often unable to provide sufficiently accurate results. In this paper, an alternative attempt is made herein to develop adaptive neuro-fuzzy inference system (ANFIS) models for predicting scour
Neuro-Fuzzy Hardware: Design, Development and Performance L.M. Reyneri
Reyneri, Leonardo
Neuro-Fuzzy Hardware: Design, Development and Performance L.M. Reyneri Dipartimento di Elettronica. This paper introduces hardware implementations of neuro-fuzzy systems, describes the technologies commonly implementation tech- niques. A. Neuro-Fuzzy Uni cation Until a few years ago, NNs, FSs and WNs were consid- ered
A Neuro-Fuzzy Approach to Hybrid Intelligent B. Lazzerini, L.M. Reyneri, M. Chiaberge
Reyneri, Leonardo
A Neuro-Fuzzy Approach to Hybrid Intelligent Control B. Lazzerini, L.M. Reyneri, M. Chiaberge Abstract This paper presents a neuro-fuzzy approach to the devel- opment of high-performance real with feed- back in hybrid neuro-fuzzy systems. Section V describes a few hardware implementations for hybrid
Neuro-fuzzy controller to navigate an unmanned vehicle.
Selma, Boumediene; Chouraqui, Samira
2013-12-01
A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. 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). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN). PMID:23705105
A Transductive Neuro-Fuzzy Force Control: An Ethernet-Based Application to a Drilling Process
Agustín Gajate; Rodolfo E. Haber; Pastora Vega
2009-01-01
This paper presents the application of a neural fuzzy inference method to the field of control systems using the internal\\u000a model control paradigm (IMC). Through a transductive reasoning system, a neuro-fuzzy inference system enables local models\\u000a to be created for each input\\/output set in the system at issue. These local models are created for modeling the direct and\\u000a inverse dynamics
De, Rajat Kumar
Unsupervised feature selection using a neuro-fuzzy approach Jayanta Basak, Rajat K. De, Sankar K December 1997; received in revised form 19 June 1998 Abstract A neuro-fuzzy methodology is described which of fuzzy set theory and ANN under the heading `neuro-fuzzy computing' for making the systems arti
A neuro-fuzzy method to learn fuzzy classification rules from data
Detlef Nauck; Rudolf Kruse
1997-01-01
Neuro-fuzzy systems have recently gained a lot of interest in research and application. Neuro-fuzzy models as we understand them are fuzzy systems that use local learning strategies to learn fuzzy sets and fuzzy rules. Neuro-fuzzy techniques have been developed to support the development of e.g. fuzzy controllers and fuzzy classifiers. In this paper we discuss a learning method for fuzzy
A Neuro-Fuzzy Linguistic Approach in Optimizing the Flow Rate of a Plastic Extruder Process
S. A. Oke; A. O. Johnson; O. E. Charles-Owaba; F. A. Oyawale; I. O. Popoola
The plastic extruder system is an important process in the solid waste recycling system. This paper optimizes the flow rate of this process with the application of a neuro-fuzzy model. The model identifies a specified desired output from a large number of input parameters. The methodology adopted is neuro-fuzzy. The concept of neuro-fuzzy is not new as a research methodology
Ali Azadeh; Najme Neshat; Afsaneh Kazemi; Mortezza Saberi
In this paper, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and partial least squares (PLS)\\u000a approaches are applied to predictive control of a drying process. In the proposed approaches, the PLS analysis is used to\\u000a pre-process actual data and to provide the necessary background to apply ANN and ANFIS approaches. A reasonable section of\\u000a this study is assigned
A Neuro-Fuzzy Method of Power Disturbances Recognition and Reduction Leon Reznik1
Reznik, Leon
A Neuro-Fuzzy Method of Power Disturbances Recognition and Reduction Leon Reznik1 , Michael neuro-fuzzy applications in power engineering: stabilizing power systems at a generation stage networks, and seeking measures for power service improvement [1]. This paper combines two major neuro
A Neuro-Fuzzy Approach as Medical Diagnostic R. Brause, F. Friedrich
Brause, R.
A Neuro-Fuzzy Approach as Medical Diagnostic Interface R. Brause, F. Friedrich J life. With the appearance of neuro-fuzzy systems which use vague, human-like categories the situation has changed. Based on the well-known mechanisms of learning for RBF networks, a special neuro
Error estimation of a neuro-fuzzy predictor for prognostic purpose
Paris-Sud XI, Université de
Error estimation of a neuro-fuzzy predictor for prognostic purpose Mohamed El-Koujok, Rafael of the evolving eXtended Tagaki-Sugeno system as a neuro- fuzzy predictor. A method to estimate the probability to online applications. Keywords: Prognostic; prediction of degradation; confidence interval; neuro
Neuro-fuzzy networks for voltage security monitoring based on synchronized phasor measurements
Chih-Wen Liu; Chen-Sung Chang; Mu-Chun Su
1998-01-01
The ability to rapidly acquire synchronized phasor measurements from around a power network opens up new possibilities for power system operation and control. A novel neuro-fuzzy network, the fuzzy hyperrectangular composite neural network, is proposed for voltage security monitoring (VSM) using synchronized phasor measurements as input patterns. This paper demonstrates how neuro-fuzzy networks can be constructed offline and then utilized
Applications of Fuzzy and Neuro-Fuzzy in Biomedical Health Sciences
R. Ranjan; A. Awasthi; N. Aggarawal; J. Gulati
2006-01-01
Fuzzy and neuro fuzzy logic concepts have been used for variety of applications, where machine intelligence can be used for the system. The fuzzy logic concept has been used for a number of biomedical applications also. We have used fuzzy and neuro fuzzy approach for tackling surgery procedures of a patient suffering from stress urinary incontinence (SUI). We have further
A phenomenological dynamic model of a magnetorheological damper using a neuro-fuzzy system
NASA Astrophysics Data System (ADS)
Zeinali, Mohammadjavad; Amri Mazlan, Saiful; Yasser Abd Fatah, Abdul; Zamzuri, Hairi
2013-12-01
A magnetorheological (MR) damper is a promising appliance for semi-active suspension systems, due to its capability of damping undesired movement using an adequate control strategy. This research has been carried out a phenomenological dynamic model of two MR dampers using an adaptive-network-based fuzzy inference system (ANFIS) approach. Two kinds of Lord Corporation MR damper (a long stroke damper and a short stroke damper) were used in experiments, and then modeled using the experimental results. In addition, an investigation of the influence of the membership function selection on predicting the behavior of the MR damper and obtaining a mathematical model was conducted to realize the relationship between input current, displacement, and velocity as the inputs and force as output. The results demonstrate that the proposed models for both short stroke and long stroke MR dampers have successfully predicted the behavior of the MR damper with adequate accuracy, and an equation is presented to precisely describe the behavior of each MR damper.
Liu, Cheng-Li
2009-05-01
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
Likhitruangsilp, Visit
2002-01-01
Dampers . . . . . . . . . , . . . . . . . . . . . . . . . . . . . . . . . . . . . 2. 3 Neural Network Approach in Modeling of MR Dampers. . . . . . . . . . 2. 4 Descriptions of Fuzzy Logic 2. 5 Development of Adaptive Neuro-Fuzzy Inference Systems.... (1999), the reliability and safety of a neural network used in an applicadon is still questionable. After taking into consideration the drawbacks of neural networks, a more proinising method of inodeling MR dampers is to use fuzzy logic, established...
Adaptive Neuro-fuzzy Control System by RBF and GRNN Neural Networks
Teo Lian Seng; Marzuki Khalid; Rubiyah Yusof; Sigeru Omatu
1998-01-01
Recently, adaptive control systems utilizing artificial intelligent techniques are being actively investigated in many applications. Neural networks with their powerful learning capability are being sought as the basis for many adaptive control systems where on-line adaptation can be implemented. Fuzzy logic on the other hand have been proven to be rather popular in many control system applications providing a rule-base
D. R. Kalbande; Nilesh Deotale; Priyank Singhal; Sumiran Shah; G. T. Thampi
2011-01-01
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
A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems
Wael A. Farag; Victor H. Quintana; Germano Lambert-torres
1998-01-01
Linguistic modeling of complex irregular systems constitutes the heart of many control and decision making systems, and fuzzy logic represents one of the most effective algorithms to build such linguistic models. In this paper, a linguistic (qualitative) modeling approach is proposed. The approach combines the merits of the fuzzy logic theory, neural networks, and genetic algorithms (GAs). The proposed model
Expert system based on neuro-fuzzy rules for diagnosis breast cancer
Ali Keles; Aytürk Keles; Ugur Yavuz
2011-01-01
Recent advances in the field of artificial intelligence have led to the emergence of expert systems for medical applications. Moreover, in the last few decades computational tools have been designed to improve the experiences and abilities of physicians for making decisions about their patients.Breast cancer is the commonest cancer in women and is the second leading cause of cancer death
Predictability in space launch vehicle anomaly detection using intelligent neuro-fuzzy systems
NASA Technical Reports Server (NTRS)
Gulati, Sandeep; Toomarian, Nikzad; Barhen, Jacob; Maccalla, Ayanna; Tawel, Raoul; Thakoor, Anil; Daud, Taher
1994-01-01
Included in this viewgraph presentation on intelligent neuroprocessors for launch vehicle health management systems (HMS) are the following: where the flight failures have been in launch vehicles; cumulative delay time; breakdown of operations hours; failure of Mars Probe; vehicle health management (VHM) cost optimizing curve; target HMS-STS auxiliary power unit location; APU monitoring and diagnosis; and integration of neural networks and fuzzy logic.
Tuning of a neuro-fuzzy controller by genetic algorithm
Teo Lian Seng; Marzuki Bin Khalid; Rubiyah Yusof
1999-01-01
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
Prediction of conductivity by adaptive neuro-fuzzy model.
Akbarzadeh, S; Arof, A K; Ramesh, S; Khanmirzaei, M H; Nor, R M
2014-01-01
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
Prediction of Conductivity by Adaptive Neuro-Fuzzy Model
Akbarzadeh, S.; Arof, A. K.; Ramesh, S.; Khanmirzaei, M. H.; Nor, R. M.
2014-01-01
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
Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models.
Mendoza, Leonardo Forero; Vellasco, Marley; Figueiredo, Karla
2014-12-01
This paper presents the research and development of two hybrid neuro-fuzzy models for the hierarchical coordination of multiple intelligent agents. The main objective of the models is to have multiple agents interact intelligently with each other in complex systems. We developed two new models of coordination for intelligent multiagent systems, which integrates the Reinforcement Learning Hierarchical Neuro-Fuzzy model with two proposed coordination mechanisms: the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with a market-driven coordination mechanism (MA-RL-HNFP-MD) and the MultiAgent Reinforcement Learning Hierarchical Neuro-Fuzzy with graph coordination (MA-RL-HNFP-CG). In order to evaluate the proposed models and verify the contribution of the proposed coordination mechanisms, two multiagent benchmark applications were developed: the pursuit game and the robot soccer simulation. The results obtained demonstrated that the proposed coordination mechanisms greatly improve the performance of the multiagent system when compared with other strategies. PMID:25406641
Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process
Agustin Gajate; Rodolfo Haber; Raul del Toro; Pastora Vega; Andres Bustillo
Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related\\u000a with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the\\u000a basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming\\u000a at exploiting the
Neuro-fuzzy control of vertical vibrations in railcars using magnetorheological dampers
Atray, Vipul Sunil
2002-01-01
. Fuzzy Logic in Vibration Control of Automobiles. . . . . Neuro-Fuzzy Systems. Other Neuro-Fuzzy Techniques. Summary and Conclusion. 5 9 15 17 19 25 25 3 DESIGN AND FABRICATION OF MR DAMPERS . 27 3. 1 3. 2 3. 3 3. 4 3. 5 3. 6 3. 7... schemes for railcars. Next the review treats one of the most prominent damping devices that has been recently developed ? the magnetorheological damper. Another subsection reviews recent efforts to control vibrations of vehicles using fuzzy logic...
GA Based Neuro Fuzzy Techniques for Breast Cancer Identification
Arpita Das; Mahua Bhattacharya
2008-01-01
An intelligent computer-aided diagnostics system may be developed to assist the radiologists to recognize the masses\\/lesions appearing in breast in different groups of benignancy\\/malignancy. In present work we have attempted to develop a computer assisted treatment planning system implementing Genetic algorithm-based Neuro-fuzzy approaches. The boundary based features of the tumor lesions appearing in breast have been extracted for classification. The
Sensor selection in neuro-fuzzy modelling for fault diagnosis
Yimin Zhou; Argyrios Zolotas
2010-01-01
In this paper, sensor selection relating to neuro-fuzzy modeling for the purpose of fault diagnosis is discussed. The input\\/output selection in fuzzy modelling plays an important role in the performance of the derived model. In addition, with respect to fault tolerant issues, the impact of the faults on the system, i.e. possible incipient and abrupt faults, should be detected in
Daily soil temperature modeling using neuro-fuzzy approach
NASA Astrophysics Data System (ADS)
Hosseinzadeh Talaee, P.
2014-11-01
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.
Brain-Inspired Evolving Neuro-Fuzzy System for Financial Forecasting and Trading of the S&P500 Index
Weng Luen Ho; Whye Loon Tung; Chai Quek
2010-01-01
\\u000a An interday financial trading system with a predictive model empowered by a novel brain-inspired evolving Mamdani-Takagi-Sugeno\\u000a Neural-Fuzzy Inference System (eMTSFIS) is proposed in this paper. The eMTSFIS predictive model possesses synaptic mechanisms\\u000a and information processing capabilities of the human hippocampus, resulting in a more robust and adaptive forecasting model\\u000a as compared to existing econometric and neural-fuzzy techniques. The trading strategy
A neuro-fuzzy based oil\\/gas producibility estimation method
Heidar A. Malki; Jeff Baldwin
2002-01-01
We present a hybrid neuro-fuzzy technique for predicting producibility of a well. First, multilayer neural networks are used to compute petrophysical parameters such as quality control curves and permeability. In particular, neural networks are used to predict the permeability from nuclear magnetic resonance (NMR) logs. Next, the permeability is used as one of the input to a fuzzy logic inference
Khademi, Mahmoud; Manzuri-Shalmani, Mohammad T; Kiaei, Ali A
2010-01-01
In this paper an accurate real-time sequence-based system for representation, recognition, interpretation, and analysis of the facial action units (AUs) and expressions is presented. Our system has the following characteristics: 1) employing adaptive-network-based fuzzy inference systems (ANFIS) and temporal information, we developed a classification scheme based on neuro-fuzzy modeling of the AU intensity, which is robust to intensity variations, 2) using both geometric and appearance-based features, and applying efficient dimension reduction techniques, our system is robust to illumination changes and it can represent the subtle changes as well as temporal information involved in formation of the facial expressions, and 3) by continuous values of intensity and employing top-down hierarchical rule-based classifiers, we can develop accurate human-interpretable AU-to-expression converters. Extensive experiments on Cohn-Kanade database show the superiority of the proposed method, in comparison with support vect...
Recognition of Handwritten Arabic words using a neuro-fuzzy network
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
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.
Adaptive Neuro-Fuzzy Inference System Based Autonomous Flight Control of Unmanned Air Vehicles
Sefer Kurnaz; Okyay Kaynak; Ekrem Konakoglu
2007-01-01
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
NASA Astrophysics Data System (ADS)
Pradhan, Biswajeet; Lee, Saro; Buchroithner, Manfred
Landslides are the most common natural hazards in Malaysia. Preparation of landslide suscep-tibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. In this study, a new attempt is tried to produce landslide susceptibility map of a part of Cameron Valley of Malaysia. This paper develops an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment for landslide susceptibility mapping. To ob-tain the neuro-fuzzy relations for producing the landslide susceptibility map, landslide locations were identified from interpretation of aerial photographs and high resolution satellite images, field surveys and historical inventory reports. Landslide conditioning factors such as slope, plan curvature, distance to drainage lines, soil texture, lithology, and distance to lineament were extracted from topographic, soil, and lineament maps. Landslide susceptible areas were analyzed by the ANFIS model and mapped using the conditioning factors. Furthermore, we applied various membership functions (MFs) and fuzzy relations to produce landslide suscep-tibility maps. The prediction performance of the susceptibility map is checked by considering actual landslides in the study area. Results show that, triangular, trapezoidal, and polynomial MFs were the best individual MFs for modelling landslide susceptibility maps (86
NEFCLASSmdash;a neuro-fuzzy approach for the classification of data
Detlauf Nauck; Rudolf Kruse
1995-01-01
In this paper we present NEFCLASS, a neuro--fuzzy systemfor the classification of data. This approach is based on ourgeneric model of a fuzzy perceptron which can be used toderive fuzzy neural networks or neural fuzzy systems for specificdomains. The presented model derives fuzzy rules fromdata to classify patterns into a number of (crisp) classes.NEFCLASS uses a supervised learning algorithm based
Modeling and control of carbon monoxide concentration using a neuro-fuzzy technique
Kazuo Tanaka; Manabu Sano; Hiroyuki Watanabe
1995-01-01
Modeling and control of carbon monoxide (CO) concentration using a neuro-fuzzy technique are discussed. A self-organizing fuzzy identification algorithm (SOFIA) for identifying complex systems such as CO concentration is proposed. The main purpose of SOFIA is to reduce the computational requirement for identifying a fuzzy model. In particular, the authors simplify a procedure for finding the optimal structure of fuzzy
A neuro-fuzzy controller for axial power distribution an nuclear reactors
Man Gyun Na; B. R. Upadhyaya
1998-01-01
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
Terrorism Event Classification Using Fuzzy Inference Systems
Inyaem, Uraiwan; Meesad, Phayung; Tran, Dat
2010-01-01
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...
NASA Astrophysics Data System (ADS)
El-Sebakhy, Emad A.
2009-09-01
Pressure-volume-temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson-Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.
Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.
Shamshirband, Shahaboddin; Petkovi?, Dalibor; Hashim, Roslan; Motamedi, Shervin
2014-01-01
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
Improving Premise Structure in Evolving Takagi-Sugeno Neuro-Fuzzy Classifiers
Boyer, Edmond
Improving Premise Structure in Evolving Takagi-Sugeno Neuro-Fuzzy Classifiers Abdullah Almaksour.Anquetil}@irisa.fr Abstract--We present in this paper a new method for the design of evolving neuro-fuzzy classifiers. The presented approach is based on a first-order Takagi- Sugeno neuro-fuzzy model. We propose a modification
Power Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris
Steele, Adam
Power Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris DePaul University in a uniform fashion. We will show how implementing a neuro- fuzzy solution will not only provide a more and Simulation, Fuzzy CLIPS, Neuro-Fuzzy Introduction Power networks are a classic example of a large-scale re
EL-SIM: a Development Environment for Neuro-Fuzzy Intelligent Controllers
Reyneri, Leonardo
EL-SIM: a Development Environment for Neuro-Fuzzy Intelligent Controllers M. Chiaberge , G. Di Bene are commonly integrated into the neuro-fuzzy ap- proach, which has proven well adapted to non- linear control- tectures by appropriately combining and opti- mizing them. However, although the neuro-fuzzy approach alone
Neuro-Fuzzy Knowledge Representation for Toxicity Prediction of Organic Compounds
Gini, Giuseppina
1 Neuro-Fuzzy Knowledge Representation for Toxicity Prediction of Organic Compounds Dan Neagu1 on neural and neuro-fuzzy structures are developed to represent knowledge about a large data set containing chemical descriptors of organic compounds, commonly used in industrial processes. The neuro-fuzzy models
Iterative Identification of Neuro-Fuzzy-Based Hammerstein Model with Global Convergence
Ge, Shuzhi Sam
Iterative Identification of Neuro-Fuzzy-Based Hammerstein Model with Global Convergence Li Jia, Min Engineering, National University of Singapore, Singapore 119260 In this paper, a neuro-fuzzy-based model of the neuro-fuzzy-based Hammerstein model, and an updating algorithm guaranteeing the global convergence
Intelligent Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris
Steele, Adam
Intelligent Network Control Using Neuro-Fuzzy Term-Rewriting Adam Steele Ashley Morris De in a uniform fashion. We will show how implementing a neuro- fuzzy solution will not only provide a more and Simulation, Fuzzy CLIPS, Neuro-Fuzzy Introduction Power networks are a classic example of a large-scale re
M Marseguerra; E Zio; P Avogadri
2004-01-01
In this paper we present a neuro-fuzzy technique which allows building a predictive model of an evolving signal. The fuzzy if-then rules are inferred from the available input-output data through a training procedure. During operation, in correspondence of each incoming input pattern the corresponding output is predicted and a measure of the strength of the model rules is computed: the
Fault Classification using Pseudomodal Energies and Neuro-fuzzy modelling
Marwala, Tshilidzi; Chakraverty, Snehashish
2007-01-01
This paper presents a fault classification method which makes use of a Takagi-Sugeno neuro-fuzzy model and Pseudomodal energies calculated from the vibration signals of cylindrical shells. The calculation of Pseudomodal Energies, for the purposes of condition monitoring, has previously been found to be an accurate method of extracting features from vibration signals. This calculation is therefore used to extract features from vibration signals obtained from a diverse population of cylindrical shells. Some of the cylinders in the population have faults in different substructures. The pseudomodal energies calculated from the vibration signals are then used as inputs to a neuro-fuzzy model. A leave-one-out cross-validation process is used to test the performance of the model. It is found that the neuro-fuzzy model is able to classify faults with an accuracy of 91.62%, which is higher than the previously used multilayer perceptron.
Unsupervised feature selection using a neuro-fuzzy approach
Jayanta Basak; Rajat K. De; Sankar K. Pal
1998-01-01
A neuro-fuzzy methodology is described which involves connectionist minimization of a fuzzy feature evaluation index with unsupervised training. The concept of a flexible membership function incorporating weighed distance is in- troduced in the evaluation index to make the modeling of clusters more appropriate. A set of optimal weighing coeÅ- cients in terms of networks parameters representing individual feature importance is
Recurrent neuro-fuzzy networks for nonlinear process modeling
Jie Zhang; A. Julian Morris
1999-01-01
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
Unsupervised feature evaluation: a neuro-fuzzy approach
Sankar K. Pal; Rajat K. De; Jayanta Basak
2000-01-01
Demonstrates a way of formulating neuro-fuzzy approaches for both feature selection and extraction under unsupervised learning. A fuzzy feature evaluation index for a set of features is defined in terms of degree of similarity between two patterns in both the original and transformed feature spaces. A concept of flexible membership function incorporating weighted distance is introduced for computing membership values
Neuro-fuzzy rule generation: survey in soft computing framework
Sushmita Mitra; Yoichi Hayashi
2000-01-01
The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a
Towards ant colony optimization of neuro-fuzzy interval rules
J. Paetz
2005-01-01
Neuro-fuzzy rules can be used in their fuzzy form and in an interval form that is a cut of the corresponding membership function. Such interval rules can be derived whenever a precise interval rule is useful in the application area. An example where interval rules can be applied is the area of virtual screening in chemistry. Current research focusses on
Optimal neuro-fuzzy control of parallel hybrid electric vehicles
M. Mohebbi; M. Charkhgard; M. Farrokhi
2005-01-01
In this paper an optimal method based on neuro-fuzzy for controlling parallel hybrid electric vehicles is presented. In parallel hybrid electric vehicles the required torque for driving and operating the onboard accessories is generated by a combination of internal combustion engine and an electric motor. The power sharing between the internal combustion engine and the electric motor is the key
Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia
NASA Astrophysics Data System (ADS)
Karimi, Sepideh; Kisi, Ozgur; Shiri, Jalal; Makarynskyy, Oleg
2013-03-01
Accurate predictions of sea level with different forecast horizons are important for coastal and ocean engineering applications, as well as in land drainage and reclamation studies. The methodology of tidal harmonic analysis, which is generally used for obtaining a mathematical description of the tides, is data demanding requiring processing of tidal observation collected over several years. In the present study, hourly sea levels for Darwin Harbor, Australia were predicted using two different, data driven techniques, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Multi linear regression (MLR) technique was used for selecting the optimal input combinations (lag times) of hourly sea level. The input combination comprises current sea level as well as five previous level values found to be optimal. For the ANFIS models, five different membership functions namely triangular, trapezoidal, generalized bell, Gaussian and two Gaussian membership function were tested and employed for predicting sea level for the next 1 h, 24 h, 48 h and 72 h. The used ANN models were trained using three different algorithms, namely, Levenberg-Marquardt, conjugate gradient and gradient descent. Predictions of optimal ANFIS and ANN models were compared with those of the optimal auto-regressive moving average (ARMA) models. The coefficient of determination, root mean square error and variance account statistics were used as comparison criteria. The obtained results indicated that triangular membership function was optimal for predictions with the ANFIS models while adaptive learning rate and Levenberg-Marquardt were most suitable for training the ANN models. Consequently, ANFIS and ANN models gave similar forecasts and performed better than the developed for the same purpose ARMA models for all the prediction intervals.
Stieler, Florian; Yan, Hui; Lohr, Frank; Wenz, Frederik; Yin, Fang-Fang
2009-01-01
Background Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT) is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI) guided system was developed and examined. Methods The AI system can automatically accomplish the optimization process based on prior knowledge operated by several fuzzy inference systems (FIS). Prior knowledge, which was collected from human planners during their routine trial-and-error process of inverse planning, has first to be "translated" to a set of "if-then rules" for driving the FISs. To minimize subjective error which could be costly during this knowledge acquisition process, it is necessary to find a quantitative method to automatically accomplish this task. A well-developed machine learning technique, based on an adaptive neuro fuzzy inference system (ANFIS), was introduced in this study. Based on this approach, prior knowledge of a fuzzy inference system can be quickly collected from observation data (clinically used constraints). The learning capability and the accuracy of such a system were analyzed by generating multiple FIS from data collected from an AI system with known settings and rules. Results Multiple analyses showed good agreements of FIS and ANFIS according to rules (error of the output values of ANFIS based on the training data from FIS of 7.77 ± 0.02%) and membership functions (3.9%), thus suggesting that the "behavior" of an FIS can be propagated to another, based on this process. The initial experimental results on a clinical case showed that ANFIS is an effective way to build FIS from practical data, and analysis of ANFIS and FIS with clinical cases showed good planning results provided by ANFIS. OAR volumes encompassed by characteristic percentages of isodoses were reduced by a mean of between 0 and 28%. Conclusion The study demonstrated a feasible way to automatically perform parameter optimization of inverse treatment planning under guidance of prior knowledge without human intervention other than providing a set of constraints that have proven clinically useful in a given setting. PMID:19781059
Kayacan, Erkan; Kayacan, Erdal; Ramon, Herman; Saeys, Wouter
2012-07-01
As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations. PMID:22773047
Adaptive Neuro-Fuzzy Extended Kalman Filtering for Robot Localization
Havangi, Ramazan; Teshnehlab, Mohammad
2010-01-01
Extended Kalman Filter (EKF) has been a popular approach to localization a mobile robot. However, the performance of the EKF and the quality of the estimation depends on the correct a priori knowledge of process and measurement noise covariance matrices (Qk and Rk, respectively). Imprecise knowledge of these statistics can cause significant degradation in performance. This paper proposed the development of an Adaptive Neuro- Fuzzy Extended Kalman Filtering (ANFEKF) for localization of robot. The Adaptive Neuro-Fuzzy attempts to estimate the elements of Qk and Rk matrices of the EKF algorithm, at each sampling instant when measurement update step is carried out. The ANFIS supervises the performance of the EKF with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences. The free parameters of ANFIS are trained using the steepest gradient descent (SD) to minimize the differences of the actual value of the covariance of the residual with its theoretical value as...
A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers
NASA Astrophysics Data System (ADS)
Dzung Nguyen, Sy; Choi, Seung-Bok
2012-08-01
This paper proposes a new algorithm, named establishing neuro-fuzzy system (ENFS), to identify dynamic characteristics of smart dampers such as magnetorheological (MR) and electrorheological (ER) dampers. In the ENFS, data clustering is performed based on the proposed algorithm named partitioning data space (PDS). Firstly, the PDS builds data clusters in joint input-output data space with appropriate constraints. The role of these constraints is to create reasonable data distribution in clusters. The ENFS then uses these clusters to perform the following tasks. Firstly, the fuzzy sets expressing characteristics of data clusters are established. The structure of the fuzzy sets is adjusted to be suitable for features of the data set. Secondly, an appropriate structure of neuro-fuzzy (NF) expressed by an optimal number of labeled data clusters and the fuzzy-set groups is determined. After the ENFS is introduced, its effectiveness is evaluated by a prediction-error-comparative work between the proposed method and some other methods in identifying numerical data sets such as ‘daily data of stock A’, or in identifying a function. The ENFS is then applied to identify damping force characteristics of the smart dampers. In order to evaluate the effectiveness of the ENFS in identifying the damping forces of the smart dampers, the prediction errors are presented by comparing with experimental results.
CardeÃ±osa, JesÃºs
Data Mining in Incomplete Numerical and Categorical Data Sets: a Neuro-Fuzzy Approach P. Rey incomplete numerical and categorical data sets using an extension of an existing neuro-fuzzy approach evolutionary algorithms and neural networks. Neuro-fuzzy computation is one of the most popular hybridizations
On-Line Heart Beat Recognition Using Hermite Polynomials And Neuro-Fuzzy
Osowski, Stanislaw
On-Line Heart Beat Recognition Using Hermite Polynomials And Neuro-Fuzzy Network Tran Hoai Linh, Stanis¨aw Osowski and Maciej Stodolski Abstract The paper presents the neuro-fuzzy approach-mail: sto@iem.pw.edu.pl). #12;ON-LINE HEART BEAT RECOGNITION USING HERMITE POLYNOMIALS AND NEURO
Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models
Granada, Universidad de
Forecasting airborne pollen concentration time series with neural and neuro-fuzzy models JoseÂ´ Luis, few works have used more sophisticated tools based in Artificial Intelligence, as are neural or neuro on each interval. Experimental results show an advantage of the neuro-fuzzy models against classical
WAVELET PACKET TRANSFORM AND NEURO-FUZZY APPROACH TO HANDWRITTEN CHARACTER RECOGNITION
Schwiebert, Loren
function. Since fuzzy sets and fuzzy logic remains as a means for representing, manipulating and utilizingWAVELET 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
Kazemipoor, Mahnaz; Hajifaraji, Majid; Radzi, Che Wan Jasimah Bt Wan Mohamed; Shamshirband, Shahaboddin; Petkovi?, Dalibor; Mat Kiah, Miss Laiha
2015-01-01
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
Evolutionary Local Search of Fuzzy Rules through a novel Neuro-Fuzzy encoding method.
Carrascal, A; Manrique, D; Ríos, J; Rossi, C
2003-01-01
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
Trends and Issues in Fuzzy Control and Neuro-Fuzzy Modeling
NASA Technical Reports Server (NTRS)
Chiu, Stephen
1996-01-01
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.
A neuro-fuzzy computing technique for modeling hydrological time series
P. C Nayak; K. P Sudheer; D. M Rangan; K. S Ramasastri
2004-01-01
Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proven to be efficient when applied individually to a variety of problems. Recently there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have evolved. This approach has been tested and evaluated in the field of signal processing
Neuro-Fuzzy Controller of a Sensorless PM Motor Drive For Washing Machines
machines have been replaced with belt-driven induction motors with tachometer feedback, digital controlNeuro-Fuzzy Controller of a Sensorless PM Motor Drive For Washing Machines Kasim M. Al towards the adoption of direct drive washing machines utilizing PM motors is increasing due
Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators
Jean Bosco Mbede; Xinhan Huang; Min Wang
2003-01-01
A new robust neuro-fuzzy controller for autonomous and intelligent robot manipulators in dynamic and partially known environments containing moving obstacles is presented. The navigation is based on a fuzzy technique for the idea of artificial potential fields (APFs) using analytic harmonic functions. Unlike the fuzzy technique, the development of APFs is computationally intensive. A computationally efficient processing scheme for fuzzy
Design of a neuro-fuzzy controller for speed control applied to AC servo motor
Sang Hoon Kim; Lark Kyo Kim
2001-01-01
In this study, a neuro-fuzzy controller which has the characteristic of fuzzy control and an artificial neural network is designed. A fuzzy rule to be applied is automatically selected by the allocated neurons. The neurons correspond to fuzzy rules that are created by an expert. To adapt the more precise modeling, error backpropagation learning of adjusting the link-weight of fuzzy
A neuro-fuzzy graphic object classifier with modified distance measure estimator
R. A. Aliev; B. G. Guirimov; R. R. Aliev
2003-01-01
The paper analyses issues leading to errors in graphic object classifiers. The distance measures suggested in literature and used as a basis in Traditional, fuzzy, and Neuro-Fuzzy classifiers are found to be not very suitable for classification of non-stylized or fuzzy objects in which the features of classes are much more difficult to recognize because of significant uncertainties in their
Melih Iphar; Mahmut Yavuz; Hakan Ak
2008-01-01
The aim of this study is to predict the peak particle velocity (PPV) values from both presently constructed simple regression\\u000a model and fuzzy-based model. For this purpose, vibrations induced by bench blasting operations were measured in an open-pit\\u000a mine operated by the most important magnesite producing company (MAS) in Turkey. After gathering the ordered pairs of distance\\u000a and PPV values,
Performance Evaluation of a ground source heat pump system based on ANN and ANFIS models
Sun, W.; Hu, P.; Lei, F.; Zhu, N.; Zhang,J.
2014-01-01
: The aim of this work is to calculate the heat pump coefficient of performance (COP) and the system COP of a ground source heat pump (GSHP) system based on an artificial neural network (ANN) model and (adaptive neuro-fuzzy inference system (ANFIS) model... and reliability for calculating performance indexes of GSHP system with fewer parameters. Keywords: Artificial neural network ANFIS Ground source heat pump COP 1. Introduction GSHP uses the low grade energy stored in the shallow level of the earth...
ANN and ANFIS models for performance evaluation of a vertical ground source heat pump system
Hikmet Esen; Mustafa Inalli
2010-01-01
The aim of this study is to demonstrate the comparison of an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) for the prediction performance of a vertical ground source heat pump (VGSHP) system. The VGSHP system using R-22 as refrigerant has a three single U-tube ground heat exchanger (GHE) made of polyethylene pipe with a 40mm outside
A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification
Debrup Chakraborty; Nikhil R. Pal
2004-01-01
Most methods of classification either ignore feature analysis or do it in a separate phase, offline prior to the main classification task. This paper proposes a neuro-fuzzy scheme for designing a classifier along with feature selection. It is a four-layered feed-forward network for realizing a fuzzy rule-based classifier. The network is trained by error backpropagation in three phases. In the
An effective neuro-fuzzy paradigm for machinery condition health monitoring
Gary G. Yen; Phayung Meesad
2001-01-01
An innovative neuro-fuzzy network appropriate for fault detection and classification in a machinery condition health monitoring environment is proposed. The network, called an incremental learning fuzzy neural (ILFN) network, uses localized neurons to represent the distributions of the input space and is trained using a one-pass, on-line, and incremental learning algorithm that is fast and can operate in real time.
Multiple response optimization using Taguchi methodology and neuro-fuzzy based model
Jiju Antony; Raj Bardhan Anand; Maneesh Kumar; M. K. Tiwari
2006-01-01
Purpose – To provide a good insight into solving a multi-response optimization problem using neuro-fuzzy model and Taguchi method of experimental design. Design\\/methodology\\/approach – Over the last few years in many manufacturing organizations, multiple response optimization problems were resolved using the past experience and engineering judgment, which leads to increase in uncertainty during the decision-making process. In this paper, a
PREOPERATIVE OVARIAN CANCER DIAGNOSIS USING NEURO-FUZZY APPROACH E.O. Madu, V. Stalbovskaya, B In this paper, we propose a neuro-fuzzy model for preoperative prediction of malignancy in ovarian tumours operating characteristic curve of 0.85. Keywords: ovarian cancer, medical diagnosis, neural networks, neuro
Mitchell, Richard
On Relation Between Neuro-Fuzzy and CMAC Controller - 1 IEEE SMC UK&RI Applied Cybernetics © Dr Richard Mitchell 2005 On the Relation between Neuro Fuzzy and CMAC Controller Dr N.H.Siddique, School Engineering, University of Reading This paper proposes a learning mechanism where the rule base of the neuro
NASA Astrophysics Data System (ADS)
Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.
2009-08-01
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.
Methodology for assessing system performance loss within a proactive maintenance framework
Cocheteux, Pierre; Levrat, Eric; Iung, Benoît
2009-01-01
Maintenance plays now a critical role in manufacturing for achieving important cost savings and competitive advantage while preserving product conditions. It suggests moving from conventional maintenance practices to predictive strategy. Indeed the maintenance action has to be done at the right time based on the system performance and component Remaining Useful Life (RUL) assessed by a prognostic process. In that way, this paper proposes a methodology in order to evaluate the performance loss of the system according to the degradation of component and the deviations of system input flows. This methodology is supported by the neuro-fuzzy tool ANFIS (Adaptive Neuro-Fuzzy Inference Systems) that allows to integrate knowledge from two different sources: expertise and real data. The feasibility and added value of such methodology is then highlighted through an application case extracted from the TELMA platform used for education and research.
Neuro-fuzzy expert system for breast cancer diagnosis
Manisha Arora; Dinesh Tagra
2012-01-01
Malignant Neoplasm commonly referred as cancer is caused by uncontrolled growth of cells in the body. According to the American Cancer Society nearly 7.6 million people died from cancer during 2007. The true inspiration for this paper comes from the paper \\
Supervised and Reinforcement Evolutionary Learning for Wavelet-based Neuro-fuzzy Networks
Cheng-jian Lin; Yong-cheng Liu; Chi-yung Lee
2008-01-01
This study presents a wavelet-based neuro-fuzzy network (WNFN). The proposed WNFN model combines the traditional Takagi–Sugeno–Kang\\u000a (TSK) fuzzy model and the wavelet neural networks (WNN). This study adopts the non-orthogonal and compactly supported functions\\u000a as wavelet neural network bases. A novel supervised evolutionary learning, called WNFN-S, is proposed to tune the adjustable\\u000a parameters of the WNFN model. The proposed WNFN-S
Streamflow Forecasting Using Nuero-Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Nanduri, U. V.; Swain, P. C.
2005-12-01
The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A Neuro-Fuzzy model is developed to forecast ten-daily flows into the Hirakud reservoir on River Mahanadi in the state of Orissa in India. Correlation analysis is carried out to find out the most influential variables on the ten daily flow at Hirakud. Based on this analysis, four variables, namely, flow during the previous time period, ql1, rainfall during the previous two time periods, rl1 and rl2, and flow during the same period in previous year, qpy, are identified as the most influential variables to forecast the ten daily flow. Performance measures such as Root Mean Square Error (RMSE), Correlation Coefficient (CORR) and coefficient of efficiency R2 are computed for training and testing phases of the model to evaluate its performance. The results indicate that the ten-daily forecasting model is efficient in predicting the high and medium flows with reasonable accuracy. The forecast of low flows is associated with less efficiency. REFERENCES Jang, J.S.R. (1993). "ANFIS: Adaptive - network- based fuzzy inference system." IEEE Trans. on Systems, Man and Cybernetics, 23 (3), 665-685. Shamseldin, A.Y. (1997). "Application of a neural network technique to rainfall-runoff modeling." Journal of Hydrology, 199, 272-294. World Meteorological Organization (1975). Intercomparison of conceptual models used in operational hydrological forecasting. World Meteorological Organization, Technical Report No.429, Geneva, Switzerland.
Verifying Stability of Dynamic Soft-Computing Systems
NASA Technical Reports Server (NTRS)
Wen, Wu; Napolitano, Marcello; Callahan, John
1997-01-01
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.
Assessment of arsenic concentration in stream water using neuro fuzzy networks with factor analysis.
Chang, Fi-John; Chung, Chang-Han; Chen, Pin-An; Liu, Chen-Wuing; Coynel, Alexandra; Vachaud, Georges
2014-10-01
We propose a systematical approach to assessing arsenic concentration in a river through: important factor extraction by a nonlinear factor analysis; arsenic concentration estimation by the neuro-fuzzy network; and impact assessment of important factors on arsenic concentration by the membership degrees of the constructed neuro-fuzzy network. The arsenic-contaminated Huang Gang Creek in northern Taiwan is used as a study case. Results indicate that rainfall, nitrite nitrogen and temperature are important factors and the proposed estimation model (ANFIS(GT)) is superior to the two comparative models, in which 50% and 52% improvements in RMSE are made over ANFIS(CC) and ANFIS(all), respectively. Results reveal that arsenic concentration reaches the highest in an environment of lower temperature, higher nitrite nitrogen concentration and larger one-month antecedent rainfall; while it reaches the lowest in an environment of higher temperature, lower nitrite nitrogen concentration and smaller one-month antecedent rainfall. It is noted that these three selected factors are easy-to-collect. We demonstrate that the proposed methodology is a useful and effective methodology, which can be adapted to other similar settings to reliably model water quality based on parameters of interest and/or study areas of interest for universal usage. The proposed methodology gives a quick and reliable way to estimate arsenic concentration, which makes good contribution to water environment management. PMID:25046611
De, Rajat Kumar
Evaluation: A Neuro-Fuzzy Approach Sankar K. Pal, Fellow, IEEE, Rajat K. De, Member, IEEE, and Jayanta Basak, Senior Member, IEEE Abstract--The present article demonstrates a way of formu- lating neuro, attempts are being made to integrate the merits of fuzzy set theory and ANN under the heading "neuro
Teresa Orlowska-Kowalska; Mateusz Dybkowski; Krzysztof Szabat
2010-01-01
In this paper, the concept of a model reference adaptive control of a sensorless induction motor (IM) drive with elastic joint is proposed. An adaptive speed controller uses fuzzy neural network equipped with an additional option for online tuning of its chosen parameters. A sliding-mode neuro-fuzzy controller is used as the speed controller, whose connective weights are trained online according
Anjan Sarkar; Arka Majumdar; Shaunak Chatterjee; Debapriya Chatterjee; Shibendu S. Ray; B. Kartikeyan
2008-01-01
This work proposes a neuro?fuzzy method for suggesting alternative crop production over a region using integrated data obtained from land?survey maps as well as satellite imagery. The methodology proposed here uses an artificial neural network (multilayer perceptron, MLP) to predict alternative crop production. For each pixel, the MLP takes vector input comprising elevation, rainfall and goodness values of different existing
A neuro-fuzzy classifier and its applications
Chuen-Tsai Sun; Jyh-Shing Jang
1993-01-01
The authors propose a general fuzzy classification scheme with learning ability using an adaptive network. System parameters, such as the membership functions defined for each feature and the parameterized t-norms used to combine conjunctive conditions, are calibrated with backpropagation. To explain this approach, the concept of adaptive networks is introduced and a supervised learning procedure based on a gradient descent
Neuro-fuzzy knowledge processing in intelligent learning environments
Magoulas, George D.
used to drive pedagogical decisions depending on the student learning style. The neuro aspects of student's learning style in a discovery-learning envi- ronment that aims to help students systems; Discovery learning environments; Learning styles 1. Introduction User and student modeling
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...
Adaptive neuro-fuzzy fusion of sensor data
NASA Astrophysics Data System (ADS)
Petkovi?, Dalibor
2014-11-01
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.
Speed Control of Multi Level Inverter Designed DC Series Motor with Neuro-Fuzzy Controllers
MadhusudhanaRao, G
2009-01-01
This paper describes the speed control of a DC series motor for an accurate and high-speed performance. A neural network based controlling operation with fuzzy modeling is suggested in this paper. The driver units of these machines are designed with a Multi-level inverter operation and are controlled by a common current control mechanism for an accurate and efficient driving technique for DC series motor. The neuro-fuzzy logic control technique is introduced to eliminate uncertainties in the plant parameters of the DC Series motors, and also considered as potential candidate for different applications to prove adequacy of the proposed control algorithm through simulations. The simulation result with such an approach is made and observed efficient over other controlling technique.
A Simplified Self-Tuned Neuro-Fuzzy Controller Based Speed Control of an Induction Motor Drive
M. Nasir Uddin; Z. Rui Huang; Muminul I. Chy
2007-01-01
In this paper a novel and simplified self-tuned neuro-fuzzy controller (NFC) is developed for speed control of an induction motor (IM) drive. The proposed NFC combines fuzzy logic and a four-layer artificial neural network (ANN) scheme. Based on the knowledge of motor control and intelligent algorithms an unsupervised self-tuning method is developed to adjust membership functions and weights of the
Dipankar Ray; D. Dutta Majumder
2009-01-01
A neuro-fuzzy clustering framework has been presented for a meaningful segmentation of Magnetic Resonance medical images.\\u000a MR imaging provides detail soft tissue descriptions of the target body object and it has immense importance in today’s non-invasive\\u000a therapeutic planning and diagnosis methods. The unlabeled image data has been classified using fuzzy c-means approach and\\u000a then the data has been used for
Fuzzy Inference Systems Optimization
Patel, Pretesh
2011-01-01
This paper compares various optimization methods for fuzzy inference system optimization. The optimization methods compared are genetic algorithm, particle swarm optimization and simulated annealing. When these techniques were implemented it was observed that the performance of each technique within the fuzzy inference system classification was context dependent.
Sam Darvishi; Ahmed Al-Ani
2007-01-01
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
Clustering of noisy image data using an adaptive neuro-fuzzy system
NASA Technical Reports Server (NTRS)
Pemmaraju, Surya; Mitra, Sunanda
1992-01-01
Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noisy real data.
A neuro-fuzzy system for tool condition monitoring in metal cutting
Mesina, Omez Samoon
1993-01-01
found it harder to complete my research. TABLE OF CONTENTS CHAPTER I INTRODUCTION I. 1 Literature Review Page I. 2 Organization of the Thesis . . . . . II PRELIMINARIES. II. 1 Fuzzy Logic H. l. l Fuzzy Set Theory. II. 1. 2 Knowledge Rule Base... is a feedforward mulfi-layered network which integrates the basic elements and funcuons of a uaditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. Gupta and Gorzalczany [16] have presented a model...
Type Inference for COBOL Systems
Arie Van Deursen; Leon Moonen
1998-01-01
Types are a good starting point for various software reengi- neering tasks. Unfortunately, programs requiring reengi- neering most desperately are written in languages without an adequate type system (such as COBOL). To solve this problem, we propose a method of automated type inference for these languages. The main ingredients are that if vari- ables are compared using some relational operator
Inference Concerning Physical Systems
NASA Astrophysics Data System (ADS)
Wolpert, David H.
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.
System Support for Forensic Inference
NASA Astrophysics Data System (ADS)
Gehani, Ashish; Kirchner, Florent; Shankar, Natarajan
Digital evidence is playing an increasingly important role in prosecuting crimes. The reasons are manifold: financially lucrative targets are now connected online, systems are so complex that vulnerabilities abound and strong digital identities are being adopted, making audit trails more useful. If the discoveries of forensic analysts are to hold up to scrutiny in court, they must meet the standard for scientific evidence. Software systems are currently developed without consideration of this fact. This paper argues for the development of a formal framework for constructing “digital artifacts” that can serve as proxies for physical evidence; a system so imbued would facilitate sound digital forensic inference. A case study involving a filesystem augmentation that provides transparent support for forensic inference is described.
Neuro-fuzzy model of superelastic shape memory alloys with application to seismic engineering
Ozbulut, Osman Eser
2009-05-15
Shape memory alloys (SMAs) have recently attracted much attention as a smart material that can be used in passive protection systems such as energy dissipating devices and base isolation systems. For the purpose of ...
F. Gravot; J. D. Muller; S. Muller
1999-01-01
We show that parametrized gradient descent is very efficient to train fuzzy expert systems with examples. We first present how fuzzy expert systems work and explain their relevance compared to neural classifiers. Then, we describe the proposed learning algorithm. We further explain in more detail its application in each parameter of the fuzzy expert system: the position and the width
Exploiting expert systems in cardiology: a comparative study.
Economou, George-Peter K; Sourla, Efrosini; Stamatopoulou, Konstantina-Maria; Syrimpeis, Vasileios; Sioutas, Spyros; Tsakalidis, Athanasios; Tzimas, Giannis
2015-01-01
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
Implementation issues of neuro-fuzzy hardware: going toward HW\\/SW codesign
Leonardo Maria Reyneri
2003-01-01
This paper presents an annotated overview of existing hardware implementations of artificial neural and fuzzy systems and points out limitations, advantages, and drawbacks of analog, digital, pulse stream (spiking), and other implementation techniques. We analyze hardware performance parameters and tradeoffs, and the bottlenecks which are intrinsic in several implementation methodologies. The constraints posed by hardware technologies onto algorithms and performance
Iterative image fusion technique using fuzzy and neuro fuzzy logic and applications
Rahul Ranjan; H. Singh; T. Meitzler; G. R. Gerhart
2005-01-01
Image fusion has attracted a widespread attention owing to applications in medical imaging, automotive and remote sensing. Image fusion deals with integrating data obtained from different sources of information for intelligent systems. Image fusion provides output as a single image from a set of input images obtained from different sources or techniques. Different approaches in image fusion provide different type
Jose Antonio Piedra; Manuel Canton; Francisco Guindos
Our goal in this work has been to study methods for the automatic interpretation of ocean satellite images by means of the recognition of meso and macroscalar ocean structures. The difficulty of the image analysis and understanding problem for ocean satellite data is due, in large part, to the lack of a precise mathematical description of the observed structures and
Inference System Integration Via Logic Morphisms
NASA Technical Reports Server (NTRS)
Bjorner, Nikolaj S.; Espinosa, David
2000-01-01
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.
TAS | A Generic Window Inference System
Christoph Luth
This paper presents work on technology for transformational proof and program development, as used by window inference calculi and transformation systems. The calculi are characterised by a certain class of theorems in the underlying logic. Our transformation system TAS compiles these rules to concrete deduction support, complete with a graphical user interface with command-language-free user interaction by gestures like drag&drop
Era Purwanto; Sato Yukihiko; P Mauridhi Herry; Gigih Prabowo
2006-01-01
This paper proposed the simple method to develop observer for detects the speed of induction motor. Direct field-oriented induction motor drive system need rotor flux observer and rotor angular speed identifier. ANFIS is used for identifying parameter dynamics and system variable estimation, linear either non-linear. ANFIS with back propagation learning algorithm has applied to estimate flux rotor and identify rotor
Shahnaz Shahbazova; Manfred Grauer; Musa Suleymanov
2011-01-01
This paper considers the problem of recognizing the visual and sound information by constructing a virtual environment, which allows to qualitatively simplify the system and to carry out of experiments, and to create an algorithmic model of pattern recognition comparable to human capabilities. Our research is aimed at obtaining an algorithmic model that can extract from the surrounding world \\
B. Dixon
2005-01-01
Modeling groundwater vulnerability reliably and cost effectively for non-point source (NPS) pollution at a regional scale remains a major challenge. In recent years, Geographic Information Systems (GIS), neural networks and fuzzy logic techniques have been used in several hydrological studies. However, few of these research studies have undertaken an extensive sensitivity analysis. The overall objective of this research is to
NASA Astrophysics Data System (ADS)
Ali, Ali H.; Tarter, Alex
2009-05-01
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.
An Ada inference engine for expert systems
NASA Technical Reports Server (NTRS)
Lavallee, David B.
1986-01-01
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.
FPGA Implementation of Fuzzy Inference System for Embedded Applications
FPGA Implementation of Fuzzy Inference System for Embedded Applications Dr. Kasim M. Al the software program stack to embedded processors on the FPGA to improve performance and reduce the cost of the whole system. A fuzzy inference system has been implemented on an FPGA, and used to control a PM motor
Type Inference for COBOL Systems Arie van Deursen
van Deursen, Arie
Type Inference for COBOL Systems Arie van Deursen CWI, P.O. Box 94079 1090 GB Amsterdam in languages without an adequate type system (such as COBOL). To solve this problem, we propose a method system and inference rules for this approach, show their effect on vari- ous real life COBOL fragments
NASA Astrophysics Data System (ADS)
Nadhir, Ahmad; Naba, Agus; Hiyama, Takashi
An optimal control for maximizing extraction of power in variable-speed wind energy conversion system is presented. Intelligent gradient detection by fuzzy inference system (FIS) in maximum power point tracking control is proposed to achieve power curve operating near optimal point. Speed rotor reference can be adjusted by maximum power point tracking fuzzy controller (MPPTFC) such that the turbine operates around maximum power. Power curve model can be modelled by using adaptive neuro fuzzy inference system (ANFIS). It is required to simply well estimate just a few number of maximum power points corresponding to optimum generator rotor speed under varying wind speed, implying its training can be done with less effort. Using the trained fuzzy model, some estimated maximum power points as well as their corresponding generator rotor speed and wind speed are determined, from which a linear wind speed feedback controller (LWSFC) capable of producing optimum generator speed can be obtained. Applied to a squirrel-cage induction generator based wind energy conversion system, MPPTFC and LWSFC could maximize extraction of the wind energy, verified by a power coefficient stay at its maximum almost all the time and an actual power line close to a maximum power efficiency line reference.
Subjective bayesian methods for rule-based inference systems
Richard O. Duda; Peter E. Hart; Nils J. Nilsson
1976-01-01
The general problem of drawing inferences from uncertain or incomplete evidence has invited a variety of technical approaches, some mathematically rigorous and some largely informal and intuitive. Most current inference systems in artificial intelligence have emphasized intuitive methods, because the absence of adequate statistical samples forces a reliance on the subjective judgment of human experts. We describe in this paper
Evaluating functional network inference using simulations of complex biological systems
V. Anne Smith; Erich D. Jarvis; Alexander J. Hartemink
2002-01-01
Motivation: Although many network inference algorithms have been presented in the bioinformatics literature, no suitable approach has been formulated for evaluating their effectiveness at recovering models of complex biological systems from limited data. To overcome this limitation, we propose an approach to evaluate network inference algorithms according to their ability to recover a complex functional network from biologically reasonable simulated
Causal Inferences in the Campbellian Validity System
ERIC Educational Resources Information Center
Lund, Thorleif
2010-01-01
The purpose of the present paper is to critically examine causal inferences and internal validity as defined by Campbell and co-workers. Several arguments are given against their counterfactual effect definition, and this effect definition should be considered inadequate for causal research in general. Moreover, their defined independence between…
Understanding COBOL Systems using Inferred Types
Arie Van Deursen; Leon Moonen
1999-01-01
In a typical COBOLprogram, the data division consists of 50% of the lines of code. Automatic type inference can help to un- derstand the large collections of variable declarations co n- tained therein, showing how variables are related based on their actual usage. The most problematic aspect of type infe r- ence is pollution, the phenomenon that types become too
ANFIS: adaptive-network-based fuzzy inference system
Jyh-Shing Roger Jang
1993-01-01
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,
LOWER LEVEL INFERENCE CONTROL IN STATISTICAL DATABASE SYSTEMS
Lipton, D.L.; Wong, H.K.T.
1984-02-01
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.
Conditioning and inference in intelligent systems
Giulianella Coletti; Romano Scozzafava
1999-01-01
We maintain that among the research trends concerning the various aspects and methodologies for the management of partial\\u000a and revisable information in automated reasoning and giving particular emphasis to conditioning and inference, conditional\\u000a events and conditional probability (in a coherent– in the sense of de Finetti – framework) play a central role: we will review some of our and related
Adaptive IMC using fuzzy neural networks for the control on non linear systems
E. Gómez Sánchez; J. M. Cano Izquierdo; M. J. Araúzo Bravo; Y. A. Dimitriadis; J. López Coronado
This paper introduces the use of FasBack neuro-fuzzy system for the identification and control of non linear MIMO plants within IMC scheme. FasBack presents fast stable learning guided by matching and error minimisation, and presents good MIMO identification performance. Emphasis is made on the on-line adaptive capability of FasBack that can be used to develop adaptive IMC strategies, which are
INFeRS: Interactive Numeric Files Retrieval System. Final Report.
ERIC Educational Resources Information Center
Chiang, Katherine; And Others
In 1988 Mann Library at Cornell University proposed to develop a computer system that would support interactive access to significant electronic files in agriculture and the life sciences. This system was titled the Interactive Numeric Files Retrieval System (INFeRS). This report describes how project goals were met and it presents the project's…
A computational model for inference chains in expert systems
Jozsef Sziray
2009-01-01
This paper deals with the calculations performed in the reasoning process of rule-based expert systems, where inference chains are applied. It presents a logic model for representing the rules and the rule base of a given system. Also, the fact base of the same expert system is involved in the logic model. The proposed equivalent representation manifests itself in a
Comparison of inference results of two otoneurological expert systems
Yrjö Auramo; Martti Juhola
1995-01-01
In this paper, two different otoneurological expert systems, Vertigo and One, the latter developed by us, are considered. The expert systems are evaluated as regards their correctness in reasoning diagnoses. In the light of our data collected from randomly selected test patients, One, being a newer technique, is more effective, since it could infer more cases than vertigo did. All
Connectionist Inference Systems Hans Werner Gusgen
Hoelldobler, Steffen
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
Huang, Shuangbing; Liu, Changrong; Wang, Yanxin; Zhan, Hongbin
2014-01-01
The effects of various geochemical processes on arsenic enrichment in a high-arsenic aquifer at Jianghan Plain in Central China were investigated using multivariate models developed from combined adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR). The results indicated that the optimum variable group for the AFNIS model consisted of bicarbonate, ammonium, phosphorus, iron, manganese, fluorescence index, pH, and siderite saturation. These data suggest that reductive dissolution of iron/manganese oxides, phosphate-competitive adsorption, pH-dependent desorption, and siderite precipitation could integrally affect arsenic concentration. Analysis of the MLR models indicated that reductive dissolution of iron(III) was primarily responsible for arsenic mobilization in groundwaters with low arsenic concentration. By contrast, for groundwaters with high arsenic concentration (i.e., > 170 ?g/L), reductive dissolution of iron oxides approached a dynamic equilibrium. The desorption effects from phosphate-competitive adsorption and the increase in pH exhibited arsenic enrichment superior to that caused by iron(III) reductive dissolution as the groundwater chemistry evolved. The inhibition effect of siderite precipitation on arsenic mobilization was expected to exist in groundwater that was highly saturated with siderite. The results suggest an evolutionary dominance of specific geochemical process over other factors controlling arsenic concentration, which presented a heterogeneous distribution in aquifers. Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of Environmental Science and Health, Part A, to view the supplemental file. PMID:24345245
Nonparametric predictive inference for voting systems
F. P. A. Coolen; P. Coolen-Schrijner
We present upper and lower probabilities for reliability of voting systems, also known as k-out- of-m systems, which include series- and parallel-systems. We restrict attention to systems with identical components. These interval probabilities are based on the nonparametric predictive inferential (NPI) approach for Bernoulli data presented by Coolen (1998). In this approach, it is assumed that test data are available
A Graph-Aided Inference Browser for Developing Knowledge-Based Systems
Yoo, SukIn
A Graph-Aided Inference Browser for Developing Knowledge-Based Systems Suk I. Yoo and Chang H. Park consists of three modules, the inference knowledge module (IKM), the inference graph module at each inferring step so as to develop the knowledge- based systems easily. The efficient graph drawing
Understanding COBOL Systems using Inferred Types Arie van Deursen
van Deursen, Arie
Understanding COBOL Systems using Inferred Types Arie van Deursen CWI, P.O. Box 94079 1090 GB SJ Amsterdam, The Netherlands http://adam.wins.uva.nl/~leon/ ABSTRACT In a typical COBOL program will be concerned with the variables occur- ring in a COBOL program. The two main parts of a COBOL program
Gago, Jorge; Martínez-Núñez, Lourdes; Landín, Mariana; Flexas, Jaume; Gallego, Pedro P.
2014-01-01
Background Plant acclimation is a highly complex process, which cannot be fully understood by analysis at any one specific level (i.e. subcellular, cellular or whole plant scale). Various soft-computing techniques, such as neural networks or fuzzy logic, were designed to analyze complex multivariate data sets and might be used to model large such multiscale data sets in plant biology. Methodology and Principal Findings In this study we assessed the effectiveness of applying neuro-fuzzy logic to modeling the effects of light intensities and sucrose content/concentration in the in vitro culture of kiwifruit on plant acclimation, by modeling multivariate data from 14 parameters at different biological scales of organization. The model provides insights through application of 14 sets of straightforward rules and indicates that plants with lower stomatal aperture areas and higher photoinhibition and photoprotective status score best for acclimation. The model suggests the best condition for obtaining higher quality acclimatized plantlets is the combination of 2.3% sucrose and photonflux of 122–130 µmol m?2 s?1. Conclusions Our results demonstrate that artificial intelligence models are not only successful in identifying complex non-linear interactions among variables, by integrating large-scale data sets from different levels of biological organization in a holistic plant systems-biology approach, but can also be used successfully for inferring new results without further experimental work. PMID:24465829
Decremental Learning of Evolving Fuzzy Inference Systems
Boyer, Edmond
. In particular, it is shown that decre- mental learning allow to adapt to concept drifts. It is also demonstrated; Recursive Least Squares; Concept Drifts; Forgetting 1 Introduction Evolving classication systems have-called concept drifts). The target application of this work is the use of online handwritten gesture classiers
Diagnosis of arthritis through fuzzy inference system.
Singh, Sachidanand; Kumar, Atul; Panneerselvam, K; Vennila, J Jannet
2012-06-01
Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusion. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Arthritis is a chronic disease and about three fourth of the patients are suffering from osteoarthritis and rheumatoid arthritis which are undiagnosed and the delay of detection may cause the severity of the disease at higher risk. Thus, earlier detection of arthritis and treatment of its type of arthritis and related locomotry abnormalities is of vital importance. Thus the work was aimed to design a system for the diagnosis of Arthitis using fuzzy logic controller (FLC) which is, a successful application of Zadeh's fuzzy set theory. It is a potential tool for dealing with uncertainty and imprecision. Thus, the knowledge of a doctor can be modelled using an FLC. The performance of an FLC depends on its knowledge base which consists of a data base and a rule base. It is observed that the performance of an FLC mainly depends on its rule base, and optimizing the membership function distributions stored in the data base is a fine tuning process. PMID:20927572
AN INVESTIGATION OF THE EFFECT OF INPUT REPRESENTATION IN ANFIS MODELLING
Aickelin, Uwe
AN INVESTIGATION OF THE EFFECT OF INPUT REPRESENTATION IN ANFIS MODELLING OF BREAST CANCER SURVIVAL.K. {hzh, jmg}@cs.nott.ac.uk Keywords: Adaptive neuro-fuzzy inference system, Survival analysis, Breast cancer, Nottingham prognostic index. Abstract: Fuzzy inference systems have been applied in recent years
A knowledge-based expert system for inferring vegetation characteristics
NASA Technical Reports Server (NTRS)
Kimes, Daniel S.; Harrison, Patrick R.; Ratcliffe, P. A.
1991-01-01
A prototype knowledge-based expert system VEG is presented that focuses on extracting spectral hemispherical reflectance using any combination of nadir and/or directional reflectance data as input. The system is designed to facilitate expansion to handle other inferences regarding vegetation properties such as total hemispherical reflectance, leaf area index, percent ground cover, phosynthetic capacity, and biomass. This approach is more robust and accurate than conventional extraction techniques previously developed.
Evaluation of fuzzy inference systems using fuzzy least squares
NASA Technical Reports Server (NTRS)
Barone, Joseph M.
1992-01-01
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.
Inference and learning in sparse systems with multiple states
Braunstein, A. [Human Genetics Foundation, Via Nizza 52, I-10126 Torino (Italy); Politecnico di Torino, C.so Duca degli Abruzzi 24, I-10129 Torino (Italy); Ramezanpour, A.; Zhang, P. [Politecnico di Torino, C.so Duca degli Abruzzi 24, I-10129 Torino (Italy); Zecchina, R. [Politecnico di Torino, C.so Duca degli Abruzzi 24, I-10129 Torino (Italy); Human Genetics Foundation, Via Nizza 52, I-10126 Torino (Italy); Collegio Carlo Alberto, Via Real Collegio 30, I-10024 Moncalieri (Italy)
2011-05-15
We discuss how inference can be performed when data are sampled from the nonergodic phase of systems with multiple attractors. We take as a model system the finite connectivity Hopfield model in the memory phase and suggest a cavity method approach to reconstruct the couplings when the data are separately sampled from few attractor states. We also show how the inference results can be converted into a learning protocol for neural networks in which patterns are presented through weak external fields. The protocol is simple and fully local, and is able to store patterns with a finite overlap with the input patterns without ever reaching a spin-glass phase where all memories are lost.
Topological augmentation to infer hidden processes in biological systems
Sunnåker, Mikael; Zamora-Sillero, Elias; López García de Lomana, Adrián; Rudroff, Florian; Sauer, Uwe; Stelling, Joerg; Wagner, Andreas
2014-01-01
Motivation: A common problem in understanding a biochemical system is to infer its correct structure or topology. This topology consists of all relevant state variables—usually molecules and their interactions. Here we present a method called topological augmentation to infer this structure in a statistically rigorous and systematic way from prior knowledge and experimental data. Results: Topological augmentation starts from a simple model that is unable to explain the experimental data and augments its topology by adding new terms that capture the experimental behavior. This process is guided by representing the uncertainty in the model topology through stochastic differential equations whose trajectories contain information about missing model parts. We first apply this semiautomatic procedure to a pharmacokinetic model. This example illustrates that a global sampling of the parameter space is critical for inferring a correct model structure. We also use our method to improve our understanding of glutamine transport in yeast. This analysis shows that transport dynamics is determined by glutamine permeases with two different kinds of kinetics. Topological augmentation can not only be applied to biochemical systems, but also to any system that can be described by ordinary differential equations. Availability and implementation: Matlab code and examples are available at: http://www.csb.ethz.ch/tools/index. Contact: mikael.sunnaker@bsse.ethz.ch; andreas.wagner@ieu.uzh.ch Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24297519
Nonparametric predictive inference for voting systems F.P.A. Coolen
Coolen, Frank
Nonparametric predictive inference for voting systems F.P.A. Coolen Department of Mathematicala) have developed a novel statistical theory entitled Nonparametric Predictive Inference (NPI. These interval probabilities are based on the nonparametric predictive inferential (NPI) approach for Bernoulli
The Dynamic Behavioral Model of RF Power Amplifiers With the Modified ANFIS
Jianfeng Zhai; Jianyi Zhou; Lei Zhang; Jianing Zhao; Wei Hong
2009-01-01
The adaptive neuro-fuzzy inference system (ANFIS) has been widely used for modeling different kinds of nonlinear systems including RF power amplifiers (PAs). The modified ANFIS (MANFIS) architecture is simpler than that of ANFIS, but with nearly the same performance for modeling nonlinear systems. In this paper, the MANFIS is applied to model RF PAs with memory effects. The simulation and
Fuzzy surface roughness modeling of CNC down milling of Alumic-79
F. Dweiri; M. Al-Jarrah; H. Al-Wedyan
2003-01-01
Machining processes are complex and highly dynamic systems that can have many variables affecting the desired results. Fuzzy modeling proved to be effective in modeling such complex systems. Down milling machining process of Alumic-79 was modeled in this paper using the adaptive neuro fuzzy inference system (ANFIS) to predict the effect of machining variables (spindle speed, feed rate, depth of
ANUBIS: artificial neuromodulation using a Bayesian inference system.
Smith, Benjamin J H; Saaj, Chakravarthini M; Allouis, Elie
2013-01-01
Gain tuning is a crucial part of controller design and depends not only on an accurate understanding of the system in question, but also on the designer's ability to predict what disturbances and other perturbations the system will encounter throughout its operation. This letter presents ANUBIS (artificial neuromodulation using a Bayesian inference system), a novel biologically inspired technique for automatically tuning controller parameters in real time. ANUBIS is based on the Bayesian brain concept and modifies it by incorporating a model of the neuromodulatory system comprising four artificial neuromodulators. It has been applied to the controller of EchinoBot, a prototype walking rover for Martian exploration. ANUBIS has been implemented at three levels of the controller; gait generation, foot trajectory planning using Bézier curves, and foot trajectory tracking using a terminal sliding mode controller. We compare the results to a similar system that has been tuned using a multilayer perceptron. The use of Bayesian inference means that the system retains mathematical interpretability, unlike other intelligent tuning techniques, which use neural networks, fuzzy logic, or evolutionary algorithms. The simulation results show that ANUBIS provides significant improvements in efficiency and adaptability of the three controller components; it allows the robot to react to obstacles and uncertainties faster than the system tuned with the MLP, while maintaining stability and accuracy. As well as advancing rover autonomy, ANUBIS could also be applied to other situations where operating conditions are likely to change or cannot be accurately modeled in advance, such as process control. In addition, it demonstrates one way in which neuromodulation could fit into the Bayesian brain framework. PMID:22970879
Computational Intelligence for Medical Knowledge Acquisition with Application to Glaucoma
Ulieru, Mihaela
Computational Intelligence for Medical Knowledge Acquisition with Application to Glaucoma Nicolae of glaucoma. The knowledge acquired is embedded in a fuzzy logic inference system. The resulting Neuro-fuzzy Glaucoma Diagnosis and Prediction System is expected to lower the effort, difficulties and risk cost
Predicting foaming slag quality in electric arc furnace using power quality indices and ANFIS
Amir Parsapoor; Behzad Mirzaeian Dehkordi; Mehdi Moallem
2010-01-01
Foaming slag quality is an important parameter that can be used to improve the efficiency and quality of electric arc furnace process. However due to its fast and unpredictable changes, its quality is difficult to control. In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) is used to determine slag quality based on power quality indices in electric arc
Predicting Foaming Slag Quality in Electric Arc Furnace Using Power Quality Indices and Fuzzy Method
Behzad Mirzaeian Dehkordi; Mehdi Moallem; Amir Parsapour
2011-01-01
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
Artificial Intelligence Techniques for Steam Generator Modelling
Sarah Wright; Tshilidzi Marwala
2008-01-01
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
A Self-Tuning Kalman Filter for Autonomous Navigation Using the Global Positioning System (GPS)
NASA Technical Reports Server (NTRS)
Truong, Son H.
1999-01-01
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.
A Hypermedia Inference Language as an Alternative to Rule-based Expert Systems
Gerry Stahl
1991-01-01
This paper reports on the development of a hypermedia inference language designed to strengthen the ability of hypermedia systems to be used effectively in applications that might otherwise require cumbersome rule-based expert systems. The inference language grew out of a primitive query language which provided the mechanism for navigation in a hypertext system. As the language gained logical and computational
Training data collection system for a learning-based photographic aesthetic quality inference engine
Razvan Orendovici; James Ze Wang
2010-01-01
We present a novel data collection system deployed for the ACQUINE - Aesthetic Quality Inference Engine. The goal of the system is to collect online user opinions, both structured and unstructured, for training future generation learning-based aesthetic quality inference engines. The development of the system was based on an analysis of over 60,000 user comments of photographs. For photos processed
A Self-Tuning Kalman Filter for Autonomous Navigation using the Global Positioning System (GPS)
NASA Technical Reports Server (NTRS)
Truong, S. H.
1999-01-01
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.
Perturbation Biology: Inferring Signaling Networks in Cellular Systems
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
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
The role of probability-based inference in an intelligent tutoring system
Robert J. Mislevy; Drew H. Gitomer
1995-01-01
Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring systems (ITSs). Basic concepts of the approach are briefly reviewed, but the emphasis is on the
Automatic Road Gap Detection Using Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Hashemi, S.; Valadan Zoej, M. J.; Mokhtarzadeh, M.
2011-09-01
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.
A Hypermedia Inference Language as an Alternative to Rule-based Expert Systems
G. Stahl; R. Mccall; G. Peper
1992-01-01
Abstract This paper,reports on the development,of a hypermedia,inference language,designed,to strengthen the ability of hypermedia systems to be used effectively in applications that might otherwise require cumbersome,rule-based expert systems. The inference language,grew out of a primitive query language,which provided the mechanism,for navigation in a hypertext system. As the language gained logical and computational capabilities it became increasingly embedded in the nodes
Inferring Presence in a Context-Aware Instant Messaging System
Mikko Perttunen; Jukka Riekki
2004-01-01
The increasing volume of digital communication is raising new chal- lenges in the management of the information flow. We discuss the usage of con- text to infer presence information automatically for instant messaging applica- tions. This results in easy-to-use applications and more reliable presence information. We suggest a new model, context relation, for representing the contexts that are relevant for
Decremental Learning of Evolving Fuzzy Inference Systems Using a Sliding Window
Boyer, Edmond
to adapt to concept drifts and that we face a precision reactiveness trade-off. It is also demonstrated-Incremental Learning; Decremental Learning; Evolving Fuzzy Inference System; Recursive Least Squares; Concept Drifts
From free energy measurements to free energy inference in small systems Felix Ritort
Potsdam, Universität
From free energy measurements to free energy inference in small systems Abstract. Fluctuation theorems (FTs) have provided new methods to extract free the knowledge of the free energy of nucleic acid and protein structures
Extending Hypermedia with an Inference Language: an Alternative to Rule-Based Expert Systems
G Stahl; R Mccall; G Peper
1992-01-01
Abstract Hypermedia,systems can provide an alternative to expert systems in domains that call for navigation rather than inference. This paper reports on research to extend the hypermedia model to include an inferencing capability, so that the benefits of this approach can be gained in applications that require a mix of navigation and inference. An application in the domain,of academic,advising was,developed,to
Intelligent control for a drone by self-tunable Fuzzy Inference System
K. M. Zemalache; H. Maaref
2009-01-01
The work describes an automatically online self-tunable fuzzy inference system (STFIS) of a new configuration of mini-flying called XSF (X4 stationary flyer) drone. A fuzzy controller based on online optimization of a zero order Takagi-Sugeno fuzzy inference system (FIS) by a back propagation-like algorithm is successfully applied. It is used to minimize a cost function that is made up of
Controlling a drone: Comparison between a based model method and a fuzzy inference system
Kadda Meguenni Zemalache; Hichem Maaref
2009-01-01
The work describes an automatically on-line self-tunable fuzzy inference system (STFIS) of a new configuration of mini-flying called XSF (X4 Stationnary Flyer) drone. A fuzzy controller based on on-line optimization of a zero order Takagi–Sugeno fuzzy inference system (FIS) by a back propagation-like algorithm is successfully applied. It is used to minimize a cost function that is made up of
Cloud-computing-based framework for multi-camera topology inference in smart city sensing system
Ye Wen; Xiaokang Yang; Yi Xu
2010-01-01
This paper proposes a cloud-computing-based algorithmic framework which is scalable and adaptive to online smart city video sensing system. One of the most cost-expensive works in such a system is to infer the topology structure of video camera network, thus spatio-temporal relationship inference for large-scale camera network is simulated on a cloud-computing platform to validate the proposed framework. The simulation
An expert system shell for inferring vegetation characteristics: Prototype help system (Task 1)
NASA Technical Reports Server (NTRS)
1993-01-01
The NASA Vegetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. A prototype of the VEG subgoal HELP.SYSTEM has been completed and the Help System has been added to the VEG system. It is loaded when the user first clicks on the HELP.SYSTEM option in the Tool Box Menu. The Help System provides a user tool to support needed user information. It also provides interactive tools the scientist may use to develop new help messages and to modify existing help messages that are attached to VEG screens. The system automatically manages system and file operations needed to preserve new or modified help messages. The Help System was tested both as a help system development and a help system user tool.
A neuro-computational approach to chiller fault identification and isolation
Prabhu, Rahul Srinivas
2002-01-01
28 30 CHAPTER IV A BR&F INTRODUCTION TO FUZZY LOGIC. . . . . Fuzzy Logic Fuzzy Sets. Membership Functions . . . . . . . . . . . . . . If-Then Rules. Fuzzy Inference System (FIS) . . Data Analysis Techniques Neuro-Fuzzy Systems . . Adaptive... is attempted in this thesis and the work is carried out using Matlab neural network toolbox. Fault Diagnosis using Fuzzy Inference Systems Significant pioneering work in the field of fuzzy logic was carried out by Lotfi Zadeh. In his 1965 paper, Zadeh...
Object Metrics for Aspect Systems: Limiting Empirical Inference Based on Modularity
Shiu Lun Tsang; Siobhán Clarke; Elisa Baniassad
In empirical comparisons of Aspect-Oriented (AO) to Object- Oriented (OO) systems, system properties such as understandability, maintainability, reusability, and testability have often been inferred from other metrics, such as lines of code, sites of change, and modularity. However, in traditional OO metrics suites such system properties are assessed separately from these measures. We applied OO metrics for comparing an AO
Systems biology GNU MCSim: Bayesian statistical inference for SBML-coded
Boyer, Edmond
. The source code is freely available under GNU GPL and can be compiled for any system, provided an ANSI C compliant compiler. The code can be freely modified. The software consists in two parts. A model generator1 Systems biology GNU MCSim: Bayesian statistical inference for SBML-coded systems biology models
Kumar, Ratnesh
of an underlying system using their own set of sensors, and jointly diagnose the occurrence of a failure based in a decentralized setting. For each event-trace executed by a system being monitored, each local diagnoser issues be detected within a uniformly bounded number of steps (or "delay"), we introduce the notion of N-inference-diagnosability
FPGA-Based Fuzzy Inference System for Real-time Embedded Applications
FPGA-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 the interest of the real-time applications. With enhanced capabilities most of the processing tasks can
New developments of a knowledge based system (VEG) for inferring vegetation characteristics
NASA Technical Reports Server (NTRS)
Kimes, D. S.; Harrison, P. A.; Harrison, P. R.
1992-01-01
An extraction technique for inferring physical and biological surface properties of vegetation using nadir and/or directional reflectance data as input has been developed. A knowledge-based system (VEG) accepts spectral data of an unknown target as input, determines the best strategy for inferring the desired vegetation characteristic, applies the strategy to the target data, and provides a rigorous estimate of the accuracy of the inference. Progress in developing the system is presented. VEG combines methods from remote sensing and artificial intelligence, and integrates input spectral measurements with diverse knowledge bases. VEG has been developed to (1) infer spectral hemispherical reflectance from any combination of nadir and/or off-nadir view angles; (2) test and develop new extraction techniques on an internal spectral database; (3) browse, plot, or analyze directional reflectance data in the system's spectral database; (4) discriminate between user-defined vegetation classes using spectral and directional reflectance relationships; and (5) infer unknown view angles from known view angles (known as view angle extension).
Reducing Failure Rates of Robotic Systems though Inferred Invariants Monitoring
Farritor, Shane
. An application of the technique on a system consisting of a unmanned aerial vehicle (UAV) landing on a moving, the scenario illustrated in Fig- ure 1 where a small unmanned aerial vehicle (UAV) is autonomously following, and then initiating the landing sequence. Using a standard message passing system such as ROS (Robot Operating System
Inferring prey perception of relative danger in large-scale marine systems
Alejandro Frid; Lawrence M. Dill; Richard E. Thorne; Gail M. Blundell
2007-01-01
Problem: Infer relative danger from spatially segregated predators in large-scale marine systems, using individual differences in prey foraging behaviour. Mathematical models: Optimization of trade-offs between predation risk and energy gain. Key assumption: Foraging individuals choosing to incur higher risk of predation should experience higher energetic gain than individuals choosing safer foraging options. Organisms: Alaskan harbour seals foraging under predation risk
Machine Learning and Inference Laboratory The LEM3 System for Non-Darwinian Evolutionary
Michalski, Ryszard S.
Reports Machine Learning and Inference Laboratory The LEM3 System for Non-Darwinian Evolutionary-2 P 05-5 October, 2005 MLI 04-1- School of Computational Sciences George Mason University #12;THE LEM3 Institute of Computer Science Polish Academy of Sciences Abstract LEM3 is the newest implementation
Erina Sakamoto; Hitoshi Iba
2001-01-01
Describes an evolutionary method for identifying a gene regulatory network from the observed time series data of the gene's expression. We use a system of ordinary differential equations as a model of the network and infer their right-hand sides by using genetic programming (GP). To explore the search space more effectively in the course of evolution, the least mean squares
Research on soft sensing model of loose of jig bed based on fuzzy inference system
Jian Cheng; Yi'nan Guo; Wei Sun; Ming Li
2004-01-01
According to the jig's separation effect affected strongly by loose of Jig bed, soft sensing model of a loose Jig bed was put forward via fuzzy inference system (FIS). The corresponding fuzzy rules were originated from the operational experiences. Using the main evaluating index of the jig's separation effect-imperfection (I) and total misplaced material (Cz), status of the loose jig
Koksal Erenturk
2009-01-01
This paper presents application of adaptive network based fuzzy inference system (ANFIS) to estimate critical flashover voltage on polluted insulators. Diameter, height, creepage distance, form factor and equivalent salt deposit density were used as input variables for ANFIS, and critical flashover voltage was estimated. In order to train the network and to test its performance, the data sets are derived
NASA Technical Reports Server (NTRS)
Harrison, P. Ann
1992-01-01
The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented. Some techniques operate on sample data at a single wavelength. The techniques previously incorporated in VEG for other subgoals operated on data at a single wavelength so implementing the additional single wavelength techniques required no changes to the structure of VEG. Two techniques which use data at multiple wavelengths to infer proportion ground cover were also implemented. This work involved modifying the structure of VEG so that multiple wavelength techniques could be incorporated. All the new techniques were tested using both the VEG 'Research Mode' and the 'Automatic Mode.'
Gray, Jeffrey G.
1 Title Page Title: MARS: A Metamodel Recovery System Using Grammar Inference Author 1. In this paper we describe MARS, a semi-automatic grammar-centric system that leverages grammar inference is a sequence of intermediate metamodel versions that represent the evolving definition of a specific modeling
FINDS: A fault inferring nonlinear detection system. User's guide
NASA Technical Reports Server (NTRS)
Lancraft, R. E.; Caglayan, A. K.
1983-01-01
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.
Optimizing Information Retrieval Using Evolutionary Algorithms and Fuzzy Inference System
Václav Snásel; Ajith Abraham; Suhail S. J. Owais; Jan Platos; Pavel Krömer
2009-01-01
With the rapid growth of the amount of data available in electronic libraries, through Internet and enterprise network mediums,\\u000a advanced methods of search and information retrieval are in demand. Information retrieval systems, designed for storing, maintaining\\u000a and searching large-scale sets of unstructured documents, are the subject of intensive investigation. An information retrieval\\u000a system, a sophisticated application managing underlying documentary databases,
Earth system sensitivity inferred from Pliocene modelling and data
Lunt, D.J.; Haywood, A.M.; Schmidt, G.A.; Salzmann, U.; Valdes, P.J.; Dowsett, H.J.
2010-01-01
Quantifying the equilibrium response of global temperatures to an increase in atmospheric carbon dioxide concentrations is one of the cornerstones of climate research. Components of the Earths climate system that vary over long timescales, such as ice sheets and vegetation, could have an important effect on this temperature sensitivity, but have often been neglected. Here we use a coupled atmosphere-ocean general circulation model to simulate the climate of the mid-Pliocene warm period (about three million years ago), and analyse the forcings and feedbacks that contributed to the relatively warm temperatures. Furthermore, we compare our simulation with proxy records of mid-Pliocene sea surface temperature. Taking these lines of evidence together, we estimate that the response of the Earth system to elevated atmospheric carbon dioxide concentrations is 30-50% greater than the response based on those fast-adjusting components of the climate system that are used traditionally to estimate climate sensitivity. We conclude that targets for the long-term stabilization of atmospheric greenhouse-gas concentrations aimed at preventing a dangerous human interference with the climate system should take into account this higher sensitivity of the Earth system. ?? 2010 Macmillan Publishers Limited. All rights reserved.
Asymptotic inference in system identification for the atom maser
Catalin Catana; Merlijn van Horssen; Madalin Guta
2011-12-09
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.
On inferring autonomous system relationships in the internet
Lixin Gao
2001-01-01
The Internet consists of rapidly increasing number of hosts interconnected by constantly evolving networks of links and routers. Interdomain routing in the Internet is coordinated by the Border Gateway Protocol (BGP). BGP allows each autonomous system (AS) to choose its own administrative policy in selecting routes and propagating reachability information to others. These routing policies are constrained by the contractual
Asymptotic Inference on Cointegrating Rank in Partial Systems
Søren Johansen; Bent Nielsen; Anders Rahbek
1998-01-01
The likelihood ratio test for cointegrating rank is analyzed for partial (or conditional) systems in the vector autoregressive error-correction model. Under the assumption of weak exogeneity for the cointegrating parameters, the asymptotic distributions are given and tables of critical values are provided. A discussion is given of some of the assumptions of the model, why they are needed, and how
Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis
Sinan Altug; Mo-Yuen Chen; H. Joel Trussell
1999-01-01
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
generation tactical communications systems is introduced. In this algorithm, handoff decision metrics-pong effect. Received signal strength (RSS) based handoff algorithm associates mobile host to the base station1 A FUZZY INFERENCE SYSTEM FOR THE HANDOFF DECISION ALGORITHMS IN THE VIRTUAL CELL LAYOUT BASED
S. Paramasivam; S. Vijayan; M. Vasudevan; R. Arumugam; Ramu Krishnan
2007-01-01
This paper presents real-time verification of an artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) based rotor position estimation techniques for a 6\\/4 pole switched reluctance motor (SRM) drive system. The techniques estimate rotor position by measuring the three-phase voltages and currents and using magnetic characteristics of the SRM, with the aid of an ANN and ANFIS, in
Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology
Murakami, Yohei
2014-01-01
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
Large-Scale Optimization for Bayesian Inference in Complex Systems
Willcox, Karen [MIT] [MIT; Marzouk, Youssef [MIT] [MIT
2013-11-12
The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimization) Project focused on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimization and inversion methods. The project was a collaborative effort among MIT, the University of Texas at Austin, Georgia Institute of Technology, and Sandia National Laboratories. The research was 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. The MIT--Sandia component of the SAGUARO Project addressed 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.
Design of a Software Sensor for Feedwater Flow Measurement Using a Fuzzy Inference System
Na, Man Gyun; Shin, Sun Ho; Jung, Dong Won [Chosun University (Korea, Republic of)
2005-06-15
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.
Application of ANFIS to Phase Estimation for Multiple Phase Shift Keying
NASA Technical Reports Server (NTRS)
Drake, Jeffrey T.; Prasad, Nadipuram R.
2000-01-01
The paper discusses a novel use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for estimating phase in Multiple Phase Shift Keying (M-PSK) modulation. A brief overview of communications phase estimation is provided. The modeling of both general open-loop, and closed-loop phase estimation schemes for M-PSK symbols with unknown structure are discussed. Preliminary performance results from simulation of the above schemes are presented.
NASA Astrophysics Data System (ADS)
Gultekin, Nurgul; Sezer, Ebru; Gokceoglu, Candan
2010-05-01
In several rock engineering applications, various prediction tools have been used to estimate strength and deformation parameters of intact rock. Commony, simple and linear multivariable regression methods have been employed. However, recently, some soft computing methods such as fuzzy inference systems, artificial neural Networks and neuro-fuzzy models have been used and they have yielded successful results, because the problems at hand have generally nonlinear nature. The purpose of the present study is to apply neuro-fuzzy modeling to estimate uniaxial compressive strength and modulus of elasticity of some granitic rocks from their physical and index properties. For the purpose of the study, sampling works on seven different granitic rocks from different locations in Turkey were performed. On these samples, unit weight, porosity, void ratio, water absorption by weight, P-wave velocity, point load index, block punch index, tensile strength, uniaxial compressive strength and modulus of elasticity were determined in laboratory. A total of 88 specimens were used during the laboratory tests. In the first stage of the analyses, stepwise multiple regression analyses were performed. By using the input parameters of the most successful regression models, some models based on adaptive neuro-fuzzy inference system (ANFIS) were developed to predict uniaxial compressive strength and modulus of elasticity. The general performances of the ANFIS models are considerably high. This results show that prediction of some intact rock properties is a nonlinear problem. For this reason, when predicting the intact rock properties, the nonlinear methods such as fuzzy inference system, artificial neural networks, adaptive neuro-fuzzy inference system or nonlinear multiple regression methods should be considered.
Application of MR damper in structural control using ANFIS method
Zhi Q. Gu; S. Olutunde Oyadiji
2008-01-01
Protection of civil engineering structures from excessive vibration due to uncontrollable events such as earthquake has increasingly been of importance for the purpose of maintaining structural integrity and serviceability. This paper presents the development of an adaptive neuro-fuzzy inference system (ANFIS) controller for reduction of environmentally induced vibration in multiple-degree-of-freedom (MDOF) building structure with MR damper. The LQG control method
Nero-fuzzy modeling of the convection heat transfer coefficient for the nanofluid
NASA Astrophysics Data System (ADS)
Salehi, H.; Zeinali-Heris, S.; Esfandyari, M.; Koolivand, M.
2013-04-01
In this study, experiments were performed by six different volume fractions of Al2O3 nanoparticles in distilled water. Then, actual nanofluid Nusslet number compared by Adaptive neuro fuzzy inference system (ANFIS) predicted number in square cross-section duct in laminar flow under uniform heat flux condition. Statistical values, which quantify the degree of agreement between experimental observations and numerically calculated values, were found greater than 0.99 for all cases.
NSDL National Science Digital Library
Mrs. Devitry
2012-03-23
Sometimes someone will try to tell you something without coming right out and saying it. He will imply it. When you understand what is implied, you infer. Sometimes you can infer the truth even when the speaker or writer isn?t trying to be helpful. That?s called ?reading between the lines.? Complete Inference Activities 1-8 with 100% accuracy! 1. Making Inferences or Predictions Lesson 2. Making Inferences or Predictions Practice 3. Making Inferences Lesson 4. Making Inferences Practice 5. Inferring Character Feelings Lesson 6. Inferring Character Feelings Practice 7. Inference Lesson 8. Inference Practice ...
Application of fuzzy inference system in the prediction of wave parameters
M. H. Kazeminezhad; A. Etemad-Shahidi; S. J. Mousavi
2005-01-01
Wave prediction is one of the most important issues in coastal and ocean engineering studies. In this study, the performance of Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Coastal Engineering Manual (CEM) methods for predicting wave parameters were investigated. The data set used in this study comprises of fetch-limited wave data and over water wind data gathered from deep-water location in
Linear Inference for Type-II Censored Lifetime Data of Reliability Systems With Known Signatures
Narayanaswamy Balakrishnan; Hon Keung Tony Ng; Jorge Navarro
2011-01-01
In this paper, we discuss linear inference for the life- time distribution of components based on a Type-II censored life- time data of reliability systems with known signatures. We derive the best linear unbiased estimators (BLUE) for the parameter(s) in general scale and location-scale parameter families. The exact computational formulas of the BLUE and their variances and co- variance are
Power control optimization in LMDS networks operating above 20GHz through Fuzzy Inference Systems
Konstantinos S. Chaloulos; Dimitris E. Charilas; Athanasios D. Panagopoulos
2011-01-01
Local Multipoint Distribution Service (LMDS) systems operate at frequency bands above 20GHz. Code division multiple access (CDMA) schemes have been proposed for this technology. LMDS networks at millimeter wave bands are susceptible to tropospheric propagation phenomena and their Quality of Service (QoS) performance mainly suffers from rain attenuation and inter-cell interference. In this paper we propose a new Fuzzy Inference
A novel bridge scour monitoring and prediction system
NASA Astrophysics Data System (ADS)
Valyrakis, Manousos; Michalis, Panagiotis; Zhang, Hanqing
2015-04-01
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 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 use of a novel methodology is proposed for the prediction of bridge scour. Specifically, the use of 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. Training of the system to new bridge geometries and flow conditions can be achieved by obtaining real time data, via novel electromagnetic sensors monitoring scour depth. Once the model is trained with data representative of the new system, bridge scour prediction can be performed for high/design flows or floods.
Toni, Tina; Welch, David; Strelkowa, Natalja; Ipsen, Andreas; Stumpf, Michael P H
2009-02-01
Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculate likelihoods. In this paper, we discuss and apply an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models. We show that ABC SMC provides information about the inferability of parameters and model sensitivity to changes in parameters, and tends to perform better than other ABC approaches. The algorithm is applied to several well-known biological systems, for which parameters and their credible intervals are inferred. Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus. PMID:19205079
K. Guney; N. Sarikaya
2009-01-01
This paper presents a method based on adaptive-network-based fuzzy inference system (ANFIS) to compute the resonant frequency\\u000a of a circular microstrip antenna (MSA). The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy\\u000a inference systems (FISs). Seven optimization algorithms, least-squares, nelder-mead, differential evolution, genetic, hybrid\\u000a learning, particle swarm, and simulated annealing, are used to determine optimally
Video-based cargo fire verification system with fuzzy inference engine for commercial aircraft
NASA Astrophysics Data System (ADS)
Sadok, Mokhtar; Zakrzewski, Radek; Zeliff, Bob
2005-02-01
Conventional smoke detection systems currently installed onboard aircraft are often subject to high rates of false alarms. Under current procedures, whenever an alarm is issued the pilot is obliged to release fire extinguishers and to divert to the nearest airport. Aircraft diversions are costly and dangerous in some situations. A reliable detection system that minimizes false-alarm rate and allows continuous monitoring of cargo compartments is highly desirable. A video-based system has been recently developed by Goodrich Corporation to address this problem. The Cargo Fire Verification System (CFVS) is a multi camera system designed to provide live stream video to the cockpit crew and to perform hotspot, fire, and smoke detection in aircraft cargo bays. In addition to video frames, the CFVS uses other sensor readings to discriminate between genuine events such as fire or smoke and nuisance alarms such as fog or dust. A Mamdani-type fuzzy inference engine is developed to provide approximate reasoning for decision making. In one implementation, Gaussian membership functions for frame intensity-based features, relative humidity, and temperature are constructed using experimental data to form the system inference engine. The CFVS performed better than conventional aircraft smoke detectors in all standardized tests.
Application of Soft Computing in Coherent Communications Phase Synchronization
NASA Technical Reports Server (NTRS)
Drake, Jeffrey T.; Prasad, Nadipuram R.
2000-01-01
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.
NASA Astrophysics Data System (ADS)
Zhang, Daili
Increasing societal demand for automation has led to considerable efforts to control large-scale complex systems, especially in the area of autonomous intelligent control methods. The control system of a large-scale complex system needs to satisfy four system level requirements: robustness, flexibility, reusability, and scalability. Corresponding to the four system level requirements, there arise four major challenges. First, it is difficult to get accurate and complete information. Second, the system may be physically highly distributed. Third, the system evolves very quickly. Fourth, emergent global behaviors of the system can be caused by small disturbances at the component level. The Multi-Agent Based Control (MABC) method as an implementation of distributed intelligent control has been the focus of research since the 1970s, in an effort to solve the above-mentioned problems in controlling large-scale complex systems. However, to the author's best knowledge, all MABC systems for large-scale complex systems with significant uncertainties are problem-specific and thus difficult to extend to other domains or larger systems. This situation is partly due to the control architecture of multiple agents being determined by agent to agent coupling and interaction mechanisms. Therefore, the research objective of this dissertation is to develop a comprehensive, generalized framework for the control system design of general large-scale complex systems with significant uncertainties, with the focus on distributed control architecture design and distributed inference engine design. A Hybrid Multi-Agent Based Control (HyMABC) architecture is proposed by combining hierarchical control architecture and module control architecture with logical replication rings. First, it decomposes a complex system hierarchically; second, it combines the components in the same level as a module, and then designs common interfaces for all of the components in the same module; third, replications are made for critical agents and are organized into logical rings. This architecture maintains clear guidelines for complexity decomposition and also increases the robustness of the whole system. Multiple Sectioned Dynamic Bayesian Networks (MSDBNs) as a distributed dynamic probabilistic inference engine, can be embedded into the control architecture to handle uncertainties of general large-scale complex systems. MSDBNs decomposes a large knowledge-based system into many agents. Each agent holds its partial perspective of a large problem domain by representing its knowledge as a Dynamic Bayesian Network (DBN). Each agent accesses local evidence from its corresponding local sensors and communicates with other agents through finite message passing. If the distributed agents can be organized into a tree structure, satisfying the running intersection property and d-sep set requirements, globally consistent inferences are achievable in a distributed way. By using different frequencies for local DBN agent belief updating and global system belief updating, it balances the communication cost with the global consistency of inferences. In this dissertation, a fully factorized Boyen-Koller (BK) approximation algorithm is used for local DBN agent belief updating, and the static Junction Forest Linkage Tree (JFLT) algorithm is used for global system belief updating. MSDBNs assume a static structure and a stable communication network for the whole system. However, for a real system, sub-Bayesian networks as nodes could be lost, and the communication network could be shut down due to partial damage in the system. Therefore, on-line and automatic MSDBNs structure formation is necessary for making robust state estimations and increasing survivability of the whole system. A Distributed Spanning Tree Optimization (DSTO) algorithm, a Distributed D-Sep Set Satisfaction (DDSSS) algorithm, and a Distributed Running Intersection Satisfaction (DRIS) algorithm are proposed in this dissertation. Combining these three distributed algorithms and a Distributed Belief Propagation (DBP) algo
2013-01-01
Background Model selection and parameter inference are complex problems that have yet to be fully addressed in systems biology. In contrast with parameter optimisation, parameter inference computes both the parameter means and their standard deviations (or full posterior distributions), thus yielding important information on the extent to which the data and the model topology constrain the inferred parameter values. Results We report on the application of nested sampling, a statistical approach to computing the Bayesian evidence Z, to the inference of parameters, and the estimation of log Z in an established model of circadian rhythms. A ten-fold difference in the coefficient of variation between degradation and transcription parameters is demonstrated. We further show that the uncertainty remaining in the parameter values is reduced by the analysis of increasing numbers of circadian cycles of data, up to 4 cycles, but is unaffected by sampling the data more frequently. Novel algorithms for calculating the likelihood of a model, and a characterisation of the performance of the nested sampling algorithm are also reported. The methods we develop considerably improve the computational efficiency of the likelihood calculation, and of the exploratory step within nested sampling. Conclusions We have demonstrated in an exemplar circadian model that the estimates of posterior parameter densities (as summarised by parameter means and standard deviations) are influenced predominately by the length of the time series, becoming more narrowly constrained as the number of circadian cycles considered increases. We have also shown the utility of the coefficient of variation for discriminating between highly-constrained and less-well constrained parameters. PMID:23899119
Hanemaaijer, Mark; Röling, Wilfred F. M.; Olivier, Brett G.; Khandelwal, Ruchir A.; Teusink, Bas; Bruggeman, Frank J.
2015-01-01
Microbial communities play important roles in health, industrial applications and earth's ecosystems. With current molecular techniques we can characterize these systems in unprecedented detail. However, such methods provide little mechanistic insight into how the genetic properties and the dynamic couplings between individual microorganisms give rise to their dynamic activities. Neither do they give insight into what we call “the community state”, that is the fluxes and concentrations of nutrients within the community. This knowledge is a prerequisite for rational control and intervention in microbial communities. Therefore, the inference of the community structure from experimental data is a major current challenge. We will argue that this inference problem requires mathematical models that can integrate heterogeneous experimental data with existing knowledge. We propose that two types of models are needed. Firstly, mathematical models that integrate existing genomic, physiological, and physicochemical information with metagenomics data so as to maximize information content and predictive power. This can be achieved with the use of constraint-based genome-scale stoichiometric modeling of community metabolism which is ideally suited for this purpose. Next, we propose a simpler coarse-grained model, which is tailored to solve the inference problem from the experimental data. This model unambiguously relate to the more detailed genome-scale stoichiometric models which act as heterogeneous data integrators. The simpler inference models are, in our opinion, key to understanding microbial ecosystems, yet until now, have received remarkably little attention. This has led to the situation where the modeling of microbial communities, using only genome-scale models is currently more a computational, theoretical exercise than a method useful to the experimentalist. PMID:25852671
Welding Penetration Control of Fixed Pipe in TIG Welding Using Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Baskoro, Ario Sunar; Kabutomori, Masashi; Suga, Yasuo
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.
A Neuro-Fuzzy Multi Swarm FastSLAM Framework
Havangi, R; Nekoui, M A
2010-01-01
FastSLAM is a framework for simultaneous localization using a Rao-Blackwellized particle filter. In FastSLAM, particle filter is used for the mobile robot pose (position and orientation) estimation, and an Extended Kalman Filter (EKF) is used for the feature location's estimation. However, FastSLAM degenerates over time. This degeneracy is due to the fact that a particle set estimating the pose of the robot loses its diversity. One of the main reasons for loosing particle diversity in FastSLAM is sample impoverishment. It occurs when likelihood lies in the tail of the proposal distribution. In this case, most of particle weights are insignificant. Another problem of FastSLAM relates to the design of an extended Kalman filter for landmark position's estimation. The performance of the EKF and the quality of the estimation depends heavily on correct a priori knowledge of the process and measurement noise covariance matrices (Q and R) that are in most applications unknown. On the other hand, an incorrect a priori...
ECG beat classification using neuro-fuzzy network
Mehmet Engin
2004-01-01
In this paper we have studied the application on the fuzzy-hybrid neural network for electrocardiogram (ECG) beat classification. Instead of original ECG beat, we have used; autoregressive model coefficients, higher-order cumulant and wavelet transform variances as features. Tested with MIT\\/BIH arrhytmia database, we observe significant performance enhancement using proposed method.
An evolutionary neuro-fuzzy approach to breast cancer diagnosis
Ridha El Hamdi; Mohamed Njah; Mohamed Chtourou
2010-01-01
The important role that mammography is playing in breast cancer detection can be attributed largely to the technical improvements and dedication of radiologists to breast imaging. A lot of work is being done to ensure that these diagnosing steps are becoming smoother, faster and more accurate in classifying whether the abnormalities seen in mammogram images are benign or malignant. In
Seismic events discrimination by neuro-fuzzy-based data merging
S. Muller; J.-F. Legrand; J.-D. Muller; Y. Cansi; R. Crusem; P. Garda
1998-01-01
This article involves an original method to classify low magnitude seismic events recorded in France by a network of seismometers. This method is based on the merging of high-level data with possibly incomplete low-level data extracted from seismic signals. The merging is performed by a multi-layer neural network. A fuzzy coding is applied to the neural network's inputs to process
Real Case Study of a Neuro-Fuzzy Intelligent Car
V. Lupu; C. Lupu
2007-01-01
This paper present a real case study of an intelligent car. There exist an important number of applications, wherein it is required to recognize some plan forms. In these applications, but also in the case that the forms aren't anymore plans, the contour of the forms is an important descriptor from which we can leave in the process of similarity
Classification of delaminated composites using neuro-fuzzy image analysis
Martin, Ralph R.
composites by shearography imaging. Detection and characterisation of damage and malformation in laminated. The application is on detection of flaws in lami- nated composite materials. Initial results indicate that great-time nondestructive evaluation technique that has been widely used for damage detection. The nondestructive procedure
Time Series Forecasting Using Hybrid Neuro-Fuzzy Regression Model
Arindam Chaudhuri; Kajal De
2009-01-01
During the past few decades various time-series forecasting methods have been developed for financial market forecasting leading\\u000a to improved decisions and investments. But accuracy remains a matter of concern in these forecasts. The quest is thus on improving\\u000a the effectiveness of time-series models. Artificial neural networks (ANN) are flexible computing paradigms and universal approximations\\u000a that have been applied to a
2011-01-01
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 still missing. Results Three multiplex genotyping assays, based on single-base primer extension technology, were developed targeting a total of 36 coding-region mtDNA variants that together differentiate 43 matrilineal haplo-/paragroups. These include the major diagnostic haplogroups for Africa, Western Eurasia, Eastern Eurasia and Native America. The assays show high sensitivity with respect to the amount of template DNA: successful amplification could still be obtained when using as little as 4 pg of genomic DNA and the technology is suitable for medium-throughput analyses. Conclusions We introduce an efficient and sensitive multiplex genotyping system for bio-geographic ancestry inference from mtDNA that provides resolution on the continental level. The method can be applied in forensics, to aid tracing unknown suspects, as well as in population studies, genealogy and personal ancestry testing. For more complete inferences of overall bio-geographic ancestry from DNA, the mtDNA system provided here can be combined with multiplex systems for suitable autosomal and, in the case of males, Y-chromosomal ancestry-sensitive DNA markers. PMID:21429198
GenSo-EWS: a novel neural-fuzzy based early warning system for predicting bank failures.
Tung, W L; Quek, C; Cheng, P
2004-05-01
Bank failure prediction is an important issue for the regulators of the banking industries. The collapse and failure of a bank could trigger an adverse financial repercussion and generate negative impacts such as a massive bail out cost for the failing bank and loss of confidence from the investors and depositors. Very often, bank failures are due to financial distress. Hence, it is desirable to have an early warning system (EWS) that identifies potential bank failure or high-risk banks through the traits of financial distress. Various traditional statistical models have been employed to study bank failures [J Finance 1 (1975) 21; J Banking Finance 1 (1977) 249; J Banking Finance 10 (1986) 511; J Banking Finance 19 (1995) 1073]. However, these models do not have the capability to identify the characteristics of financial distress and thus function as black boxes. This paper proposes the use of a new neural fuzzy system [Foundations of neuro-fuzzy systems, 1997], namely the Generic Self-organising Fuzzy Neural Network (GenSoFNN) [IEEE Trans Neural Networks 13 (2002c) 1075] based on the compositional rule of inference (CRI) [Commun ACM 37 (1975) 77], as an alternative to predict banking failure. The CRI based GenSoFNN neural fuzzy network, henceforth denoted as GenSoFNN-CRI(S), functions as an EWS and is able to identify the inherent traits of financial distress based on financial covariates (features) derived from publicly available financial statements. The interaction between the selected features is captured in the form of highly intuitive IF-THEN fuzzy rules. Such easily comprehensible rules provide insights into the possible characteristics of financial distress and form the knowledge base for a highly desired EWS that aids bank regulation. The performance of the GenSoFNN-CRI(S) network is subsequently benchmarked against that of the Cox's proportional hazards model [J Banking Finance 10 (1986) 511; J Banking Finance 19 (1995) 1073], the multi-layered perceptron (MLP) and the modified cerebellar model articulation controller (MCMAC) [IEEE Trans Syst Man Cybern: Part B 30 (2000) 491] in predicting bank failures based on a population of 3635 US banks observed over a 21 years period. Three sets of experiments are performed-bank failure classification based on the last available financial record and prediction using financial records one and two years prior to the last available financial statements. The performance of the GenSoFNN-CRI(S) network as a bank failure classification and EWS is encouraging. PMID:15109685
The Gaia astrophysical parameters inference system (Apsis). Pre-launch description
Bailer-Jones, C A L; Arcay, B; Astraatmadja, T; Bellas-Velidis, I; Berihuete, A; Bijaoui, A; Carrión, C; Dafonte, C; Damerdji, Y; Dapergolas, A; de Laverny, P; Delchambre, L; Drazinos, P; Drimmel, R; Frémat, Y; Fustes, D; García-Torres, M; Guédé, C; Heiter, U; Janotto, A -M; Karampelas, A; Kim, D -W; Knude, J; Kolka, I; Kontizas, E; Kontizas, M; Korn, A J; Lanzafame, A C; Lebreton, Y; Lindstrøm, H; Liu, C; Livanou, E; Lobel, A; Manteiga, M; Martayan, C; Ordenovic, Ch; Pichon, B; Recio-Blanco, A; Rocca-Volmerange, B; Sarro, L M; Smith, K; Sordo, R; Soubiran, C; Surdej, J; Thévenin, F; Tsalmantza, P; Vallenari, A; Zorec, J
2013-01-01
The Gaia satellite will survey the entire celestial sphere down to 20th magnitude, obtaining astrometry, photometry, and low resolution spectrophotometry on one billion astronomical sources, plus radial velocities for over one hundred million stars. Its main objective is to take a census of the stellar content of our Galaxy, with the goal of revealing its formation and evolution. Gaia's unique feature is the measurement of parallaxes and proper motions with hitherto unparalleled accuracy for many objects. As a survey, the physical properties of most of these objects are unknown. Here we describe the data analysis system put together by the Gaia consortium to classify these objects and to infer their astrophysical properties using the satellite's data. This system covers single stars, (unresolved) binary stars, quasars, and galaxies, all covering a wide parameter space. Multiple methods are used for many types of stars, producing multiple results for the end user according to different models and assumptions. ...
NASA Astrophysics Data System (ADS)
Hancock, P. L.; Barka, A. A.
The 1200-km long North Anatolian fault zone is a right-lateral, intracontinental transform boundary which was initiated in the Late Neogene. Sediments of Pliocene to Holocene age in basins between Cerkes and Erbaa, within the convex-northwards arc of the fault zone, are deformed by syn-sedimentary and post-depositional mesoscopic faults and joints. The mesofractures, which strike obliquely to the fault zone, include reverse faults, normal faults, normal shear joints, conjugate vertical joints and strike-slip faults. Each type of structure occurs in two geometrical groups, one comprises four systems of fractures, the other is made up of five systems. The directions of secondary compression and/or extension inferred from the first group of mesofractures, which are restricted to sediments of Pliocene to Early Pleistocene age, are interpreted as being related to left-lateral shear along the North Anatolian fault zone. The directions of compression and/or extension inferred from the second group of mesofractures, which cut sediments of Pliocene to late Holocene age, were generated during right-lateral shear. The presence of the second group of mesofractures is understandable because they are related to the shear sense which operates at the present-day, but those interpreted as being related to left-lateral shear are more puzzling: their development implies one or more reversals of the dominant sense of displacement. Several tentative models to explain such reversals are proposed, including regional and local influences, the latter related to mechanical constraints and/or the effects of other fault systems.
Macroscopic Time Evolution and MaxEnt Inference for Closed Systems with Hamiltonian Dynamics
NASA Astrophysics Data System (ADS)
Kui?, Domagoj; Županovi?, Paško; Jureti?, Davor
2012-02-01
MaxEnt inference algorithm and information theory are relevant for the time evolution of macroscopic systems considered as problem of incomplete information. Two different MaxEnt approaches are introduced in this work, both applied to prediction of time evolution for closed Hamiltonian systems. The first one is based on Liouville equation for the conditional probability distribution, introduced as a strict microscopic constraint on time evolution in phase space. The conditional probability distribution is defined for the set of microstates associated with the set of phase space paths determined by solutions of Hamilton's equations. The MaxEnt inference algorithm with Shannon's concept of the conditional information entropy is then applied to prediction, consistently with this strict microscopic constraint on time evolution in phase space. The second approach is based on the same concepts, with a difference that Liouville equation for the conditional probability distribution is introduced as a macroscopic constraint given by a phase space average. We consider the incomplete nature of our information about microscopic dynamics in a rational way that is consistent with Jaynes' formulation of predictive statistical mechanics, and the concept of macroscopic reproducibility for time dependent processes. Maximization of the conditional information entropy subject to this macroscopic constraint leads to a loss of correlation between the initial phase space paths and final microstates. Information entropy is the theoretic upper bound on the conditional information entropy, with the upper bound attained only in case of the complete loss of correlation. In this alternative approach to prediction of macroscopic time evolution, maximization of the conditional information entropy is equivalent to the loss of statistical correlation, and leads to corresponding loss of information. In accordance with the original idea of Jaynes, irreversibility appears as a consequence of gradual loss of information about possible microstates of the system.
Portable inference engine: An extended CLIPS for real-time production systems
NASA Technical Reports Server (NTRS)
Le, Thach; Homeier, Peter
1988-01-01
The present C-Language Integrated Production System (CLIPS) architecture has not been optimized to deal with the constraints of real-time production systems. Matching in CLIPS is based on the Rete Net algorithm, whose assumption of working memory stability might fail to be satisfied in a system subject to real-time dataflow. Further, the CLIPS forward-chaining control mechanism with a predefined conflict resultion strategy may not effectively focus the system's attention on situation-dependent current priorties, or appropriately address different kinds of knowledge which might appear in a given application. Portable Inference Engine (PIE) is a production system architecture based on CLIPS which attempts to create a more general tool while addressing the problems of real-time expert systems. Features of the PIE design include a modular knowledge base, a modified Rete Net algorithm, a bi-directional control strategy, and multiple user-defined conflict resolution strategies. Problems associated with real-time applications are analyzed and an explanation is given for how the PIE architecture addresses these problems.
An expert system shell for inferring vegetation characteristics: Atmospheric techniques (Task G)
NASA Technical Reports Server (NTRS)
Harrison, P. Ann; Harrison, Patrick R.
1993-01-01
The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The VEG Subgoals have been reorganized into categories. A new subgoal category 'Atmospheric Techniques' containing two new subgoals has been implemented. The subgoal Atmospheric Passes allows the scientist to take reflectance data measured at ground level and predict what the reflectance values would be if the data were measured at a different atmospheric height. The subgoal Atmospheric Corrections allows atmospheric corrections to be made to data collected from an aircraft or by a satellite to determine what the equivalent reflectance values would be if the data were measured at ground level. The report describes the implementation and testing of the basic framework and interface for the Atmospheric Techniques Subgoals.
Tribal particle swarm optimization for neurofuzzy inference systems and its prediction applications
NASA Astrophysics Data System (ADS)
Chen, Cheng-Hung; Liao, Yen-Yun
2014-04-01
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.
Grain Size Estimation of Superalloy Inconel 718 After Upset Forging by a Fuzzy Inference System
NASA Astrophysics Data System (ADS)
Toro, Luis; Cavazos, Alberto; Colás, Rafael
2009-12-01
A fuzzy logic inference system was designed to predict the grain size of Inconel 718 alloy after upset forging. The system takes as input the original grain size, temperature, and reduction rate at forging and predicts the final grain size at room temperature. It is assumed that the system takes into account the effects that the heterogeneity of deformation and grain growth exerts in this particular material. Experimental trials were conducted in a factory that relies on upset forging to produce preforms for ring rolling. The grain size was reported as ASTM number, as this value is used on site. A first attempt was carried out using a series of 15 empirically based set of rules; the estimation error with these was above two ASTM numbers; which is considered to be very high. The system was modified and expanded to take into account 28 rules; the estimation error of this new system resulted to be close to one ASTM number, which is considered to be adequate for the prediction.
Petrov, S.
1996-10-01
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.
The Role of Probability-Based Inference in an Intelligent Tutoring System.
ERIC Educational Resources Information Center
Mislevy, Robert J.; Gitomer, Drew H.
Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring…
Bayesian Model Comparison and Parameter Inference in Systems Biology Using Nested Sampling
Pullen, Nick; Morris, Richard J.
2014-01-01
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
Jianqiang Yi; Naoyoshi Yubazaki; Kaoru Hirota
2001-01-01
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.
NASA Technical Reports Server (NTRS)
Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.
1985-01-01
This paper presents the performance analysis results of a fault inferring nonlinear detection system (FINDS) using integrated avionics sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment. First, an overview of the FINDS algorithm structure is given. Then, aircraft state estimate time histories and statistics for the flight data sensors are discussed. This is followed by an explanation of modifications made to the detection and decision functions in FINDS to improve false alarm and failure detection performance. Next, the failure detection and false alarm performance of the FINDS algorithm are analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minutes of flight data. Results indicate that the detection speed, failure level estimation, and false alarm performance show a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed is faster for filter measurement sensors such as MLS than for filter input sensors such as flight control accelerometers. Finally, the progress in modifications of the FINDS algorithm design to accommodate flight computer constraints is discussed.
A neural network mode inference engine for the advisory system for training and safety
Nguyen, Thinh Xuan
1996-01-01
nominal flight conditions. The ANNSR performed better than the fuzzy logic based SR in flight tests on the Engineering Flight Simulator (EFS). The ANNSR returned correct mode inferences which were slightly more accurate than the fuzzy logic SR during...
A neural network mode inference engine for the advisory system for training and safety
Nguyen, Thinh Xuan
1996-01-01
logic membership functions. Although functional, the limitations of this method have prompted the development of an artificial neural network based SR (ANNSR). The goal of the ANNSR was to provide more accurate mode inferences, particularly during off...
Tfwala, Samkele S.; Wang, Yu-Min; Lin, Yu-Chieh
2013-01-01
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
The Gaia astrophysical parameters inference system (Apsis). Pre-launch description
NASA Astrophysics Data System (ADS)
Bailer-Jones, C. A. L.; Andrae, R.; Arcay, B.; Astraatmadja, T.; Bellas-Velidis, I.; Berihuete, A.; Bijaoui, A.; Carrión, C.; Dafonte, C.; Damerdji, Y.; Dapergolas, A.; de Laverny, P.; Delchambre, L.; Drazinos, P.; Drimmel, R.; Frémat, Y.; Fustes, D.; García-Torres, M.; Guédé, C.; Heiter, U.; Janotto, A.-M.; Karampelas, A.; Kim, D.-W.; Knude, J.; Kolka, I.; Kontizas, E.; Kontizas, M.; Korn, A. J.; Lanzafame, A. C.; Lebreton, Y.; Lindstrøm, H.; Liu, C.; Livanou, E.; Lobel, A.; Manteiga, M.; Martayan, C.; Ordenovic, Ch.; Pichon, B.; Recio-Blanco, A.; Rocca-Volmerange, B.; Sarro, L. M.; Smith, K.; Sordo, R.; Soubiran, C.; Surdej, J.; Thévenin, F.; Tsalmantza, P.; Vallenari, A.; Zorec, J.
2013-11-01
The Gaia satellite will survey the entire celestial sphere down to 20th magnitude, obtaining astrometry, photometry, and low resolution spectrophotometry on one billion astronomical sources, plus radial velocities for over one hundred million stars. Itsmain objective is to take a census of the stellar content of our Galaxy, with the goal of revealing its formation and evolution. Gaia's unique feature is the measurement of parallaxes and proper motions with hitherto unparalleled accuracy for many objects. As a survey, the physical properties of most of these objects are unknown. Here we describe the data analysis system put together by the Gaia consortium to classify these objects and to infer their astrophysical properties using the satellite's data. This system covers single stars, (unresolved) binary stars, quasars, and galaxies, all covering a wide parameter space. Multiple methods are used for many types of stars, producing multiple results for the end user according to different models and assumptions. Prior to its application to real Gaia data the accuracy of these methods cannot be assessed definitively. But as an example of the current performance, we can attain internal accuracies (rms residuals) on F, G, K, M dwarfs and giants at G = 15 (V = 15-17) for a wide range of metallicites and interstellar extinctions of around 100 K in effective temperature (Teff), 0.1 mag in extinction (A0), 0.2 dex in metallicity ([Fe/H]), and 0.25 dex in surface gravity (log g). The accuracy is a strong function of the parameters themselves, varying by a factor of more than two up or down over this parameter range. After its launch in December 2013, Gaia will nominally observe for five years, during which the system we describe will continue to evolve in light of experience with the real data.
Alberto Segre; Charles Elkan; Daniel Scharstein; Geoffrey Gordon; Alexander Russell
Automatically improving the performance of inference engines is a central issue in automated deduction research. This paper\\u000a describes and evaluates mechanisms for speeding up search in an inference engine used in research on reactive planning. The\\u000a inference engine is adaptive in the sense that its performance improves with experience. This improvement is obtained via\\u000a a combination of several different learning
Robson, Barry
2007-08-01
What is the Best Practice for automated inference in Medical Decision Support for personalized medicine? A known system already exists as Dirac's inference system from quantum mechanics (QM) using bra-kets and bras where A and B are states, events, or measurements representing, say, clinical and biomedical rules. Dirac's system should theoretically be the universal best practice for all inference, though QM is notorious as sometimes leading to bizarre conclusions that appear not to be applicable to the macroscopic world of everyday world human experience and medical practice. It is here argued that this apparent difficulty vanishes if QM is assigned one new multiplication function @, which conserves conditionality appropriately, making QM applicable to classical inference including a quantitative form of the predicate calculus. An alternative interpretation with the same consequences is if every i = radical-1 in Dirac's QM is replaced by h, an entity distinct from 1 and i and arguably a hidden root of 1 such that h2 = 1. With that exception, this paper is thus primarily a review of the application of Dirac's system, by application of linear algebra in the complex domain to help manipulate information about associations and ontology in complicated data. Any combined bra-ket can be shown to be composed only of the sum of QM-like bra and ket weights c(), times an exponential function of Fano's mutual information measure I(A; B) about the association between A and B, that is, an association rule from data mining. With the weights and Fano measure re-expressed as expectations on finite data using Riemann's Incomplete (i.e., Generalized) Zeta Functions, actual counts of observations for real world sparse data can be readily utilized. Finally, the paper compares identical character, distinguishability of states events or measurements, correlation, mutual information, and orthogonal character, important issues in data mining and biomedical analytics, as in QM. PMID:17608401
Barrier Effects of Tungsten Infer-Layer for Aluminum Diffusion in Aluminum\\/Silicon Ohmic-Contact System
Tohru Hara; Shuichi Enomoto; Noboru Ohtsuka; Shohei Shima
1985-01-01
The barrier effects of tungsten inter-layers for aluminum diffusion have been studied in an aluminum\\/silicon ohmic-contact system, where the tungsten layers were deposited by chemical vapor deposition (CVD) and sputtering. Sintering was carried out at temperatures ranging from 450°C to 550°C and the interfacial reaction was then studied by 1.5 MeV He+ Rutherford backscattering spectroscopy. In the CVD tungsten infer-layer,
Yoichiro KannoJason; Jason C. Vokoun; Benjamin H. Letcher
2011-01-01
Brook trout Salvelinus fontinalis populations have declined in much of the native range in eastern North America and populations are typically relegated to\\u000a small headwater streams in Connecticut, USA. We used sibship reconstruction to infer mating systems, dispersal and effective\\u000a population size of resident (non-anadromous) brook trout in two headwater stream channel networks in Connecticut. Brook trout\\u000a were captured via
Neto, Elias Chaibub; Keller, Mark P.; Attie, Alan D.; Yandell, Brian S.
2010-01-01
Causal inference approaches in systems genetics exploit quantitative trait loci (QTL) genotypes to infer causal relationships among phenotypes. The genetic architecture of each phenotype may be complex, and poorly estimated genetic architectures may compromise the inference of causal relationships among phenotypes. Existing methods assume QTLs are known or inferred without regard to the phenotype network structure. In this paper we develop a QTL-driven phenotype network method (QTLnet) to jointly infer a causal phenotype network and associated genetic architecture for sets of correlated phenotypes. Randomization of alleles during meiosis and the unidirectional influence of genotype on phenotype allow the inference of QTLs causal to phenotypes. Causal relationships among phenotypes can be inferred using these QTL nodes, enabling us to distinguish among phenotype networks that would otherwise be distribution equivalent. We jointly model phenotypes and QTLs using homogeneous conditional Gaussian regression models, and we derive a graphical criterion for distribution equivalence. We validate the QTLnet approach in a simulation study. Finally, we illustrate with simulated data and a real example how QTLnet can be used to infer both direct and indirect effects of QTLs and phenotypes that co-map to a genomic region. PMID:21218138
Inductive Inference: Theory and Methods
Dana Angluin; Carl H. Smith
1983-01-01
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
On quantum statistical inference
Ole E. Barndorff-Nielsen; Richard D. Gill; Peter E. Jupp
2003-01-01
Interest in problems of statistical inference connected to measurements of quantum systems has recently increased substantially, in step with dramatic new developments in experimental techniques for studying small quantum systems. Furthermore, developments in the theory of quantum measurements have brought the basic mathematical framework for the probability calculations much closer to that of classical probability theory. The present paper reviews
NASA Technical Reports Server (NTRS)
Sigwarth, John B.; Bekerat, Hamed A.
2008-01-01
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.
Ganesan, S; Victoire, T Aruldoss Albert; Vijayalakshmy, G
2014-01-01
In this paper, the work is mainly concentrated on removing non-linear parameters to make the physiological signals more linear and reducing the complexity of the signals. This paper discusses three different types of techniques that can be successfully utilised to remove non-linear parameters in EEG and ECG. (i) Transformation technique using Discrete Walsh-Hadamard Transform (DWHT); (ii) application of fuzzy logic control and (iii) building the Adaptive Neuro-Fuzzy Inference System (ANFIS) model for fuzzy. This work has been inspired by the need to arrive at an efficient, simple, accurate and quicker method for analysis of bio-signal. PMID:24589837
Daili Zhang
2010-01-01
Increasing societal demand for automation has led to considerable efforts to control large-scale complex systems, especially in the area of autonomous intelligent control methods. The control system of a large-scale complex system needs to satisfy four system level requirements: robustness, flexibility, reusability, and scalability. Corresponding to the four system level requirements, there arise four major challenges. First, it is difficult
Compatibility and reuse in component-based systems via type and unit inference Christian Kuhnel1
systems like adaptive cruise control, engine man- agement, or electronic brake systems, reuse is often con- proach to systems design is predominant, e. g., as in embed- ded control systems which are often modelled, such as automotive and embedded control systems in general. Tool chains used in these domains are often based on MAT
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
2010-01-01
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. PMID:20542910
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
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.
Adaptive fuzzy system for 3-D vision
NASA Technical Reports Server (NTRS)
Mitra, Sunanda
1993-01-01
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.
The Birth Environment of the Solar System Inferred from a "Mixing-Fallback" Supernova Model
NASA Astrophysics Data System (ADS)
Miki, J.; Takigawa, A.; Tachibana, S.; Huss, G. R.
2007-03-01
The birth environment of the solar system was evaluated from abundances of short-lived radionuclides and a mixing-fallback supernova model. The solar system may have formed within several parsec from a massive star with >20 solar mass.
Inference and learning methodology of belief-rule-based expert system for pipeline leak detection
Dong-ling Xu; Jun Liu; Jian-bo Yang; Guo-ping Liu; Jin Wang; Ian Jenkinson; Jun Ren
2007-01-01
Belief rule based expert systems are an extension of traditional rule based systems and are capable of representing more complicated causal relationships using different types of information with uncertainties. This paper describes how the belief rule based expert systems can be trained and used for pipeline leak detection. Pipeline operations under different conditions are modelled by a belief rule base
A hybrid conceptual-fuzzy inference streamflow modelling for the Letaba River system in South Africa
NASA Astrophysics Data System (ADS)
Katambara, Zacharia; Ndiritu, John G.
There has been considerable water resources developments in South Africa and other regions in the world in order to meet the ever-increasing water demands. These developments have not been matched with a similar development of hydrological monitoring systems and hence there is inadequate data for managing the developed water resources systems. The Letaba River system ( Fig. 1) is a typical case of such a system in South Africa. The available water on this river is over-allocated and reliable daily streamflow modelling of the Letaba River that adequately incorporates the main components and processes would be an invaluable aid to optimal operation of the system. This study describes the development of a calibrated hybrid conceptual-fuzzy-logic model and explores its capability in reproducing the natural processes and human effects on the daily stream flow in the Letaba River. The model performance is considered satisfactory in view of the complexity of the system and inadequacy of relevant data. Performance in modelling streamflow improves towards the downstream and matches that of a stand-alone fuzzy-logic model. The hybrid model obtains realistic estimates of the major system components and processes including the capacities of the farm dams and storage weirs and their trajectories. This suggests that for complex data-scarce River systems, hybrid conceptual-fuzzy-logic modelling may be used for more detailed and dependable operational and planning analysis than stand-alone fuzzy modelling. Further work will include developing and testing other hybrid model configurations.
Bugs as deviant behavior: a general approach to inferring errors in systems code
Dawson Engler; David Yu Chen; Seth Hallem; Andy Chou; Benjamin Chelf
2001-01-01
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
Inferring population history from genealogies
Lohse, Konrad R.
2010-01-01
This thesis investigates a range of genealogical approaches to making quantitative inferences about the spatial and demographic history of populations with application to two insect systems: A local radiation of high ...
Magnetospheric current systems as inferred from SYM and ASY mid-latitude indices
NASA Astrophysics Data System (ADS)
Ganushkina, Natalia; Dubyagin, Stepan
2015-04-01
Separating the contributions from different current systems from point magnetic field measurements and interpreting them as belonging to one system or another is very difficult, and caution must be used when deciphering near-Earth currents from either data or modeling results. At the same time, there are other continuously measured quantities, which can provide, though indirectly, information about the dynamics of the magnetospheric current systems. The SYM-H and ASY-H indices, computed from the observations of magnetic field at low latitude ground-based stations, contain contributions from major magnetospheric current systems, such as the symmetric and asymmetric ring current, tail current, magnetopause currents and field-aligned currents. Highly distorted magnetospheric magnetic field during storm times due to disturbances in the current systems is reflected in the SYM-H and ASY-H observed variations. Using empirical magnetospheric models we study the relative contribution from different current systems to the SYM and ASY mid-latitude indices. It was found that the models can reproduce ground based mid-latitude indices rather well. The good agreement between the indices computed using magnetospheric models and real ones indicates that purely ionospheric current systems, on average, give modest contribution to these indices. The superposed epoch analysis of the indices computed using the models shows that the cross-tail current gives dominant contribution to SYM-H index during the main phase though this contribution can not be separated from FAC region 2 and partial ring current contributions since these systems are overlapped. The relative contribution from symmetric ring current to SYM-H starts to increase a bit prior or just after SYM-H minimum and attains its maximum during recovery phase. The ASY-H and ASY-D indices are controlled by interplay between three current systems which close via the ionosphere. The region 1 FAC gives the largest contribution to ASY-H and ASY-D indices during the main phase, though, region 2 FAC and partial ring current contributions are also prominent. The partial ring current is the main contributor to the ASY indices during the recovery phase. In addition, we discuss the application of these results to resolving the long-debated inconsistencies of the substorm-controlled geomagnetic storm scenario.
Simultaneous Nonparametric Inference in a One-Way Layout Using the SAS® System
Paul Juneau
The process of discovering a novel medicine is one fraught with many unknowns. Even if a fundamental understanding of the biological systems involved in the disease process exists, the effect of a novel agent or agents on an organism may be difficult to predict or characterize. Moreover, knowledge about the corresponding measurement properties (e.g., distribution) is generally very limited. For
WiP Abstract: Can Cyber-Physical Systems be Predictable? Inferring Cyber-Workloads
Rajkumar, Ragunathan
a uni-modal task or a multi-modal task. We apply a conventional workload analysis [1] to uni-modal tasks that the traditional work- load analysis works well for uni-modal tasks and the regression methods are suitable medical de- vices, smart cars, distributed transportation systems, smart grids, and planetary robots
Towards Automatic Inference of Task Hierarchies in Complex Systems , Chongnan Gao
Wang, Deli
their detection and analysis difficult. The inherent complexity of such systems further ob- structs understanding client requests, in a series of stages, which can be distributed across multiple machines, processes synchronization points. Based on the task model, developers can better understand the structures of components
NASA Astrophysics Data System (ADS)
Bearden, Kathryn
Interpenetrating polymer networks (IPNs), where polymer chains mechanically entangle during network formation, are of interest for their unique properties. The reaction sequence of a DGEBF epoxy/polybutadiene-dimethacrylate simultaneous IPN system was varied with differing catalysts to observe the correlation between reaction steps and physical properties. When the acrylate components were reacted first an IPN with two glass transitions and discrete phase separation was observed via scanning electron microscopy (SEM). When all components were reacted in parallel, two glass transitions were also observed but the morphology presented a single phase or a visible macro phase seperation. The IPN showed an increase in fracture toughness but a decrease in tensile strength compared to the single phase system and an epoxy control. Varying the amounts of polybutadiene-dimethacrylate in relation to the epoxy also showed a limit to the toughening effect.
W. E. Holt; N. Chamot-Rooke; X. Le Pichon; A. J. Haines; B. Shen-Tu; J. Ren
2000-01-01
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
Mannis van Oven; Mark Vermeulen; Manfred Kayser
2011-01-01
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
Breast cancer image assessment using an adaptative network-based fuzzy inference system
F. C. Fernandes; L. M. Brasil; J. M. Lamas; R. Guadagnin
2010-01-01
In Brazil breast cancer is the foremost cause of fatality by cancer for women. Given that the causes are unidentified, it\\u000a cannot be prevented. Mammography is one of the most reliable exams for breast cancer detection and it is based on image analysis\\u000a by radiologists. Early detection is the key issue for breast cancer control and computer-aided diagnosis system can
Early history of Earth's crust-mantle system inferred from hafnium isotopes in chondrites
Martin Bizzarro; Joel A. Baker; Henning Haack; David Ulfbeck; Minik Rosing
2003-01-01
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
Woodford, Neil; Eastaway, Anne T.; Ford, Michael; Leanord, Alistair; Keane, Chloe; Quayle, Reinhard M.; Steer, Jane A.; Zhang, Jiancheng; Livermore, David M.
2010-01-01
We assessed the ability of three commercial systems to infer carbapenem resistance mechanisms in 39 carbapenemase-producing and 16 other carbapenem-resistant Enterobacteriaceae. The sensitivity/specificity values for “flagging” a likely carbapenemase were 100%/0% (BD Phoenix), 82 to 85%/6 to 19% (MicroScan), and 74%/38% (Vitek 2), respectively. OXA-48 producers were poorly detected, but all systems reliably detected isolates with KPC and most with metallo-carbapenemases. PMID:20534805
NASA Astrophysics Data System (ADS)
Zlotnicki, Jacques; Toutain, Jean Paul; Sasai, Yoichi; Villacorte, Egardo; Bernard, Alain; Fauquet, Frederic; Nagao, Toshiyatsu
2010-05-01
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.
NASA Astrophysics Data System (ADS)
Daud, H.; Razali, R.; Low, T. J.; Sabdin, M.; Zafrul, S. Z. Mohd
2014-06-01
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.
Davies, Andrew J; Hope, Max J
2015-07-15
Contingency plans are essential in guiding the response to marine oil spills. However, they are written before the pollution event occurs so must contain some degree of assumption and prediction and hence may be unsuitable for a real incident when it occurs. The use of Bayesian networks in ecology, environmental management, oil spill contingency planning and post-incident analysis is reviewed and analysed to establish their suitability for use as real-time environmental decision support systems during an oil spill response. It is demonstrated that Bayesian networks are appropriate for facilitating the re-assessment and re-validation of contingency plans following pollutant release, thus helping ensure that the optimum response strategy is adopted. This can minimise the possibility of sub-optimal response strategies causing additional environmental and socioeconomic damage beyond the original pollution event. PMID:26006775
Network-assisted crop systems genetics: network inference and integrative analysis.
Lee, Tak; Kim, Hyojin; Lee, Insuk
2015-04-01
Although next-generation sequencing (NGS) technology has enabled the decoding of many crop species genomes, most of the underlying genetic components for economically important crop traits remain to be determined. Network approaches have proven useful for the study of the reference plant, Arabidopsis thaliana, and the success of network-based crop genetics will also require the availability of a genome-scale functional networks for crop species. In this review, we discuss how to construct functional networks and elucidate the holistic view of a crop system. The crop gene network then can be used for gene prioritization and the analysis of resequencing-based genome-wide association study (GWAS) data, the amount of which will rapidly grow in the field of crop science in the coming years. PMID:25698380
NASA Technical Reports Server (NTRS)
Harrison, P. Ann
1993-01-01
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.
NASA Astrophysics Data System (ADS)
Mohamed, A.; Sultan, M.; Ahmed, M.; Yan, E.
2014-12-01
The Nubian Sandstone Aquifer System (NSAS) is shared by Egypt, Libya, Chad and Sudanand is one of the largest (area: ~ 2 × 106 km2) groundwater systems in the world. Despite its importance to the population of these countries, major hydrological parameters such as modern recharge and extraction rates remain poorly investigated given: (1) the large extent of the NSAS, (2) the absence of comprehensive monitoring networks, (3) the general inaccessibility of many of the NSAS regions, (4) difficulties in collecting background information, largely included in unpublished governmental reports, and (5) limited local funding to support the construction of monitoring networks and/or collection of field and background datasets. Data from monthly Gravity Recovery and Climate Experiment (GRACE) gravity solutions were processed (Gaussian smoothed: 100 km; rescaled) and used to quantify the modern recharge to the NSAS during the period from January 2003 to December 2012. To isolate the groundwater component in GRACE data, the soil moisture and river channel storages were removed using the outputs from the most recent Community Land Model version 4.5 (CLM4.5). GRACE-derived recharge calculations were performed over the southern NSAS outcrops (area: 835 × 103 km2) in Sudan and Chad that receive average annual precipitation of 65 km3 (77.5 mm). GRACE-derived recharge rates were estimated at 2.79 ± 0.98 km3/yr (3.34 ± 1.17 mm/yr). If we take into account the total annual extraction rates (~ 0.4 km3; CEDARE, 2002) from Chad and Sudan the average annual recharge rate for the NSAS could reach up to ~ 3.20 ± 1.18 km3/yr (3.84 ± 1.42 mm/yr). Our recharge rates estimates are similar to those calculated using (1) groundwater flow modelling in the Central Sudan Rift Basins (4-8 mm/yr; Abdalla, 2008), (2) WaterGAP global scale groundwater recharge model (< 5 mm/yr, Döll and Fiedler, 2008), and (3) chloride tracer in Sudan (3.05 mm/yr; Edmunds et al. 1988). Given the available global coverage of the temporal GRACE solutions for the past twelve years and plans are underway for the deployment of a GRACE follow-On and GRACE-II missions, we suggest that within the next few years, GRACE will probably become the most practical, informative, and cost-effective tool for monitoring the recharge of large aquifers across the globe.
Early accretion of protoplanets inferred from a reduced inner solar system 26Al inventory
NASA Astrophysics Data System (ADS)
Schiller, Martin; Connelly, James N.; Glad, Aslaug C.; Mikouchi, Takashi; Bizzarro, Martin
2015-06-01
The mechanisms and timescales of accretion of 10-1000 km sized planetesimals, the building blocks of planets, are not yet well understood. With planetesimal melting predominantly driven by the decay of the short-lived radionuclide 26Al (26Al?26Mg; t1/2 = 0.73 Ma), its initial abundance determines the permissible timeframe of planetesimal-scale melting and its subsequent cooling history. Currently, precise knowledge about the initial 26Al abundance [(26Al/27Al)0] exists only for the oldest known solids, calcium aluminum-rich inclusions (CAIs) - the so-called canonical value. We have determined the 26Al/27Al of three angrite meteorites, D'Orbigny, Sahara 99555 and NWA 1670, at their time of crystallization, which corresponds to (3.98 ± 0.15) ×10-7, (3.64 ± 0.18) ×10-7, and (5.92 ± 0.59) ×10-7, respectively. Combined with a newly determined absolute U-corrected Pb-Pb age for NWA 1670 of 4564.39 ± 0.24 Ma and published U-corrected Pb-Pb ages for the other two angrites, this allows us to calculate an initial (26Al/27Al)0 of (1.33-0.18+0.21) ×10-5 for the angrite parent body (APB) precursor material at the time of CAI formation, a value four times lower than the accepted canonical value of 5.25 ×10-5. Based on their similar 54Cr/52Cr ratios, most inner solar system materials likely accreted from material containing a similar 26Al/27Al ratio as the APB precursor at the time of CAI formation. To satisfy the abundant evidence for widespread planetesimal differentiation, the subcanonical 26Al budget requires that differentiated planetesimals, and hence protoplanets, accreted rapidly within 0.25 ± 0.15 Ma of the formation of canonical CAIs.
Final Report: Large-Scale Optimization for Bayesian Inference in Complex Systems
Ghattas, Omar [The University of Texas at Austin] [The University of Texas at Austin
2013-10-15
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.
Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien
2013-01-01
Background An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. Methods The ANFIS and ANN models were compared in terms of six statistical indices calculated by comparing their prediction results with actual data: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R2). Graphical plots were also used for model comparison. Conclusions The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. PMID:23705023
M. Jayabharata Reddy; Dusmanta Kumar Mohanta
2008-01-01
This paper employs a wavelet multiresolution analysis (MRA) along with the adaptive-network-based fuzzy inference system to overcome the difficulties associated with conventional voltage- and current-based measurements for transmission-line fault location algorithms, due to the effect of factors such as fault inception angle, fault impedance, and fault distance. This proposed approach is different from conventional algorithms that are based on deterministic
Artificial Intelligence in Public Health Prevention of Legionelosis in Drinking Water Systems
Sin?ak, Peter; Ondo, Jaroslav; Kaposztasova, Daniela; Vir?ikova, Maria; Vranayova, Zuzana; Sabol, Jakub
2014-01-01
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
Artificial intelligence in public health prevention of legionelosis in drinking water systems.
Sin?ak, Peter; Ondo, Jaroslav; Kaposztasova, Daniela; Vir?ikova, Maria; Vranayova, Zuzana; Sabol, Jakub
2014-08-01
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
David B. Goldstein; Gary W. Roemer; Deborah A. Smith; David E. Reich; Aviv Bergman; Robert K. Wayne
1999-01-01
To assess the reliability of genetic markers it is important to compare inferences that are based on them to a priori expectations. In this article we present an analysis of microsatellite variation within and among populations of island foxes (Urocyon littoralis) on California's Channel Islands. We first show that microsatel- lite variation at a moderate number of loci (19) can
NASA Astrophysics Data System (ADS)
Minami, S.; Iguchi, M.; Mikada, H.; Goto, T.; Takekawa, J.
2010-12-01
We studied the ground deformation associated with the eruption at the Showa crater of Sakurajima, which has been active since 2006. Using the Mogi’s spherical pressure model, a volume change of magma chambers can be estimated from the displacement, tilt, or strain observations near the ground surface. After the application of the Mogi’s model to data in the past observations, the existence of two magma chambers has been inferred beneath the Sakurajima down to a depth of 5 km. The tilt and strain data in 2 underground tunnel sites observed 36 hours before an eruption in April 9, 2009, are analyzed to reveal the behavior of magma leading to eruption. From these data, there seems to be a time lag in the inflation between the two magma chambers at a depth of 4km and 0.1km, respectively. In addition, the order of the volume change of the shallow source is about one tenth of that of the deep one. A system which consists of shallow and deep magma chambers and a vertical conduit connecting them is numerically modeled to investigate the mechanism of the time lag and why the difference in the magnitude of the volumetric changes in the two chambers appears as described above. The initial values of magma properties in the deep magma chamber are assumed from the volcanic ejecta of Sakurajima volcano. We assumed that magma is supplied with a constant rate to the deep magma chamber. The two different pressure limits are assigned to the deep and shallow chamber, respectively: (1) one triggers the magma uprise from the deep to the shallow, and (2) the other to start to erupt. In a one-dimentional steady flow model of a magma conduit, we consider the vesiculation of volatile-bearing magma, gas escape and overpressure in the bubble due to the viscous resistance, which largely influences the physical properties of magma. Although our simulation results cannot exactly describe the data, we confirmed that our hypothetical model with two triggers could explain the time lag of the inflation and the difference in the order of the volume change. We would like to propose that the numerical simulation could be a powerful tool for understanding the behavior of magma before eruption once ground deformations are well observed.
NASA Astrophysics Data System (ADS)
Wolpert, David H.
2008-07-01
We show that physical devices that perform observation, prediction, or recollection share an underlying mathematical structure. We call devices with that structure “inference devices”. We present a set of existence and impossibility results concerning inference devices. These results hold independent of the precise physical laws governing our universe. In a limited sense, 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, these impossibility results can be viewed as a non-quantum-mechanical “uncertainty principle”. The mathematics of inference devices has close connections to the mathematics of Turing Machines (TMs). In particular, the impossibility results for inference devices are similar to the Halting theorem for TMs. Furthermore, one can define an analog of Universal TMs (UTMs) for inference devices. We call those analogs “strong inference devices”. We use strong inference devices to define the “inference complexity” of an inference task, which is the analog of the Kolmogorov complexity of computing a string. A task-independent bound is derived on how much the inference complexity of an inference task can differ for two different inference devices. This is analogous to the “encoding” bound governing how much the Kolmogorov complexity of a string can differ between two UTMs used to compute that string. However no universe can contain more than one strong inference device. So whereas the Kolmogorov complexity of a string is arbitrary up to specification of the UTM, there is no such arbitrariness in the inference complexity of an inference task. We informally discuss the philosophical implications of these results, e.g., for whether the universe “is” a computer. We also derive some graph-theoretic properties governing any set of multiple inference devices. We also present an extension of the framework to address physical devices used for control. We end with an extension of the framework to address probabilistic inference.
Neuro-fuzzy controller for gas turbine in biomass-based electric power plant
Francisco Jurado; Manuel Ortega; Antonio Cano; José Carpio
2002-01-01
Biomass gasification is a technology that transforms solid biomass into syngas. The gas turbine controller regulates both the gas turbine and the gas turbine generator. Two fuzzy logic controllers have been developed using speed and mechanical power deviations, and a neural network has been designed to tune the gains of the fuzzy logic controllers based on the operating conditions of
Neuro-fuzzy controller in biomass-based electric power plant
Francisco Jurado; A. Lopez; J. R. Saenz
2001-01-01
Biomass gasification is a technology that transforms solid biomass into gas. The gas motor controller regulates both the gas motor and the gas motor generator. In this paper two fuzzy logic controllers have been developed using speed and mechanical power deviations, and a neural network has been designed to tune the gains of the fuzzy logic controllers based on the
Neuro-fuzzy based Motion Control of a Robotic Exoskeleton: Considering End-effector Force Vectors
Kazuo Kiguchi; Mohammad Habibur Rahman; Makoto Sasaki
2006-01-01
To assist physically disabled, injured, and\\/or elderly persons, we have been developing a 3DOF exoskeleton robot for assisting upper-limb motion, since upper-limb motion is involved in a lot of activities of everyday life. The exoskeleton robot is mainly is controlled by the skin surface electromyogram (EMG) signals, since EMG signals of muscles directly reflect how the user intends to move.
Neuro-fuzzy control of a robotic exoskeleton with EMG signals
Kazuo Kiguchi; Takakazu Tanaka; Toshio Fukuda
2004-01-01
We have been developing robotic exoskeletons to assist motion of physically weak persons such as elderly, disabled, and injured persons. The robotic exoskeleton is controlled basically based on the electromyogram (EMG) signals, since the EMG signals of human muscles are important signals to understand how the user intends to move. Even though the EMG signals contain very important information, however,
Representation and Reasoning Under Uncertainty in Deception Detection: A Neuro-Fuzzy Approach
Lina Zhou; Azene Zenebe
2008-01-01
An analysis of the process and human cognitive model of deception detection (DD) shows that DD is infused with uncertainty, especially in high-stake situations. There is a recent trend toward automating DD in computer-mediated communication. However, extant approaches to automatic DD overlook the importance of representation and reasoning under uncertainty in DD. They represent uncertain cues as crisp values and
Total organic carbon content determined from well logs using ?LogR and Neuro Fuzzy techniques
Mohammad Reza Kamali; Ahad Allah Mirshady
2004-01-01
Total organic carbon (TOC) content present in potential source rocks significantly affects the response of several types of well logs. Wireline logs can be used to identify source rocks and serve as an indicator for the source rock potential. Because the source rock intervals generally show a lower density, higher sonic transit time, higher porosity and higher resistivity than other
Neuro-fuzzy control of an MDOF building with a magnetorheological damper using acceleration feedback
Schurter, Kyle Christopher
2000-01-01
techniques are again used with the introduction of a new form of expert control. With this new method, fuzzy logic controllers (FLC) are developed to diminish seismically induced vibrations of single and multiple DOF buildings. The controllers specify a time...
Daniel Fischer; Peter Kohlhepp
2000-01-01
This paper presents a novel solution to the reconstruction of 3D geometry models from partial, segmented (2.5D or 3D) range views. First, the geometric fusion works entirely on sparse symbolic information, i.e. attributed surface graphs, rather than point data or triangulated meshes. Thus, new sensor data can always be integrated with an existing partial model available for symbolic action planning.
Towards intelligent self-care: Multisensor monitoring and neuro-fuzzy behavior modelling
E. Chan; D. Wang; M. Pasquier
2008-01-01
The work reported in this paper investigates the problem of intelligent patient monitoring, whereby a combination of smart sensor technology and computational intelligence techniques is used to observe then understand the behavior of a person in a self-care context, such as a patient in his hospital room or an elderly in her home. The key objective is to automatically derive
Neuro-fuzzy control of vertical vibrations in railcars using magnetorheological dampers
Atray, Vipul Sunil
2002-01-01
Introduction. State Space Representation of Quarter Car Model of Rail Truck. . . . . . Track Irregularity Data. Determination of Design Parameters. Design of MR Dampers Modification of the Piston Staging Area. Conclusion . . 27 . . . . 29 34 35 40...-1000 MR Damper (Spencer et al. 1997). TABLE 2. Simulation Parameters for Quarter Car Model. . TABLE 3. Damping Force for Fully Loaded Railcar with Linear Viscous Damping . . . TABLE 4. Design Parameters for Magnetorheological Damper TABLE 5...
A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller
NASA Astrophysics Data System (ADS)
Amor, Mohamed Anis Ben; Oda, Takeshi; Watanabe, Shigeyoshi
This paper is concerned with the development of a car-steering model for traffic simulation. Our focus in this paper is to propose a model of the steering behavior of a human driver for different driving scenarios. These scenarios are modeled in a unified framework using the idea of target position. The proposed approach deals with the driver’s approximation and decision-making mechanisms in tracking a target position by means of fuzzy set theory. The main novelty in this paper lies in the development of a learning algorithm that has the intention to imitate the driver’s self-learning from his driving experience and to mimic his maneuvers on the steering wheel, using linear networks as local approximators in the corresponding fuzzy areas. Results obtained from the simulation of an obstacle avoidance scenario show the capability of the model to carry out a human-like behavior with emphasis on learned skills.
Neuro-fuzzy model of superelastic shape memory alloys with application to seismic engineering
Ozbulut, Osman Eser
2009-05-15
of SMAs in seismic engineering applications a soft computing approach, namely a neurofuzzy technique is used to model dynamic behavior of CuAlBe shape memory alloy wires. Experimental data are collected from two test programs that have been performed...
Multisensor data fusion for helicopter guidance using neuro-fuzzy estimation algorithms
R. S. Doyle; C. J. Harris
1995-01-01
The main objective of this paper is to present some algorithms to fuse information about obstacles, whose dynamics are a-priori unknown, in a helicopter's environment, provided by multiple spatially separate sensors. The fused information can then be used to help helicopters locate obstacles in hazardous conditions so that it can avoid them. Obstacle track estimation has been commonly carried out
Estimation and optimization of thermal performance of evacuated tube solar collector system
NASA Astrophysics Data System (ADS)
Dikmen, Erkan; Ayaz, Mahir; Ezen, H. Hüseyin; Küçüksille, Ecir U.; ?ahin, Arzu ?encan
2014-05-01
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.
ERIC Educational Resources Information Center
Allen, JoBeth
Since E.L. Thorndike's landmark 1917 study of the complexity of reading comprehension, inferential research has generally focused on either inference as a developmental process or the nature of inferences made during reading. In his 1930 study, R. W. Tyler established that inference could be objectively measured. S. G. Paris conducted several…
Inferring Hierarchical Pronunciation Rules from a Phonetic Dictionary
Bogliolo, Alessandro
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
Eight challenges in phylodynamic inference.
Frost, Simon D W; Pybus, Oliver G; Gog, Julia R; Viboud, Cecile; Bonhoeffer, Sebastian; Bedford, Trevor
2015-03-01
The field of phylodynamics, which attempts to enhance our understanding of infectious disease dynamics using pathogen phylogenies, has made great strides in the past decade. Basic epidemiological and evolutionary models are now well characterized with inferential frameworks in place. However, significant challenges remain in extending phylodynamic inference to more complex systems. These challenges include accounting for evolutionary complexities such as changing mutation rates, selection, reassortment, and recombination, as well as epidemiological complexities such as stochastic population dynamics, host population structure, and different patterns at the within-host and between-host scales. An additional challenge exists in making efficient inferences from an ever increasing corpus of sequence data. PMID:25843391
St. Onge, K. R.; Palmé, A. E.; Wright, S. I.; Lascoux, M.
2012-01-01
Most species have at least some level of genetic structure. Recent simulation studies have shown that it is important to consider population structure when sampling individuals to infer past population history. The relevance of the results of these computer simulations for empirical studies, however, remains unclear. In the present study, we use DNA sequence datasets collected from two closely related species with very different histories, the selfing species Capsella rubella and its outcrossing relative C. grandiflora, to assess the impact of different sampling strategies on summary statistics and the inference of historical demography. Sampling strategy did not strongly influence the mean values of Tajima’s D in either species, but it had some impact on the variance. The general conclusions about demographic history were comparable across sampling schemes even when resampled data were analyzed with approximate Bayesian computation (ABC). We used simulations to explore the effects of sampling scheme under different demographic models. We conclude that when sequences from modest numbers of loci (<60) are analyzed, the sampling strategy is generally of limited importance. The same is true under intermediate or high levels of gene flow (4Nm > 2–10) in models in which global expansion is combined with either local expansion or hierarchical population structure. Although we observe a less severe effect of sampling than predicted under some earlier simulation models, our results should not be seen as an encouragement to neglect this issue. In general, a good coverage of the natural range, both within and between populations, will be needed to obtain a reliable reconstruction of a species’s demographic history, and in fact, the effect of sampling scheme on polymorphism patterns may itself provide important information about demographic history. PMID:22870403
Inference on Counterfactual Distributions
Chernozhukov, Victor V.
Counterfactual distributions are important ingredients for policy analysis and decomposition analysis in empirical economics. In this article, we develop modeling and inference tools for counterfactual distributions based ...
ODE parameter inference using adaptive gradient matching with Gaussian processes
Filippone, Maurizio
predicted from the differen- tial equations, offer a computationally ap- pealing shortcut to the inferenceODE parameter inference using adaptive gradient matching with Gaussian processes F. Dondelinger M Parameter inference in mechanistic models based on systems of coupled differential equa- tions is a topical
Bayesian Inference on Proportional Elections
Brunello, Gabriel Hideki Vatanabe; Nakano, Eduardo Yoshio
2015-01-01
Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software. PMID:25786259
Lienkaemper, James J.; McFarland, Forrest S.; Simpson, Robert W.; Caskey, S. John
2014-01-01
Surface creep rate, observed along five branches of the dextral San Andreas fault system in northern California, varies considerably from one section to the next, indicating that so too may the depth at which the faults are locked. We model locking on 29 fault sections using each section’s mean long?term creep rate and the consensus values of fault width and geologic slip rate. Surface creep rate observations from 111 short?range alignment and trilateration arrays and 48 near?fault, Global Positioning System station pairs are used to estimate depth of creep, assuming an elastic half?space model and adjusting depth of creep iteratively by trial and error to match the creep observations along fault sections. Fault sections are delineated either by geometric discontinuities between them or by distinctly different creeping behaviors. We remove transient rate changes associated with five large (M?5.5) regional earthquakes. Estimates of fraction locked, the ratio of moment accumulation rate to loading rate, on each section of the fault system provide a uniform means to inform source parameters relevant to seismic?hazard assessment. From its mean creep rates, we infer the main branch (the San Andreas fault) ranges from only 20%±10% locked on its central creeping section to 99%–100% on the north coast. From mean accumulation rates, we infer that four urban faults appear to have accumulated enough seismic moment to produce major earthquakes: the northern Calaveras (M 6.8), Hayward (M 6.8), Rodgers Creek (M 7.1), and Green Valley (M 7.1). The latter three faults are nearing or past their mean recurrence interval.
Nonparametric Predictive Utility Inference
B. Houlding; F. P. A. Coolen
This work considers the combination of two strands of recent statistical research: that of decision making with uncertain utility and that of nonparametric predictive inference. In doing so we discuss the use of Nonparametric Predictive Utility Inference (NPUI) within a sequential decision selection problem for the situation of a Decision Maker (DM) who is confronted with a choice set that
Dominic Duggan; Frederick Bent
1996-01-01
Type inference is the compile-time process of reconstructi ng missing type information in a program based on the usage of its variables. ML and Haskell are two languages where this aspect of compilation has enjoyed some popularity, allowing type information to be omitted while static type checking is still performed. Type inference may be expected to have some application in
NSDL National Science Digital Library
Kevin D. Finson
2010-10-01
Learning about what inferences are, and what a good inference is, will help students become more scientifically literate and better understand the nature of science in inquiry. Students in K-4 should be able to give explanations about what they investigat
A Real-Time Intelligent Wireless Mobile Station Location Estimator with Application to TETRA Network
Faihan D. Alotaibi; Adel Abdennour; Adel Ahmed Ali
2009-01-01
Mobile location estimation has received considerable interest over the past few years due to its great potential in different applications such as logistics, patrol, and fleet management. Many mobile location estimation techniques had been proposed to improve the accuracy of location estimation. Location estimation based on artificial intelligence techniques is a recent alternative approach. In this paper, adaptive neuro-fuzzy inference
NASA Technical Reports Server (NTRS)
Aires, Filipe; Rossow, William B.; Hansen, James E. (Technical Monitor)
2001-01-01
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.
Levine, Daniel S., Ph. D. Massachusetts Institute of Technology
2014-01-01
In resource-constrained inferential settings, uncertainty can be efficiently minimized with respect to a resource budget by incorporating the most informative subset of observations - a problem known as active inference. ...
Social Inference Through Technology
NASA Astrophysics Data System (ADS)
Oulasvirta, Antti
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.
Martin, Ralph R.
recognition and general image processing. Neural network and fuzzy logic, together with genetic algorithms. Printed in the Netherlands. 309 Editorial: Neural-Fuzzy Applications in Computer Vision 1. What is Neuro-Fuzzy? Neural networks and fuzzy logic are two bio-mimetic techniques that are used to provide approximations
NASA Technical Reports Server (NTRS)
Jones, Patricia S.; Mitchell, Christine M.; Rubin, Kenneth S.
1988-01-01
The authors proposes an architecture for an expert system that can function as an operator's associate in the supervisory control of a complex dynamic system. Called OFMspert (operator function model (OFM) expert system), the architecture uses the operator function modeling methodology as the basis for the design. The authors put emphasis on the understanding capabilities, i.e., the intent referencing property, of an operator's associate. The authors define the generic structure of OFMspert, particularly those features that support intent inferencing. They also describe the implementation and validation of OFMspert in GT-MSOCC (Georgia Tech-Multisatellite Operations Control Center), a laboratory domain designed to support research in human-computer interaction and decision aiding in complex, dynamic systems.
Pan, Wei
How can we model influence between individuals in a social system, even when the network of interactions is unknown? In this article, we review the literature on the “influence model,” which utilizes independent time series ...
Inference of Stellar Coronal Structure
NASA Astrophysics Data System (ADS)
Schmitt, J. H. M. M.
Unlike the solar corona, stellar coronae cannot be --- directly --- spatially resolved at X-ray wavelengths. Yet stellar coronae are likely to exhibit similar amounts of structure as the solar corona. Currently structural information from such spatially unresolved data can be inferred from rotational modulation of the X-ray emission for single stars and/or eclipses in the case of binary systems as well as from coronal density measurements, which can be obtained from suitably chosen density sensitive line ratios. The most powerful information on structure is contained in Doppler data, however, the spectral resolution of currently X-ray available instrumentation does not permit such measurements. I will discuss some of the observations obtained, and review the methods used to infer structure from these data. Particular emphasis will be placed on the ill-conditioned nature of the inversion problem, that makes it rather difficult to infer the possibly three-dimensional structure of stellar coronae. Finally I will address the prospects of obtaining structural information on other stars with the next generation of X-ray telescopes.
NASA Astrophysics Data System (ADS)
Pedersen, R.; Sigmundsson, F.
2002-12-01
We present measurements of volcano deformation from a series of 18 interferograms spanning the years 1993-2000. The detected deformation originates from repeated intrusions in the Eyjafjallaj”kull system, an icecap covered stratovolcano situated in, what is considered to be, a propagating rift zone in southern Iceland. The volcano erupts infrequently, with only two known eruptions in historic time (last 1100 years). The eruptive products are alkaline in composition, with only small volumes produced in recent eruptions. In spite of the apparent silence of this system two intrusive episodes have been detected within the last decade, causing major concern in the local community. In 1994, and again in 1999, seismic unrest associated with magmatic intrusions occurred in the system. Crustal deformation associated with the events was detected by dry-tilt, GPS and interferometry. During the 1994 episode, the center of deformation was situated underneath the icecap, and the area experiencing maximum uplift was therefore within the zone of decorrelation. The deformation shows an oval fringe pattern, which reaches well beyond the icecap, covering more than 300 km2 in total. Up to 15 cm of LOS ("line of sight") displacement is observed. The temporal resolution of the InSAR images during the 1999 intrusive episode is better and it is possible to follow the development of the intrusive event through time. The center of deformation does not coincide with the center from the 1994 event, but is situated just south of the icecap. The deformation during this event amounts to about 20 cm of LOS. Several of the interferograms cover the whole time-span of the 1999 intrusion, but three interferograms cover different periods of the intrusive event. The data set enables us to follow the temporal development of the crustal deformation created by the intrusion, and hence the growth of the intrusion itself through time. A previous study based on forward modeling of GPS and tilt data suggests similar models for the two events with a Mogi point-source at 3.5 km depth, approximately in the same location 4 km south of the summit crater. The patterns of deformation seen in the interferogram series suggest that this is an oversimplification, as it is evident that the two events had two distinctly separate centers of maximum uplift. We model the deformation created by magma migration through time, by applying a two-step inversion method, in an attempt to generate an image of the magmatic plumbing system within the Eyjafjallaj”kull volcanic system. Relations between the areas of elevated seismicity at the volcano, and the derived plumbing system will be of great importance for improving future hazard assessment.
The CO2 system in the Mediterranean Sea inferred from a 3D coupled physical-biogeochemical model
NASA Astrophysics Data System (ADS)
Ulses, Caroline; Kessouri, Fayçal; Estournel, Claude; Marsaleix, Patrick; Beuvier, Jonathan; Somot, Samuel; Touratier, Frank; Goyet, Catherine; Coppola, Laurent; Diamond, Emilie; Metzl, Nicolas
2015-04-01
The semi-enclosed Mediterranean Sea characterized by short residence times is considered as a region particularly sensitive to natural and anthropogenic forcing. Due to scarce CO2 measurements in the whole basin, the CO2 system, for instance the air-sea CO2 exchanges and the effects of the increase of atmospheric CO2, are poorly characterized. 3D physical-biogeochemical coupled models are unique tools that can provide integrated view and gain understanding in the temporal and spatial variation of the CO2 system variables (dissolved inorganic carbon, total alkalinity, partial pressure of CO2 and pH). An extended version of the biogeochemical model Eco3m-S (Auger et al., 2014), that describes the cycles of carbon, nitrogen, phosphorus and silica, was forced by a regional circulation model (Beuvier et al., 2012) to investigate the CO2 system in the Mediterranean Sea over a 13-years period (2001-2013). First, the quality of the modelling was evaluated through comparisons with satellite and in situ observations collected in the whole basin over the study period (Touratier and Goyet, 2009; 2011 ; Rivaro et al., 2010 ; Pujo-Pay et al., 2011 ; Alvarez et al, 2014). The model reasonably reproduced the various biological regimes (north-western phytoplanctonic bloom regime, oligotrophic eastern regime, etc.) as well as the recorded spatial distribution and temporal variations of the carbonate system variables. The coupled model was then used to estimate the air-sea pCO2 exchanges and the transport of DIC and TA towards the Atlantic Ocean at the Strait of Gibraltar.
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
NASA Astrophysics Data System (ADS)
Tahmasebi, Pejman; Hezarkhani, Ardeshir
2012-05-01
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.
A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation
Tahmasebi, Pejman; Hezarkhani, Ardeshir
2012-01-01
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. PMID:25540468
Chelgani, S.C.; Hart, B.; Grady, W.C.; Hower, J.C.
2011-01-01
The relationship between maceral content plus mineral matter and gross calorific value (GCV) for a wide range of West Virginia coal samples (from 6518 to 15330 BTU/lb; 15.16 to 35.66MJ/kg) has been investigated by multivariable regression and adaptive neuro-fuzzy inference system (ANFIS). The stepwise least square mathematical method comparison between liptinite, vitrinite, plus mineral matter as input data sets with measured GCV reported a nonlinear correlation coefficient (R2) of 0.83. Using the same data set the correlation between the predicted GCV from the ANFIS model and the actual GCV reported a R2 value of 0.96. It was determined that the GCV-based prediction methods, as used in this article, can provide a reasonable estimation of GCV. Copyright ?? Taylor & Francis Group, LLC.
Application of ANN and ANFIS models for reconstructing missing flow data.
Dastorani, Mohammad T; Moghadamnia, Alireza; Piri, Jamshid; Rico-Ramirez, Miguel
2010-07-01
Hydrological yearbooks, especially in developing countries, are full of gaps in flow data series. Filling missing records is needed to make feasibility studies, potential assessment, and real-time decision making. In this research project, it was tried to predict the missing data of gauging stations using data from neighboring sites and a relevant architecture of artificial neural networks (ANN) as well as adaptive neuro-fuzzy inference system (ANFIS). To be able to evaluate the results produced by these new techniques, two traditionally used methods including the normal ratio method and the correlation method were also employed. According to the results, although in some cases all four methods presented acceptable predictions, the ANFIS technique presented a superior ability to predict missing flow data especially in arid land stations with variable and heterogeneous data. Comparing the results, ANN was also found as an efficient method to predict the missing data in comparison to the traditional approaches. PMID:19543999
Real-Time Inference of Mental States from Facial Expressions and Upper Body Gestures
Baltrusaitis, Tadas
We present a real-time system for detecting facial action units and inferring emotional states from head and shoulder gestures and facial expressions. The dynamic system uses three levels of inference on progressively ...
NASA Astrophysics Data System (ADS)
Li, B.; Atakan, K.; Sorensen, M. B.; Havskov, J.
2014-12-01
Earthquake focal mechanisms of the Shanxi rift system, North China, are investigated for the time period 1965 - Apr. 2014. A total of 143 focal mechanisms of ML ? 3.0 earthquakes were compiled. Among them, 105 solutions are newly determined by combining the P-wave first motions and full waveform inversion, and 38 solutions are from available published data. Stress tensor inversion was then performed based on the new database. The results show that most solutions exhibit normal or strike-slip faulting, and the regional stress field is characterized by a stable, dominating NNW-SSE extension and an ENE-WSW compression. This correlates well with results from GPS data, geological field observations and leveling measurements across the faults. Heterogeneity exists in the regional stress field, as indicated by individual stress tensor inversions conducted for five subzones. While the minimum stress axis (?3) appears to be consistent and stable, the orientations, especially the plunges, of the maximum and intermediate stresses (?1 and ?2) vary significantly among the different subzones. Based on our results and combining multidisciplinary observations from geological surveys, GPS and cross-fault monitoring, a kinematic model is proposed, to illustrate the present-day stress field and its correlation with the regional tectonics, as well as the current crustal deformation of the Shanxi rift system. Results obtained in this study, may help to understand the geodynamics, neotectonic activity, active seismicity and potential seismic hazard in this region of North China.
NASA Astrophysics Data System (ADS)
Nimalsiri, Thusitha Bandara; Suriyaarachchi, Nuwan Buddhika; Hobbs, Bruce; Manzella, Adele; Fonseka, Morrel; Dharmagunawardena, H. A.; Subasinghe, Nalaka Deepal
2015-06-01
First comprehensive geothermal exploration in Sri Lanka was conducted in 2010 encompassing seven thermal springs, of which Kapurella records the highest temperature. The study consisted of passive magnetotelluric (MT) soundings, in which static shifts were corrected using time domain electromagnetic method (TDEM). A frequency range of 12,500-0.001 Hz was used for MT acquisition and polar diagrams were employed for dimensionality determination. MT and TDEM data were jointly inverted and 2D models were created using both transverse electric and transverse magnetic modes. A conductive southeast dipping structure is revealed from both phase pseudosections and the preferred 2D inversion model. A conductive formation starting at a depth of 7.5 km shows a direct link with the dipping structure. We suggest that these conductive structures are accounted for deep circulation and accumulation of groundwater. Our results show the geothermal reservoir of Kapurella system with a lateral extension of around 2.5 km and a depth range of 3 km. It is further found that the associated dolerite dike is not the source of heat although it could be acting as an impermeable barrier to form the reservoir. The results have indicated the location of the deep reservoir and the possible fluid path of the Kapurella system, which could be utilized to direct future geothermal studies. This pioneering study makes suggestions to improve future MT data acquisition and to use boreholes and other geophysical methods to improve the investigation of structures at depth.
"Groundwater ages" of the Lake Chad multi-layer aquifers system inferred from 14C and 36Cl data
NASA Astrophysics Data System (ADS)
Bouchez, Camille; Deschamps, Pierre; Goncalves, Julio; Hamelin, Bruno; Seidel, Jean-Luc; Doumnang, Jean-Claude
2014-05-01
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
Hanson, K.M.; Cunningham, G.S.
1996-04-01
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.
Hybrid Inference for Sensor Network Localization Using a Mobile Robot
Dimitri Marinakis; David Meger; Ioannis M. Rekleitis; Gregory Dudek
2007-01-01
In this paper, we consider a hybrid solution to the sensor net- work position inference problem, which combines a real-time filtering system with information from a more expensive, global inference procedure to improve accuracy and prevent divergence. Many online solutions for this problem make use of simplifying assumptions, such as Gaussian noise models and linear system behaviour and also adopt
NASA Astrophysics Data System (ADS)
McCubbin, Francis Michael
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.
NASA Technical Reports Server (NTRS)
1993-01-01
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.
Predictive Inference and Discontinuities
Elja Arjas
2002-01-01
The purpose of this note is to study the consequences, mainly in the form of simple examples, which the fundamental ideas of deFinetti on predictive inference and exchangeable random variables have in the context of reliability problems. In particular, the role of unpredictable observations, or innovations, is related to discontinuities in the process of learning from the data. It is
Aaron M. Ellison
2004-01-01
Bayesian inference is an important statistical tool that is increasingly being used by ecologists. In a Bayesian analysis, information available before a study is conducted is summarized in a quantitative model or hypothesis: the prior probability distribution. Bayes Theorem uses the prior probability distribution and the likelihood of the data to generate a posterior probability distribution. Posterior probability distributions are
ERIC Educational Resources Information Center
Watson, Jane
2007-01-01
Inference, or decision making, is seen in curriculum documents as the final step in a statistical investigation. For a formal statistical enquiry this may be associated with sophisticated tests involving probability distributions. For young students without the mathematical background to perform such tests, it is still possible to draw informal…
Inference for Categorical Data
NSDL National Science Digital Library
Lacey, Michelle
This site, created by the Department of Statistics at Yale University, gives an explanation, a definition and an example of inference for categorical data. Topics include confidence intervals and significance tests for a single proportion, as well as comparison of two proportions. Overall, this is a great resource for any mathematics classroom studying statistics.
Perception as Unconscious Inference
Hatfield, Gary
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
Sampling in Statistical Inference
NSDL National Science Digital Library
Lacey, Michelle
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.
2011-01-01
Background Main waterfowl migration systems are well understood through ringing activities. However, in mallards (Anas platyrhynchos) ringing studies suggest deviations from general migratory trends and traditions in waterfowl. Furthermore, surprisingly little is known about the population genetic structure of mallards, and studying it may yield insight into the spread of diseases such as Avian Influenza, and in management and conservation of wetlands. The study of evolution of genetic diversity and subsequent partitioning thereof during the last glaciation adds to ongoing discussions on the general evolution of waterfowl populations and flyway evolution. Hypothesised mallard flyways are tested explicitly by analysing mitochondrial mallard DNA from the whole northern hemisphere. Results Phylogenetic analyses confirm two mitochondrial mallard clades. Genetic differentiation within Eurasia and North-America is low, on a continental scale, but large differences occur between these two land masses (FST = 0.51). Half the genetic variance lies within sampling locations, and a negligible portion between currently recognised waterfowl flyways, within Eurasia and North-America. Analysis of molecular variance (AMOVA) at continent scale, incorporating sampling localities as smallest units, also shows the absence of population structure on the flyway level. Finally, demographic modelling by coalescence simulation proposes a split between Eurasia and North-America 43,000 to 74,000 years ago and strong population growth (~100fold) since then and little migration (not statistically different from zero). Conclusions Based on this first complete assessment of the mallard's world-wide population genetic structure we confirm that no more than two mtDNA clades exist. Clade A is characteristic for Eurasia, and clade B for North-America although some representatives of clade A are also found in North-America. We explain this pattern by evaluating competing hypotheses and conclude that a complex mix of historical, recent and anthropogenic factors shaped the current mallard populations. We refute population classification based on flyways proposed by ornithologists and managers, because they seem to have little biological meaning. Our results have implications for wetland management and conservation, with special regard to the release of farmed mallards for hunting, as well as for the possible transmission of Avian Influenza by mallards due to migration. PMID:22093799
NASA Astrophysics Data System (ADS)
Iepure, S.; Namiotko, T.; Montanari, A.; Brugiapaglia, E.; Mainiero, M.; Mariani, S.; Fiebig, M.
2012-04-01
Cave water and sediments from an extensive sulfidic, chemioautotrophic subterranean ecosystem in the hypogenic karst complex of Frasassi (northeastern Apennines of Italy) was analysed for modern and fossil ostracode assemblages. 22 extant and 16 extinct ostracode species make of this continental sulphidic ecosystem one of the richest worldwide. Both modern and fossil assemblages show the expected pattern of species diversity after the simulation procedure for taxonomic distinctness, which indicates no major extinction events since the Pleistocene. Extant species display patchy distribution according to habitat heterogeneity within the sulphidic environment. Fossil assemblages from a 3 m thick fluvial deposit trapped near the entrance of the Caverna del Carbone (CDC) at about 30 m above present river level, and a fine sand deposit resting at about the same elevation in Sala Duecento (SDS) within the Grotta Grande del Vento preliminarily dated with OSL at 111±17 ka are being investigated. The former deposit has yet to be dated but it represents probably a normal stratigraphic succession spanning a few tens of kyr, which was deposited when the cave entrance was at the reach of fluvial flooding, potentially recording the transition from the last interglacial Riss-Würm to the glacial Würm. Sediment samples from the SDS site yielded an ostracode assemblage represented by 12 species with a d18O signature of -5‰ and a well-diversified palinoflora assemblage indicating a transitional condition between steppe and temperate forest. The top sediment from the CDC site is characterized by a less diversified ostracode assemblage represented by 8 species, d18O of -3‰, and a poorly diversified palinoflora dominated by herbaceous plants and lesser pines, indicating a colder environment in the early stage of the last glacial. Additional information on the geometric morphometry approach of B-splines method applied to extant and fossil specimens of the hypogean Mixtacandona ostracode was used to identify microevolutionary patterns and environmentally cued variation. Analyses indicate the presence of one morphotype of a new species A of the group Mixtacandona riongessa, and three distinctive morphotypes of a species B of the group M. laisi-chappuisi occurring in stratigraphically distinct fluvial-cave sediments. Apparent difference in the disparity level between these species could be associated with their survival in different environmental conditions. Species A is found nowadays living exclusively in sulphidic cave waters, and was present in the system since at least the end of the last interglacial. The extraordinary high taxonomic and morphological diversity of ostracods reflects in situ evolutionary processes that have occurred under the cumulative effect of high environmental energy availability of subterranean sulphidic ecosystems, heterogeneous environmental conditions, and spatial and temporal isolation.
NASA Astrophysics Data System (ADS)
Kuria, Z. N.; Woldai, T.; van der Meer, F. D.; Barongo, J. O.
2010-06-01
Southern Kenya Rift has been known as a region of high geodynamic activity expressed by recent volcanism, geothermal activity and high rate of seismicity. The active faults that host these activities have not been investigated to determine their subsurface geometry, faulting intensity and constituents (fluids, sediments) for proper characterization of tectonic rift extension. Two different models of extension direction (E-W to ESE-WNW and NW-SE) have been proposed. However, they were based on limited field data and lacked subsurface investigations. In this research, we delineated active fault zones from ASTER image draped on ASTER DEM, together with relocated earthquakes. Subsequently, we combined field geologic mapping, electrical resistivity, ground magnetic traverses and aeromagnetic data to investigate the subsurface character of the active faults. Our results from structural studies identified four fault sets of different age and deformational styles, namely: normal N-S; dextral NW-SE; strike slip ENE-WSW; and sinistral NE-SW. The previous studies did not recognize the existence of the sinistral oblique slip NE-SW trending faults which were created under an E-W extension to counterbalance the NW-SE faults. The E-W extension has also been confirmed from focal mechanism solutions of the swarm earthquakes, which are located where all the four fault sets intersect. Our findings therefore, bridge the existing gap in opinion on neo-tectonic extension of the rift suggested by the earlier authors. Our results from resistivity survey show that the southern faults are in filled with fluid (0.05 and 0.2 ?m), whereas fault zones to the north contain high resistivity (55-75 ?m) material. The ground magnetic survey results have revealed faulting activity within active fault zones that do not contain fluids. In addition, the 2D inversion of the four aero-magnetic profiles (209 km long) revealed: major vertical to sub vertical faults (dipping 75-85° east or west); an uplifted, heavily fractured and deformed basin to the north (highly disturbed magnetic signatures) characteristic of on going active rifting; and a refined architecture of the asymmetry graben to the south with an intrarift horst, whose western graben is 4 km deep and eastern graben is much deeper (9 km), with a zone of significant break in magnetic signatures at that depth, interpreted as source of the hot springs south of Lake Magadi (a location confirmed near surface by ground magnetic and resistivity data sets). The magnetic sources to the north are shallow at 15 km depth compared to 22 km to the south. The loss of magnetism to the north is probably due to increased heat as a result of magmatic intrusion supporting active rifting model. Conclusively, the integrated approach employed in this research confirms that fault system delineated to the north is actively deforming under E-W normal extension and is a potential earthquake source probably related to magmatic intrusion, while the presence of fluids within the south fault zone reduce intensity of faulting activity and explains lack of earthquakes in a continental rift setting.
Quantum inference on Bayesian networks
Low, Guang Hao
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. ...
Jessica Staddon; Philippe Golle; Bryce Zimny
Newly published data, when combined with existing public knowledge, allows for complex and sometimes unintended inferences. We propose semi-automated tools for detecting these inferences prior to releasing data. Our tools give data owners a fuller understanding of the implications of releasing data and help them ad- just the amount of data they release to avoid unwanted inferences. Our tools first
NASA Astrophysics Data System (ADS)
Resmini, Ronald G.; Marsh, Bruce D.
1995-11-01
The Dome Mountain ( DM) lavas, located in the moat of the Timber Mountain caldera of the SW Nevada volcanic field, cap a thick sequence of welded tuffs, massive and bedded tuffs, and mafic and rhyolitic lava flows. These 10.9-m.y.-old flows comprise a pile of some twenty flows with a total thickness of about 300 m. Individual flows range in thickness from 3 to 20 m and are characterized by a basal vesicular zone, a massive central region and an upper vesicular region containing, in most cases, cooling fractures. The flows dip gently away from the summit of DM, a prominent topographic feature of the region with an elevation of 2065 m. Silica contents range from 48.8 wt.% at the base of the pile to 59.4 wt.% at the top, although most of the flows are andesitic. Crystal size distributions (CSDs) for plagioclase in these comagmatic lavas have been determined to provide information on magma storage times. These times, when coupled with phase equilibria, can be used to infer cooling rates, which are indicative of the relative dimensions of magmatic bodies and the vigor of various magmatic cooling processes. Most of the CSDs show no sign of crystal fractionation or accumulation. Assuming a growth rate of 10 -10 cm/s, calculated mean crystal residence times for most lavas cluster in the range of 1.5 to 4 years. Two of the lower lavas have residence times in the range of 4 to 9 years. The residence times correlate inversely with nucleation density and broadly correlate inversely with silica content: high-residence-time, low-silica lavas occur at the base of the pile, whereas low-residence-time, high-silica lavas occur at the top of the pile. One interpretation of the residence time data is that the lavas are from an open system of more or less constant residence time but varying spatially in system locations; e.g., a magma chamber in which steady-state recharge and eruption may have been reached. Alternatively, these lavas may represent small samples, closely spaced in time, of a slowly cooling, large batch or reservoir of magma. The greater residence times associated with the lower flows may indicate the increased amount of time necessary to form the conduit of the magma-volcano system by the oldest magma or simply that part of the system with the highest liquidus temperature. A minimum volume of 1.4 to 2.5 km 3 for the magma chamber is estimated for the DM system using the estimated residence times in a simplified cooling model of a sheet of aspect ratio R. The actual system volume depends on R, which is strictly unknown, and thus the actual system volume may be much larger (e.g., ~ 200 km 3). A mean paleoeffusion rate of 0.05 km 3/yr is obtained for the lavas by assuming that the residence time of each lava corresponds to the amount of time elapsed since the eruption of the underlying flow.
A. F. Emery; E. Valenti; D. Bardot
2007-01-01
Parameter estimation is generally based upon the maximum likelihood approach and often involves regularization. Typically it is desired that the results be unbiased and of minimum variance. However, it is often better to accept biased estimates that have minimum mean square error. Bayesian inference is an attractive approach that achieves this goal and incorporates regularization automatically. More importantly, it permits
Type Inference with Extended Pattern Matching and Subtypes
Mitchell, John C.
Type Inference with Extended Pattern Matching and Subtypes guarantees on run-time behavior. In recent years, there has been substantial research on type inference@theory.lcs.mit.edu mitchell@cs.stanford.edu Abstract. We study a type system, in the spirit of ML and related lan
Reliability of the Granger causality inference
NASA Astrophysics Data System (ADS)
Zhou, Douglas; Zhang, Yaoyu; Xiao, Yanyang; Cai, David
2014-04-01
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.
Quantum Inference on Bayesian Networks
NASA Astrophysics Data System (ADS)
Yoder, Theodore; Low, Guang Hao; Chuang, Isaac
2014-03-01
Because quantum physics is naturally probabilistic, it seems reasonable to expect physical systems to describe probabilities and their evolution in a natural fashion. Here, we use quantum computation to speedup sampling from a graphical probability model, the Bayesian network. A specialization of this sampling problem is approximate Bayesian inference, where the distribution on query variables is sampled given the values e of evidence variables. Inference is a key part of modern machine learning and artificial intelligence tasks, but is known to be NP-hard. Classically, a single unbiased sample is obtained from a Bayesian network on n variables with at most m parents per node in time ?(nmP(e) - 1 / 2) , depending critically on P(e) , the probability the evidence might occur in the first place. However, by implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking ?(n2m P(e) -1/2) time per sample. The speedup is the result of amplitude amplification, which is proving to be broadly applicable in sampling and machine learning tasks. In particular, we provide an explicit and efficient circuit construction that implements the algorithm without the need for oracle access.
Structure Inference for Bayesian Multisensory Scene Understanding
Timothy M. Hospedales; Sethu Vijayakumar
2008-01-01
We investigate a solution to the problem of multi-sensor scene understanding by formulating it in the framework of Bayesian model selection and structure inference. Humans robustly associate multimodal data as appropriate, but previous modelling work has focused largely on optimal fusion, leaving segregation unaccounted for and unexploited by machine perception systems. We illustrate a unifying, Bayesian solution to multi-sensor perception
Network topologies: inference, modeling, and generation
Hamed Haddadi; Miguel Rio; Gianluca Iannaccone; ANDREW MOORE; Richard Mortier
2008-01-01
Abstract, Accurate measurement, inference and mod-elling techniques are fundamental to Internet topology re-search. Spatial analysis of the Internet is needed to develop network planning, optimal routing algorithms and failure detection measures. A first step towards achieving such goals is the availability of network topologies at different levels of granularity, facilitating realistic simulations of new Internet systems.
New trends in information aggregation
Imre J. Rudas
2001-01-01
Information aggregation is one of the key issues in the development of intelligent systems, like neural networks, neuro-fuzzy systems, fuzzy knowledge based systems, vision and decision making systems, etc. Fuzzy set theory provides a host of attractive aggregation operators for integrating the membership values representing uncertain information. The variety of these operators might be confusing and make it difficult to
NASA Technical Reports Server (NTRS)
Wheeler, Kevin; Timucin, Dogan; Rabbette, Maura; Curry, Charles; Allan, Mark; Lvov, Nikolay; Clanton, Sam; Pilewskie, Peter
2002-01-01
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.
Bimal K. Bose; Nitin R. Patel
1997-01-01
A high-performance stator flux oriented speed sensorless direct vector-controlled induction motor drive is described in the paper. The drive can start from stand-still, operate at any speed in all the quadrants including the field-weakening region, and then stop at zero speed. The drive incorporated the following novel features: (1) software-programmable cascaded low-pass filter units permit machine terminal voltage integration to
Ersin Kolay; Kamil Kayabali; Yuksel Tasdemir
2010-01-01
Clay bearing, weathered and other weak rocks cause major problems in engineering practice due to their interactions with water.\\u000a The slake durability index (I\\u000a d2) is an important tool used to assess the resistance of these rocks to erosion and degradation, but sample preparation for\\u000a this test is tedious. The paper reports an attempt to define I\\u000a d2 through statistical
Likhitruangsilp, Visit
2002-01-01
University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Appmved as to style and content by: Paul N. Roschke (Chair of Committee) Aniruddha Datta (Member) Luciana R. Barroso (Member) iedzw ki ead of Deparnn t May...
Structural inference for uncertain networks
Martin, Travis; Newman, M E J
2015-01-01
In the study of networked systems such as biological, technological, and social networks the available data are often uncertain. Rather than knowing the structure of a network exactly, we know the connections between nodes only with a certain probability. In this paper we develop methods for the analysis of such uncertain data, focusing particularly on the problem of community detection. We give a principled maximum-likelihood method for inferring community structure and demonstrate how the results can be used to make improved estimates of the true structure of the network. Using computer-generated benchmark networks we demonstrate that our methods are able to reconstruct known communities more accurately than previous approaches based on data thresholding. We also give an example application to the detection of communities in a protein-protein interaction network.
Inferring Horizontal Gene Transfer
Lassalle, Florent; Dessimoz, Christophe
2015-01-01
Horizontal or Lateral Gene Transfer (HGT or LGT) is the transmission of portions of genomic DNA between organisms through a process decoupled from vertical inheritance. In the presence of HGT events, different fragments of the genome are the result of different evolutionary histories. This can therefore complicate the investigations of evolutionary relatedness of lineages and species. Also, as HGT can bring into genomes radically different genotypes from distant lineages, or even new genes bearing new functions, it is a major source of phenotypic innovation and a mechanism of niche adaptation. For example, of particular relevance to human health is the lateral transfer of antibiotic resistance and pathogenicity determinants, leading to the emergence of pathogenic lineages [1]. Computational identification of HGT events relies upon the investigation of sequence composition or evolutionary history of genes. Sequence composition-based ("parametric") methods search for deviations from the genomic average, whereas evolutionary history-based ("phylogenetic") approaches identify genes whose evolutionary history significantly differs from that of the host species. The evaluation and benchmarking of HGT inference methods typically rely upon simulated genomes, for which the true history is known. On real data, different methods tend to infer different HGT events, and as a result it can be difficult to ascertain all but simple and clear-cut HGT events. PMID:26020646
Moment inference from tomograms
Day-Lewis, F. D.; Chen, Y.; Singha, K.
2007-01-01
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.
Knowledge Based Approach for Diagnosis of Breast Cancer
A. Shukla; R. Tiwari; P. Kaur
2009-01-01
This paper presents a novel approach to simulate a Knowledge Based System for diagnosis of Breast Cancer using Soft Computing tools like artificial neural networks (ANNs) and Neuro Fuzzy Systems. The feed-forward neural network has been trained using three ANN algorithms, the back propagation algorithm (BPA), the radial basis function (RBF) Networks and the learning vector quantization (LVQ) Networks; and
Soft Computing and its Application B. M. `Dan' Wilamowski
Wilamowski, Bogdan Maciej
Systems Genetic Algorithms Hardware implementation of neuro-fuzzy systems Conclusion nn.uidaho.edu wialm odTT - = - xxxw LMS AND REGRESSION ALGORITHMS If a single layer of neurons is considered, error back generated by the minimum distance classifier. LMS algorithm AW ALGORITHM The total error for one neuron j
Composable Probabilistic Inference with Blaise
Bonawitz, Keith A
2008-07-23
Probabilistic inference provides a unified, systematic framework for specifying and solving these problems. Recent work has demonstrated the great value of probabilistic models defined over complex, structured domains. ...
Bayes factors and multimodel inference
Link, W.A.; Barker, R.J.
2009-01-01
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.
Marateb, Hamid Reza; Goudarzi, Sobhan
2015-01-01
Background: Coronary heart diseases/coronary artery diseases (CHDs/CAD), the most common form of cardiovascular disease (CVD), are a major cause for death and disability in developing/developed countries. CAD risk factors could be detected by physicians to prevent the CAD occurrence in the near future. Invasive coronary angiography, a current diagnosis method, is costly and associated with morbidity and mortality in CAD patients. The aim of this study was to design a computer-based noninvasive CAD diagnosis system with clinically interpretable rules. Materials and Methods: In this study, the Cleveland CAD dataset from the University of California UCI (Irvine) was used. The interval-scale variables were discretized, with cut points taken from the literature. A fuzzy rule-based system was then formulated based on a neuro-fuzzy classifier (NFC) whose learning procedure was speeded up by the scaled conjugate gradient algorithm. Two feature selection (FS) methods, multiple logistic regression (MLR) and sequential FS, were used to reduce the required attributes. The performance of the NFC (without/with FS) was then assessed in a hold-out validation framework. Further cross-validation was performed on the best classifier. Results: In this dataset, 16 complete attributes along with the binary CHD diagnosis (gold standard) for 272 subjects (68% male) were analyzed. MLR + NFC showed the best performance. Its overall sensitivity, specificity, accuracy, type I error (?) and statistical power were 79%, 89%, 84%, 0.1 and 79%, respectively. The selected features were “age and ST/heart rate slope categories,” “exercise-induced angina status,” fluoroscopy, and thallium-201 stress scintigraphy results. Conclusion: The proposed method showed “substantial agreement” with the gold standard. This algorithm is thus, a promising tool for screening CAD patients. PMID:26109965
Quantum-Like Representation of Non-Bayesian Inference
NASA Astrophysics Data System (ADS)
Asano, M.; Basieva, I.; Khrennikov, A.; Ohya, M.; Tanaka, Y.
2013-01-01
This research is related to the problem of "irrational decision making or inference" that have been discussed in cognitive psychology. There are some experimental studies, and these statistical data cannot be described by classical probability theory. The process of decision making generating these data cannot be reduced to the classical Bayesian inference. For this problem, a number of quantum-like coginitive models of decision making was proposed. Our previous work represented in a natural way the classical Bayesian inference in the frame work of quantum mechanics. By using this representation, in this paper, we try to discuss the non-Bayesian (irrational) inference that is biased by effects like the quantum interference. Further, we describe "psychological factor" disturbing "rationality" as an "environment" correlating with the "main system" of usual Bayesian inference.
ERACER: A Database Approach for Statistical Inference and Data Cleaning
Neville, Jennifer
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
BIE: Bayesian Inference Engine
NASA Astrophysics Data System (ADS)
Weinberg, Martin D.
2013-12-01
The Bayesian Inference Engine (BIE) is an object-oriented library of tools written in C++ designed explicitly to enable Bayesian update and model comparison for astronomical problems. To facilitate "what if" exploration, BIE provides a command line interface (written with Bison and Flex) to run input scripts. The output of the code is a simulation of the Bayesian posterior distribution from which summary statistics e.g. by taking moments, or determine confidence intervals and so forth, can be determined. All of these quantities are fundamentally integrals and the Markov Chain approach produces variates heta distributed according to P( heta|D) so moments are trivially obtained by summing of the ensemble of variates.
Causal Inference in Retrospective Studies.
ERIC Educational Resources Information Center
Holland, Paul W.; Rubin, Donald B.
1988-01-01
The problem of drawing causal inferences from retrospective case-controlled studies is considered. A model for causal inference in prospective studies is applied to retrospective studies. Limitations of case-controlled studies are formulated concerning relevant parameters that can be estimated in such studies. A coffee-drinking/myocardial…
Reductionistic Inferences in Modern Biology.
ERIC Educational Resources Information Center
Chiaraviglio, Lucio
The author analyzes the logic of inferences in modern biology which serve as reductionist bridges between the organismic and molecular levels of explanation. He distinguishes pragmatic validity from deductive or inductive validity, and discusses the requirements for validity of pragmatic inferences (which are accepted or rejected in terms of the…
Improving Inferences from Multiple Methods.
ERIC Educational Resources Information Center
Shotland, R. Lance; Mark, Melvin M.
1987-01-01
Multiple evaluation methods (MEMs) can cause an inferential challenge, although there are strategies to strengthen inferences. Practical and theoretical issues involved in the use by social scientists of MEMs, three potential problems in drawing inferences from MEMs, and short- and long-term strategies for alleviating these problems are outlined.…
Causal Inference and Developmental Psychology
ERIC Educational Resources Information Center
Foster, E. Michael
2010-01-01
Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether…
Science Shorts: Observation Versus Inference
NSDL National Science Digital Library
Craig R. Leager
2008-02-01
When you observe something, how do you know for sure what you are seeing, feeling, smelling, or hearing? Asking students to think critically about their encounters with the natural world will help to strengthen their understanding and application of the science-process skills of observation and inference. In the following lesson, students make observations and inferences of an object and some mystery photos.
Inverse Ising inference with correlated samples
NASA Astrophysics Data System (ADS)
Obermayer, Benedikt; Levine, Erel
2014-12-01
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.
Double jeopardy in inferring cognitive processes
Fific, Mario
2014-01-01
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
CONTROL OF THE PENICILLIN PRODUCTION WITH ADAPTIVE IMC USING FUZZY NEURAL NETWORKS
M. J. Araúzo Bravo; E. Gómez Sánchez; J. M. Cano Izquierdo; J. López Coronado; M. J. López Nieto; A. Collados de la Vieja
This paper introduces the use adaptation in IMC strategy for the control of a simulated penicillin plant. The plant model and control modules are built using FasBack neuro-fuzzy system, featuring fast stable learning guided by matching and error minimisation and good identification performance. Control results show good general performance both in the nominal case and in the presence of noise.
Statistical and soft-computing techniques for the prediction of upper arm articular synergies
S. Micera; J. Carpaneto; P. Dario; M. Popovic
2002-01-01
The feasibility of predicting elbow position from shoulder angular trajectories during pointing movements was analyzed. Aiming to achieve this result a hybrid strategy (composed of statistical and soft computing algorithms) was developed. Using a statistical procedure we first clustered the different trajectories and then a neuro-fuzzy system was trained for each group. The results show the feasibility of this approach
K. Zhao; B. R. Upadhyaya
2005-01-01
An adaptive fuzzy inference causal graph is presented as an integrated approach for fault detection and isolation of field devices including sensors, actuators, and controllers in nuclear power plants. In this approach, nuclear plant systems are represented as a causal graph consisting of individual process variables connected with adaptive fuzzy inference system models. The adaptive fuzzy inference system models generated
Declarative Modeling and Bayesian Inference of Dark Matter Halos
Kronberger, Gabriel
2013-01-01
Probabilistic programming allows specification of probabilistic models in a declarative manner. Recently, several new software systems and languages for probabilistic programming have been developed on the basis of newly developed and improved methods for approximate inference in probabilistic models. In this contribution a probabilistic model for an idealized dark matter localization problem is described. We first derive the probabilistic model for the inference of dark matter locations and masses, and then show how this model can be implemented using BUGS and Infer.NET, two software systems for probabilistic programming. Finally, the different capabilities of both systems are discussed. The presented dark matter model includes mainly non-conjugate factors, thus, it is difficult to implement this model with Infer.NET.
Bayesian inference of substrate properties from film behavior
NASA Astrophysics Data System (ADS)
Aggarwal, R.; Demkowicz, M. J.; Marzouk, Y. M.
2015-01-01
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.
NASA Astrophysics Data System (ADS)
Krasilenko, Vladimir G.; Nikolskyy, Aleksandr I.; Lazarev, Alexander A.
2013-12-01
We consider design and modeling of hardware realizations of reconfigurable multifunctional continuous logic devices (R MCL D) as advanced components of the next generation high-performance MIMO-systems for the processing and interconnection. The R MCL D realize function of two-valued and continuous logics with current inputs and current outputs on the basis of CMOS current mirrors and circuits which realize the limited difference functions. We show advantages of such elements consisting in encoding of variables by the photocurrent levels, that allows easily providing optical inputs (by photo-detectors (PD)) and optical outputs (by LED). The conception of construction of R MCL D consists in the use of a current mirrors realized on 1.5?m technology CMOS transistors. Presence of 55÷65 transistors, 1 PD and 1 LED makes the offered circuits quite compact and allows their integration in 1D and 2D arrays. In the presentation we consider the capabilities of the offered circuits, show the simulation results and possible prospects of application of the circuits in particular for time-pulse coding for multivalued, continuous, neuro-fuzzy and matrix logics. The simulation results of NOT, MIN, MAX, equivalence (EQ) and other functions, that implemented R MCL D, showed that the level of logical variables can change from 1 ?A to 10 ?A for low-power consumption variants. The base cell of the R MCL D have low power consumption <1mW and processing time about 1÷11?S at supply voltage 2.4÷3.3V. Modeling of such cells in OrCad is made.
Intersection Bounds: Estimation and Inference
Chernozhukov, Victor V.
We develop a practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or, equivalently, the value of a linear ...
Bayesian Inference: with ecological applications
Link, William A.; Barker, Richard J.
2010-01-01
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.
Statistical Inference: The Big Picture
Kass, Robert E.
2011-01-01
Statistics has moved beyond the frequentist-Bayesian controversies of the past. Where does this leave our ability to interpret results? I suggest that a philosophy compatible with statistical practice, labelled here statistical pragmatism, serves as a foundation for inference. Statistical pragmatism is inclusive and emphasizes the assumptions that connect statistical models with observed data. I argue that introductory courses often mis-characterize the process of statistical inference and I propose an alternative “big picture” depiction. PMID:21841892
On Asymptotic Quantum Statistical Inference
Gill, Richard D
2011-01-01
We study asymptotically optimal statistical inference concerning the unknown state of N identical quantum systems, using two complementary approaches: a "poor man's approach" based on the van Trees inequality, and a rather more sophisticated approach using the recently developed quantum form of Le Cam's theory of Local Asymptotic Normality. In the first approach, we make use of a Bayesian version of a quantum Cramer-Rao bound due to Holevo. Holevo's bound can be thought of as a bound on the set of Fisher information matrices for the unknown parameters of the state, as we consider arbitrary measurements on that state. Heuristically one can expect the bound to be asymptotically sharp. We show in various important examples that it is asymptotically attained by measurement-and-estimation schemes which have been proposed by physicists either on ad-hoc grounds or through explicit optimization under rather special prior and loss function. On the way we obtain a family of "dual Holevo bounds" of independent interest....
Active inference, communication and hermeneutics.
Friston, Karl J; Frith, Christopher D
2015-07-01
Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others - during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions - both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then - in principle - they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. PMID:25957007
Active inference, communication and hermeneutics?
Friston, Karl J.; Frith, Christopher D.
2015-01-01
Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others – during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions – both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then – in principle – they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. PMID:25957007
Mark Claypool; David Brown; Phong Le; Makoto Waseda
2001-01-01
Recommender systems provide personalized suggestions about items that users will find interesting. Typically, recommender systems require a user interface that can determine the interest of a user and use this information to make suggestions. The common solution, explicit ratings, where users tell the system what they think about a piece of information, is well-understood and fairly precise. However, having to
Strategic Production of Predictive Inferences During Comprehension
ERIC Educational Resources Information Center
Allbritton, David
2004-01-01
Although some types of inferences are mandatory for readers, predictive inferences (inferences for what will happen next) are generally considered elaborative or optional. Three experiments measuring probe word lexical decision latencies produced evidence for the online generation of predictive inferences during narrative text comprehension.…
Memory-Based Inferences during Consumer Choice
Alan Dick; Dipankar Chakravarti; Gabriel Biehal
1990-01-01
This study explores consumers' inference strategies in a mixed choice task involving memory,external information, and missing information on attribute values for some brands. Accessibility of relevant information was manipulated, and both instructed and uninstructed or natural inferences were studied. Instructed inference by low accessibility subjects confirmed more with prior overall evaluations of the brands, displaying evaluative consistency. Instructed inferences by
Static Universe Type Inference using a SAT-Solver
Matthias Niklaus
2006-01-01
Abstract The Universe type system allows to restrict the possible aliasing in object-oriented programs and thereby enables static reasoning about individual components. Compared to other ownership type systems, the Universe type system is lightweight, but annotating existing software is still a considerable eort. To ease the eort of annotation, static inference of Universe modifiers from Java source code is an
A symbolic-connectionist theory of relational inference and generalization.
Hummel, John E; Holyoak, Keith J
2003-04-01
The authors present a theory of how relational inference and generalization can be accomplished within a cognitive architecture that is psychologically and neurally realistic. Their proposal is a form of symbolic connectionism: a connectionist system based on distributed representations of concept meanings, using temporal synchrony to bind fillers and roles into relational structures. The authors present a specific instantiation of their theory in the form of a computer simulation model, Learning and Inference with Schemas and Analogies (LISA). By using a kind of self-supervised learning, LISA can make specific inferences and form new relational generalizations and can hence acquire new schemas by induction from examples. The authors demonstrate the sufficiency of the model by using it to simulate a body of empirical phenomena concerning analogical inference and relational generalization. PMID:12747523
Inference of Kinetic Ising Model on Sparse Graphs
NASA Astrophysics Data System (ADS)
Zhang, Pan
2012-08-01
Based on dynamical cavity method, we propose an approach to the inference of kinetic Ising model, which asks to reconstruct couplings and external fields from given time-dependent output of original system. Our approach gives an exact result on tree graphs and a good approximation on sparse graphs, it can be seen as an extension of Belief Propagation inference of static Ising model to kinetic Ising model. While existing mean field methods to the kinetic Ising inference e.g., naïve mean-field, TAP equation and simply mean-field, use approximations which calculate magnetizations and correlations at time t from statistics of data at time t-1, dynamical cavity method can use statistics of data at times earlier than t-1 to capture more correlations at different time steps. Extensive numerical experiments show that our inference method is superior to existing mean-field approaches on diluted networks.
Inferring Epidemic Network Topology from Surveillance Data
Wan, Xiang; Liu, Jiming; Cheung, William K.; Tong, Tiejun
2014-01-01
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
Inferring Network Topology from Complex Dynamics
Srinivas Gorur Shandilya; Marc Timme
2010-07-09
Inferring network topology from dynamical observations is a fundamental problem pervading research on complex systems. Here, we present a simple, direct method to infer the structural connection topology of a network, given an observation of one collective dynamical trajectory. The general theoretical framework is applicable to arbitrary network dynamical systems described by ordinary differential equations. No interference (external driving) is required and the type of dynamics is not restricted in any way. In particular, the observed dynamics may be arbitrarily complex; stationary, invariant or transient; synchronous or asynchronous and chaotic or periodic. Presupposing a knowledge of the functional form of the dynamical units and of the coupling functions between them, we present an analytical solution to the inverse problem of finding the network topology. Robust reconstruction is achieved in any sufficiently long generic observation of the system. We extend our method to simultaneously reconstruct both the entire network topology and all parameters appearing linear in the system's equations of motion. Reconstruction of network topology and system parameters is viable even in the presence of substantial external noise.
Multistability and Perceptual Inference
Gershman, Samuel J.
Ambiguous images present a challenge to the visual system: How can uncertainty about the causes of visual inputs be represented when there are multiple equally plausible causes? A Bayesian ideal observer should represent ...
Thermodynamics of cellular statistical inference
NASA Astrophysics Data System (ADS)
Lang, Alex; Fisher, Charles; Mehta, Pankaj
2014-03-01
Successful organisms must be capable of accurately sensing the surrounding environment in order to locate nutrients and evade toxins or predators. However, single cell organisms face a multitude of limitations on their accuracy of sensing. Berg and Purcell first examined the canonical example of statistical limitations to cellular learning of a diffusing chemical and established a fundamental limit to statistical accuracy. Recent work has shown that the Berg and Purcell learning limit can be exceeded using Maximum Likelihood Estimation. Here, we recast the cellular sensing problem as a statistical inference problem and discuss the relationship between the efficiency of an estimator and its thermodynamic properties. We explicitly model a single non-equilibrium receptor and examine the constraints on statistical inference imposed by noisy biochemical networks. Our work shows that cells must balance sample number, specificity, and energy consumption when performing statistical inference. These tradeoffs place significant constraints on the practical implementation of statistical estimators in a cell.
How forgetting aids heuristic inference.
Schooler, Lael J; Hertwig, Ralph
2005-07-01
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 research programs that take an ecological approach to studying cognition. Specifically, they implement fast and frugal heuristics within the ACT-R cognitive architecture. Simulations of the recognition heuristic, which relies on systematic failures of recognition to infer which of 2 objects scores higher on a criterion value, demonstrate that forgetting can boost accuracy by increasing the chances that only 1 object is recognized. Simulations of the fluency heuristic, which arrives at the same inference on the basis of the speed with which objects are recognized, indicate that forgetting aids the discrimination between the objects' recognition speeds. PMID:16060753
Inferring Diversity: Life After Shannon
NASA Astrophysics Data System (ADS)
Giffin, Adom
The diversity of a community that cannot be fully counted must be inferred. The two preeminent inference methods are the MaxEnt method, which uses information in the form of constraints and Bayes' rule which uses information in the form of data. It has been shown that these two methods are special cases of the method of Maximum (relative) Entropy (ME). We demonstrate how this method can be used as a measure of diversity that not only reproduces the features of Shannon's index but exceeds them by allowing more types of information to be included in the inference. A specific example is solved in detail. Additionally, the entropy that is found is the same form as the thermodynamic entropy.
Using Bayesian networks with rule extraction to infer the risk of weed infestation in a corn-crop
Gláucia M. Bressan; Vilma A. de Oliveira; Estevam R. Hruschka Jr.; Maria do Carmo Nicoletti
2009-01-01
This paper describes the modeling of a weed infestation risk inference system that implements a collaborative inference scheme based on rules extracted from two Bayesian network classifiers. The first Bayesian classifier infers a categorical variable value for the weed–crop competitiveness using as input categorical variables for the total density of weeds and corresponding proportions of narrow and broad-leaved weeds. The
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
The ventral pallidum and orbitofrontal cortex support food pleasantness inferences
Simmons, W. Kyle; Rapuano, Kristina M.; Ingeholm, John E.; Avery, Jason; Kallman, Seth; Hall, Kevin D.; Martin, Alex
2013-01-01
Food advertisements often promote choices that are driven by inferences about the hedonic pleasures of eating a particular food. Given the individual and public health consequences of obesity, it is critical to address unanswered questions about the specific neural systems underlying these hedonic inferences. For example, although regions such as the orbitofrontal cortex (OFC) are frequently observed to respond more to pleasant food images than less hedonically pleasing stimuli, one important hedonic brain region in particular has largely remained conspicuously absent among human studies of hedonic response to food images. Based on rodent research demonstrating that activity in the ventral pallidum underlies the hedonic pleasures experienced upon eating food rewards, one might expect that activity in this important ‘hedonic hotspot’ might also track inferred food pleasantness. To date, however, no human studies have assessed this question. We thus asked human subjects to undergo fMRI and make item-by-item ratings of how pleasant it would be to eat particular visually perceived foods. Activity in the ventral pallidum was strongly modulated with pleasantness inferences. Additionally, activity within a region of the orbitofrontal cortex that tracks the pleasantness of tastes was also modulated with inferred pleasantness. Importantly, the reliability of these findings is demonstrated by their replication when we repeated the experiment at a new site with new subjects. These two experiments demonstrate that the ventral pallidum, in addition to the OFC, plays a central role in the moment-to-moment hedonic inferences that influence food-related decision-making. PMID:23397317
A Toolkit for Constraintbased Inference Engines Tee Yong Chew, Martin Henz, and Ka Boon Ng
Henz, Martin
based inference engines that go beyond depth first search. Several constraint programming systems support the pro gramming of such inference engines through programming abstractions. For example, the Mozart system for Oz engines that achieves high reusability and supports rapid prototyp ing of search algorithms
Controlling Selection Bias in Causal Inference Elias Bareinboim Judea Pearl
California at Los Angeles, University of
Controlling Selection Bias in Causal Inference Elias Bareinboim Judea Pearl Cognitive Systems@cs.ucla.edu Cognitive Systems Laboratory Department of Computer Science University of California, Los Angeles Los Angeles, CA. 90095 judea@cs.ucla.edu Abstract Selection bias, caused by preferential exclu- sion
Adaptive Neural Network Fuzzy Inference Controller Using Predictive Evolutionary Tuning
Gordon K. Lee; Edward Grant
2007-01-01
Abstract - The design of intelligent controllers for nonlinear systems continues to ,be a ,challenging problem, particularly when the system is uncertain or the environment noisy. A nonparametric approach which has gained success is to employ a neural network to learn about the unknown plant and fuzzy inference to compensate for the uncertainty (GANFIS control). Inherent in the design of
AS relationships: inference and validation
Xenofontas A. Dimitropoulos; Dmitri V. Krioukov; Marina Fomenkov; Bradley Huffaker; Young Hyun; Kimberly C. Claffy; George F. Riley
2007-01-01
Research on performance, robustness, and evolution of the global Internet is fundamentally handicapped without accu- rate and thorough knowledge of the nature and structure of the contractual relationships between Autonomous Sys- tems (ASs). In this work we introduce novel heuristics for inferring AS relationships. Our heuristics improve upon pre- vious works in several technical aspects, which we outline in detail
Nonparametric Applications of Bayesian Inference
Gary Chamberlain; Guido Imbens
1996-01-01
The paper evaluates the usefulness of a nonparametric approach to Bayesian inference by presenting two applications. The approach is due to Ferguson (1973, 1974) and Rubin (1981). Our first application considers an educational choice problem. We focus on obtaining a predictive distribution for earnings corresponding to various levels of schooling. This predictive distribution incorporates the parameter uncertainty, so that it
Nonparametric Applications of Bayesian Inference
Gary Chamberlain; Guido W Imbens
2003-01-01
This article evaluates the usefulness of a nonparametric approach to Bayesian inference by presenting two applications. Our first application considers an educational choice problem. We focus on obtaining a predictive distribution for earnings corresponding to various levels of schooling. This predictive distribution incorporates the parameter uncertainty, so that it is relevant for decision making under uncertainty in the expected utility
Bayesian Inference for Stable Distributions
D. J. Buckle
1995-01-01
Very little work on stable distribution parameter estimation and inference appears in the literature due to the nonexistence of the probability density function. This has led in particular to a dearth of Bayesian work in this area. But Bayesian computation via Markov chain Monte Carlo allows us to sample from the distribution of the parameters of the stable distributions, by
Statistical Inference in Graphical Models
Kevin Gimpel
2006-01-01
ABSTRACT Graphical models fuse probability theory and graph theory in such a way as to permit ef- ficient representation and computation with probability distributions. They intuitively capture statistical relationships among,random,variables in a distribution and exploit these relationships to permit tractable algorithms for statistical inference. In recent years, certain types of graphical models, particularly undirected graphical models, Bayesian networks, and dynamic
Statistical Inference in Inverse Problems
Xun, Xiaolei
2012-07-16
an object, when the background noise dominates. The goal is to reach the signal-to-noise ratio levels on the order of 10^-3. We develop a Bayesian approach to this problem in two-dimension. The method allows inference not only about the existence...
Foundational issues in statistical inference
C. J. Albers; O. J. W. F. Kardaun; W. Schaafsma; A. G. M. Steerneman; A. Stein
Statistical inference is about using statistical data (x) to formulate an opinion about something that is dened well, but unknown ( y). Testing a hypothesis H about y is one of the possibilities, the estimation or prediction ofy is another one. We concentrate the attention on estimation or prediction in the sense that an opinion is required in the form
Perceptual Inference and Autistic Traits
ERIC Educational Resources Information Center
Skewes, Joshua C; Jegindø, Else-Marie; Gebauer, Line
2015-01-01
Autistic people are better at perceiving details. Major theories explain this in terms of bottom-up sensory mechanisms or in terms of top-down cognitive biases. Recently, it has become possible to link these theories within a common framework. This framework assumes that perception is implicit neural inference, combining sensory evidence with…