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1

Structure identification of generalized adaptive neuro-fuzzy inference systems  

Microsoft Academic Search

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

Mohammad Fazle Azeem; Madasu Hanmandlu; Nesar Ahmad

2003-01-01

2

A Novel Adaptive Neuro Fuzzy Inference System Based CPU Scheduler for Multimedia Operating System  

Microsoft Academic Search

In this paper we propose a novel CPU Scheduler based on Adaptive Neuro Fuzzy Inference System (ANFIS), to support the execution of multimedia applications along with conventional applications in multimedia operating system. Adaptive Neuro-Fuzzy Inference System (ANFIS) can be used to solve highly non-linear dynamic problems. This paper shows how an ANFIS can be used to optimize CPU scheduling in

Mohammad Atique; Sadique S. Ali

2007-01-01

3

Streamflow Forecasting Using Neuro-Fuzzy Inference System  

Microsoft Academic Search

This paper presents combined approaches of neural network analysis and fuzzy inference techniques to the problem of streamflow forecasting. 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

Prakash C. Swain

4

Flexible neuro-fuzzy systems  

Microsoft Academic Search

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

Leszek Rutkowski; Krzysztof Cpalka

2003-01-01

5

Input Resistance Calculation for Circular Microstrip Antennas Using Adaptive Neuro-Fuzzy Inference System  

Microsoft Academic Search

This paper presents a new method based on adaptive neuro-fuzzy inference system (ANFIS) to calculate the input resistance of circular microstrip patch antennas. The ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of FIS with learning power of neural networks. A hybrid learning algorithm based

K. Guney; N. Sarikaya

2004-01-01

6

Adaptive neuro-fuzzy inference system for prediction of water level in reservoir  

Microsoft Academic Search

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

Fi-John Chang; Ya-Ting Chang

2006-01-01

7

Adaptive Neuro-fuzzy Inference System for Classification of EEG Signals Using Fractal Dimension  

Microsoft Academic Search

This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) and Principle Component Analysis(PCA), for classification of electroencephalogram (EEG) signals. Different mental tasks have been used to understand the process in our mind and we have chosen relaxation and imagination for our study. As well as normal conscious state, we have considered mental tasks performed in hypnosis which is

Maryam Vatankhah; Mehdi Yaghubi

2009-01-01

8

An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM  

Microsoft Academic Search

A wire electrical discharge machined (WEDM) surface is characterized by its roughness and metallographic properties. Surface roughness and white layer thickness (WLT) are the main indicators of quality of a component for WEDM. In this paper an adaptive neuro-fuzzy inference system (ANFIS) model has been developed for the prediction of the white layer thickness (WLT) and the average surface roughness

Ulas Çaydas; Ahmet Hasçalik; Sami Ekici

2009-01-01

9

Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients  

Microsoft Academic Search

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

?nan Güler; Elif Derya Übeyli

2005-01-01

10

Design of Adaptive Neuro-Fuzzy Inference System for Estimation of Grinding Performance  

Microsoft Academic Search

The grinding performance is influenced and determined by the disc dressing conditions due to effects of dressing process on the wheel surface topography. In this way, prediction of the grinding specific energy helps to optimize the disc dressing conditions to increase grinding performance. The objective of this study is the design of adaptive neuro-fuzzy inference system (ANFIS) for estimation of

H. Baseri

2011-01-01

11

HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems  

Microsoft Academic Search

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

Jaesoo Kim; Nikola K. Kasabov

1999-01-01

12

A Neuro-Fuzzy Inference System Combining Wavelet Denoising, Principal Component Analysis, and Sequential Probability Ratio Test for Sensor Monitoring  

SciTech Connect

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.

Na, Man Gyun [Chosun University (Korea, Republic of); Oh, Seungrohk [Dankook University (Korea, Republic of)

2002-11-15

13

An Adaptive Speed Controller for Induction Motor Drives Using Adaptive Neuro-Fuzzy Inference System  

Microsoft Academic Search

This study develops an adaptive speed controller from the adaptive neuro-fuzzy inference system (ANFIS) for an indirect field-oriented\\u000a (IFO) induction motor drive. First, a two-degree-of-freedom controller (2DOFC) is designed quantitatively to meet the prescribed\\u000a speed command tracking and load regulation responses at the nominal case. When system parameters and operating conditions\\u000a vary, the prescribed control specifications cannot be satisfied. To

Kuei-hsiang Chao; Yu-ren Shen

2007-01-01

14

Self-adaptive neuro-fuzzy inference systems for classification applications  

Microsoft Academic Search

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

Jeen-Shing Wang; C. S. George Lee

2002-01-01

15

A novel edge detection method based on adaptive neuro-fuzzy inference system  

NASA Astrophysics Data System (ADS)

In this paper, we present a novel adaptive neuro-fuzzy inference system (ANFIS) for edge detection in digital images. In the proposed method, the edges in the image are directly determined by an ANFIS network. The proposed ANFIS edge detector is tested on popular images having different image properties and also compared with popular edge detectors from the literature. Experimental results show that the proposed ANFIS edge detector exhibits much better performance than the competing operators and may efficiently be used for the detection of edges in digital images.

Zhang, Lei; Xiao, Mei; Ma, Jian; Song, Hongxun

2009-07-01

16

Fracture density estimation from petrophysical log data using the adaptive neuro-fuzzy inference system  

NASA Astrophysics Data System (ADS)

Fractures as the most common and important geological features have a significant share in reservoir fluid flow. Therefore, fracture detection is one of the important steps in fractured reservoir characterization. Different tools and methods are introduced for fracture detection from which formation image logs are considered as the common and effective tools. Due to the economical considerations, image logs are available for a limited number of wells in a hydrocarbon field. In this paper, we suggest a model to estimate fracture density from the conventional well logs using an adaptive neuro-fuzzy inference system. Image logs from two wells of the Asmari formation in one of the SW Iranian oil fields are used to verify the results of the model. Statistical data analysis indicates good correlation between fracture density and well log data including sonic, deep resistivity, neutron porosity and bulk density. The results of this study show that there is good agreement (correlation coefficient of 98%) between the measured and neuro-fuzzy estimated fracture density.

Ja'fari, Ahmad; Kadkhodaie-Ilkhchi, Ali; Sharghi, Yoosef; Ghanavati, Kiarash

2012-02-01

17

Adaptive neuro-fuzzy inference system for bearing fault detection in induction motors using temperature, current, vibration data  

Microsoft Academic Search

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

Malik S. Yilmaz; Emine Ayaz

2009-01-01

18

Design of a biped locomotion controller based on adaptive neuro-fuzzy inference systems  

NASA Astrophysics Data System (ADS)

This paper proposes a method for the design of a biped locomotion controller based on the ANFIS (Adaptive Neuro-Fuzzy Inference System) inverse learning model. In the model developed here, an integrated ANFIS structure is trained to function as the system identifier for the modeling of the inverse dynamics of a biped robot. The parameters resulting from the modeling process are duplicated and integrated as those of the biped locomotion controller to provide favorable control action. As the simulation results show, the proposed controller is able to generate a stable walking cycle for a biped robot. Moreover, the experimental results demonstrate that the performance of the proposed controller is satisfactory under conditions when the robot stands in different postures or moves on a rugged surface.

Shieh, M.-Y.; Chang, K.-H.; Lia, Y.-S.

2008-02-01

19

Visual servoing system based on ANFIS (adaptive neuro fuzzy inference system)  

NASA Astrophysics Data System (ADS)

Research in this visual servoing field in the past few decades has produced remarkable results, leading to many exciting expectations as well as new challenges. However, because of the complicated calculation of the inverse Jacobian, it is difficult to implement in real time. Therefore, instead of using the inverse Jacobian, this paper employs the ANFIS (Adaptive Neuro Fuzzy Inference System) approach for visual servo control of a robot manipulator. It is based on visual feedback and no prior information about the kinematics of robot and the camera calibration are unnecessary. Firstly, to efficiently control a manipulator, 3D space is divided into two 2D spaces. And then, we acquire training data from each 2D space and ANFIS is learned by the training data. We categorize the robot movement into two kinds of actions. That is, TOWARD action is performed, in the xy plane, by joint 1 and APPROACH action is performed, in the plane orthogonal to the xy plane, by joint 2 and joint 3. The time varying object can be tracked by controlling both actions in each plane and the simulation results show the validation of our approach.

Choi, Gyu-Jong; Lee, Kyoung-Soo; Ahn, Doo-Sung

2001-10-01

20

Memristive Neuro-Fuzzy System.  

PubMed

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

Merrikh-Bayat, Farnood; Bagheri Shouraki, Saeed

2012-07-25

21

Model reduction and optimization of reactive batch distillation based on the adaptive neuro-fuzzy inference system and differential evolution  

Microsoft Academic Search

This paper considers the application of the adaptive neuro-fuzzy inference system (ANFIS) instead of the highly nonlinear\\u000a model of a reactive batch distillation column for optimization. The architecture has been developed for fuzzy modeling that\\u000a learns information from a data set, in order to compute the membership function and rule base in accordance with the given\\u000a input–output data. In this

S. M. Khazraee; A. H. Jahanmiri; S. A. Ghorayshi

2011-01-01

22

Adaptive neuro-fuzzy inference system (ANFIS) to predict the forced convection heat transfer from a v-shaped plate  

NASA Astrophysics Data System (ADS)

This paper reports the application of the adaptive neuro-fuzzy inference system to model the forced convection heat transfer from v-shaped plate internal surfaces exposed to an air impingement slot jet. The aim of the current study is to consider the effects of the angle of the v-shaped plate (Upphi ) , slot-to-plate spacing ratio (Z/W) and the Reynolds number (Re) variation on the average heat transfer from the v-shaped plate.

Karami, Alimohammad; Yousefi, Tooraj; Ebrahimi, Saeid; Rezaei, Ehsan; Mahmoudinezhad, Sajjad

2013-06-01

23

A new battery capacity indicator for lithium-ion battery powered electric vehicles using adaptive neuro-fuzzy inference system  

Microsoft Academic Search

This paper describes a new adaptive neuro-fuzzy inference system (ANFIS) model to estimate accurately the battery residual capacity (BRC) of the lithium-ion (Li-ion) battery for modern electric vehicles (EVs). The key to this model is to adopt newly both the discharged\\/regenerative capacity distributions and the temperature distributions as the inputs and the state of available capacity (SOAC) as the output,

K. T. Chau; K. C. Wu; C. C. Chan

2004-01-01

24

Human action recognition using meta-cognitive neuro-fuzzy inference system.  

PubMed

We propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference System (McFIS) to efficiently recognize human actions from video sequence. Optical flow information between two consecutive image planes can represent actions hierarchically from local pixel level to global object level, and hence are used to describe the human action in McFIS classifier. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: (i) Sample deletion (ii) Sample learning and (iii) Sample reserve. Performance of proposed McFIS based human action recognition system is evaluated using benchmark Weizmann and KTH video sequences. The simulation results are compared with well known SVM classifier and also with state-of-the-art action recognition results reported in the literature. The results clearly indicates McFIS action recognition system achieves better performances with minimal computational effort. PMID:23186277

Subramanian, K; Suresh, S

2012-12-01

25

Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS)  

NASA Astrophysics Data System (ADS)

The quality of radiation therapy delivered for treating cancer patients is related to set-up errors and organ motion. Due to the margins needed to ensure adequate target coverage, many breast cancer patients have been shown to develop late side effects such as pneumonitis and cardiac damage. Breathing-adapted radiation therapy offers the potential for precise radiation dose delivery to a moving target and thereby reduces the side effects substantially. However, the basic requirement for breathing-adapted radiation therapy is to track and predict the target as precisely as possible. Recent studies have addressed the problem of organ motion prediction by using different methods including artificial neural network and model based approaches. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro fuzzy inference system) for predicting respiratory motion in breast cancer patients. In ANFIS, we combine both the learning capabilities of a neural network and reasoning capabilities of fuzzy logic in order to give enhanced prediction capabilities, as compared to using a single methodology alone. After training ANFIS and checking for prediction accuracy on 11 breast cancer patients, it was found that the RMSE (root-mean-square error) can be reduced to sub-millimetre accuracy over a period of 20 s provided the patient is assisted with coaching. The average RMSE for the un-coached patients was 35% of the respiratory amplitude and for the coached patients 6% of the respiratory amplitude.

Kakar, Manish; Nyström, Håkan; Rye Aarup, Lasse; Jakobi Nøttrup, Trine; Rune Olsen, Dag

2005-10-01

26

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

NASA Astrophysics Data System (ADS)

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

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

2012-11-01

27

Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir  

NASA Astrophysics Data System (ADS)

Compressional-wave (Vp) data are key information for estimation of rock physical properties and formation evaluation in hydrocarbon reservoirs. However, the absence of Vp will significantly delay the application of specific risk-assessment approaches for reservoir exploration and development procedures. Since Vp is affected by several factors such as lithology, porosity, density, and etc., it is difficult to model their non-linear relationships using conventional approaches. In addition, currently available techniques are not efficient for Vp prediction, especially in carbonates. There is a growing interest in incorporating advanced technologies for an accurate prediction of lacking data in wells. The objectives of this study, therefore, are to analyze and predict Vp as a function of some conventional well logs by two approaches; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR). Also, the significant impact of selected input parameters on response variable will be investigated. A total of 2156 data points from a giant Middle Eastern carbonate reservoir, derived from conventional well logs and Dipole Sonic Imager (DSI) log were utilized in this study. The quality of the prediction was quantified in terms of the mean squared error (MSE), correlation coefficient (R-square), and prediction efficiency error (PEE). Results show that the ANFIS outperforms MLR with MSE of 0.0552, R-square of 0.964, and PEE of 2%. It is posited that porosity has a significant impact in predicting Vp in the investigated carbonate reservoir.

Zoveidavianpoor, Mansoor; Samsuri, Ariffin; Shadizadeh, Seyed Reza

2013-02-01

28

Adaptive neuro-fuzzy inference system for acoustic analysis of 4-channel phonocardiograms using empirical mode decomposition.  

PubMed

The heart's mechanical activity can be appraised by auscultation recordings, taken from the 4-Standard Auscultation Areas (4-SAA), one for each cardiac valve, as there are invisible murmurs when a single area is examined. This paper presents an effective approach for cardiac murmur detection based on adaptive neuro-fuzzy inference systems (ANFIS) over acoustic representations derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of 4-channel phonocardiograms (4-PCG). The 4-PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated on the combination of different intrinsic mode functions (IMFs). A fuzzy-rough feature selection (FRFS) was applied in order to reduce complexity. An ANFIS network was implemented on the feature space, randomly initialized, adjusted using heuristic rules and trained using a hybrid learning algorithm made up by least squares and gradient descent. Global classification for 4-SAA was around 98.9% with satisfactory sensitivity and specificity, using a 50-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-PCG and the neuro-fuzzy inference of acoustic features offered a high performance to detect cardiac murmurs. PMID:24109851

Becerra, Miguel A; Orrego, Diana A; Delgado-Trejos, Edilson

2013-07-01

29

An application of adaptive neuro-fuzzy inference system to landslide susceptibility mapping (Klang valley, Malaysia)  

NASA Astrophysics Data System (ADS)

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.

Sezer, Ebru; Pradhan, Biswajeet; Gokceoglu, Candan

2010-05-01

30

Adaptive neuro-fuzzy inference systems for semi-automatic discrimination between seismic events: a study in Tehran region  

NASA Astrophysics Data System (ADS)

Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289 signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are almost 223 earthquakes with M ? 2.2 included in this database. Data sets from the south, east, and southeast of the city of Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results confirmed that the proposed ANFIS model has good potential for determining seismic events.

Vasheghani Farahani, Jamileh; Zare, Mehdi; Lucas, Caro

2012-04-01

31

Modelling of An Agricultural Robot Applying Neuro-Fuzzy Inference System Approach  

Microsoft Academic Search

This paper emphasizes the modelling of an agricultural robot, API. It is expected to build a model by the Neuro-Fuzzy method according to its high nonlinearity, multivariate and time-variation. Firstly the neuro-fuzzy model is built by ANFIS algorithm. Particularly, the equivalent wheel is proposed in the paper. It decreases the number of the fuzzy rules sharply, hence to enhance the

Jun Xie; Xinying Xu; Keming Xie

2007-01-01

32

Application of Artificial Neuro-Fuzzy Logic Inference System for Predicting the Microbiological Pollution in Fresh Water  

NASA Astrophysics Data System (ADS)

The classical methods for detecting the micro biological pollution in water are based on the detection of the coliform bacteria which indicators of contamination. But to check each water supply for these contaminants would be a time-consuming job and a qualify operators. In this study, we propose a novel intelligent system which provides a detection of microbiological pollution in fresh water. The proposed system is a hierarchical integration of an Artificial Neuro-Fuzzy Inference System (ANFIS). This method is based on the variations of the physical and chemical parameters occurred during bacteria growth. The instantaneous result obtained by the measurements of the variations of the physical and chemical parameters occurred during bacteria growth-temperature, pH, electrical potential and electrical conductivity of many varieties of water (surface water, well water, drinking water and used water) on the number Escherichia coli in water. The instantaneous result obtained by measurements of the inputs parameters of water from sensors.

Bouharati, S.; Benmahammed, K.; Harzallah, D.; El-Assaf, Y. M.

33

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

ERIC Educational Resources Information Center

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

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

2012-01-01

34

Fuzzy logic and adaptive neuro-fuzzy inference system for characterization of contaminant exposure through selected biomarkers in African catfish.  

PubMed

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

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

2012-07-01

35

Utility of coactive neuro-fuzzy inference system for pan evaporation modeling in comparison with multilayer perceptron  

NASA Astrophysics Data System (ADS)

Estimation of pan evaporation ( E pan) using black-box models has received a great deal of attention in developing countries where measurements of E pan are spatially and temporally limited. Multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CANFIS) models were used to predict daily E pan for a semi-arid region of Iran. Six MLP and CANFIS models comprising various combinations of daily meteorological parameters were developed. The performances of the models were tested using correlation coefficient ( r), root mean square error (RMSE), mean absolute error (MAE) and percentage error of estimate (PE). It was found that the MLP6 model with the Momentum learning algorithm and the Tanh activation function, which requires all input parameters, presented the most accurate E pan predictions ( r = 0.97, RMSE = 0.81 mm day-1, MAE = 0.63 mm day-1 and PE = 0.58 %). The results also showed that the most accurate E pan predictions with a CANFIS model can be achieved with the Takagi-Sugeno-Kang (TSK) fuzzy model and the Gaussian membership function. Overall performances revealed that the MLP method was better suited than CANFIS method for modeling the E pan process.

Tabari, Hossein; Hosseinzadeh Talaee, P.; Abghari, Hirad

2012-05-01

36

Neuro-fuzzy systems for function approximation  

Microsoft Academic Search

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

Detlef Nauck; Rudolf Kruse

1999-01-01

37

Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS  

NASA Astrophysics Data System (ADS)

The objective of this study is to investigate a potential application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a relatively new approach for landslide susceptibility mapping in the Hoa Binh province of Vietnam. Firstly, a landslide inventory map with a total of 118 landslide locations was constructed from various sources. Then the landslide inventory was randomly split into a testing dataset 70% (82 landslide locations) for training the models and the remaining 30% (36 landslides locations) was used for validation purpose. Ten landslide conditioning factors such as slope, aspect, curvature, lithology, land use, soil type, rainfall, distance to roads, distance to rivers, and distance to faults were considered in the analysis. The hybrid learning algorithm and six different membership functions (Gaussmf, Gauss2mf, Gbellmf, Sigmf, Dsigmf, Psigmf) were applied to generate the landslide susceptibility maps. The validation dataset, which was not considered in the ANFIS modeling process, was used to validate the landslide susceptibility maps using the prediction rate method. The validation results showed that the area under the curve (AUC) for six ANFIS models vary from 0.739 to 0.848. It indicates that the prediction capability depends on the membership functions used in the ANFIS. The models with Sigmf (0.848) and Gaussmf (0.825) have shown the highest prediction capability. The results of this study show that landslide susceptibility mapping in the Hoa Binh province of Vietnam using the ANFIS approach is viable. As far as the performance of the ANFIS approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.

Tien Bui, Dieu; Pradhan, Biswajeet; Lofman, Owe; Revhaug, Inge; Dick, Oystein B.

2012-08-01

38

Prediction of Refrigerant Mass Flow Rates through Capillary Tubes Using Adaptive Neuro-fuzzy Inference System  

Microsoft Academic Search

A capillary tube is a common expansion device widely used in small-scale refrigeration and air conditioning systems. Generalized correlation method for refrigerant flow rate through adiabatic capillary tubes is developed by combining dimensional analysis and adaptive neuron-fuzzy inference system (ANFIS).Dimensional analysis is utilized to provide the generalized dimensionless parameters and reduce the number of input parameters, while a five-layer feedforward

Hui Xie; Fei Ma; Huifang Fan; Yanqiang Di

2009-01-01

39

Neuro-Fuzzy Systems: Review And Prospects  

Microsoft Academic Search

This paper reviews neuro-fuzzy systems, which combine methods from neural network theory with fuzzysystems. Such combinations have been considered for several years already. However, the term neuro-fuzzystill lacks proper definition, and still has the flavour of a buzzword to it. Surprisingly few neuro-fuzzyapproaches do actually employ neural networks, even though they are very often depicted in form of somekind of

Detlef Nauck

1997-01-01

40

Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department.  

PubMed

Unexpected disease outbreaks and disasters are becoming primary issues facing our world. The first points of contact either at the disaster scenes or emergency department exposed the frontline workers and medical physicians to the risk of infections. Therefore, there is a persuasive demand for the integration and exploitation of heterogeneous biomedical information to improve clinical practice, medical research and point of care. In this paper, a primary triage model was designed using two different methods: an adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).When the patient is presented at the triage counter, the system will capture their vital signs and chief complains beside physiology stat and general appearance of the patient. This data will be managed and analyzed in the data server and the patient's emergency status will be reported immediately. The proposed method will help to reduce the queue time at the triage counter and the emergency physician's burden especially duringdisease outbreak and serious disaster. The models have been built with 2223 data set extracted from the Emergency Department of the Universiti Kebangsaan Malaysia Medical Centre to predict the primary triage category. Multilayer feed forward with one hidden layer having 12 neurons has been used for the ANN architecture. Fuzzy subtractive clustering has been used to find the fuzzy rules for the ANFIS model. The results showed that the RMSE, %RME and the accuracy which evaluated by measuring specificity and sensitivity for binary classificationof the training data were 0.14, 5.7 and 99 respectively for the ANN model and 0.85, 32.00 and 96.00 respectively for the ANFIS model. As for unseen data the root mean square error, percentage the root mean square error and the accuracy for ANN is 0.18, 7.16 and 96.7 respectively, 1.30, 49.84 and 94 respectively for ANFIS model. The ANN model was performed better for both training and unseen data than ANFIS model in term of generalization. It was therefore chosen as the technique to develop the primary triage prediction model. This primary triage model will be combined with the secondary triage prediction model to produce the final triage category as a tool to assist the medical officer in the emergency department. PMID:24052927

Azeez, Dhifaf; Ali, Mohd Alauddin Mohd; Gan, Kok Beng; Saiboon, Ismail

2013-08-29

41

Short term wind power forecasting using adaptive neuro-fuzzy inference systems  

Microsoft Academic Search

As the global political will to address climate change gains momentum, the issues associated with integrating an increasing penetration of wind power into power systems need to be addressed. This paper summarises the current trends in wind power and how it is accepted into electricity markets. The need for accurate short term wind power forecasting is highlighted with particular reference

Peter L. Johnson; Michael Negnevitsky; Kashem M. Muttaqi

2007-01-01

42

Learning competition in robot soccer game based on an adapted neuro-fuzzy inference system  

Microsoft Academic Search

RoboCup is a worldwide popular research domain. Because of the complexity of the systems, how to describe cooperation and competition between agents is a great challenge in the RoboCup Simulation Game. On one hand, the rich experience of a human soccer player is of great service to the robot players. On the other hand, the difference between the simulation game

Li Shi; Chen Jiang; Ye Zhen; Sun Zengqi

2001-01-01

43

Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages  

NASA Astrophysics Data System (ADS)

This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.

Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.

2013-09-01

44

Neuro-fuzzy methods for nonlinear system identification  

Microsoft Academic Search

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

Robert Babuška; Henk Verbruggen

2003-01-01

45

Differentiating between good credits and bad credits using neuro-fuzzy systems  

Microsoft Academic Search

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.

Rashmi Malhotra; D. K. Malhotra

2002-01-01

46

Use of an adaptive neuro-fuzzy inference system to obtain the correspondence among balance, gait, and depression for Parkinson's disease  

NASA Astrophysics Data System (ADS)

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

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

2013-03-01

47

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

Microsoft Academic Search

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

Alexandre Evsukoff; Sylviane Gentil

2005-01-01

48

Prognosis of machine health condition using neuro-fuzzy systems  

Microsoft Academic Search

A reliable machine fault prognostic system can be used to forecast damage propagation trend in rotary machinery and to provide an alarm before a fault reaches critical levels. Currently, there are several techniques available in the literature for time-series prediction. Among the most promising methods are recurrent neural networks (RNNs) and neuro-fuzzy (NF) systems. In this paper, the performance of

Wilson Q. Wang; M. Farid Golnaraghi; Fathy Ismail

2004-01-01

49

Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system  

NASA Astrophysics Data System (ADS)

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

Iphar, Melih; Yavuz, Mahmut; Ak, Hakan

2008-11-01

50

Winter Rainfall Prediction Based on climatic large scale signals by Using Adaptive Neuro Fuzzy Inference System (ANFIS)  

NASA Astrophysics Data System (ADS)

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

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

2009-09-01

51

Forecasting stock market short-term trends using a neuro-fuzzy based methodology  

Microsoft Academic Search

A neuro-fuzzy system composed of an Adaptive Neuro Fuzzy Inference System (ANFIS) controller used to control the stock market process model, also identified using an adaptive neuro-fuzzy technique, is derived and evaluated for a variety of stocks. Obtained results challenge the weak form of the Efficient Market Hypothesis (EMH) by demonstrating much improved and better predictions, compared to other approaches,

George S. Atsalakis; Kimon P. Valavanis

2009-01-01

52

Modeling uncertainty analysis in flow and solute transport model using Adaptive Neuro Fuzzy Inference System and particle swarm optimization  

Microsoft Academic Search

Groundwater flow and contaminant transport simulations require the determination of various hydro geological parameters such\\u000a as transmissivity, aquifer thickness, seepage velocity, dispersibility etc. Due to the complex behavior of Groundwater flow\\u000a and contaminant transport simulations, reliable measurement of the parameters involved is often not possible while performing\\u000a groundwater system simulations. Hence a methodology is developed in this study wherein a

Sudheer Ch; Shashi Mathur

2010-01-01

53

Neuro-fuzzy modeling and control  

Microsoft Academic Search

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

JYH-SHING ROGER JANG; Chuen-Tsai Sun

1995-01-01

54

Anomaly Detection using Neuro Fuzzy system  

Microsoft Academic Search

? Abstract— As the network based technologies become omnipresent, demands to secure networks\\/systems against threat increase. One of the effective ways to achieve higher security is through the use of intrusion detection systems (IDS), which are a software tool to detect anomalous in the computer or network. In this paper, an IDS has been developed using an improved machine learning

Fatemeh Amiri; Caro Lucas; Nasser Yazdani

2009-01-01

55

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

NASA Astrophysics Data System (ADS)

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

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

2012-11-01

56

Adaptive neuro-fuzzy control with sliding mode learning algorithm: Application to Antilock Braking System  

Microsoft Academic Search

A neuro-fuzzy adaptive control approach for nonlinear dynamical systems, which are coupled with unknown dynamics, modeling errors, and various sorts of disturbances, is proposed and used to design a wheel slip regulating controller. The implemented control structure consists of a conventional controller and a neuro-fuzzy network-based feedback controller. The former is provided both to guarantee global asymptotic stability in compact

Andon V. Topalov; Erdal Kayacan; Yesim Oniz; Okyay Kaynak

2009-01-01

57

A neuro-fuzzy online fault detection and diagnosis algorithm for nonlinear and dynamic systems  

Microsoft Academic Search

This paper presents a new fault detection and diagnosis approach for nonlinear dynamic plant systems with a neuro-fuzzy based\\u000a approach to prevent developing of fault as soon as possible. By comparison of plants and neuro-fuzzy estimator outputs in\\u000a the presence of noise, residual signal is generated and compared with a predefined threshold, the fault can be detected. To\\u000a diagnose the

Mohsen Shabanian; Mohsen Montazeri

2011-01-01

58

On-line identification of thermal process using a modified ts-type neuro-fuzzy system  

Microsoft Academic Search

In this paper, a modified TS-type neuro-fuzzy system (MTSNFS) for on-line identification is proposed, which possesses six layers of neurons to perform the fuzzy inference. A modified self-organizing competitive learning algorithm with capabilities of dynamical rules recruitment and cancellation is proposed for structure identification. A hybrid learning algorithm combining recursive least squares (RLS) estimation and ordered derivative learning is used

Zhanbo Dong; Wenguo Xiang; Xiaocen Xue; Shiyi Chen; Xin Wang

2011-01-01

59

Classification and quantification of hydrometeors based on polarimetric radar measurements: Development of fuzzy logic and neuro-fuzzy systems and in situ verification  

Microsoft Academic Search

Fuzzy logic and Neuro-Fuzzy systems for the classification of hydrometeor type based on polarimetric radar measurements is developed. The hydrometeor classification system is implemented where the fuzzy logic is used to infer hydrometeor type, and the neural network learning algorithm is used for automatic adjustment of the parameters of the fuzzy sets in the fuzzy logic system. Five radar measurements,

Hongping Liu; V. Chandrasekar

2000-01-01

60

A system-on-chip development of a neuro-fuzzy embedded agent for ambient-intelligence environments.  

PubMed

This paper presents the development of a neuro-fuzzy agent for ambient-intelligence environments. The agent has been implemented as a system-on-chip (SoC) on a reconfigurable device, i.e., a field-programmable gate array. It is a hardware/software (HW/SW) architecture developed around a MicroBlaze processor (SW partition) and a set of parallel intellectual property cores for neuro-fuzzy modeling (HW partition). The SoC is an autonomous electronic device able to perform real-time control of the environment in a personalized and adaptive way, anticipating the desires and needs of its inhabitants. The scheme used to model the intelligent agent is a particular class of an adaptive neuro-fuzzy inference system with piecewise multilinear behavior. The main characteristics of our model are computational efficiency, scalability, and universal approximation capability. Several online experiments have been performed with data obtained in a real ubiquitous computing environment test bed. Results obtained show that the SoC is able to provide high-performance control and adaptation in a life-long mode while retaining the modeling capabilities of similar agent-based approaches implemented on larger computing machines. PMID:22010155

del Campo, Inés; Basterretxea, Koldo; Echanobe, Javier; Bosque, Guillermo; Doctor, Faiyaz

2011-10-14

61

A transductive neuro-fuzzy controller: application to a drilling process.  

PubMed

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

Gajate, Agustín; Haber, Rodolfo E; Vega, Pastora I; Alique, José R

2010-07-01

62

A neuro-fuzzy system modeling with self-constructing rule generationand hybrid SVD-based learning  

Microsoft Academic Search

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

Shie-Jue Lee; Chen-Sen Ouyang

2003-01-01

63

Hybrid Intelligence modeling of cut edge quality for Mn-Mo in laser machining by adaptive neuro-fuzzy inference system (ANFIS)  

Microsoft Academic Search

Past few decades have seen a resurgent trend towards establishment of intelligent manufacturing systems, which are capable of using advanced knowledge-bases and intelligence techniques in aiding critical operational procedures in manufacturing. Increasing demands on productivity and quality with the increase in global competitiveness have necessitated development of sound predictive models and optimization strategies. This paper presents the modeling technique and

Sivarao

2007-01-01

64

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

NASA Astrophysics Data System (ADS)

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

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

1998-06-01

65

Inference of S-wave velocities from well logs using a Neuro-Fuzzy Logic (NFL) approach  

NASA Astrophysics Data System (ADS)

The knowledge of S-wave velocity values is important for a complete characterization and understanding of reservoir rock properties. It could help in determining fracture propagation and also to improve porosity prediction (Cuddy and Glover, 2002). Nevertheless the acquisition of S-wave velocity data is rather expensive; hence, for most reservoirs usually this information is not available. In the present work we applied a hybrid system, that combines Neural Networks and Fuzzy Logic, in order to infer S-wave velocities from porosity (?), water saturation (Sw) and shale content (Vsh) logs. The Neuro-Fuzzy Logic (NFL) technique was tested in two wells from the Guafita oil field, Apure Basin, Venezuela. We have trained the system using 50% of the data randomly taken from one of the wells, in order to obtain the inference equations (Takani-Sugeno-Kang (TSK) fuzzy model). Equations using just one of the parameters as input (i.e. ??, Sw or Vsh), combined by pairs and all together were obtained. These equations were tested in the whole well. The results indicate that the best inference (correlation between inferred and experimental data close to 80%) is obtained when all the parameters are considered as input data. An increase of the equation number of the TSK model, when one or just two parameters are used, does not improve the performance of the NFL. The best set of equations was tested in a nearby well. The results suggest that the large difference in the petrophysical and lithological characteristics between these two wells, avoid a good inference of S-wave velocities in the tested well and allowed us to analyze the limitations of the method.

Aldana, Milagrosa; Coronado, Ronal; Hurtado, Nuri

2010-05-01

66

Intelligent control of aircraft dynamic systems with a new hybrid neuro-fuzzy-fractal approach  

Microsoft Academic Search

We describe in this paper a hybrid method for adaptive model-based control of non-linear dynamic systems using Neural Networks, Fuzzy Logic and Fractal Theory. The new neuro–fuzzy–fractal method combines Soft Computing (SC) techniques with the concept of the fractal dimension for the domain of Non-Linear Dynamic System Control. The new method for adaptive model-based control has been implemented as a

Patricia Melin; Oscar Castillo

2002-01-01

67

Intelligent adaptive control of aircraft dynamic systems with a new neuro-fuzzy-fractal approach  

Microsoft Academic Search

We describe a general method for adaptive model-based control of nonlinear dynamic systems using neural networks, fuzzy logic and fractal theory. The new neuro-fuzzy-fractal method combines soft computing techniques with the concept of the fractal dimension for the domain of nonlinear dynamic system control. The new method for adaptive model-based control has been implemented as a computer program to show

Patricia Melin; O. Castillo

1999-01-01

68

Neuro-fuzzy ART-based document management system: application to mail distribution and digital libraries  

Microsoft Academic Search

A new document management system is proposed in this paper. Its kernel is based on a new set of neuro-fuzzy systems of the ART family: FasArt and RFasArt. The first one, FasArt, is used to support a simple Optical Character Recognition (OCR) that inherits fine properties of ART architectures, such as fast and incremental learning, stability and modularity. On the

G. I. Sainz Palmero; Y. A. Dimitriadis; R. Sanz Guadarrama; J. M. Cano Izquierdo

2002-01-01

69

CLUSTERED BASED TAKAGI-SUGENO NEURO-FUZZY MODELING OF A MULTIVARIABLE NONLINEAR DYNAMIC SYSTEM  

Microsoft Academic Search

This research frame work investigates the application of a clustered based Neuro-fuzzy system to nonlinear dynamic system modeling from a set of input-output training patterns. It is concentrated on the modeling via Ta- kagi-Sugeno (T-S) modeling technique and the employment of fuzzy cluster- ing to generate suitable initial membership functions. Hence, such created ini- tial memberships are then employed to

E. A. Al-Gallaf

2005-01-01

70

Simulink-based HW/SW codesign of embedded neuro-fuzzy systems.  

PubMed

We propose a semi-automatic HW/SW codesign flow for low-power and low-cost Neuro-Fuzzy embedded systems. Applications range from fast prototyping of embedded systems to high-speed simulation of Simulink models and rapid design of Neuro-Fuzzy devices. The proposed codesign flow works with different technologies and architectures (namely, software, digital and analog). We have used The Mathworks' Simulink environment for functional specification and for analysis of performance criteria such as timing (latency and throughput), power dissipation, size and cost. The proposed flow can exploit trade-offs between SW and HW as well as between digital and analog implementations, and it can generate, respectively, the C, VHDL and SKILL codes of the selected architectures. PMID:11011793

Reyneri, L M; Chiaberge, M; Lavagno, L

2000-06-01

71

Efficient Reinforcement Hybrid Evolutionary Learning for Recurrent Wavelet-Based Neuro-fuzzy Systems  

Microsoft Academic Search

This paper proposes a recurrent wavelet-based neuro-fuzzy system (RWNFS) with the reinforcement hybrid evolutionary learning\\u000a algorithm (R-HELA) for solving various control problems. The proposed R-HELA combines the compact genetic algorithm (CGA)\\u000a and the modified variable-length genetic algorithm (MVGA), performs the structure\\/ parameter learning for dynamically constructing\\u000a the RWNFS. That is, both the number of rules and the adjustment of parameters

Cheng-hung Chen; Cheng-jian Lin; Chi-yung Lee

2007-01-01

72

Development of an adaptive neuro-fuzzy method for supply air pressure control in HVAC system  

Microsoft Academic Search

An adaptive neuro-fuzzy (ANF) method is developed for the supply air pressure control loop of a heating, ventilation and air-conditioning (HVAC) system. Although a well-tuned PID controller performs well around normal working points, its tolerance to process parameter variations is severely affected due to the nature of PID controllers. The ANF controller developed overcomes this weakness. The controller design involves

Wu Jian; Cai Wenjian

2000-01-01

73

An adaptive neuro-fuzzy system for automatic image segmentation and edge detection  

Microsoft Academic Search

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

Victor Boskovitz; Hugo Guterman

2002-01-01

74

Neuro-fuzzy Controlled Autonomous Mobile Robotics System  

Microsoft Academic Search

This article discusses a Neurofuzzy navigation strategy for sensor-based mobile robotics system. A Transputer computation power is used to carry out complicated needed computation (reading sensors data, deciding actions, outputting wheels data, … system monitoring). Robot control mythology was run on a parallel computing environment known as Transputers. The Transputer embedded real-time controller was used on board the robot to

Khalid Al Mutib; Ebrahim Mattar

2011-01-01

75

A neuro-fuzzy system for extracting environment features based on ultrasonic sensors.  

PubMed

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

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

2009-12-09

76

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

PubMed Central

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

Marichal, Graciliano Nicolas; Hernandez, Angela; Acosta, Leopoldo; Gonzalez, Evelio Jose

2009-01-01

77

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Sp?ka? Ska? Przy Pracach Strza?owych W Kopalniach Odkrywkowych Przy U?yciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym  

NASA Astrophysics Data System (ADS)

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 (R2) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.

Bazzazi, Abbas Aghajani; Esmaeili, Mohammad

2012-12-01

78

A simple direct-torque neuro-fuzzy control of PWM-inverter-fed induction motor drive  

Microsoft Academic Search

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

Pawel Z. Grabowski; Marian P. Kazmierkowski; Bimal K. Bose; Frede Blaabjerg

2000-01-01

79

Neuro-fuzzy decision trees for content popularity model and multi-genre movie recommendation system over social network  

Microsoft Academic Search

In this paper, we propose a framework of multi-genre movie recommender system based on neuro-fuzzy decision tree (NFDT) methodology. The system is capable of recommending list of movies in descending order of preference in response to user queries and profiles. The system also takes care of attempt to vote stuffing using novel application of fuzzy c-means clustering algorithm. Typical user

R. B. Bhatt

2009-01-01

80

1.15mW mixed-mode neuro-fuzzy accelerator for keypoint localization in image processing  

Microsoft Academic Search

A mixed-mode neuro-fuzzy accelerator is proposed for keypoint localization of image features of Scale Invariant Feature Transform (SIFT) algorithm. To reduce processing time of keypoint localization with low power consumption, analog Adaptive Neuro-Fuzzy Inference System (ANFIS) and digital controller are implemented together. It is implemented in O.13J.1.m CMOS process and achieves 1.15mW power consumption. Compared to the conventional digital standalone

Injoon Hong; Jinwook Oh; Hoi-Jun Yoo

2011-01-01

81

Prediction of Breeding Values for Dairy Cattle Using Artificial Neural Networks and Neuro-Fuzzy Systems  

PubMed Central

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

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

2012-01-01

82

A neuro-fuzzy approach to hybrid intelligent control  

Microsoft Academic Search

This paper presents a neuro-fuzzy approach to the development of high-performance real-time intelligent and adaptive controllers for nonlinear plants. Several paradigms derived from cognitive sciences are considered and analyzed in this work, such as neural networks, fuzzy inference systems, genetic algorithms, etc. The different control strategies are also integrated with finite-state automata, and the theory of fuzzy-state automata is derived

Beatrice Lazzerini; Leonardo M. Reyneri; Marcello Chiaberge

1999-01-01

83

Classification and quantification of hydrometeors based on polarimetric radar measurements: Development of fuzzy logic and neuro-fuzzy systems and in situ verification  

NASA Astrophysics Data System (ADS)

Fuzzy logic and Neuro-Fuzzy systems for the classification of hydrometeor type based on polarimetric radar measurements is developed. The hydrometeor classification system is implemented where the fuzzy logic is used to infer hydrometeor type, and the neural network learning algorithm is used for automatic adjustment of the parameters of the fuzzy sets in the fuzzy logic system. Five radar measurements, namely, horizontal reflectivity (ZH ), differential reflectivity (ZDR), differential propagation phase shift (KDP), correlation coefficient (?HV(0)), and linear depolarization ratio (LDR), and corresponding altitude have been used as input variables to the hydrometeor classifier. The output is one of the many possible hydrometeor types, namely (1)drizzle, (2)rain, (3)dry and low density snow, (4)dry and high density crystals, (5)wet and melting snow, (6)dry graupel, (7)wet graupel, (8)small hail, (9)large hail, and (10)mixture of rain and hail. The Neuro-Fuzzy classifier is more advantageous than a simple Neural Network or a fuzzy logic classifier because it is transparent rather than a ``black box'' (unlike a neural network), and can learn the parameters of the system from the past data (unlike a fuzzy logic system). The Neuro-Fuzzy hydrometeor classifier has been applied to several case studies and the results are compared against in-situ observations. A novel scheme of adaptively updating the structure and parameters of the neural network for rainfall estimation is presented. This adaptive neural network scheme enables the network to implement the nonstationary relationship between radar measurements and precipitation estimation with change of season and other environment conditions, and also can incorporate new information, without re- training the complete network from the beginning. It was shown that the adaptive neural network is much faster, more efficient and convenient for real time rainfall estimation to be used with WSR-88D. Another important issue for the application of radar rainfall algorithm is the detection of rain/no-rain conditions on the ground. Vertical reflectivity profiles of radar observations are used as input variables to the rain/no-rain determination. Radar data and ground raingage measurements are used to train the neural network. Results indicate that rain/no-rain conditions on the ground can be inferred from the procedure developed in this paper fairly accurately. It is shown that by using rain/no-rain classification scheme the accuracy of rainfall accumulation estimates can be improved greatly. (Abstract shortened by UMI.)

Liu, Hongping

84

Indirect adaptive control of nonlinear systems based on bilinear neuro-fuzzy approximation.  

PubMed

In this paper, we investigate the indirect adaptive regulation problem of unknown affine in the control nonlinear systems. The proposed approach consists of choosing an appropriate system approximation model and a proper control law, which will regulate the system under the certainty equivalence principle. The main difference from other relevant works of the literature lies in the proposal of a potent approximation model that is bilinear with respect to the tunable parameters. To deploy the bilinear model, the components of the nonlinear plant are initially approximated by Fuzzy subsystems. Then, using appropriately defined fuzzy rule indicator functions, the initial dynamical fuzzy system is translated to a dynamical neuro-fuzzy model, where the indicator functions are replaced by High Order Neural Networks (HONNS), trained by sampled system data. The fuzzy output partitions of the initial fuzzy components are also estimated based on sampled data. This way, the parameters to be estimated are the weights of the HONNs and the centers of the output partitions, both arranged in matrices of appropriate dimensions and leading to a matrix to matrix bilinear parametric model. Based on the bilinear parametric model and the design of appropriate control law we use a Lyapunov stability analysis to obtain parameter adaptation laws and to regulate the states of the system. The weight updating laws guarantee that both the identification error and the system states reach zero exponentially fast, while keeping all signals in the closed loop bounded. Moreover, introducing a method of "concurrent" parameter hopping, the updating laws are modified so that the existence of the control signal is always assured. The main characteristic of the proposed approach is that the a priori experts information required by the identification scheme is extremely low, limited to the knowledge of the signs of the centers of the fuzzy output partitions. Therefore, the proposed scheme is not vulnerable to initial design assumptions. Simulations on selected examples of well-known benchmarks illustrate the potency of the method. PMID:23924413

Boutalis, Yiannis; Christodoulou, Manolis; Theodoridis, Dimitrios

2013-07-16

85

Neuro-fuzzy controller to navigate an unmanned vehicle.  

PubMed

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

Selma, Boumediene; Chouraqui, Samira

2013-04-27

86

Characterization of Lithofacies in an oil well via experimental measument of bulk magnetic properties and their inference through Neuro Fuzzy Logic analysis  

NASA Astrophysics Data System (ADS)

We have measured NRM, room temperature magnetic susceptibility, S ratios and Königsberger ratios in 134 samples that encompass aproximately 670 meters of depth in an oil well drilled in eastern Colombia. These samples are sandstones and siltstones from the Guayabo, León and Carbonera Formations (Oligocene/Miocene/Pliocene). Our main goal is to asses the potential of the Neuro Fuzzy Logic analysis to infer magnetic parameters such as S ratios and Königsberger ratios from magnetic susceptibility experimental data. This method has been previously used with some success to obtain other petrophysical properties such as permeability out of porosity experimental data, however this is the first time it is applied to bulk magnetic properties. The results obtained here are then compared and integrated with their experimental counterparts. They are also used to study the variability of the paleoenvironmental conditions during the formation of the Barinas Apure sedimentary basin in eastern Colombia and western Venezuela.

Hurtado, N.; López, D.; Costanzo-Alvarez, V.; Aldana, M.

2009-04-01

87

Thermodynamic evaluation of the Afyon geothermal district heating system by using neural network and neuro-fuzzy  

NASA Astrophysics Data System (ADS)

In this study, energy and exergy analysis of the Afyon geothermal district heating system (AGDHS) in Afyon, Turkey using artificial neural network (ANN) and adaptive neuro-fuzzy (ANFIS) methods is carried out. Actual system data in the analysis of the AGDHS are used. The results of ANN are compared with ANFIS in which the same data sets are used. ANN model is slightly better than ANFIS in determining the energy and exergy rates. In addition, new formulations obtained from ANN are presented for the determination of the energy and exergy rates of the AGDHS. The R2-values obtained when unknown data were used in the networks were 0.999999847 and 0.99999997 for the energy and exergy rates respectively, which are very satisfactory.

?encan ?ahin, Arzu; Yaz?c?, Hilmi

2012-07-01

88

A Wavelet-neuro-fuzzy Combined Approach for Digital Relaying of Transmission Line Faults  

Microsoft Academic Search

The proposed algorithm for fault location, different from conventional algorithms that are based on deterministic computations on a well-defined model to be protected, employs wavelet transform together with fuzzy inference system (FIS) and the adaptive neuro-fuzzy inference system (ANFIS) to incorporate expert evaluation so as to extract important features from wavelet multi-resolution analysis (MRA) coefficients for obtaining coherent conclusions regarding

M. Jayabharata Reddy; Dusmanta Kumar Mohanta

2007-01-01

89

Modeling of Turbine Cycle Using Neuro-fuzzy Based Approach to Predict Turbine-Generator Output for Nuclear Power Plants  

Microsoft Academic Search

Due to the very complex sets of component systems, interrelated thermodynamic processes and seasonal change in operating conditions, it is relatively difficult to find a suitable model for turbine cycle of nuclear power plants (NPPs). This paper deals with the modeling of turbine cycle to predict turbine-generator output using an adaptive neuro-fuzzy inference system (ANFIS) for Unit 1 of Kuosheng

Yea-Kuang Chan; Jyh-Cherng Gu

2012-01-01

90

Application of a neuro-fuzzy network for gait event detection using electromyography in the child with Cerebral palsy  

Microsoft Academic Search

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

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

2005-01-01

91

A new neuro-fuzzy-fractal approach for adaptive model-based control of non-linear dynamic systems: the case of controlling aircraft dynamics  

Microsoft Academic Search

Describes a general method for adaptive model-based control of non-linear dynamic systems using neural networks, fuzzy logic and fractal theory. The new neuro-fuzzy-fractal method combines soft computing (SC) techniques with the concept of the fractal dimension for the domain of non-linear dynamic system control. The new method for adaptive model-based control has been implemented as a computer program to show

Patricia Melin; Oscar Castillo

1999-01-01

92

In vitro–in vivo correlations of self-emulsifying drug delivery systems combining the dynamic lipolysis model and neuro-fuzzy networks  

Microsoft Academic Search

The aim of the current study was to evaluate the potential of the dynamic lipolysis model to simulate the absorption of a poorly soluble model drug compound, probucol, from three lipid-based formulations and to predict the in vitro–in vivo correlation (IVIVC) using neuro-fuzzy networks. An oil solution and two self-micro and nano-emulsifying drug delivery systems were tested in the lipolysis

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

2008-01-01

93

A neuro-fuzzy method to learn fuzzy classification rules from data  

Microsoft Academic Search

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

Detlef Nauck; Rudolf Kruse

1997-01-01

94

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

NASA Astrophysics Data System (ADS)

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

Krishnamurthy, Karthik

2000-10-01

95

Some Approaches to Improve the Interpretability of Neuro-Fuzzy Classifiers  

Microsoft Academic Search

Neuro-fuzzy classification systems make it possible to obtain a suitable fuzzy classifier by learningfrom data. Nevertheless, in some cases the derived rule base is hard to interpret. In this paper wediscuss some approaches to improvetheinterpretability of neuro-fuzzy classification systems. We presentmodified learning strategies to derive fuzzy classification rules from data, and some methods to simplifythe found rule base to improve

Aljoscha Klose; Andreas Nürnberger; Detlef Nauck

1998-01-01

96

In vitro-in vivo correlations of self-emulsifying drug delivery systems combining the dynamic lipolysis model and neuro-fuzzy networks.  

PubMed

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

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

2008-02-01

97

Design and implementation of a neuro-fuzzy system for longitudinal control of autonomous vehicles  

Microsoft Academic Search

The control of nonlinear systems has been putting especial attention in the use of Artificial Intelligent techniques, where fuzzy logic presents one of the best alternatives due to the exploit of human knowledge. However, several fuzzy logic real-world applications use manual tuning (human expertise) to adjust control systems. On the other hand, in the Intelligent Transport Systems (ITS) field, the

Joshué Pérez; Agustín Gajate; Vicente Milanés; Enrique Onieva; Matilde Santos

2010-01-01

98

Design of Decision Making Unit for Neuro-fuzzy Control of Dynamic Systems  

Microsoft Academic Search

In this work is presented a design and implemen- tation of an intelligent controller applied to dynamic systems. The main objective is to design a management intelligent system that acts in the controllers of Direct current machine (DC) and thermo-eletric control systems drives. The purpose is to evaluate the performance of algorithms for intelligent control that integrate DC and thermal

Gustavo Araujo de Andrade

2011-01-01

99

A neuro-fuzzy system for looper tension control in rolling mills  

Microsoft Academic Search

A looper tension control system is common to many rolling processes. Conventional tension controllers for mill actuation systems are based on a rolling model. They therefore cannot deal effectively with unmodeled dynamics and large parameter variations that can lead to scrap runs and machinery damage. In this paper, this problem is tackled by designing a fuzzy controller that possesses different

F. Janabi-Sharifi

2005-01-01

100

A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems  

Microsoft Academic Search

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

Wael A. Farag; Victor H. Quintana; Germano Lambert-torres

1998-01-01

101

Predictability in Space Launch Vehicle Anomaly Detection Using Intelligent Neuro-Fuzzy Systems.  

National Technical Information Service (NTIS)

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; fail...

S. Gulati N. Toomarian J. Barhen A. Maccalla R. Tawel

1994-01-01

102

Development of a neuro-fuzzy expert system for predictive maintenance  

NASA Astrophysics Data System (ADS)

In this paper, a method for automatic constructing a fuzzy expert system from numerical data using the ILFN network and the Genetic Algorithm is presented. The Incremental Learning Fuzzy Neural (ILFN) network was developed for pattern classification applications. The ILFN network, employed fuzzy sets and neural network theory, is a fast, one-pass, on-line, and incremental nearing algorithm. After trained, the ILFN network stored numerical knowledge in hidden units, which can then be directly mapped into if-then rule bases. A knowledge base for fuzzy expert systems can then be extracted from the hidden units of the ILFN classifier. A genetic algorithm is then invoked, in an iterative manner, to reduce number of rules and select only important features of input patterns needed to provide to a fuzzy rule-based system. Three computer simulations using the Wisconsin breast cancer data set were performed. Using 400 patterns for training and 299 patterns for testing, the derived fuzzy expert system achieved 99.5% and 98.33% correct classification on the training set and the test set, respectively.

Yen, Gary G.; Meesad, Phayung

2001-07-01

103

Development of a neuro-fuzzy expert system for predictive maintenance  

Microsoft Academic Search

In this paper, a method for automatic constructing a fuzzy expert system from numerical data using the ILFN network and the Genetic Algorithm is presented. The Incremental Learning Fuzzy Neural (ILFN) network was developed for pattern classification applications. The ILFN network, employed fuzzy sets and neural network theory, is a fast, one-pass, on-line, and incremental nearing algorithm. After trained, the

Gary G. Yen; Phayung Meesad

2001-01-01

104

ADAPTIVE GENETIC SEARCH FOR OPTIMIZATION OF FUZZY AND NEURO-FUZZY SYSTEMS  

Microsoft Academic Search

It is known that the performance of a fuzzy control system may be significantly improved if the fuzzy reasoning model is s upplemented b y a genetic-based learning mechanism. In this paper an adaptive genetic search p rocedure for optimization of membership functions' ( MF) shape forming parameters based on d irect definition of the search ranges or certainty degrees

Alexander P. Topchy; Victor V. Miagkikh; Roman N. Kononenko; Ascold N. Melikhov

105

Neuro-fuzzy and soft computing in classification of remote sensing data  

NASA Astrophysics Data System (ADS)

Hybrid intelligent systems are discussed. These systems combine neural networks, which recognize patterns and adapt themselves to cope with changing environments, and fuzzy inference systems that incorporate human knowledge and perform inferencing and decision making. The integration of these complimentary techniques along with derivative-free optimization techniques based on genetic algorithms, results in a novel discipline called neuro-fuzzy and soft computing. These approaches will be discussed and applied in classification of multisource remote sensing and geographic data. Both the rationale of the approaches and the results obtained by the methods will be compared to more traditional techniques.

Benediktsson, Jon A.; Benediktsson, Helgi

1999-12-01

106

Classification of Sleep Stages in Infants: A Neuro Fuzzy Approach.  

National Technical Information Service (NTIS)

An ANFIS based neuro-fuzzy system to classify sleep-waking states and stages in healthy infants has been developed. The classifier takes five input patterns identified from polysomnographic recordings on 20 s frames and assigns them to one out of five pos...

J. E. Heiss C. M. Held P. A. Estevez C. A. Perez C. A. Holzmann

2001-01-01

107

Application of Neuro-Fuzzy Controller for Sumo Robot control  

Microsoft Academic Search

This paper proposes the application of Neuro-Fuzzy (NF) hybrid system for Sumo Robot (SR) control. This robot is frequently designed by engineering students for robotic competition. As the relation between sensors output signals and motors control pulses is highly nonlinear in SR, soft computing techniques can be used to define this nonlinear relation and control of the robot in a

Hamit Erdem

2011-01-01

108

Tuning of a neuro-fuzzy controller by genetic algorithm  

Microsoft Academic Search

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

Teo Lian Seng; Marzuki Bin Khalid; Rubiyah Yusof

1999-01-01

109

Self-Adaptive Recurrent Neuro-Fuzzy Control for an Autonomous Underwater Vehicle  

Microsoft Academic Search

This paper presents the utilization of a self-adaptive recurrent neuro-fuzzy control as a feedforward controller and a proportional-plus-derivative (PD) control as a feedback controller for controlling an autonomous underwater vehicle (AUV) in an unstructured environment. Without a priori knowledge, the recurrent neuro-fuzzy system is first trained to model the inverse dynamics of the AUV and then it utilized as a

Jeen-shing Wang; C. S. George Lee

2002-01-01

110

Self-adaptive recurrent neuro-fuzzy control of an autonomous underwater vehicle  

Microsoft Academic Search

This paper presents the utilization of a self-adaptive recurrent neuro-fuzzy control as a feedforward controller and a proportional-plus-derivative (PD) control as a feedback controller for controlling an autonomous underwater vehicle (AUV) in an unstructured environment. Without a priori knowledge, the recurrent neuro-fuzzy system is first trained to model the inverse dynamics of the AUV and then utilized as a feedforward

Jeen-Shing Wang; C. S. George Lee

2003-01-01

111

Stock trading using RSPOP: a novel rough set-based neuro-fuzzy approach.  

PubMed

This paper investigates the method of forecasting stock price difference on artificially generated price series data using neuro-fuzzy systems and neural networks. As trading profits is more important to an investor than statistical performance, this paper proposes a novel rough set-based neuro-fuzzy stock trading decision model called stock trading using rough set-based pseudo outer-product (RSPOP) which synergizes the price difference forecast method with a forecast bottleneck free trading decision model. The proposed stock trading with forecast model uses the pseudo outer-product based fuzzy neural network using the compositional rule of inference [POPFNN-CRI(S)] with fuzzy rules identified using the RSPOP algorithm as the underlying predictor model and simple moving average trading rules in the stock trading decision model. Experimental results using the proposed stock trading with RSPOP forecast model on real world stock market data are presented. Trading profits in terms of portfolio end values obtained are benchmarked against stock trading with dynamic evolving neural-fuzzy inference system (DENFIS) forecast model, the stock trading without forecast model and the stock trading with ideal forecast model. Experimental results showed that the proposed model identified rules with greater interpretability and yielded significantly higher profits than the stock trading with DENFIS forecast model and the stock trading without forecast model. PMID:17001989

Ang, Kai Keng; Quek, Chai

2006-09-01

112

Adaptive neuro fuzzy for image segmentation and edge detection  

Microsoft Academic Search

Adaptive Neuro-Fuzzy system for automatic multilevel image segmentation and edge detection. This system consists of a multilayer perceptron (MLP)-like network that performs image segmentation using thresholds automatically pre selected by Fuzzy C-means clustering algorithm. The learning technique employed is self supervised allowing, therefore, automatic adaptation of the neural network. This system does not require a priori assumptions whatsoever are made

B. R. Vikram; M. A. Bhanu; S. C. Venkateswarlu; M. R. Babu

2010-01-01

113

Adaptive Neuro - Fuzzy Controller for Stabilizing Autonomous Bicycle  

Microsoft Academic Search

The authors present an adaptive neuro-fuzzy controller for stabilizing an autonomous bicycle system. The controller has been designed and verified using simulation experiments in MATLAB. The controller has been found successful in balancing an autonomous bicycle system by running a generalized bicycle model under its control. The results show that it balances the bicycle within lean values of plusmn2.5deg around

N. Umashankar; Himanshu Dutt Sharma

2006-01-01

114

Estimating the crowding level with a neuro-fuzzy classifier  

NASA Astrophysics Data System (ADS)

This paper introduces a neuro-fuzzy system for the estimation of the crowding level in a scene. Monitoring the number of people present in a given indoor environment is a requirement in a variety of surveillance applications. In the present work, crowding has to be estimated from the image processing of visual scenes collected via a TV camera. A suitable preprocessing of the images, along with an ad hoc feature extraction process, is discussed. Estimation of the crowding level in the feature space is described in terms of a fuzzy decision rule, which relies on the membership of input patterns to a set of partially overlapping crowding classes, comprehensive of doubt classifications and outliers. A society of neural networks, either multilayer perceptrons or hyper radial basis functions, is trained to model individual class-membership functions. Integration of the neural nets within the fuzzy decision rule results in an overall neuro-fuzzy classifier. Important topics concerning the generalization ability, the robustness, the adaptivity and the performance evaluation of the system are explored. Experiments with real-world data were accomplished, comparing the present approach with statistical pattern recognition techniques, namely linear discriminant analysis and nearest neighbor. Experimental results validate the neuro-fuzzy approach to a large extent. The system is currently working successfully as a part of a monitoring system in the Dinegro underground station in Genoa, Italy.

Boninsegna, Massimo; Coianiz, Tarcisio; Trentin, Edmondo

1997-07-01

115

Flexible Process Control - A Neuro-Fuzzy Approach in Intelligent Workflow Model Based on Extended Petri Nets  

Microsoft Academic Search

With business applications going toward collectivization and internationalization, many researchers have focused their attention on intelligent workflow for inter-organizational cooperation. This paper mainly introduces a neuro-fuzzy approach for the realization of intelligent workflow management systems (WfMSs). The formal model of intelligent workflow is called intelligent neuro-fuzzy extended Petri nets (INFEPN). INFEPN not only takes the descriptive advantages of Petri Nets,

Jianchuan Xing; Zhishu Li; Jingyu Zhang

2006-01-01

116

Composition Estimation of Reactive Batch Distillation by Using Adaptive Neuro-Fuzzy Inference System  

Microsoft Academic Search

Composition estimation plays very important role in plant operation and control. Extended Kalman filter (EKF) is one of the most common estimators, which has been used in composition estimation of reactive batch distillation, but its performance is heavily dependent on the thermodynamic modeling of vapor-liquid equilibrium, which is difficult to initialize and tune. In this paper an inferential state estimation

S. M. Khazraee; A. H. Jahanmiri

2010-01-01

117

Intelligent control of a stepping motor drive using an adaptive neuro-fuzzy inference system  

Microsoft Academic Search

Stepping motors are widely used in robotics and in the numerical control of machine tools where they have to perform high-precision positioning operations. However, the variations of the mechanical configuration of the drive, which are common to these two applications, can lead to a loss of synchronism for high stepping rates. Moreover, the classical open-loop speed control is weak and

Patricia Melin; Oscar Castillo

2005-01-01

118

Adaptive Neuro-Fuzzy Inference System for generating scenarios in business strategic planning  

Microsoft Academic Search

The aim of this study is to investigate a new method for generating scenarios in order to cope with the data shortage and linguistic expression of an expert in scenario planning This study incorporates the concepts of neural network and fuzzy logic. The proposed methodology includes: (1) defining the scope and internal and external variables (2) determining rules from experts

Sorousha Moayer; Parisa A. Bahri; Ali Nooraii

2007-01-01

119

Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters  

Microsoft Academic Search

Modified 9Cr-1Mo ferritic steel is used as a structural material for steam generator components of power plants. Generally,\\u000a tungsten inert gas (TIG) welding is preferred for welding of these steels in which the depth of penetration achievable during\\u000a autogenous welding is limited. Therefore, activated flux TIG (A-TIG) welding, a novel welding technique, has been developed\\u000a in-house to increase the depth

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

2011-01-01

120

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

Microsoft Academic Search

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

Sefer Kurnaz; Okyay Kaynak; Ekrem Konakoglu

2007-01-01

121

Manifestation of a neuro-fuzzy model to produce landslide susceptibility map using remote sensing data derived parameters  

NASA Astrophysics Data System (ADS)

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

Pradhan, Biswajeet; Lee, Saro; Buchroithner, Manfred

122

Comparison of MLP Neural Network and Neuro-fuzzy System in Transcranial Doppler Signals Recorded from the Cerebral Vessels  

Microsoft Academic Search

Transcranial Doppler signals recorded from cerebral vessels of 110 patients were transferred to a personal computer by using\\u000a a 16 bit sound card. Spectral analyses of Transcranial Doppler signals were performed for determining the Multi Layer Perceptron\\u000a (MLP) neural network and neuro Ankara-fuzzy system inputs. In order to do a good interpretation and rapid diagnosis, FFT parameters\\u000a of Transcranial Doppler

Firat Hardalaç

2008-01-01

123

A neuro-fuzzy approach for estimation of time-to-flashover characteristic of polluted insulators  

Microsoft Academic Search

Function estimation is one of the major fields of fuzzy logic applications. Because of the useful properties of fuzzy systems such as adaptivity and nonlinearity, they are well suited to function estimation tasks where the equation describing the function is unknown as the only prerequisite is a representative sample of the function behavior. In this paper, a neuro-fuzzy approach for

M. Savaghebi; A. Gholami; A. Jalilian; H. Hooshyar

2008-01-01

124

Symbolic, Neural and Neuro-fuzzy Approaches to Pattern Recognition in Cardiotocograms  

Microsoft Academic Search

In this paper, several approaches to computer supported recognition of accelerative and decelerative patterns in the Foetal Heart Rate signal are presented, in order to automate the diagnosis of foetal well being. The classifiers discussed in here evolve from a rule-based approach to a neuro-fuzzy system, through classical neural network architectures. The main problem regarding the symbolic approach was a

Oscar Fontenla-romero; Bertha Guijarro-berdiñas; Amparo Alonso-betanzos

2002-01-01

125

Combining neuro-fuzzy and machine learning for fault diagnosis of a DC motor  

Microsoft Academic Search

An approach for the diagnosis of faults in dynamic systems based on a neuro-fuzzy scheme is presented. The simple structure that represents fuzzy rules in a neural network uses a rule extraction mechanism varying from most other approaches as it is based on concepts of machine learning. An additional, straightforward optimization eventually enhances the performance of the diagnosis. The approach

D. Fussel; P. Balle

1997-01-01

126

Adaptive neuro-fuzzy short-term wind-speed forecasting for Egypt's East-Coast  

Microsoft Academic Search

Use of wind energy as a renewable source of energy for electric utility systems is increasing around the world. The major challenges of wind energy generation are natural intermittency, unpredictability, and uncertainty due to wind variations. In this paper, five different adaptive neuro-fuzzy wind predictors are proposed and compared to forecast the speed of wind blowing in the East Coast

O. M. Salim; M. A. Zohdy; H. T. Dorrah; A. M. Kamel

2011-01-01

127

Modeling and control of carbon monoxide concentration using a neuro-fuzzy technique  

Microsoft Academic Search

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

Kazuo Tanaka; Manabu Sano; Hiroyuki Watanabe

1995-01-01

128

NEFCLASSmdash;a neuro-fuzzy approach for the classification of data  

Microsoft Academic Search

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

Detlauf Nauck; Rudolf Kruse

1995-01-01

129

Neuro-Fuzzy Based Clustering Approach For Content Based Image Retrieval Using 2D- Wavelet Transform  

Microsoft Academic Search

Abstract—In this paper we introduce neuro-fuzzy based clustering approach for content based image retrieval using 2D-wavelet transform(2D-DWT). Most of the ,image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity.To address this problem, we developed neural network -fuzzy logic cluster based approach for content based image retrieval using 2D-wavelet transform. The system

V. Balamurugan; P. Anandhakumar

2009-01-01

130

Neuro-fuzzy speed tracking control of traveling-wave ultrasonic motor drives using direct pulsewidth modulation  

Microsoft Academic Search

The traveling-wave ultrasonic motor (TWUM) drive offers many distinct advantages but suffers from severe system nonlinearities and parameter variations, especially during speed control. This paper presents a new speed tracking control system for the TWUM drive, which newly incorporates neuro-fuzzy control and direct pulsewidth modulation to solve the problem of nonlinearities and variations. The proposed control system is digitally implemented

K. T. Chau; S. W. Chung; C. C. Chan

2003-01-01

131

Neuro-fuzzy speed tracking control of traveling-wave ultrasonic motor drives using direct pulse width modulation  

Microsoft Academic Search

The traveling-wave ultrasonic motor (TUSM) drive offers many distinct advantages but suffers from severe system nonlinearities and parameter variations especially during speed control. This paper presents a new speed tracking control system for the TUSM drive, which newly incorporates neuro-fuzzy control and direct pulse width modulation to solve the problem of nonlinearities and variations. Increasingly, the proposed control system is

K. T. Chau; S. W. Chung

2002-01-01

132

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

Microsoft Academic Search

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

Man Gyun Na; B. R. Upadhyaya

1998-01-01

133

Neuro-Fuzzy Dynamic-Inversion-Based Adaptive Control for Robotic Manipulators—Discrete Time Case  

Microsoft Academic Search

In this paper, we present a stable discrete-time adaptive tracking controller using a neuro-fuzzy (NF) dynamic-inversion for a robotic manipulator with its dynamics approximated by a dynamic T-S fuzzy model. The NF dynamic-inversion constructed by a dynamic NF (DNF) system is used to compensate for the robot inverse dynamics for a better tracking performance. By assigning the dynamics of the

Fuchun Sun; Li Li; Han-Xiong Li; Huaping Liu

2007-01-01

134

Neuro-Fuzzy Knowledge Representation for Toxicity Prediction of Organic Compounds  

Microsoft Academic Search

Models based 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 here proposed include both, QSARs and original numerical values. The developed approaches use various techniques to insert knowledge by training, and to map rules in neuro -fuzzy structures. These

Ciprian-daniel Neagu; Emilio Benfenati; Giuseppina C. Gini; Paolo Mazzatorta; Alessandra Roncaglioni

2002-01-01

135

Ozone levels in the Empty Quarter of Saudi Arabia--application of adaptive neuro-fuzzy model.  

PubMed

In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO? concentrations and their transformations, as inputs. The root mean square error and Willmott's index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model. PMID:23111771

Rahman, Syed Masiur; Khondaker, A N; Khan, Rouf Ahmad

2012-10-31

136

A novel approach to neuro-fuzzy classification.  

PubMed

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

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

2008-10-09

137

Modeling the Malaysian motor insurance claim using artificial neural network and adaptive NeuroFuzzy inference system  

NASA Astrophysics Data System (ADS)

Artificial neural network (ANN) with back propagation algorithm (BP) and ANFIS was chosen as an alternative technique in modeling motor insurance claims. In particular, an ANN and ANFIS technique is applied to model and forecast the Malaysian motor insurance data which is categorized into four claim types; third party property damage (TPPD), third party bodily injury (TPBI), own damage (OD) and theft. This study is to determine whether an ANN and ANFIS model is capable of accurately predicting motor insurance claim. There were changes made to the network structure as the number of input nodes, number of hidden nodes and pre-processing techniques are also examined and a cross-validation technique is used to improve the generalization ability of ANN and ANFIS models. Based on the empirical studies, the prediction performance of the ANN and ANFIS model is improved by using different number of input nodes and hidden nodes; and also various sizes of data. The experimental results reveal that the ANFIS model has outperformed the ANN model. Both models are capable of producing a reliable prediction for the Malaysian motor insurance claims and hence, the proposed method can be applied as an alternative to predict claim frequency and claim severity.

Mohd Yunos, Zuriahati; Shamsuddin, Siti Mariyam; Ismail, Noriszura; Sallehuddin, Roselina

2013-04-01

138

Adaptive Neuro-Fuzzy Inference System Modeling of MRR and WIWNU in CMP Process With Sparse Experimental Data  

Microsoft Academic Search

Availability of only limited or sparse experimental data impedes the ability of current models of chemical mechanical planarization (CMP) to accurately capture and predict the underlying complex chemomechanical interactions. Modeling approaches that can effectively interpret such data are therefore necessary. In this paper, a new approach to predict the material removal rate (MRR) and within wafer nonuniformity (WIWNU) in CMP

Lih Wen-chen; Satish T. S. Bukkapatnam; Prahalada K. Rao; Naga Chandrasekharan; Ranga Komanduri

2008-01-01

139

Neuro-Fuzzy Control of Railcar Vibrations Using Semiactive Dampers  

Microsoft Academic Search

This article describes a new approach to re- ducing vertical vibrations in a 70-ton railcar using a neuro- fuzzy controller and a magnetorheological (MR) damper. A semiactive control technique is developed for a two- degree-of-freedom quarter car model of the railcar that has an installed MR damper. A fuzzy controller in real time continuously updates damping properties of the de-

Vipul S. Atray; Paul N. Roschke

2004-01-01

140

Design and Analysis of Neuro Fuzzy Color Image Restoration  

Microsoft Academic Search

Color image is degraded by a blur function and corrupted by random noise. It is necessary to find the best approximation of original image so that the corrupted image may be used for future use .The problem is to accurately remove the noise degradation and blurring without damaging the intelligence. New method based on neuro fuzzy techniques has been proposed

V. Seenivasagam; K. Ramar; P. G. Banupriya

2007-01-01

141

Optimal neuro-fuzzy control of parallel hybrid electric vehicles  

Microsoft Academic Search

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

M. Mohebbi; M. Charkhgard; M. Farrokhi

2005-01-01

142

Neuro-fuzzy rule generation: survey in soft computing framework  

Microsoft Academic Search

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

Sushmita Mitra; Yoichi Hayashi

2000-01-01

143

Unsupervised feature evaluation: a neuro-fuzzy approach  

Microsoft Academic Search

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

Sankar K. Pal; Rajat K. De; Jayanta Basak

2000-01-01

144

Recurrent neuro-fuzzy networks for nonlinear process modeling  

Microsoft Academic Search

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

Jie Zhang; A. Julian Morris

1999-01-01

145

Unsupervised feature selection using a neuro-fuzzy approach  

Microsoft Academic Search

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

Jayanta Basak; Rajat K. De; Sankar K. Pal

1998-01-01

146

Vibration suppression control of smart piezoelectric rotating truss structure by parallel neuro-fuzzy control with genetic algorithm tuning  

NASA Astrophysics Data System (ADS)

The main goal of this paper is to develop a novel approach for vibration control on a piezoelectric rotating truss structure. This study will analyze the dynamics and control of a flexible structure system with multiple degrees of freedom, represented in this research as a clamped-free-free-free truss type plate rotated by motors. The controller has two separate feedback loops for tracking and damping, and the vibration suppression controller is independent of position tracking control. In addition to stabilizing the actual system, the proposed proportional-derivative (PD) control, based on genetic algorithm (GA) to seek the primary optimal control gain, must supplement a fuzzy control law to ensure a stable nonlinear system. This is done by using an intelligent fuzzy controller based on adaptive neuro-fuzzy inference system (ANFIS) with GA tuning to increase the efficiency of fuzzy control. The PD controller, in its assisting role, easily stabilized the linear system. The fuzzy controller rule base was then constructed based on PD performance-related knowledge. Experimental validation for such a structure demonstrates the effectiveness of the proposed controller. The broad range of problems discussed in this research will be found useful in civil, mechanical, and aerospace engineering, for flexible structures with multiple degree-of-freedom motion.

Lin, J.; Zheng, Y. B.

2012-07-01

147

Autonomous parallel parking of a car-like mobile robot by a neuro-fuzzy behavior-based controller  

Microsoft Academic Search

In this paper, the concept of sensor-based behavior is used to design a neuro-fuzzy control system for a car-like-mobile-robot. The problem addressed is the parallel parking in a rectangular constrained space with just one backward maneuver. To accomplish the autonomous fuzzy behavior control, the car-like-mobile-robot has trained to park in just 2 parking dimensions based on the training data obtained

M. Khoshnejad; K. Demirli

2005-01-01

148

Assessing Aggressive Behavior in Children: A Neuro-Fuzzy Model  

Microsoft Academic Search

It is hypothesized that marginal behavioral deviations, through their aggregation, may generate impressions of discomfort and disturbance, leading in their turn to progressive social seclusion, lower self-esteem, and maladjustment. The article describes an attempt to assess the development of a stable aggressive behavior, by means of a neuro-fuzzy model of the relationships between sociometric predictors (popularity\\/refusal rates among peers, hyperactivity,

Sandro Nicole; Gian Vittorio Caprara

2005-01-01

149

Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach  

NASA Astrophysics Data System (ADS)

Machine prognosis can be considered as the generation of long-term predictions that describe the evolution in time of a fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem so that timely maintenance can be performed to avoid catastrophic failures. This paper proposes an integrated RUL prediction method using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which forecasts the time evolution of the fault indicator and estimates the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter as a model describing the fault progression. The high-order particle filter is used to estimate the current state and carry out p-step-ahead predictions via a set of particles. These predictions are used to estimate the RUL pdf. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results demonstrate that it outperforms both the conventional ANFIS predictor and the particle-filter-based predictor where the fault growth model is a first-order model that is trained via the ANFIS.

Chen, Chaochao; Vachtsevanos, George; Orchard, Marcos E.

2012-04-01

150

A new neuro-fuzzy training algorithm for identifying dynamic characteristics of smart dampers  

NASA Astrophysics Data System (ADS)

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.

Dzung Nguyen, Sy; Choi, Seung-Bok

2012-08-01

151

Backpropagation through time training of a neuro-fuzzy controller.  

PubMed

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

Koprinkova-Hristova, Petia

2010-10-01

152

Full Measuring System for Copper Electrowinning Processes Using Optibar® Inter-Cell Bars  

Microsoft Academic Search

An evolved Optibar inter-cell bar for copper EW processes with current sensing capabilities is presented. This technology upgrades the advantages of the conventional Optibar by providing a complete measuring system using magnetic sensors inside the capping board. To enhance reliability and simplicity, only half of the intercell currents are physically measured. This is accomplished using Adaptive Neuro-Fuzzy Inference System networks

Eduardo P. Wiechmann; Anibal S. Morales; Pablo E. Aqueveque

2008-01-01

153

New hybrid adaptive neuro-fuzzy algorithms for manipulator control with uncertainties- comparative study.  

PubMed

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

Alavandar, Srinivasan; Nigam, M J

2009-06-11

154

A Neuro-Fuzzy modeling for prediction of solar cycles 24 and 25  

NASA Astrophysics Data System (ADS)

The paper presents a Neuro-Fuzzy model to predict the features of the forthcoming sunspot cycles 24 and 25. The sunspot time series were analyzed with the proposed model. It is optimized based on Backpropagation scheme and applied to the yearly smoothed sunspot numbers. The appropriate number of network inputs for the sunspots data series is obtained based on sequential forward search for the Neuro-Fuzzy model. According to the model prediction the maximum amplitudes of the cycles 24 and 25 will occur in the year 2013 and year 2022 with peaks of 101±8 and 90.7±8, respectively. The correlation and error analysis are discussed to ensure the performance of the proposed Neuro-Fuzzy approach as a predictor for sunspot time series. The correlation coefficient between Neuro-Fuzzy model forecasted sunspot number values with the actual ones is 0.96.

Attia, Abdel-Fattah; Ismail, Hamed A.; Basurah, Hassan M.

2013-03-01

155

Self-adaptive neuro-fuzzy control with fuzzy basis function network for autonomous underwater vehicles  

Microsoft Academic Search

Presents an online self-adaptive neuro-fuzzy control that serves as a better alternative control scheme in controlling autonomous underwater vehicles (AUVs) in an uncertain and unstructured environment. The proposed self-adaptive neuro-fuzzy controller is a five-layer feedforward neural network that implements fuzzy basis function (FBF) expansions and is capable of self-constructing and self-restructuring its internal node connectivity and learning the parameters of

Jeen-Shing Wang; C. S. George Lee; Junku Yuh

1999-01-01

156

An online self-organizing neuro-fuzzy control for autonomous underwater vehicles  

Microsoft Academic Search

Controlling autonomous underwater vehicles (AUVs) in an uncertain and unstructured environment presents many challenging control problems. Model-based control strategies have been used with limited success. The paper presents an online self-organizing neuro-fuzzy control that serves as a better alternative control scheme in controlling AUVs. The proposed self-organizing neuro-fuzzy controller is a six-layer feedforward neural network that is capable of self-constructing

Jeen-Shing Wang; C. S. George Lee; Junku Yuh

1999-01-01

157

Neuro-fuzzy TSK network for calibration of semiconductor sensor array for gas measurements  

Microsoft Academic Search

The neuro-fuzzy network applying Takagi-Sugeno-Kang (TSK) fuzzy reasoning for the calibration of the semiconductor sensor array is developed in this paper. The structure, as well as the learning algorithm of the neuro-fuzzy network, is presented and tested on the example of estimation of the concentration of gas components in the gaseous mixture (so-called artificial nose problem). The results of numerical

Stanislaw Osowski; Tran Haoi Linh; Kazimierz Brudzewski

2004-01-01

158

An asynchronous mixed-mode neuro-fuzzy controller for energy efficient machine intelligence SoC  

Microsoft Academic Search

This paper presents an asynchronous digital-analog mixed-mode neuro-fuzzy controller that enables energy efficient implementation of machine intelligence SoC. The proposed neuro-fuzzy controller adopts an asynchronous 2-stage pipeline for analog and digital domain operations, which is the main contributor to high throughput and energy efficient machine intelligence SoC. To this end, a neuro-fuzzy controller with a delay prediction unit and a

Jinwook Oh; Gyeonghoon Kim; Hoi-Jun Yoo

2011-01-01

159

Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera  

NASA Astrophysics Data System (ADS)

The complete assessment of landslide susceptibility needs uniformly distributed detailed information on the territory. This information, which is related to the temporal occurrence of landslide phenomena and their causes, is often fragmented and heterogeneous. The present study evaluates the landslide susceptibility map of the Natural Archaeological Park of Matera (Southern Italy) (Sassi and area Rupestrian Churches sites). The assessment of the degree of "spatial hazard" or "susceptibility" was carried out by the spatial prediction regardless of the return time of the events. The evaluation model for the susceptibility presented in this paper is very focused on the use of innovative techniques of artificial intelligence such as Neural Network, Fuzzy Logic and Neuro-fuzzy Network. The method described in this paper is a novel technique based on a neuro-fuzzy system. It is able to train data like neural network and it is able to shape and control uncertain and complex systems like a fuzzy system. This methodology allows us to derive susceptibility maps of the study area. These data are obtained from thematic maps representing the parameters responsible for the instability of the slopes. The parameters used in the analysis are: plan curvature, elevation (DEM), angle and aspect of the slope, lithology, fracture density, kinematic hazard index of planar and wedge sliding and toppling. Moreover, this method is characterized by the network training which uses a training matrix, consisting of input and output training data, which determine the landslide susceptibility. The neuro-fuzzy method was integrated to a sensitivity analysis in order to overcome the uncertainty linked to the used membership functions. The method was compared to the landslide inventory map and was validated by applying three methods: a ROC (Receiver Operating Characteristic) analysis, a confusion matrix and a SCAI method. The developed neuro-fuzzy method showed a good performance in the determination of the landslide susceptibility map.

Sdao, F.; Lioi, D. S.; Pascale, S.; Caniani, D.; Mancini, I. M.

2013-02-01

160

Correlation Between Miocene Global Climatic Changes (d18O) and Magnetic Properties, Using Neuro Fuzzy Logic Analysis  

NASA Astrophysics Data System (ADS)

We have used the hybrid algorithm of neuro fuzzy logic (NFL), to establish a correlation between global climatic changes (benthic foraminiferal d18O data), experimental S-ratios and magnetic susceptibility (?), in 44 samples of the Colombian stratigraphic well Saltarín 1A (Llanos foreland basin). ? and S-ratios were linked to global d18O data based on a constant accumulation rate for the stratigraphic interval flanked by the two age constrains available. A good inference (over 64%) is obtained using 4 fuzzy clusters or TKS type relationships. A stronger correlation is perhaps prevented by the likely influence of local and regional tectonic events and climatic changes that could have affected the Colombian Llanos foreland basin during Miocene times. For the Guayabo and León lithologies, it seems that the late diagenesis of the primary magnetic minerals and the assumption of a constant accumulation rate might have a minor influence on these results.

Costanzo, Vincenzo; da Silva, Ana; Hurtado, Nuri

2010-05-01

161

An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease  

Microsoft Academic Search

Diabetes occurs when a body is unable to produce or respond properly to insulin which is needed to regulate glucose (sugar). Besides contributing to heart disease, diabetes also increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. In this paper, we have detected on diabetes disease, which is a very common and important disease using

Kemal Polat; Salih Günes

2007-01-01

162

A simplified speed controller for direct torque neuro fuzzy controlled induction machine drive based on a variable gain PI controller  

Microsoft Academic Search

This paper presents an original variable gain PI (VGPI) controller for speed control of a simplified direct torque neuro fuzzy controlled (DTNFC) induction motor drive. First, a simplified direct torque neuro fuzzy control (DTNFC) for a voltage source PWM inverter fed induction motor drive is presented. This control scheme uses the stator flux amplitude and the electromagnetic torque errors through

Azeddine Draou; Abdellah Miloudi

2010-01-01

163

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

PubMed

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

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

2003-01-01

164

Principal component analysis- adaptive neuro-fuzzy inference system modeling and genetic algorithm optimization of adsorption of methylene blue by activated carbon derived from Pistacia khinjuk.  

PubMed

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

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

2013-07-09

165

A reduced-order adaptive neuro-fuzzy inference system model as a software sensor for rapid estimation of five-day biochemical oxygen demand  

NASA Astrophysics Data System (ADS)

This study is a progress of published papers for online estimation of BOD5 (OEB).We proposed a methodology based on POD for increasing ANFIS performance for OEB.We determined uncertainty of ANFIS and reduced-order ANFIS models for OEB.We concluded that the presented methodology can improve uncertainty of ANFIS for OEB.

Noori, Roohollah; Safavi, Salman; Nateghi Shahrokni, Seyyed Afshin

2013-07-01

166

Dynamical recurrent neuro-fuzzy identification schemes employing switching parameter hopping.  

PubMed

In this paper we analyze the identification problem which consists of choosing an appropriate identification model and adjusting its parameters according to some adaptive law, such that the response of the model to an input signal (or a class of input signals), approximates the response of the real system for the same input. For identification models we use fuzzy-recurrent high order neural networks. High order networks are expansions of the first-order Hopfield and Cohen-Grossberg models that allow higher order interactions between neurons. The underlying fuzzy model is of Mamdani type assuming a standard defuzzification procedure such as the weighted average. Learning laws are proposed which ensure that the identification error converges to zero exponentially fast or to a residual set when a modeling error is applied. There are two core ideas in the proposed method: (1) Several high order neural networks are specialized to work around fuzzy centers, separating in this way the system into neuro-fuzzy subsystems, and (2) the use of a novel method called switching parameter hopping against the commonly used projection in order to restrict the weights and avoid drifting to infinity. PMID:23627590

Theodoridis, Dimitrios; Boutalis, Yiannis; Christodoulou, Manolis

2012-04-01

167

Temperature based daily incoming solar radiation modeling based on gene expression programming, neuro-fuzzy and neural network computing techniques.  

NASA Astrophysics Data System (ADS)

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

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

2012-04-01

168

Daily pan evaporation modelling using a neuro-fuzzy computing technique  

Microsoft Academic Search

Evaporation, as a major component of the hydrologic cycle, is important in water resources development and management. This paper investigates the abilities of neuro-fuzzy (NF) technique to improve the accuracy of daily evaporation estimation. Five different NF models comprising various combinations of daily climatic variables, that is, air temperature, solar radiation, wind speed, pressure and humidity are developed to evaluate

Özgür Kisi

2006-01-01

169

A neuro-fuzzy controller for speed control of a permanent magnet synchronous motor drive  

Microsoft Academic Search

This paper introduces a neuro-fuzzy controller (NFC) for the speed control of a PMSM. A four layer neural network (NN) is used to adjust input and output parameters of membership functions in a fuzzy logic controller (FLC). The back propagation learning algorithm is used for training this network. The performance of the proposed controller is verified by both simulations and

Cetin Elmas; Oguz Ustun; Hasan H. Sayan

2008-01-01

170

Global cross-station assessment of neuro-fuzzy models for estimating daily reference evapotranspiration  

NASA Astrophysics Data System (ADS)

We used neuro-fuzzy (NF) technique to model daily reference evapotranspiration. A global cross station assessment of NF model was performed. NF model was generalized (GNF) in humid and non-humid regions. Results confirmed the superiority of GNF models to the corresponding equations.

Shiri, Jalal; Nazemi, Amir Hossein; Sadraddini, Ali Ashraf; Landeras, Gorka; Kisi, Ozgur; Fard, Ahmad Fakheri; Marti, Pau

2013-02-01

171

Comparative evaluation of pattern recognition algorithms: statistical, neural, fuzzy, and neuro-fuzzy techniques  

Microsoft Academic Search

Pattern recognition by fuzzy, neural, and neuro-fuzzy approaches, has gained popularity partly because of intelligent decision processes involved in some of the above techniques, thus providing better classification and partly because of simplicity in computation required by these methods as opposed to traditional statistical approaches for complex data structures. However, the accuracy of pattern classification by various methods is often

Sunanda Mitra; Ramiro Castellanos

1998-01-01

172

A neuro-fuzzy computing technique for modeling hydrological time series  

Microsoft Academic Search

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

P. C Nayak; K. P Sudheer; D. M Rangan; K. S Ramasastri

2004-01-01

173

Neuro-fuzzy state modeling of flexible robotic arm employing dynamically varying cognitive and social component based PSO  

Microsoft Academic Search

The present paper proposes the development of a neuro-fuzzy state-space model for flexible robotic arm on the basis of real sensor data acquired. The training problem of the neuro-fuzzy architecture has been configured as a highly multidimensional stochastic global optimization problem and improved variants of particle swarm optimization (PSO) techniques have been successfully implemented for it. The effects of dynamically

Amitava Chatterjee; Ranajit Chatterjee; Fumitoshi Matsuno; Takahiro Endo

2007-01-01

174

Streamflow Forecasting Using Nuero-Fuzzy Inference System  

NASA Astrophysics Data System (ADS)

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.

Nanduri, U. V.; Swain, P. C.

2005-12-01

175

A Takagi-Sugeno type neuro-fuzzy network for determining child anemia  

Microsoft Academic Search

Decision-making is a difficult and quite responsible task for doctors. Some of the computer decision models assisted the doctor with some computer decision models. In this study, neuro-fuzzy network has been designed to determine anemia level of a child. The performance analyses have been obtained by leaving-one-out cross-validation. After statistical measurements, it was found that MPE=?0.0018, MAE=0.2090, MAPE=0.0511, RMSE=0.2743 and

Novruz Allahverdi; Ayfer Tunali; Hakan Isik; Humar Kahramanli

2011-01-01

176

Neuro-Fuzzy Actor Critic Reinforcement Learning for determination of optimal timing plans  

Microsoft Academic Search

The purpose of timing plan optimization is to decrease delay and increase the overall performance of transportation network. This paper presents an agent-based reinforcement learning framework to train optimization agents to take appropriate actions according to perceived traffic states. Neuro-Fuzzy Actor-Critic Reinforcement Learning (NFACRL) method is applied in isolated intersection control. The control agent gets knowledge of traffic states after

Linsen Chong; Montasir Abbas

2010-01-01

177

Efficient Immune-Based Particle Swarm Optimization Learning for Neuro-Fuzzy Networks Design  

Microsoft Academic Search

In order to enhance the immune algorithm (IA) performance and find the optimal solution when dealing with difficult problems, we propose an efficient immune-based particle swarm optimization (IPSO) for use in TSK-type neuro-fuzzy networks for solv- ing the identification and prediction problems. The proposed IPSO combines the im- mune algorithm (IA) and particle swarm optimization (PSO) to perform parameter learning.

Cheng-jian Lin; Cheng-hung Chen; Chi-yung Lee

2008-01-01

178

Neuro-fuzzy adaptive control based on dynamic inversion for robotic manipulators  

Microsoft Academic Search

This paper presents a stable neuro-fuzzy (NF) adaptive control approach for the trajectory tracking of the robotic manipulator with poorly known dynamics. Firstly, the fuzzy dynamic model of the manipulator is established using the Takagi-Sugeno (T-S) fuzzy framework with both structure and parameters identi\\/ed through input=output data from the robot control process. Secondly, based on the derived fuzzy dynamics of

Fuchun Sun; Zengqi Sun; Lei Li; Han-xiong Li

2003-01-01

179

A neuro-fuzzy scheme for simultaneous feature selection and fuzzy rule-based classification  

Microsoft Academic Search

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

Debrup Chakraborty; Nikhil R. Pal

2004-01-01

180

Comparison of Fuzzy and Neuro Fuzzy Image Fusion Techniques and its Applications  

NASA Astrophysics Data System (ADS)

Image fusion is the process of integrating multiple images of the same scene into a single fused image to reduce uncertainty and minimizing redundancy while extracting all the useful information from the source images. Image fusion process is required for different applications like medical imaging, remote sensing, medical imaging, machine vision, biometrics and military applications where quality and critical information is required. In this paper, image fusion using fuzzy and neuro fuzzy logic approaches utilized to fuse images from different sensors, in order to enhance visualization. The proposed work further explores comparison between fuzzy based image fusion and neuro fuzzy fusion technique along with quality evaluation indices for image fusion like image quality index, mutual information measure, fusion factor, fusion symmetry, fusion index, root mean square error, peak signal to noise ratio, entropy, correlation coefficient and spatial frequency. Experimental results obtained from fusion process prove that the use of the neuro fuzzy based image fusion approach shows better performance in first two test cases while in the third test case fuzzy based image fusion technique gives better results.

RaoD, Srinivasa; M, Seetha; Prasad MHM, Krishna

2012-04-01

181

Detailed comparison of neuro-fuzzy estimation of subpixel land-cover composition from remotely sensed data  

NASA Astrophysics Data System (ADS)

Mixed pixels, which do not follow a known statistical distribution that could be parameterized, are a major source of inconvenience in classification of remote sensing images. This paper reports on an experimental study designed for the in-depth investigation of how and why two neuro-fuzzy classification schemes, whose properties are complementary, estimate sub-pixel land cover composition from remotely sensed data. The first classifier is based on the fuzzy multilayer perceptron proposed by Pal and Mitra: the second classifier consists of a two-stage hybrid (TSH) learning scheme whose unsupervised first stage is based on the fully self- organizing simplified adaptive resonance theory clustering network proposed by Baraldi. Results of the two neuro-fuzzy classifiers are assessed by means of specific evaluation tools designed to extend conventional descriptive and analytical statistical estimators to the case of multi-membership in classes. When a synthetic data set consisting of pure and mixed pixels is processed by the two neuro-fuzzy classifiers, experimental result show that: i) the two neuro- fuzzy classifiers perform better than the traditional MLP; ii) classification accuracies of the two neuro-fuzzy classifiers are comparable; and iii) the TSH classifier requires to train less background knowledge than FMLP.

Baraldi, Andrea; Binaghi, Elisabetta; Blonda, Palma N.; Brivio, Pietro A.; Rampini, Anna

1998-10-01

182

Neuro-fuzzy speed control of traveling-wave type ultrasonic motor drive using frequency and phase modulation.  

PubMed

This paper presents a Fuzzy Neural Network (FNN) control system for a traveling-wave ultrasonic motor (TWUSM) driven by a dual mode modulation non-resonant driving circuit. First, the motor configuration and the proposed driving circuit of a TWUSM are introduced. To drive a TWUSM effectively, a novel driving circuit, that simultaneously employs both the driving frequency and phase modulation control scheme, is proposed to provide two-phase balance voltage for a TWUSM. Since the dynamic characteristics and motor parameters of the TWUSM are highly nonlinear and time-varying, a FNN control system is therefore investigated to achieve high-precision speed control. The proposed FNN control system incorporates neuro-fuzzy control and the driving frequency and phase modulation to solve the problem of nonlinearities and variations. The proposed control system is digitally implemented by a low-cost digital signal processor based microcontroller, hence reducing the system hardware size and cost. The effectiveness of the proposed driving circuit and control system is verified with hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a conventional proportional-integral control system. PMID:18501903

Chen, Tien-Chi; Yu, Chih-Hsien; Chen, Chun-Jung; Tsai, Mi-Ching

2008-05-23

183

Spatial modeling of environmental vulnerability of marine finfish aquaculture using GIS-based neuro-fuzzy techniques.  

PubMed

Combining GIS with neuro-fuzzy modeling has the advantage that expert scientific knowledge in coastal aquaculture activities can be incorporated into a geospatial model to classify areas particularly vulnerable to pollutants. Data on the physical environment and its suitability for aquaculture in an Irish fjard, which is host to a number of different aquaculture activities, were derived from a three-dimensional hydrodynamic and GIS models. Subsequent incorporation into environmental vulnerability models, based on neuro-fuzzy techniques, highlighted localities particularly vulnerable to aquaculture development. The models produced an overall classification accuracy of 85.71%, with a Kappa coefficient of agreement of 81%, and were sensitive to different input parameters. A statistical comparison between vulnerability scores and nitrogen concentrations in sediment associated with salmon cages showed good correlation. Neuro-fuzzy techniques within GIS modeling classify vulnerability of coastal regions appropriately and have a role in policy decisions for aquaculture site selection. PMID:21683421

Navas, Juan Moreno; Telfer, Trevor C; Ross, Lindsay G

2011-06-17

184

A review on applicability of expert system in designing and control of autonomous cars  

Microsoft Academic Search

This paper presents a survey on applicability of expert system in designing and control of autonomous vehicles. Each of reviewed papers categorized on five categories as expert system, fuzzy expert system, neuro-fuzzy expert system, genetic plus neuro-fuzzy expert system and transition systems. Some earlier works used only a rule based expert system for driving the car but due to the

Hurieh Khalajzadeh; Chitra Dadkhah; Mohammad Mansouri

2011-01-01

185

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS  

NASA Astrophysics Data System (ADS)

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

Pradhan, Biswajeet

2013-02-01

186

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

PubMed

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

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

2011-11-23

187

A Variable Gain PI Controller Used for Speed Control of a Direct Torque Neuro Fuzzy Controlled Induction Machine Drive  

Microsoft Academic Search

This paper presents an original variable gain PI (VGPI) controller for speed control of a direct torque neuro fuzzy controlled (DTNFC) induction motor drive. First, a VGPI speed controller is designed to replace the classical PI controller in a conventional direct torque controlled induction motor drive. Its simulated performances are then compared to those of a classical PI controller. Then,

A. MILOUDI; Eid A. AL-RADADI; A. D. DRAOU

188

Autonomous parallel parking of a car-like mobile robot by a neuro-fuzzy sensor-based controller  

Microsoft Academic Search

In this paper, a neuro-fuzzy model has been developed for autonomous parallel parking of a car-like mobile robot. In our approach we have focused on the most difficult case of parallel parking which is the case when the parking space dimensions cannot be identified. The proposed model uses the data from three sonar sensors mounted in the front left corner

Kudret Demirli; M. Khoshnejad

2009-01-01

189

Neuro-fuzzy Learning for Automated Incident Detection  

Microsoft Academic Search

Traffic incidents such as vehicle accidents, weather and construction works are a major cause of congestion. Incident detection\\u000a is thus an important function in freeway and arterial traffic management systems. Most of the large scale and operational\\u000a incident detection systems make use of data collected from inductive loop detectors. Several new approaches, such as probe\\u000a vehicles and video image processing

Murlikrishna Viswanathan; Seung-heon Lee; Young-kyu Yang

2006-01-01

190

Neuro-fuzzy approaches for pipeline condition assessment  

Microsoft Academic Search

Recent advances in electronics, transducers, ultrasonic and computing technologies, have led to the development of inspection systems for underground facilities such as water lines, sewer pipes, oil and gas pipelines. Recent inspection technologies have been developed that require no human entry into underground structures; they are now fully automated, from data acquisition to data analysis, and eventually to condition assessment,

S. Kumar; F. Taheri

2007-01-01

191

A neuro-fuzzy classifier and its applications  

Microsoft Academic Search

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

Chuen-Tsai Sun; Jyh-Shing Jang

1993-01-01

192

Classification of delaminated composites using neuro-fuzzy image analysis  

Microsoft Academic Search

Computer assisted image analysis is often required in automatic visual in- spection in manufacturing processes.However, in spite of years of research in pixel-based image processing techniques such systems are often unable to recognise characteristics that are obvious to human visual inspection.In this paper, we present a technique that combines conventional image analysis, neural networks and fuzzy decision-making.The motivation for this

Paul L. Rosin; Henry O. Nyongesa; Andrew W. Otieno

1999-01-01

193

Online neuro-fuzzy CANFIS hidden-node teaching  

Microsoft Academic Search

On-line first-order backpropagation (BP) has been widely employed for optimizing a multi-layer neural network and a fuzzy neural network. When BP is applied to a TSK fuzzy system, the interpretability of fuzzy rules may be lost. We describe a simple and effective remedy for the loss by casting the posed optimization problem into a general Bolza-type optimal-control mold so as

Eiji Mizutani; Jing-Yun Carey Fan

2011-01-01

194

Industrial batch dryer data mining using intelligent pattern classifiers: Neural network, neuro-fuzzy and Takagi–Sugeno fuzzy models  

Microsoft Academic Search

This contribution describes the pattern recognition based data analysis of an existing industrial batch dryer, and the comparison of three artificial intelligence techniques suited to perform classification tasks: neural networks trained using the Levenberg–Marquardt and the Levenberg–Marquardt method with Bayesian regularization, the neuro-fuzzy model based on clustering and grid partition, and the Takagi–Sugeno fuzzy models. The classifiers are used to

Levente L. Simon; Konrad Hungerbuhler

2010-01-01

195

Development of a Neuro-fuzzy MR Image Segmentation Approach Using Fuzzy C-Means and Recurrent Neural Network  

Microsoft Academic Search

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

Dipankar Ray; D. Dutta Majumder

2009-01-01

196

Neuro-fuzzy reaping of shear wave velocity correlations derived by hybrid genetic algorithm-pattern search technique  

NASA Astrophysics Data System (ADS)

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

Asoodeh, Mojtaba; Bagheripour, Parisa

2013-06-01

197

Bearing fault diagnosis based on wavelet transform and fuzzy inference  

NASA Astrophysics Data System (ADS)

This paper deals with a new scheme for the diagnosis of localised defects in ball bearings based on the wavelet transform and neuro-fuzzy classification. Vibration signals for normal bearings, bearings with inner race faults and ball faults were acquired from a motor-driven experimental system. The wavelet transform was used to process the accelerometer signals and to generate feature vectors. An adaptive neural-fuzzy inference system (ANFIS) was trained and used as a diagnostic classifier. For comparison purposes, the Euclidean vector distance method as well as the vector correlation coefficient method were also investigated. The results demonstrate that the developed diagnostic method can reliably separate different fault conditions under the presence of load variations.

Lou, Xinsheng; Loparo, Kenneth A.

2004-09-01

198

Neuro-fuzzy decoding of sensory information from ensembles of simultaneously recorded dorsal root ganglion neurons for functional electrical stimulation applications  

NASA Astrophysics Data System (ADS)

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

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

2011-08-01

199

Simulation and modelling of a variable gain PI controller for speed control of a direct torque neuro fuzzy controlled induction machine drive  

Microsoft Academic Search

This paper presents an original variable gain PI (VGPI) controller for speed control of a direct torque neuro fuzzy controlled (DTNFC) induction motor drive. First, a VGPI speed controller is designed to replace the classical PI controller in a conventional direct torque controlled induction motor drive. Its simulated performances are then compared to those of a classical PI controller. Then,

A. Miloudi; E. A. AI Radadi; A. Draou; Y. Miloud I

2004-01-01

200

Detection of Weak Seismo-Electric Signals Upon the Recordings of the Electrotelluric Field by Means of Neuro-Fuzzy Technology  

Microsoft Academic Search

This letter presents the development and application of prediction-based adaptive filters incorporated with neuro-fuzzy technology for the emergence and detection of weak electrotelluric potential anomalies appearing upon recordings of the Earth's electric field. Electric earthquake precursors (EEPs) are considered to be related with forthcoming seismic events and, in many cases, are hidden in the electrotelluric field background. Their detection can

A. Konstantaras; M. R. Varley; F. Vallianatos; J. P. Makris; G. Collins; P. Holifield

2007-01-01

201

Nonlinear system identification of smart structures under high impact loads  

NASA Astrophysics Data System (ADS)

The main purpose of this paper is to develop numerical models for the prediction and analysis of the highly nonlinear behavior of integrated structure control systems subjected to high impact loading. A time-delayed adaptive neuro-fuzzy inference system (TANFIS) is proposed for modeling of the complex nonlinear behavior of smart structures equipped with magnetorheological (MR) dampers under high impact forces. Experimental studies are performed to generate sets of input and output data for training and validation of the TANFIS models. The high impact load and current signals are used as the input disturbance and control signals while the displacement and acceleration responses from the structure-MR damper system are used as the output signals. The benchmark adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. Comparisons of the trained TANFIS models with experimental results demonstrate that the TANFIS modeling framework is an effective way to capture nonlinear behavior of integrated structure-MR damper systems under high impact loading. In addition, the performance of the TANFIS model is much better than that of ANFIS in both the training and the validation processes.

Sarp Arsava, Kemal; Kim, Yeesock; El-Korchi, Tahar; Park, Hyo Seon

2013-05-01

202

Strong Inference for Systems Biology  

Microsoft Academic Search

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

Daniel A. Beard; Martin J. Kushmerick

2009-01-01

203

Inference Concerning Physical Systems  

NASA Astrophysics Data System (ADS)

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

Wolpert, David H.

204

System Support for Forensic Inference  

NASA Astrophysics Data System (ADS)

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.

Gehani, Ashish; Kirchner, Florent; Shankar, Natarajan

205

Estimation of dew point temperature using neuro-fuzzy and neural network techniques  

NASA Astrophysics Data System (ADS)

This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T mean and R H variables.

Kisi, Ozgur; Kim, Sungwon; Shiri, Jalal

2013-02-01

206

Intelligent system for control of a stepping motor drive using a hybrid neuro-fuzzy approach  

Microsoft Academic Search

Stepping motors are widely used in robotics and in the numerical control of machine tools where they have to perform high-precision positioning operations. However, the variations of the mechanical configuration of the drive, which are common to these two applications, can lead to a loss of synchronism for high stepping rates. Moreover, the classical open-loop speed control is weak and

P. Melin; Oscar CASTILLO

2002-01-01

207

A neuro-fuzzy system that uses distributed learning for compact rule set generation  

Microsoft Academic Search

ARTMAP based architectures have several desirable properties that make them very suitable for pattern classification problems. However, they suffer from category proliferation. Distributed coding has been proposed as a solution for memory compression and the dARTMAP neural network has been introduced as a modification of the fuzzy ARTMAP that, due to distributed learning, achieves code compression while fast stable learning

E. Parrado Hernandez; E. Gdmez Sanchez; Y. A. Dimitriadis; J. L. Coronado

1999-01-01

208

Design of Neuro-Fuzzy Systems Using a Hybrid Evolutionary Learning Algorithm  

Microsoft Academic Search

In this paper, a TSK-type fuzzy model (TFM) with a hybrid evolutionary learning algorithm (HELA) is proposed. The proposed HELA method combines the compact ge- netic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA). Both the number of fuzzy rules and the adjustable parameters in the TFM are designed concurrently using the HELA method. In the proposed HELA method,

Cheng-jian Lin; Yong-ji Xu

2007-01-01

209

Flood Forecasting in River System Using ANFIS  

NASA Astrophysics Data System (ADS)

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

Ullah, Nazrin; Choudhury, P.

2010-10-01

210

Flood Forecasting in River System Using ANFIS  

SciTech Connect

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

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

2010-10-26

211

Needs Analysis for Inference Systems at FTD.  

National Technical Information Service (NTIS)

The report documents the first phase of the BIAS Augmentation Study, an investigation into the requirements for, and approaches to, the incorporation of deductive inference procedures into the Basic-Level Intelligence Analysis System (BIAS) at the Foreign...

J. D. Sable S. Forst

1973-01-01

212

Hierarchical motor diagnosis utilizing structural knowledge and a self-learning neuro-fuzzy scheme  

Microsoft Academic Search

Fault diagnosis requires a classification system that can distinguish between different faults based on observed symptoms of the process under investigation. Since the fault symptom relationships are not always known beforehand, a system is needed which can be learned from experimental or simulated data. A fuzzy logic based diagnosis is advantageous. It allows an easy incorporation of a-priori known rules

D. Fussel; Rolf Isermann

1998-01-01

213

Book review: Fuzzy logic and Neuro Fuzzy Applications Explained by Constantin von Altrock (Prentice Hall 1995)  

Microsoft Academic Search

, a software system for development of fuzzy logic-based controllers from INFORM Corporation, Aachen, Germany. A demonstration version of FuzzyTECH with an example is included on 2 diskettes (PC version only).

Marek Pat. yra

1997-01-01

214

An efficient Neuro-Fuzzy approach to nuclear power plant transient identification  

Microsoft Academic Search

Transient identification in nuclear power plants (NPP) is often a computational very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in NPPs. The bases for the transient identification relay on the evidence that different system faults

Rafael Gomes da Costa; Antônio Carlos de Abreu Mol; Paulo Victor R. de Carvalho; Celso Marcelo Franklin Lapa

2011-01-01

215

A NEURO-FUZZY TECHNIQUE FOR DISCRIMINATION BETWEEN INTERNAL FAULTS AND MAGNETIZING INRUSH CURRENTS IN TRANSFORMERS  

Microsoft Academic Search

This paper presents the application of the fuzzy-neuro method to investigate transformer inrush current. Recently, the frequency environment of power systems has been made more complicated and the magnitude of the second harmonic in inrush current has been decreased because of the improvement of cast steel. Therefore, traditional approaches will likely mal-operate in the case of magnetizing inrush with low

H. KHORASHADI-ZADEH; M. R. AGHAEBRAHIMI

2005-01-01

216

An efficient and robust face detection method using neuro-fuzzy approach  

Microsoft Academic Search

Person identification plays a major role in any secured and safety system. Face is one of the major biometric explored by many researchers for human identification. The problem becomes more complex by means of any occlusion of objects, due to different illumination, expression and pose. We propose a novel pattern recognition approach for face detection in this paper. The approach

S. Logesh; S. Arun Bharathi; P. V. S. S. R. Chandra Mouli

2011-01-01

217

Implementation issues of neuro-fuzzy hardware: going toward HW\\/SW codesign  

Microsoft Academic Search

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

Leonardo Maria Reyneri

2003-01-01

218

Book review: Fuzzy logic and Neuro Fuzzy Applications Explained by Constantin von Altrock (Prentice Hall 1995)  

Microsoft Academic Search

Fuzzy Logic and News Fuzzy Applications Explained presents three major topics: (1) an intuitive introduction to the theory of fuzzy sets, fuzzy logic and fuzzy control; (2) a plethora of engineering applications of fuzzy logic and fuzzy logic-based control, and (3) an introduction to fuzzyTECHTM, a software system for development of fuzzy logic-based controllers from INFORM Corporation, Aachen, Germany. A

Marek Pat. yra

1997-01-01

219

An Adaptive Network-based Fuzzy Inference System for the detection of thermal and TEC anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake of 11 August 2012  

NASA Astrophysics Data System (ADS)

Anomaly detection is extremely important for forecasting the date, location and magnitude of an impending earthquake. In this paper, an Adaptive Network-based Fuzzy Inference System (ANFIS) has been proposed to detect the thermal and Total Electron Content (TEC) anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake jolted in 11 August 2012 NW Iran. ANFIS is the famous hybrid neuro-fuzzy network for modeling the non-linear complex systems. In this study, also the detected thermal and TEC anomalies using the proposed method are compared to the results dealing with the observed anomalies by applying the classical and intelligent methods including Interquartile, Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. The duration of the dataset which is comprised from Aqua-MODIS Land Surface Temperature (LST) night-time snapshot images and also Global Ionospheric Maps (GIM), is 62 days. It can be shown that, if the difference between the predicted value using the ANFIS method and the observed value, exceeds the pre-defined threshold value, then the observed precursor value in the absence of non seismic effective parameters could be regarded as precursory anomaly. For two precursors of LST and TEC, the ANFIS method shows very good agreement with the other implemented classical and intelligent methods and this indicates that ANFIS is capable of detecting earthquake anomalies. The applied methods detected anomalous occurrences 1 and 2 days before the earthquake. This paper indicates that the detection of the thermal and TEC anomalies derive their credibility from the overall efficiencies and potentialities of the five integrated methods.

Akhoondzadeh, M.

2013-09-01

220

Iterative learning fuzzy inference system  

Microsoft Academic Search

This paper presents a learning fuzzy controller which can adapt with changing performance requirements. During the past decade we have witnessed a rapid growth in the number and variety of applications of fuzzy logic ranging from consumer electronics and industrial process control to decision support system and financial systems. The fuzzy controller designer faces the challenge of choosing the appropriate

S. Ashraf; E. Muhammad; F. Rashid; M. Shahzad

2009-01-01

221

Experimental comparison of uncertain inference systems  

SciTech Connect

Uncertainty is a pervasive feature of the domains in which expert systems are supposed to function. There are several mechanisms for handling uncertainty, of which the oldest and most widely used in probability theory. It is the only one derived from a formal description of rational behavior. For use in pattern-directed inference systems, or rule-based inference engines, artificial intelligence researchers have favored others, largely for reasons of simplicity and speed. The author developed techniques that measure how these alternative approximate the results of probability theory, assess how well they perform by those measures, and find out what underlying features of a problem affect performance. Because the amount of data required to fully specify a probability distribution is enormous, some technique must be used to estimate a distribution when only partial information is given. He gives intuitive and axiomatic arguments, algebraic analysis, and numerical examples that, fitting maximum entropy priors and using minimum cross-entropy updating, are the most appropriate ways to do so. For several uncertain inference systems, he performed detailed analyses of operations to elucidate both which basic problem features bias the answers and the directions of the biases. He found that the newer uncertain inference systems often re-incorporated features of general-probability theory that were eliminated in earlier systems.

Wise, B.P.

1986-01-01

222

Inference problems in multilevel secure database management systems  

Microsoft Academic Search

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

S. Jajodia; C. Meadows

1995-01-01

223

Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction  

Microsoft Academic Search

In this paper we describe a neuro-fuzzy system with adaptive capability to extract fuzzy If Then rules from input and output sample data through learning. The proposed system, called radial basis function (RBF) based adaptive fuzzy system (AFS), employs the Gaussian functions to represent the membership functions of the premise part of fuzzy rules. Three architectural deviations of the RBF

Kwang Bo Cho; Bo Hyeun Wang

1996-01-01

224

Challenges for context management systems imposed by context inference  

Microsoft Academic Search

This work gives an overview over the challenges for context management systems in Ubiquitous Computing frameworks or Personal Smart Spaces. Focused on the integration of context inference in today's context management systems (CMSs) we address important design decisions for future frameworks. The inference system we have in mind is probabilistic and relies on the concept of Bayeslets, special inference rules

Korbinian Frank; Nikos Kalatzis; Ioanna Roussaki; Nicolas Liampotis

2009-01-01

225

Application of interval and fuzzy techniques to integrated navigation systems  

Microsoft Academic Search

The paper deals with the development of a new algorithm to be used by an INS (Integrated Navigation System) for carrying out accurate position estimation for different types of surface vehicles, including cars and ships. The proposed algorithm combines a neuro-fuzzy Kalman filter with a map matching method, in order to improve the effective real-time system performance when a GPS

Antonio Tiano; Antonio Zirilli; Fausto Pizzocchero

2001-01-01

226

Chapter 7. Evolving Connectionist and Fuzzy - Connectionist Systems: Theory and Applications for Adaptive, On-line Intelligent Systems  

Microsoft Academic Search

The paper introduces one paradigm of neuro-fuzzy techniques and an approach to building on-line, adaptive intelligent systems. This approach is called evolving connectionist systems (ECOS). ECOS evolve through incremental, on- line learning, both supervised and unsupervised. They can accommodate new input data, including new features, new classes, etc. New connections and new neurons are created during the operation of the

Nikola Kasabov

227

Color image classification systems for poultry viscera inspection  

NASA Astrophysics Data System (ADS)

A neuro-fuzzy based image classification system that utilizes color-imaging features of poultry viscera in the spectral and spatial domains was developed in this study. Poultry viscera of liver and heart were separated into four classes: normal, airsacculitis, cadaver, and septicemia. Color images for the classified poultry viscera were collected in the poultry process plant. These images in RGB color space were segmented and statistical analysis was performed for feature selection. The neuro-fuzzy system utilizes hybrid paradigms of fuzzy interference system and neural networks to enhance the robustness of the classification processes. The results showed that the accuracy for separation of normal from abnormal livers were 87.5 to 92.5% when two classes of validation data were used. For two-class classification of chicken hearts, the accuracies were 92.5 to 97.5%. When neuro-fuzzy models were employed to separate chicken livers into three classes (normal, airsacculitis, and cadaver), the accuracy was 88.3% for the training data and 83.3% for the validation data. Combining features of chicken liver and heart, a generalized neuro-fuzzy model was designed to classify poultry viscera into four classes (normal, airsacculitis, cadaver, and septicemia). The classification accuracy of 86.3% was achieved for the training data and 82.5% accuracy for the validation.

Chao, Kuanglin; Chen, Yud-Ren; Early, Howard; Park, Bosoon

1999-01-01

228

Estimation of exposure time to GSM900 radiation causing auditory brainstem response changes in rabbits using neuro-fuzzy system  

Microsoft Academic Search

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

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

2011-01-01

229

A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis  

Microsoft Academic Search

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, esti- mating the demand quantity for the next period most likely appears to be crucial. This work presents a comparative forecasting methodology regarding to uncertain customer demands in a multi-level supply chain (SC)

Tugba Efendigil; Semih Önüt; Cengiz Kahraman

2009-01-01

230

Comparative study of different controllers for automatic generation control of an interconnected hydro-thermal system with generation rate constraints  

Microsoft Academic Search

This paper compares the conventional integral, fuzzy and hybrid neuro-fuzzy (HNF) controllers for the automatic generation control (AGC) of an interconnected hydro-thermal system. The design objective is to improve the transient performance of the interconnected system following a disturbance. Appropriate generation rate constraint (GRC) has been considered for the thermal and hydro plants. In the analysis, the hydro area has

S. R. Khuntia; S. Panda

2010-01-01

231

Travel Demand and Land Use, 2006. Journal of the Transportation Research Board, No. 1977.  

National Technical Information Service (NTIS)

Partial Contents: Exploratory Analysis of Children's Travel Patterns; Development of Transport Mode Choice Model by Using Adaptive Neuro-Fuzzy Inference System; Sequential Logit Dynamic Travel Demand Model and its Transferability; Use of Mixed Revealed-Pr...

2006-01-01

232

System for Automatically Inferring a Genetic Netwerk from Expression Profiles  

Microsoft Academic Search

A system is constructed to automatically infer a genetic network byapplication of graphical Gaussian modeling to the expression profiledata. Our system is composed of two parts: one part is automaticdetermination of cluster boundaries of profiles in hierarchicalclustering, and another part is inference of a genetic network byapplication of graphical Gaussian modeling to the clustered profiles.Since thousands of or tens of

H. Toh; K. Horimoto

2002-01-01

233

Subjective bayesian methods for rule-based inference systems  

Microsoft Academic Search

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

Richard O. Duda; Peter E. Hart; Nils J. Nilsson

1976-01-01

234

Neuro-fuzzy speed control of traveling-wave type ultrasonic motor drive using frequency and phase modulation  

Microsoft Academic Search

This paper presents a Fuzzy Neural Network (FNN) control system for a traveling-wave ultrasonic motor (TWUSM) driven by a dual mode modulation non-resonant driving circuit. First, the motor configuration and the proposed driving circuit of a TWUSM are introduced. To drive a TWUSM effectively, a novel driving circuit, that simultaneously employs both the driving frequency and phase modulation control scheme,

Tien-Chi Chen; Chih-Hsien Yu; Chun-Jung Chen; Mi-Ching Tsai

2008-01-01

235

LOWER LEVEL INFERENCE CONTROL IN STATISTICAL DATABASE SYSTEMS  

SciTech Connect

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

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

1984-02-01

236

Fuzzy exemplar-based inference system for flood forecasting  

NASA Astrophysics Data System (ADS)

Fuzzy inference systems have been successfully applied in numerous fields since they can effectively model human knowledge and adaptively make decision processes. In this paper we present an innovative fuzzy exemplar-based inference system (FEIS) for flood forecasting. The FEIS is based on a fuzzy inference system, with its clustering ability enhanced through the Exemplar-Aided Constructor of Hyper-rectangles algorithm, which can effectively simulate human intelligence by learning from experience. The FEIS exhibits three important properties: knowledge extraction from numerical data, knowledge (rule) modeling, and fuzzy reasoning processes. The proposed model is employed to predict streamflow 1 hour ahead during flood events in the Lan-Yang River, Taiwan. For the purpose of comparison the back propagation neural network (BPNN) is also performed. The results show that the FEIS model performs better than the BPNN. The FEIS provides a great learning ability, robustness, and high predictive accuracy for flood forecasting.

Chang, Li-Chiu; Chang, Fi-John; Tsai, Ya-Hsin

2005-02-01

237

Nonparametric predictive inference for voting systems  

Microsoft Academic Search

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

F. P. A. Coolen; P. Coolen-Schrijner

238

Daily reservoir inflow forecasting using fuzzy inference systems  

Microsoft Academic Search

This paper presents the application of a methodology for daily reservoir inflow forecasting in Brazilian hydroelectric plants. The methodology is based on Fuzzy Inference Systems (FIS) and the technique used for adjusting of the model parameters is an offline version of the Expectation Maximization (EM) algorithm. In order to automate the application of the methodology and facilitate the analysis of

Ivette Raymunda Luna Huamani; Rosangela Ballini; Ieda Geriberto Hidalgo; Paulo Sergio Franco Barbosa; Alberto Luiz Francato

2011-01-01

239

Diagnosis of arthritis through fuzzy inference system.  

PubMed

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

Singh, Sachidanand; Kumar, Atul; Panneerselvam, K; Vennila, J Jannet

2010-10-07

240

Maximum Entropy Inference for Geographical Information Systems  

Microsoft Academic Search

An immediate problem in approaching GIS (Geographic Information Systems) consists in giving a sufficiently agreed definition\\u000a of what GIS actually are. For present purposes it seems reasonable to consider GIS as being characterized by a twofold nature.\\u000a On the one hand, GIS consist of a technology used for certain purposes. From this perspective, the crucial issues in GIS research amount

Hykel Hosni; Maria Vittoria Masserotti; Chiara Renso

241

Minerva: A Scalable OWL Ontology Storage and Inference System  

Microsoft Academic Search

\\u000a With the increasing use of ontologies in Semantic Web and enterprise knowledge management, it is critical to develop scalable\\u000a and efficient ontology management systems. In this paper, we present Minerva, a storage and inference system for large-scale\\u000a OWL ontologies on top of relational databases. It aims to meet scalability requirements of real applications and provide practical\\u000a reasoning capability as well

Jian Zhou; Li Ma; Qiaoling Liu; Lei Zhang; Yong Yu; Yue Pan

2006-01-01

242

Inference and learning in sparse systems with multiple states  

SciTech Connect

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.

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

243

An Availability System with General Repair Distribution: Statistical Inference  

Microsoft Academic Search

This article we study the statistical inferences of an availability system with imperfect coverage. The time-to-failure and time-to-repair of the active and standby components are assumed to be exponential and general distribution, respectively. Assume that the coverage factor is the same for an active-component failure as that for a standby-component failure. Firstly, we propose a consistent and asymptotically normal (CAN)

Jau-Chuan Ke; Zheng-Long Su; Kuo-Hsiung Wang

2009-01-01

244

Granular Fuzzy Inference System (FIS) Design by Lattice Computing  

NASA Astrophysics Data System (ADS)

Information granules are partially/lattice-ordered. Therefore, lattice computing (LC) is proposed for dealing with them. The granules here are Intervals' Numbers (INs), which can represent real numbers, intervals, fuzzy numbers, probability distributions, and logic values. Based on two novel theoretical propositions introduced here, it is demonstrated how LC may enhance popular fuzzy inference system (FIS) design by the rigorous fusion of granular input data, the sensible employment of sparse rules, and the introduction of tunable nonlinearities.

Kaburlasos, Vassilis G.

245

Hybrid soft computing systems for reservoir PVT properties prediction  

NASA Astrophysics Data System (ADS)

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

Khoukhi, Amar

2012-07-01

246

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

PubMed Central

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

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

2010-01-01

247

Adaptive intelligent control of aircraft systems with a hybrid approach combining neural networks, fuzzy logic and fractal theory  

Microsoft Academic Search

We describe in this paper a hybrid method for adaptive model-based control of nonlinear dynamic systems using neural networks, fuzzy logic and fractal theory. The new neuro-fuzzy-fractal method combines soft computing techniques with the concept of the fractal dimension for the domain of nonlinear dynamic system control. The new method for adaptive model-based control has been implemented as a computer

Patricia Melin; Oscar Castillo

2003-01-01

248

ANUBIS: artificial neuromodulation using a Bayesian inference system.  

PubMed

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

Smith, Benjamin J H; Saaj, Chakravarthini M; Allouis, Elie

2012-09-12

249

Inference of biochemical network models in S-system using multiobjective optimization approach  

Microsoft Academic Search

Motivation: The inference of biochemical networks, such as gene regulatory networks, protein-protein interaction networks, and metabolic pathway networks, from time-course data is one of the main challenges in systems biology. The ultimate goal of inferred modeling is to obtain expressions that quantitatively understand every detail and principle of biological systems. To infer a realizable S-system structure, most articles have applied

Pang-kai Liu; Feng-sheng Wang

2008-01-01

250

A note on Pollock's system of direct inference  

Microsoft Academic Search

John Pollock's recent book [1] contains an interesting and in several respects quite novel approach to issues concerning probability and inductive inference. Unfortunately, the approach is flawed, and precisely because of the feature that constitutes Pollock's most radical departure from accepted views. The purpose of this note is to call attention to the flaw. The classical problem of direct inference,

Stephen Leeds

1994-01-01

251

ADAPTIVE FUZZY LOGIC SPEED CONTROLLER WITH TORQUE ADAPTED GAINS FUNCTION FOR PMSM DRIVE  

Microsoft Academic Search

This paper presents a 15 rules-base Function Torque Adapted Gain Fuzzy Inference System (FTAGFIS) adaptive speed controller for the Permanent Magnet Synchronous Motor (PMSM). The proposed controller was developed using Adaptive Neuro Fuzzy Inference System (ANFIS). This expanded version of the FIS not only suggests the effectiveness of using the ANFIS to develop an adaptive speed controller for motors but

MUTASIM NOUR

252

A Hypermedia Inference Language as an Alternative to Rule-based Expert Systems  

Microsoft Academic Search

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

Gerry Stahl

1991-01-01

253

Annual Rainfall Forecasting by Using Mamdani Fuzzy Inference System  

NASA Astrophysics Data System (ADS)

Long-term rainfall prediction is very important to countries thriving on agro-based economy. In general, climate and rainfall are highly non-linear phenomena in nature giving rise to what is known as "butterfly effect". The parameters that are required to predict the rainfall are enormous even for a short period. Soft computing is an innovative approach to construct computationally intelligent systems that are supposed to possess humanlike expertise within a specific domain, adapt themselves and learn to do better in changing environments, and explain how they make decisions. Unlike conventional artificial intelligence techniques the guiding principle of soft computing is to exploit tolerance for imprecision, uncertainty, robustness, partial truth to achieve tractability, and better rapport with reality. In this paper, 33 years of rainfall data analyzed in khorasan state, the northeastern part of Iran situated at latitude-longitude pairs (31°-38°N, 74°- 80°E). this research attempted to train Fuzzy Inference System (FIS) based prediction models with 33 years of rainfall data. For performance evaluation, the model predicted outputs were compared with the actual rainfall data. Simulation results reveal that soft computing techniques are promising and efficient. The test results using by FIS model showed that the RMSE was obtained 52 millimeter.

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

2009-04-01

254

Knowledge representation and inference in intelligent decision systems  

SciTech Connect

Decision making in complex domains typically consists of a series of related decisions, each of which requires a different level of support and richness of representation. Rule-based expert systems for decision support have been successful for well-structured, well-understood decision situations. As uncertainty increases and the preferred solution depends on the specific beliefs and preferences of an individual decision maker, more-powerful techniques based on single-person decision theory can be brought to bear. This research focuses on alternative means of representing and using knowledge regarding decision situations in a computer-based decision aid. A unified characterization of knowledge and inference for logical, probabilistic, and decision-theoretic reasoning is developed for intelligent decision support over a wide spectrum of decision situations. A representation of a decision domain consists of structures for representing the decision choices, alternative possible states or outcomes which might occur, the relationships between choices made and outcomes realized, and preferences of the decision maker for the various outcomes. The components are captured by an extension to first-order predicate logic in which propositions are used to represent states, alternatives, and objectives.

Breese, J.S.

1987-01-01

255

An integrated fuzzy inference based monitoring, diagnostic, and prognostic system  

NASA Astrophysics Data System (ADS)

To date the majority of the research related to the development and application of monitoring, diagnostic, and prognostic systems has been exclusive in the sense that only one of the three areas is the focus of the work. While previous research progresses each of the respective fields, the end result is a variable "grab bag" of techniques that address each problem independently. Also, the new field of prognostics is lacking in the sense that few methods have been proposed that produce estimates of the remaining useful life (RUL) of a device or can be realistically applied to real-world systems. This work addresses both problems by developing the nonparametric fuzzy inference system (NFIS) which is adapted for monitoring, diagnosis, and prognosis and then proposing the path classification and estimation (PACE) model that can be used to predict the RUL of a device that does or does not have a well defined failure threshold. To test and evaluate the proposed methods, they were applied to detect, diagnose, and prognose faults and failures in the hydraulic steering system of a deep oil exploration drill. The monitoring system implementing an NFIS predictor and sequential probability ratio test (SPRT) detector produced comparable detection rates to a monitoring system implementing an autoassociative kernel regression (AAKR) predictor and SPRT detector, specifically 80% vs. 85% for the NFIS and AAKR monitor respectively. It was also found that the NFIS monitor produced fewer false alarms. Next, the monitoring system outputs were used to generate symptom patterns for k-nearest neighbor (kNN) and NFIS classifiers that were trained to diagnose different fault classes. The NFIS diagnoser was shown to significantly outperform the kNN diagnoser, with overall accuracies of 96% vs. 89% respectively. Finally, the PACE implementing the NFIS was used to predict the RUL for different failure modes. The errors of the RUL estimates produced by the PACE-NFIS prognosers ranged from 1.2-11.4 hours with 95% confidence intervals (CI) from 0.67-32.02 hours, which are significantly better than the population based prognoser estimates with errors of ˜45 hours and 95% CIs of ˜162 hours.

Garvey, Dustin

256

A combination method for short term load forecasting used in Iran electricity market by NeuroFuzzy, Bayesian and finding similar days methods  

Microsoft Academic Search

Short term load forecasting (STLF) plays an important role for the power system operational planners and also most of the participants in the nowadays electricity markets. With the importance of the STLF in power system operation and electricity markets, many methods for arriving careful results, are represented. In this paper, a combination approach for STLF is proposed. The proposed approach

S. Barghinia; S. Kamankesh; N. Mahdavi; A. H. Vahabie; A. A. Gorji

2008-01-01

257

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

SciTech Connect

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

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

2010-03-10

258

A Hypermedia Inference Language as an Alternative to Rule-based Expert Systems  

Microsoft Academic Search

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

G. Stahl; R. Mccall; G. Peper

1992-01-01

259

Intelligent Learning Algorithms for Active Vibration Control  

Microsoft Academic Search

This correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are proposed to develop the mechanisms of an AVC system. The controller is designed on the basis

A. Madkour; M. Alamgir Hossain; Keshav P. Dahal; H. Yu

2007-01-01

260

Fuzzy surface roughness modeling of CNC down milling of Alumic-79  

Microsoft Academic Search

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

F. Dweiri; M. Al-Jarrah; H. Al-Wedyan

2003-01-01

261

Design an Optimized PID Controller for Brushless DC Motor by Using PSO and Based on NARMAX Identified Model with ANFIS  

Microsoft Academic Search

The trapezoidal back-emf synchronous motor which is called brushless DC (BLDC) motor is receiving wide attention for industrial applications because of its high torque density, high efficiency and small size. This kind of electrical motors is a typical example of highly coupled, nonlinear systems. In the first part of this paper an intelligent agent based on Adaptive Neuro-Fuzzy Inference System

Mohammad Reza Faieghi; S. M. Azimi

2010-01-01

262

Prediction of significant wave height using regressive support vector machines  

Microsoft Academic Search

Wave parameters prediction is an important issue in coastal and offshore engineering. In this literature, several models and methods are introduced. In the recent years, the well-known soft computing approaches, such as artificial neural networks, fuzzy and adaptive neuro-fuzzy inference systems and etc., have been known as novel methods to form intelligent systems, these approaches has also been used to

J. Mahjoobi; Ehsan Adeli Mosabbeb

2009-01-01

263

Hybrid intelligent scenario generator for business strategic planning by using ANFIS  

Microsoft Academic Search

The aim of this study is to investigate a new method for generating scenarios in order to cope with the data shortage and linguistic expression of experts in scenario planning. The proposed hybrid intelligent scenario generator uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) to deal with uncertain inputs. In this methodology, the strengths of expert systems, fuzzy logic and Artificial

Sorousha Moayer; Parisa A. Bahri

2009-01-01

264

Towards Leveraging Inference Web to Support Intuitive Explanations in Recommender Systems for Automated Career Counseling  

Microsoft Academic Search

We consider the problem of supporting intuitive explanations in recommender systems used for automated career counseling. Explanations enhance the transparency in operation of a recommender system and facilitate user-acceptance, adoption, and trust in the system. We leverage the inference Web (IW) Infrastructure and the proof markup language (PML) as a foundation for supporting intuitive explanations in recommender systems for automated

Tejaswini Narayanan; Deborah L. Mcguinness

2008-01-01

265

Fault classification performance of induction motor bearing using AI methods  

Microsoft Academic Search

This paper presents an approach of intelligent fault classification of induction motor bearing (IMB) using several artificial intelligent (AI) methods. The most common AI methods are FeedForward Neural Network (FFNN), Elman Network (EN), Radial Basis Function Network (RBFN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The data of IMB fault is obtained from Case Western Reserve University website in form of

Abd Kadir Mahamad; Takashi Hiyama

2010-01-01

266

Efficient and interpretable fuzzy classifiers from data with support vector learning  

Microsoft Academic Search

The maximization of the performance of the most if not all the fuzzy identification techniques is usually expressed in terms of the generalization performance of the derived neuro-fuzzy construction. Support Vector algorithms are adapted for the identification of a Support Vector Fuzzy Inference (SVFI) system that obtains robust generalization performance. However, these SVFI rules usually lack of interpretability. The accurate

Stergios Papadimitriou; Constantinos Terzidis

2005-01-01

267

A hybrid adaptive control strategy for a smart prosthetic hand  

Microsoft Academic Search

This paper presents a hybrid of a soft computing technique of adaptive neuro-fuzzy inference system (ANFIS) and a hard computing technique of adaptive control for a two-dimensional movement of a prosthetic hand with a thumb and index finger. In particular, ANFIS is used for inverse kinematics, and the adaptive control is used for linearized dynamics to minimize tracking error. The

Cheng-Hung Chen; D. Subbaram Naidu; Alba Perez-Gracia; Marco P. Schoen

2009-01-01

268

ANFIS for prediction of weld bead width in a submerged arc welding process  

Microsoft Academic Search

This paper proposes an intelligent technique, Adaptive Neuro-Fuzzy Inference System (ANFIS), to predict the weld bead width in the submerged arc welding (SAW) process for a given set of welding parameters. Experiments are designed according to Taguchi's principles and its results are used to develop a multiple regression model . Multiple sets of data from multiple regression analysis are utilized

J Edwin; Raja Dhas; S Kumanan

269

A comparison of various forecasting techniques applied to mean hourly wind speed time series  

Microsoft Academic Search

This paper presents a comparison of various forecasting approaches, using time series analysis, on mean hourly wind speed data. In addition to the traditional linear (ARMA) models and the commonly used feed forward and recurrent neural networks, other approaches are also examined including the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Neural Logic Networks. The developed models are evaluated for their

A. Sfetsos

2000-01-01

270

Inferring the Gibbs state of a small quantum system  

SciTech Connect

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

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

2011-07-15

271

Information Fusion Implementation Using Fuzzy Inference System, Term Weight and Information Theory in a Multimode Authentication System  

Microsoft Academic Search

\\u000a Fuzzy inference systems have been used in a number of systems to introduce intelligence behaviour. In this paper we attempt\\u000a to address an area of security challenges in identity management. The Sugeno-Style fuzzy inference is envisaged in the implementation\\u000a of information fusion in a multimode authentication system in an effort to provide a solution to identity theft and fraud.\\u000a Triangular

Jackson Phiri; Tie Jun Zhao; Jameson Mbale

272

Rule-base structure identification in an adaptive-network-based fuzzy inference system  

Microsoft Academic Search

We summarize Jang's architecture of employing an adaptive network and the Kalman filtering algorithm to identify the system parameters. Given a surface structure, the adaptively adjusted inference system performs well on a number of interpolation problems. We generalize Jang's basic model so that it can be used to solve classification problems by employing parameterized t-norms. We also enhance the model

Chuen-Tsai Sun

1994-01-01

273

Macroscopic Time Evolution and MaxEnt Inference for Closed Systems with Hamiltonian Dynamics  

Microsoft Academic Search

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

Domagoj Kuic; Pasko Zupanovic; Davor Juretic

2011-01-01

274

A high-precision camera operation parameter measurement system and its application to image motion inferring  

Microsoft Academic Search

Information about camera operations such as zoom, focus, pan, tilt and dollying is significant not only for efficient video coding, but also for content-based video representation. In this paper we describe a high-precision camera operation parameter measurement system and apply it to image motion inferring. First, we outline the implemented system which is designed to provide camera operation parameters with

Wentao Zheng; Yoshiaki Shishikui; Yasuaki Kanatsugu; Yutaka Tanaka; Ichiro Yuyama

2001-01-01

275

Using Viewing Time to Infer User Preference in Recommender Systems  

Microsoft Academic Search

The need for effective technologies to help Web users locate items (information or products) is increasing as the amount of information on the Web grows. Collaborative filtering is one of the most successful techniques for making recommendations; however, most CF-based systems require explicit user ratings and a large quantity of usage history to function effectively. In addition, such systems typically

Jeffrey Parsons; Paul Ralph; Katherine Gallagher

276

A Cascaded Fuzzy Inference System for Indian river water quality prediction  

Microsoft Academic Search

Now-a-days, Fuzzy Inference System (FIS) is considered as an effective tool for solution of many complex engineering systems when ambiguity and uncertainly is associated with the systems. Mamdani and Takagi, Sugeno and Kang (TSK) models poses simplicity in modeling but their system performance prediction capability is severely affected as complexity of the problem increases. In a multi-input, multi-output situation where

S. S. Mahapatra; Santosh Kumar Nanda; B. K. Panigrahy

2011-01-01

277

Optimizing Information Retrieval Using Evolutionary Algorithms and Fuzzy Inference System  

Microsoft Academic Search

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,

Václav Snásel; Ajith Abraham; Suhail S. J. Owais; Jan Platos; Pavel Krömer

2009-01-01

278

Earth system sensitivity inferred from Pliocene modelling and data  

USGS Publications Warehouse

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.

Lunt, D. J.; Haywood, A. M.; Schmidt, G. A.; Salzmann, U.; Valdes, P. J.; Dowsett, H. J.

2010-01-01

279

Isotopic abundances - Inferences on solar system and planetary evolution  

NASA Astrophysics Data System (ADS)

For matter that has been removed from a region of nucleosynthetic activity and the effects of interactions with nuclear active particles, the only changes in nuclear abundances that can occur in an isolated system derive from the decay of radioactive nuclei of an element to yield the nucleus of another element. These two related nuclei furnish the absolute chronometers of geologic and cosmic time, through the decay of spontaneously radioactive parent nuclei and the accumulation of daughter nuclei. For systems related to such cosmic processes as the formation of the solar system from the precursor interstellar medium, and involving the very early evolution of the sun, there may arise considerable complexity, due to the intrinsic isotopic heterogeneity of the medium and the presence of short-lived nuclei.

Wasserburg, G. J.

1987-12-01

280

Patent analysis-based fuzzy inference system for technological strategy planning  

Microsoft Academic Search

This paper describes a technological strategy planning method that integrates patent analysis techniques with a fuzzy inference system (FIS). The method differentiates itself from the traditional technological management decision-making tools in its knowledge base. Instead of eliciting knowledge from domain experts, the proposed method adopts global patent databases as sources of knowledge for strategy planning. The patent analysis techniques are

W.-D. Yu; S.-S. Lo

2009-01-01

281

Functional equivalence between radial basis function networks and fuzzy inference systems  

Microsoft Academic Search

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

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

1993-01-01

282

Evolutionary Fuzzy Neural Inference System for Decision Making in Geotechnical Engineering  

Microsoft Academic Search

Problems in geotechnical engineering are full of uncertain, vague, and incomplete information. In most instances, successfully solving such problems depends on experts' knowledge and experience. The primary object of this research was to develop an evolutionary fuzzy neural inference system EFNIS to imitate the decision-making processes in the human brain in order to facilitate geotechnical expert decision making. First, an

Min-Yuan Cheng; Hsing-Chih Tsai; Chien-Ho Ko; Wen-Te Chang

2008-01-01

283

Inference of genetic networks using S-system: information criteria for model selection  

Microsoft Academic Search

In this paper we present an evolutionary approach for infer- ring the structure and dynamics in gene circuits from ob- served expression kinetics. For representing the regulatory interactions in a genetic network the decoupled S-system for- malism has been used. We proposed an Information Criteria based fitness evaluation for model selection instead of the traditional Mean Squared Error (MSE) based

Nasimul Noman; Hitoshi Iba

2006-01-01

284

Extending the functional equivalence of radial basis function networks and fuzzy inference systems  

Microsoft Academic Search

We establish the functional equivalence of a generalized class of Gaussian radial basis function (RBFs) networks and the full Takagi-Sugeno model (1983) of fuzzy inference. This generalizes an existing result which applies to the standard Gaussian RBF network and a restricted form of the Takagi-Sugeno fuzzy system. The more general framework allows the removal of some of the restrictive conditions

Kenneth J. Hunt; Roland Haas; Roderick Murray-Smith

1996-01-01

285

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

Microsoft Academic Search

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

David Barber; Bertrand Mesot

2006-01-01

286

Web enabled expert systems using hyperlink-based inference  

Microsoft Academic Search

With the proliferation of the WWW, providing more intelligent Websites has become a major concern in the e-business industry. Recently, this trend has been even more accelerated by the success of Customer Relationship Management (CRM) in terms of product recommendation, and self after service, etc. As a result, many e-companies are eager to embed Web-enabled, rule-based systems, i.e. that is,

Wooju Kim; Yong U. Song; June S. Hong

287

Gesture Recognition and Generation for HOAP-2 Robots by Fuzzy Inference System  

Microsoft Academic Search

Since HOAP series robots resemble human body structure, a HOAP robot is expected to interact with others in real-time. However, it has proven hard in terms of learning, recognition, and interaction in real-time. In this paper a Fuzzy Inference System (FIS) is proposed, which learns gestures with segmentation and motion primitives, recognize gestures with created rule-based system in learning phase,

Rajesh Doriya; Parikshit Agarwal; Pavan Chakraborty; G. C. Nandi

2011-01-01

288

Macroscopic time evolution and MaxEnt inference for closed systems with Hamiltonian dynamics  

Microsoft Academic Search

MaxEnt inference algorithm is relevant for the problem of time evolution of\\u000amacroscopic systems. Two different approaches are considered, both applied to\\u000apredicting macroscopic time evolution of closed Hamiltonian systems. The first\\u000ais based on Liouville equation as a strict microscopic constraint on time\\u000aevolution in phase space. Phase space paths determined by Hamilton's equations\\u000aare introduced. Path probability distribution

Domagoj Kuic; Pasko Zupanovic; Davor Juretic

2010-01-01

289

Petrophysical data prediction from seismic attributes using committee fuzzy inference system  

NASA Astrophysics Data System (ADS)

This study presents an intelligent model based on fuzzy systems for making a quantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation ( S w) and porosity, are predicted from seismic attributes using various fuzzy inference systems (FISs), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a committee fuzzy inference system (CFIS) is constructed using a hybrid genetic algorithms-pattern search (GA-PS) technique. The inputs of the CFIS model are the outputs and averages of the FIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a probabilistic neural network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method.

Kadkhodaie-Ilkhchi, Ali; Rezaee, M. Reza; Rahimpour-Bonab, Hossain; Chehrazi, Ali

2009-12-01

290

Causal graphical models in systems genetics: A unified framework for joint inference of causal network and genetic architecture for correlated phenotypes  

Microsoft Academic Search

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

Elias Chaibub Neto; Mark P. Keller; Alan D. Attie; Brian S. Yandell

2010-01-01

291

Thermal Error Modeling of a Machining Center Using Grey System Theory and Adaptive Network-Based Fuzzy Inference System  

Microsoft Academic Search

Thermal effect on machine tools is a well-recognized problem in an environment of increasing demand for product quality. The performance of a thermal error compensation system typically depends on the accuracy and robustness of the thermal error model. This work presents a novel thermal error model utilizing two mathematic schemes: the grey system theory and the adaptive network-based fuzzy inference

Kun-Chieh Wang; Pai-Chung Tseng; Kuo-Ming Lin

2006-01-01

292

Fuzzy inference system for the risk assessment of liquefied natural gas carriers during loading\\/offloading at terminals  

Microsoft Academic Search

A multiple attribute risk assessment approach using a fuzzy inference system is developed in this work. The approach is based on the use of fuzzy sets, a rule base and a fuzzy inference engine. Traditional input probabilities and consequences used in risk assessment are represented by fuzzy sets to take into account uncertainties associated with the assignment of their values.

Tarek Elsayed

2009-01-01

293

A novel fuzzy logic inference system for decision support in weaning from mechanical ventilation.  

PubMed

Weaning from mechanical ventilation represents one of the most challenging issues in management of critically ill patients. Currently used weaning predictors ignore many important dimensions of weaning outcome and have not been uniformly successful. A fuzzy logic inference system that uses nine variables, and five rule blocks within two layers, has been designed and implemented over mathematical simulations and random clinical scenarios, to compare its behavior and performance in predicting expert opinion with those for rapid shallow breathing index (RSBI), pressure time index and Jabour' weaning index. RSBI has failed to predict expert opinion in 52% of scenarios. Fuzzy logic inference system has shown the best discriminative power (ROC: 0.9288), and RSBI the worst (ROC: 0.6556) in predicting expert opinion. Fuzzy logic provides an approach which can handle multi-attribute decision making, and is a very powerful tool to overcome the weaknesses of currently used weaning predictors. PMID:20703599

Kilic, Yusuf Alper; Kilic, Ilke

2009-06-11

294

Development of Rainfall–Runoff Models Using Mamdani-Type Fuzzy Inference Systems  

Microsoft Academic Search

This study explores the application of Mamdani-type fuzzy inference systems (FIS) to the development of rainfall–runoff models\\u000a operating on a daily basis. The model proposed uses a Rainfall Index, obtained from the weighted sum of the most recently\\u000a observed rainfall values, as input information. The model output is the daily discharge amount at the catchment outlet. The\\u000a membership function parameters

A. P. Jacquin; A. Y. Shamseldin

295

A Novel Reinforcement Learning Approach for Automatic Generation of Fuzzy Inference Systems  

Microsoft Academic Search

In this paper, a novel approach termed dynamic self-generated fuzzy Q-learning (DSGFQL) for automatically generating fuzzy inference systems (FISs) is presented. The DSGFQL methodology can automatically create, delete and adjust fuzzy rules without any priori knowledge. Compared with conventional fuzzy Q-learning (FQL) approaches which only use reinforcement learning (RL) for the consequents part of an FIS, the most salient feature

Meng Joo Er; Yi Zhou

2006-01-01

296

Predicting Spring rainfall Based on Remote Linkage controlling using Adaptive Neural-Fuzzy Inference System (ANFIS)  

NASA Astrophysics Data System (ADS)

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

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

2009-04-01

297

Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems  

PubMed Central

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.

Toni, Tina; Welch, David; Strelkowa, Natalja; Ipsen, Andreas; Stumpf, Michael P.H.

2008-01-01

298

Evaluation of Probabilistic and Logical Inference for a SNP Annotation System  

PubMed Central

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

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

2010-01-01

299

Video-based cargo fire verification system with fuzzy inference engine for commercial aircraft  

NASA Astrophysics Data System (ADS)

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.

Sadok, Mokhtar; Zakrzewski, Radek; Zeliff, Bob

2005-02-01

300

Multi-agent based control of large-scale complex systems employing distributed dynamic inference engine  

NASA Astrophysics Data System (ADS)

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

Zhang, Daili

301

Adaptive fuzzy control of underactuated robotic systems with the use of differential flatness theory  

NASA Astrophysics Data System (ADS)

An adaptive fuzzy controller is designed for a class of underactuated nonlinear robotic manipulators, under the constraint that the system's model is unknown. The control algorithm aims at satisfying the H? tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the robotic system into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system's parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H? tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked in the case of a 2-DOF planar robotic manipulator that has the structure of a closed-chain mechanism.

Rigatos, Gerasimos G.

2013-10-01

302

The early solar system abundance of /sup 244/Pu as inferred from the St. Severin chondrite  

SciTech Connect

We describe the analysis of Xe released in stepwise heating of neutron-irradiated samples of the St. Severin chondrite. This analysis indicates that at the time of formation of most chondritic meteorites, approximately 4.56 x 10/sup 9/ years ago, the atomic ratio of /sup 244/Pu//sup 238/U was 0.0068 +- 0.0010 in chondritic meteorites. We believe that this value is more reliable than that inferred from earlier analyses of St. Severin. We feel that this value is currently the best available estimate for the early solar system abundance of /sup 244/Pu. 42 refs., 2 tabs.

Hudson, G.B.; Kennedy, B.M.; Podosek, F.A.; Hohenberg, C.M.

1987-03-01

303

Prediction of the Uniaxial Compressive Strength of a Greywacke by Fuzzy Inference System  

NASA Astrophysics Data System (ADS)

Rock engineering projects require the uniaxial compressive strength of intact rock. High quality core samples are needed for the application of uniaxial compressive strength in laboratory. In this study, to establish some predictive models taking into consideration multiple regression techniques and fuzzy inference system is aimed. Ankara greywackes is selected as the material, because of its highly problematic nature as mentioned by many previous researchers. For this purpose, a series of rock mechanics tests were carried out, and uniaxial compressive strength, point load index, block punch index, unit weight, apparent porosity, water absorption by weight, P-wave velocity, Schmidt hardness and tensile strength of greywacke were obtained. Using the obtained results, two prediction models were constructed to predict the uniaxial compressive strength of selected greywacke. The values account for and root mean square error indices were calculated as 41.49% and 15.62 the multiple regression model; 81.24% and 13.06 for the fuzzy inference system. As a result, these indices revealed that the prediction performances of the fuzzy model are higher than that of multiple regression equations.

Zorlu, Kívanc; Gokceoglu, Candan; Sonmez, Harun

304

Ecological inference  

PubMed Central

Ecological inference is the process of drawing conclusions about individual-level behavior from aggregate-level data. Recent advances involve the combination of statistical and deterministic means to produce such inferences.

Schuessler, Alexander A.

1999-01-01

305

Dynamic Inference.  

National Technical Information Service (NTIS)

When from the pattern of observable variables the probability distributions of process parameters is inferred and then these distributions are used to predict the future course of the process; this is called Dynamic Inference. For a military illustration ...

R. A. Howard

1964-01-01

306

Real-Time Decision-Making with Partial Information for Construction Management  

Microsoft Academic Search

Real-time decision-making with partial information is commonplace in the daily tasks of a construction manager\\/engineer. However, traditional decision support systems (DSSs) do not support partial information inference. Data pre-processing was adopted to solve the data-incompleteness problem. Unfortunately, such approach may be biased. This paper presents a newly developed neuro-fuzzy system, named Variable-attribute Fuzzy Adaptive Logic Control Network (VaFALCON), for decision-making

Wen-der Yu; Shao-Shung Lo; Gang-wei Fan

307

Fractional Fuzzy Adaptive Sliding-Mode Control of a 2DOF Direct-Drive Robot Arm  

Microsoft Academic Search

This paper presents a novel parameter adjustment scheme to improve the robustness of fuzzy sliding-mode control achieved by the use of an adaptive neuro-fuzzy inference system (ANFIS) architecture. The proposed scheme utilizes fractional-order integration in the parameter tuning stage. The controller parameters are tuned such that the system under control is driven toward the sliding regime in the traditional sense.

Mehmet Önder Efe

2008-01-01

308

Intelligent control of grid connected unified doubly-fed induction generator  

Microsoft Academic Search

A nonlinear adaptive neuro-fuzzy inference system (ANFIS) based controller is proposed for power electronic systems (PES) of grid connected unified doubly-fed induction generators (DFIG). The unified DFIG utilizes an additional series grid-side converter (SGSC) connected in series with the stator winding of the DFIG to inject voltage into the grid for compensation purposes. The SGSC is useful in tolerating voltage

Bharat Singh; Elias Kyriakides; Sri Niwas Singh

2010-01-01

309

Grain Size Estimation of Superalloy Inconel 718 After Upset Forging by a Fuzzy Inference System  

NASA Astrophysics Data System (ADS)

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.

Toro, Luis; Cavazos, Alberto; Colás, Rafael

2009-12-01

310

Spatial distribution of arsenic in the Texas Gulf Coastal Aquifer System and inferences regarding hydrogeochemical controls  

NASA Astrophysics Data System (ADS)

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

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

2008-12-01

311

The Role of Probability-Based Inference in an Intelligent Tutoring System.  

ERIC Educational Resources Information Center

|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…

Mislevy, Robert J.; Gitomer, Drew H.

312

K2F - A Novel Framework for Converting Fuzzy Cognitive Maps into Rule-Based Fuzzy Inference Systems  

Microsoft Academic Search

\\u000a This paper focuses on a novel methodological framework for converting a Fuzzy Cognitive Map into a network of rule-based Fuzzy Inference Systems. Furthermore, it allows to obtain a crisp value representing an arbitrary parameter of the complex system’s model. This way\\u000a the system provides a quantitative answer without employing an exact mathematical model. This paper also outlines a first\\u000a possible

Lars Krüger

2010-01-01

313

Application of MR damper in structural control using ANFIS method  

Microsoft Academic Search

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

Zhi Q. Gu; S. Olutunde Oyadiji

2008-01-01

314

Fuzzy control strategies in human operator and sport modeling  

Microsoft Academic Search

The motivation behind mathematically modeling the human operator is to help explain the response characteristics of the complex\\u000a dynamical system including the human manual controller. In this paper, we present two different fuzzy logic strategies for\\u000a human operator and sport modeling: fixed fuzzy-logic inference control and adaptive fuzzy-logic control, including neuro-fuzzy-fractal\\u000a control. As an application of the presented fuzzy strategies,

Tijana T. Ivancevic; Bojan Jovanovic; Sasa Markovic

2010-01-01

315

Nero-fuzzy modeling of the convection heat transfer coefficient for the nanofluid  

NASA Astrophysics Data System (ADS)

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.

Salehi, H.; Zeinali-Heris, S.; Esfandyari, M.; Koolivand, M.

2013-04-01

316

An automatic inference system for the quality analysis of test items based on the Bloom's revised taxonomy  

Microsoft Academic Search

Good Test items can be used to effectively measure the ability of testers. In this paper, an inference system is proposed to automatically analyze the quality for test items based on the Bloom's revised taxonomy. The main concept is to find the characteristics of knowledge dimension and cognitive process dimension for test items by analyzing the ldquoverbsrdquo and ldquoquestion wordsrdquo

Yi-Hsing Chang; Huan-Wen Chen

2009-01-01

317

Upswing and stabilization control of inverted pendulum system based on the SIRMs dynamically connected fuzzy inference model  

Microsoft Academic Search

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

Jianqiang Yi; Naoyoshi Yubazaki; Kaoru Hirota

2001-01-01

318

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

NASA Astrophysics Data System (ADS)

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

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

2013-03-01

319

Adaptive network based on fuzzy inference system for equilibrated urea concentration prediction.  

PubMed

Post-dialysis urea rebound (PDUR) has been attributed mostly to redistribution of urea from different compartments, which is determined by variations in regional blood flows and transcellular urea mass transfer coefficients. PDUR occurs after 30-90min of short or standard hemodialysis (HD) sessions and after 60min in long 8-h HD sessions, which is inconvenient. This paper presents adaptive network based on fuzzy inference system (ANFIS) for predicting intradialytic (Cint) and post-dialysis urea concentrations (Cpost) in order to predict the equilibrated (Ceq) urea concentrations without any blood sampling from dialysis patients. The accuracy of the developed system was prospectively compared with other traditional methods for predicting equilibrated urea (Ceq), post dialysis urea rebound (PDUR) and equilibrated dialysis dose (eKt/V). This comparison is done based on root mean squares error (RMSE), normalized mean square error (NRMSE), and mean absolute percentage error (MAPE). The ANFIS predictor for Ceq achieved mean RMSE values of 0.3654 and 0.4920 for training and testing, respectively. The statistical analysis demonstrated that there is no statistically significant difference found between the predicted and the measured values. The percentage of MAE and RMSE for testing phase is 0.63% and 0.96%, respectively. PMID:23806679

Azar, Ahmad Taher

2013-06-24

320

S-systems and Evolutionary Algorithms for the Inference of Chemical Reaction Networks from Fed-Batch Reactor Experiments  

Microsoft Academic Search

This article aims to demonstrate the potential of the S-system methodology to construct hybrid models of fed-batch reaction systems in which both the reaction network equations and kinetic parameters are unknown. Furthermore, the article aims to show that the hybrid S-systems models can be used as an aid in the inference of the structure of unknown reaction networks from simple

D. P. Searson; M. J. Willis; S. J. Horne; A. R. Wright

2006-01-01

321

Estimating daily pan evaporation using adaptive neural-based fuzzy inference system  

NASA Astrophysics Data System (ADS)

Estimation of evaporation is important for water planning, management, and hydrological practices. There are many available methods to estimate evaporation from a water surface, comprising both direct and indirect methods. All the evaporation models are based on crisp conceptions with no uncertainty element coupled into the model structure although in daily evaporation variations there are uncontrollable effects to a certain extent. The probabilistic, statistical, and stochastic approaches require large amounts of data for the modeling purposes and therefore are not practical in local evaporation studies. It is therefore necessary to adopt a better approach for evaporation modeling, which is the fuzzy sets and adaptive neural-based fuzzy inference system (ANFIS) as used in this paper. ANFIS and fuzzy sets have been evaluated for its applicability to estimate evaporation from meteorological data which is including air and water temperatures, solar radiation, and air pressure obtained from Automated GroWheather meteorological station located near Lake E?irdir and daily pan evaporation values measured by XVIII. District Directorate of State Hydraulic Works. Results of ANFIS and fuzzy logic approaches were analyzed and compared with measured daily pan evaporation values. ANFIS approach could be employed more successfully in modeling the evaporation process than fuzzy sets.

Keskin, M. Erol; Terzi, Özlem; Taylan, Dilek

2009-09-01

322

Adaptive network-based fuzzy inference system for assessment of lower limb peripheral vascular occlusive disease.  

PubMed

Detecting lower limb peripheral vascular occlusive disease (PVOD) early is important for patients to prevent disabling claudication, ischaemic rest pain and gangrene. According to previous research, the pulse timing and shape distortion characteristics of photoplethysmography (PPG) signals tend to increase with disease severity and calibrated amplitude decreases with vascular diseases. However, this is not a reliable method of evaluating the condition of PVOD because of noise effect. In this paper, an adaptive network-based fuzzy inference system (ANFIS) is proposed to assess lower limb PVOD based on PPG signals. PPG signals are non-invasively recorded from the right and left sides at the big toe sites from twenty subjects, including normal condition (Nor), lower-grade disease (LG), and higher-grade disease (HG) groups. The number of each group is 10, 8 and 2 respectively, and the ages ranged from 24 to 65 years. With the time-domain technique, the parameters for the absolute bilateral differences (right-to-left side of foot) in pulse delay and amplitude were extracted for analyzing ANFIS. The results indicated that ANFIS based on three timing parameters base bilateral differences, including ?PTTf and ?PTTp, and ?RT has a high rate and noise tolerance of PVOD assessment. PMID:20703718

Du, Yi-Chun; Lin, Chia-Hung

2010-04-13

323

Thermal Error Modeling of a Machining Center Using Grey System Theory and Adaptive Network-Based Fuzzy Inference System  

NASA Astrophysics Data System (ADS)

Thermal effect on machine tools is a well-recognized problem in an environment of increasing demand for product quality. The performance of a thermal error compensation system typically depends on the accuracy and robustness of the thermal error model. This work presents a novel thermal error model utilizing two mathematic schemes: the grey system theory and the adaptive network-based fuzzy inference system (ANFIS). First, the measured temperature and deformation results are analyzed via the grey system theory to obtain the influence ranking of temperature ascent on thermal drift of spindle. Then, using the highly ranked temperature ascents as inputs for the ANFIS and training these data by the hybrid learning rule, a thermal compensation model is constructed. The grey system theory effectively reduces the number of temperature sensors needed on a machine structure for prediction, and the ANFIS has the advantages of good accuracy and robustness. For testing the performance of proposed ANFIS model, a real-cutting operation test was conducted. Comparison results demonstrate that the modeling schemes of the ANFIS coupled with the grey system theory has good predictive ability.

Wang, Kun-Chieh; Tseng, Pai-Chung; Lin, Kuo-Ming

324

Application of Bayesian inference to the study of hierarchical organization in self-organized complex adaptive systems  

NASA Astrophysics Data System (ADS)

We consider the application of Bayesian inference to the study of self-organized structures in complex adaptive systems. In particular, we examine the distribution of elements, agents, or processes in systems dominated by hierarchical structure. We demonstrate that results obtained by Caianiello [1] on Hierarchical Modular Systems (HMS) can be found by applying Jaynes' Principle of Group Invariance [2] to a few key assumptions about our knowledge of hierarchical organization. Subsequent application of the Principle of Maximum Entropy allows inferences to be made about specific systems. The utility of the Bayesian method is considered by examining both successes and failures of the hierarchical model. We discuss how Caianiello's original statements suffer from the Mind Projection Fallacy [3] and we restate his assumptions thus widening the applicability of the HMS model. The relationship between inference and statistical physics, described by Jaynes [4], is reiterated with the expectation that this realization will aid the field of complex systems research by moving away from often inappropriate direct application of statistical mechanics to a more encompassing inferential methodology. .

Knuth, K. H.

2001-05-01

325

Embedded prediction in feature extraction: application to single-trial EEG discrimination.  

PubMed

In this study, an analysis system embedding neuron-fuzzy prediction in feature extraction is proposed for brain-computer interface (BCI) applications. Wavelet-fractal features combined with neuro-fuzzy predictions are applied for feature extraction in motor imagery (MI) discrimination. The features are extracted from the electroencephalography (EEG) signals recorded from participants performing left and right MI. Time-series predictions are performed by training 2 adaptive neuro-fuzzy inference systems (ANFIS) for respective left and right MI data. Features are then calculated from the difference in multi-resolution fractal feature vector (MFFV) between the predicted and actual signals through a window of EEG signals. Finally, the support vector machine is used for classification. The proposed method estimates its performance in comparison with the linear adaptive autoregressive (AAR) model and the AAR time-series prediction of 6 participants from 2 data sets. The results indicate that the proposed method is promising in MI classification. PMID:23248335

Hsu, Wei-Yen

2012-12-17

326

Neuro-fuzzy technique for navigation of multiple mobile robots  

Microsoft Academic Search

In this paper, navigation techniques for several mobile robots are investigated in a totally unknown environment. In the beginning,\\u000a Fuzzy logic controllers (FLC) using different membership functions are developed and used to navigate mobile robots. First\\u000a a fuzzy controller has been used with four types of input members, two types of output members and three parameters each.\\u000a Next two types

Saroj Kumar Pradhan; Dayal Ramakrushna Parhi; Anup Kumar Panda

2006-01-01

327

An evolutionary neuro-fuzzy approach to breast cancer diagnosis  

Microsoft Academic Search

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

Ridha El Hamdi; Mohamed Njah; Mohamed Chtourou

2010-01-01

328

ECG beat classification using neuro-fuzzy network  

Microsoft Academic Search

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.

Mehmet Engin

2004-01-01

329

Entropic Inference  

NASA Astrophysics Data System (ADS)

In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEnt and Bayes' rule, and therefore unifies the two themes of these workshops--the Maximum Entropy and the Bayesian methods--into a single general inference scheme.

Caticha, Ariel

2011-03-01

330

Information Warfare-Worthy Jamming Attack Detection Mechanism for Wireless Sensor Networks Using a Fuzzy Inference System  

PubMed Central

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

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

2010-01-01

331

Sibship reconstruction for inferring mating systems, dispersal and effective population size in headwater brook trout (Salvelinus fontinalis) populations  

USGS Publications Warehouse

Brook trout Salvelinus fontinalis populations have declined in much of the native range in eastern North America and populations are typically relegated to small headwater streams in Connecticut, USA. We used sibship reconstruction to infer mating systems, dispersal and effective population size of resident (non-anadromous) brook trout in two headwater stream channel networks in Connecticut. Brook trout were captured via backpack electrofishing using spatially continuous sampling in the two headwaters (channel network lengths of 4.4 and 7.7 km). Eight microsatellite loci were genotyped in a total of 740 individuals (80-140 mm) subsampled in a stratified random design from all 50 m-reaches in which trout were captured. Sibship reconstruction indicated that males and females were both mostly polygamous although single pair matings were also inferred. Breeder sex ratio was inferred to be nearly 1:1. Few large-sized fullsib families (>3 individuals) were inferred and the majority of individuals were inferred to have no fullsibs among those fish genotyped (family size = 1). The median stream channel distance between pairs of individuals belonging to the same large-sized fullsib families (>3 individuals) was 100 m (range: 0-1,850 m) and 250 m (range: 0-2,350 m) in the two study sites, indicating limited dispersal at least for the size class of individuals analyzed. Using a sibship assignment method, the effective population size for the two streams was estimated at 91 (95%CI: 67-123) and 210 (95%CI: 172-259), corresponding to the ratio of effective-to-census population size of 0.06 and 0.12, respectively. Both-sex polygamy, low variation in reproductive success, and a balanced sex ratio may help maintain genetic diversity of brook trout populations with small breeder sizes persisting in headwater channel networks. ?? 2010 Springer Science+Business Media B.V.

Kanno, Y.; Vokoun, J. C.; Letcher, B. H.

2011-01-01

332

Chapter 1 Spatial Soil Information Systems and Spatial Soil Inference Systems: Perspectives for Digital Soil Mapping  

Microsoft Academic Search

Given the relative dearth of, and the huge demand for, quantitative spatial soil information, it is timely to develop and implement methodologies for its provision. We suggest that digital soil mapping, which can be defined as the creation, and population of spatial soil information systems (SSINFOS) by the use of field and laboratory observational methods, coupled with spatial and non-spatial

P. Lagacherie; A. B. McBratney

2006-01-01

333

Intelligent Maneuvering Decision System for Computer Generated Forces Using Predictive Fuzzy Inference System  

Microsoft Academic Search

The purpose of this paper is to develop an intelligent maneuvering decision system (IMDS) for computer generated forces (CGF). The proposed CGF can take actions similar to a human pilot to gain an advantageous status over the enemy target using the IMDS. The IMDS will produce the best control command from the control alternatives for the CGF in an air

Tsung-ying Sun; Shang-jeng Tsai; Chih-li Huo

2008-01-01

334

Application of adaptive noise cancellation with neural-network-based fuzzy inference system for visual evoked potentials estimation  

Microsoft Academic Search

This paper presents an application of adaptive noise cancellation with neural-network-based fuzzy inference system (NNFIS) for rapid estimation of visual evoked potentials (VEPs). Usually a recorded VEP is severely contaminated by background ongoing activities of the spontaneous EEG signal in the human brain. Many approaches have been adopted to enhance the signal-to-noise ratio (SNR) of the recorded signal. However, nonlinear

Haie Yin; Yanjun Zeng; Jianhua Zhang; Yinfu Pan

2004-01-01

335

Self-Generation of Fuzzy Inference Systems by Enhanced Dynamic Self-Generated Fuzzy Q-Learning  

Microsoft Academic Search

In this paper, a novel approach termed enhanced dynamic self-generated fuzzy Q-learning (EDSGFQL) for automatically generating a fuzzy inference system (FIS) is presented. In this temporal difference (TD)-based EDSGFQL approach, the structure and preconditioning parts of an FIS are generated by a hybrid reinforcement learning (RL) and unsupervised learning (UL) approach. In the EDSGFQL approach, the preconditioning parts of an

Yi Zhou; Meng Joo Er

2006-01-01

336

Neurofuzzy-model-based feedback controller for shape memory alloy actuators  

NASA Astrophysics Data System (ADS)

This paper describes development of a motion controller for Shape Memory Alloy (SMA) actuators using a dynamic model generated by a neuro-fuzzy inference system. This kind of smart alloy is known to have a unique characteristic in that its shape can be controlled by temperature that can be varied by passing a current through it. Using SMA actuators, it would be possible to design miniature mechanisms for a variety of applications. Today SMA is used for valves, latches, and locks, which are automatically activated by heat. However it has not been used as a motion control device due to difficulty in the treatment of its highly non-linear strain-stress hysteresis characteristic which is further influenced by its temperature. In this project, a dynamic model of a SMA actuator is developed using ANFIS, a neuro-fuzzy inference system provided in MATLAB environment. Using neuro-fuzzy logic, the system identification of the dynamic system is performed by observing the change of state variables (displacement and velocity) responding to a known input (voltage across the SMA actuator). Then, using the dynamic model, the estimated input voltage required to follow a desired trajectory is calculated in an open-loop manner. The actual input voltage supplied to the SMA actuator is the sum of this open-loop input voltage and an input voltage calculated from an ordinary PD control scheme. This neuro- fuzzy logic-based control scheme is a very generalized scheme that can be used for a variety of SMA actuators. Experimental results are provided to demonstrate the potential for this type of controller to control the motion of the SMA actuator.

Kumagai, Akihiko; Hozian, Paul A.; Kirkland, Michael

2000-06-01

337

Hydrothermal system beneath Aso volcano as inferred from self-potential mapping and resistivity structure  

NASA Astrophysics Data System (ADS)

We conducted self-potential (SP) surveys sequentially from part to part over the central cones of Aso volcano since August 1998 by December 2001. The compiled SP map revealed large SP anomalies on the central cones. The main feature of the SP map is a ‘W-shaped’ profile along the NS-transect over the central cones. It is probable that this characteristic SP profile is produced by the combination of hydrothermal upwelling in the middle and topographic effect. A positive anomaly showing a large concentric pattern has appeared after correcting the topographic effect. To evaluate this SP anomaly, we implemented a numerical code that calculates electric potential produced by arbitrarily positioned current sources and sinks in any three-dimensional resistivity structure. A layered structure obtained from a time-domain electromagnetic (TDEM) field experiment was used for the resistivity model. The estimated current source is 300 A, being located in a conductive layer around the sea level. Meanwhile, sinks were estimated to sit on a circular area corresponding to the marginal part of the conductive layer. Water and heat budget study gives a lower limit of water mass transfer from depth to the bottom of the crater lake of Nakadake. This value was used to estimate the equivalent current in either case of electro-kinetic (EK) [Mizutani, H., Ishido, T., 1976. A new interpretation of magnetic field variation associated with the Matsushiro earthquakes, J. Geomag. Geoelectr., 28, 179 188.] or rapid fluid disruption (RFD) process [Johnston, M.J.S., Byerlee, J.D., Lockner, D., 2001. Rapid fluid disruption: A source for self-potential anomalies on volcanoes, J. Geophys. Res. 106(B3), 4327-4335.]. This comparison suggests that the former process is preferable to explain the observed SP anomaly. From these results we infer a large-scale hydrothermal system beneath the central cones of Aso volcano, in which the fluid flow initiates from the surrounding area, converging to the central vent to transport the heat and materials up to the crater lake of Nakadake through a vapor-filled conduit.

Hase, Hideaki; Hashimoto, Takeshi; Sakanaka, Shin'ya; Kanda, Wataru; Tanaka, Yoshikazu

2005-05-01

338

Opening the black box of neural networks with fuzzy set theory to facilitate the understanding of remote sensing image processing  

Microsoft Academic Search

The purpose of this research is to facilitate the understanding of remote sensing image classifications based on the integration of neural networks with fuzzy expert systems, which is often known as neuro-fuzzy systems. A neuro-fuzzy system is basically a fuzzy system that uses a learning algorithm derived from or inspired by neural network theory to determine its parameters (fuzzy sets

Fang Qiu

2000-01-01

339

CAUSAL GRAPHICAL MODELS IN SYSTEMS GENETICS: A UNIFIED FRAMEWORK FOR JOINT INFERENCE OF CAUSAL NETWORK AND GENETIC ARCHITECTURE FOR CORRELATED PHENOTYPES.  

PubMed

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

Neto, Elias Chaibub; Keller, Mark P; Attie, Alan D; Yandell, Brian S

2010-03-01

340

Bayesian inference.  

PubMed

This chapter provides an overview of the Bayesian approach to data analysis, modeling, and statistical decision making. The topics covered go from basic concepts and definitions (random variables, Bayes' rule, prior distributions) to various models of general use in biology (hierarchical models, in particular) and ways to calibrate and use them (MCMC methods, model checking, inference, and decision). The second half of this Bayesian primer develops an example of model setup, calibration, and inference for a physiologically based analysis of 1,3-butadiene toxicokinetics in humans. PMID:23086859

Bois, Frederic Y

2013-01-01

341

Intelligent detection of hypoglycemic episodes in children with type 1 diabetes using adaptive neural-fuzzy inference system.  

PubMed

Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, an intelligent diagnostics system, using the hybrid approach of adaptive neural fuzzy inference system (ANFIS), is developed to recognize the presence of hypoglycemia. The proposed ANFIS is characterized by adaptive neural network capabilities and the fuzzy inference system. To optimize the membership functions and adaptive network parameters, a global learning optimization algorithm called hybrid particle swarm optimization with wavelet mutation (HPSOWM) is used. For clinical study, 15 children with Type 1 diabetes volunteered for an overnight study. All the real data sets are collected from the Department of Health, Government of Western Australia. Several experiments were conducted with 5 patients each, for a training set (184 data points), a validation set (192 data points) and a testing set (153 data points), which are randomly selected. The effectiveness of the proposed detection method is found to be satisfactory by giving better sensitivity, 79.09% and acceptable specificity, 51.82%. PMID:23367375

San, Phyo Phyo; Ling, Sai Ho; Nguyen, Hung T

2012-01-01

342

On quantum statistical inference  

Microsoft Academic Search

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

Ole E. Barndorff-Nielsen; Richard D. Gill; Peter E. Jupp

2003-01-01

343

Type inference for atomicity  

Microsoft Academic Search

Atomicity is a fundamental correctness property in multithreaded programs. This paper presents an algorithm for verifying atomicity via type inference. The underlying type system supports guarded, write-guarded, and unguarded fields, as well as thread-local data, parameterized classes and methods, and protected locks. We describe an implementation of this algorithm for Java and discuss its performance and usability on benchmarks totaling

Cormac Flanagan; Stephen N. Freund; Marina Lifshin

2005-01-01

344

The processes of inference  

Microsoft Academic Search

Mental models represent possibilities, and the theory of mental models postulates three systems of mental processes underlying inference: (0) the construction of an intensional representation of a premise's meaning – a process guided by a parser; (1) the building of an initial mental model from the intension, and the drawing of a conclusion based on heuristics and the model; and

Sangeet Khemlani; P. N. Johnson-Laird

2012-01-01

345

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

Microsoft Academic Search

\\u000a A new trend in runtime system monitoring is to utilize MOF-based techniques in analyzing the runtime system data. Approaches\\u000a and tools have been proposed to automatically reflect the system data as MOF compliant models, but they all require users\\u000a to manually build the meta-models that define the types and relations of the system data. To do this, users have to

Hui Song; Gang Huang; Yingfei Xiong; Franck Chauvel; Yanchun Sun; Hong Mei

2010-01-01

346

A Formal Verification Analysis of a Bayesian Inference-Based Sensors and Actuators Control System  

Microsoft Academic Search

Formal verification of a control system relies on the ability to construct a finite state transition model from a set of logical rules. An efficient search procedure can check whether a desired system property holds true in that model under perturbations of system parameters and disturbances. Formal verification methods provide full coverage of all possible cases (scenarios), and thereby check

M. Moulin

2006-01-01

347

A system for inference of spatial context of Parkinson's disease patients.  

PubMed

This work proposes a concept for indoor ambulatory monitoring for Parkinson's disease patients. In the proposed concept, a wearable inertial sensor is kept as the main monitoring device through the day, and it is expanded by an ambient sensor system in the specific living areas with high estimated probability of occurrence of freezing of gait episode. The ambient sensor system supports decisions of the wearable sensor system by providing relevant spatial context information of the user, which is obtained through precise localization. PMID:22942043

Taka?, Boris; Català, Andreu; Cabestany, Joan; Chen, Wei; Rauterberg, Matthias

2012-01-01

348

A general framework for modeling tumor-immune system competition and immunotherapy: Mathematical analysis and biomedical inferences  

NASA Astrophysics Data System (ADS)

In this work we propose and investigate a family of models, which admits as particular cases some well known mathematical models of tumor-immune system interaction, with the additional assumption that the influx of immune system cells may be a function of the number of cancer cells. Constant, periodic and impulsive therapies (as well as the non-perturbed system) are investigated both analytically for the general family and, by using the model by Kuznetsov et al. [V.A. Kuznetsov, I.A. Makalkin, M.A. Taylor, A.S. Perelson, Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis, Bull. Math. Biol. (1994) 56(2) 295 321), via numerical simulations. Simulations seem to show that the shape of the function modeling the therapy is a crucial factor only for very high values of the therapy period T, whereas for realistic values of T, the eradication of the cancer cells depends on the mean values of the therapy term. Finally, some medical inferences are proposed.

D'Onofrio, Alberto

2005-09-01

349

Decomposition of progressivity and inequality indices: Inferences from the US federal income tax system  

Microsoft Academic Search

Vertical equity is an important criterion in evaluating a tax system. Vertical equity has two elements: progressivity and income equality. In this paper, we analyze the vertical equity effects of the US income tax system during 1995–2006 and show that income inequality increased substantially during the period combined with a significant reduction in real progressivity.Using a Lorenz-curve-based graphical method, we

Govind S. Iyer; Philip M. J. Reckers

350

Deformation of the San Andreas Fault system inferred from trilateration measurements  

NASA Astrophysics Data System (ADS)

The San Andreas fault system is responsible for most of the deformation on the boundary between the North American and Pacific plates. The fault system was studied from the San Francisco Bay south to the Salton Trough because it is well covered by the USGS trilateration networks which have been measured almost every year or two since 1970. The measurements were examined to determine whether any systematic errors contribute a long term variation and how such errors influence estimates of secular fault deformation. Then, with Matsu'ura's fault model and given geological observations, trilateration measurements were used to calculate fault parameters. From San Francisco to Stalton Sea, the San Andreas fault system is divided into four regions. Cumulative fault displacements are high at Hollister/Bear Valley and Cajon Pass/Salton Sea, but low in the San Francisco Bay and in the Transverse Ranges. Shallow faultings from trilateration data match most surface displacements of locked and creeping faults.

Cheng, C. C. A.

351

Advanced Self-adaptation Learning and Inference Techniques for Fuzzy Petri Net Expert System Units  

NASA Astrophysics Data System (ADS)

In a complicated expert reasoning system, it is inefficient for commonly fuzzy production rules to depict the vague and modified knowledge. Fuzzy Petri nets are more accurate for dynamic knowledge proposition in describing expert knowledge. However, the bad learning ability of fuzzy Petri net constrains its application in dynamic knowledge expert system. In this paper, an advanced self-adaptation learning way based on error back-propagation is proposed to train parameters of fuzzy production rules in fuzzy Petri net. In order to enhance reasoning and learning efficiency, fuzzy Petri net is transformed into hierarchy model and continuous functions are built to approximate transition firing and fuzzy reasoning. Simulation results show that the designed advanced learning way can make rule parameters arrive at optimization rapidly. These techniques used in this paper are quite effective and can be applied to most practical Petri net models and fuzzy expert systems.

Zhang, Zipeng; Wang, Shuqing; Yuan, Xiaohui

352

An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange  

Microsoft Academic Search

Stock market prediction is important and of great interest because successful prediction of stock prices may promise attractive benefits. These tasks are highly complicated and very difficult. In this paper, we investigate the predictability of stock market return with Adaptive Network-Based Fuzzy Inference System (ANFIS). The objective of this study is to determine whether an ANFIS algorithm is capable of

Melek Acar Boyacioglu; Derya Avci

2010-01-01

353

A Hybrid Method Based on Combining Artificial Neural Network and Fuzzy Inference System for Simultaneous Computation of Resonant Frequencies of Rectangular, Circular, and Triangular Microstrip Antennas  

Microsoft Academic Search

A hybrid method based on a combination of artificial neural network (ANN) and fuzzy inference system (FIS) is presented to calculate simultaneously the resonant frequencies of various microstrip antennas (MSAs) of regular geometries. The ANN is trained with the Bayesian regulation algorithm. An algorithm that integrates least square method and backpropagation algorithm is used to identify the parameters of FIS.

Kerim Guney; Nurcan Sarikaya

2007-01-01

354

A knowledge-based method for inferring semantic concepts from visual models of system behavior  

Microsoft Academic Search

Software designers use visual models, such as data flow\\/control flow diagrams or object collaboration diagrams, to express system behavior in a form that can be understood easily by users and by pogrammers, and from which designers can generate a software architecture. The research described in this paper is motivated by a desire to provide an automated designer's assistant that can

Kevin L. Mills; Hassan Gomaa

2000-01-01

355

A new approach to the diagnosis of solid insulation systems based on PD signal inference  

Microsoft Academic Search

The authors have developed digital instrumentation that can measure the amplitude, shape, phase, and number of pulses per cycle. Examples are given of results obtained from an induction motor, a hydrogenerator, a polymeric cable system with a defective joint, and a high-voltage current transformer.

A. Cavallini; G. C. Montanari; A. Contin; F. Pulletti

2003-01-01

356

Probabilistic Identification of Spin Systems and their Assignments including Coil–Helix Inference as Output (PISTACHIO)  

Microsoft Academic Search

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

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

2005-01-01

357

Spatial distribution of arsenic in the Texas Gulf Coastal Aquifer System and inferences regarding hydrogeochemical controls  

Microsoft Academic Search

Arsenic is a prominent trace element in the Gulf Coastal Aquifer System (GCAS) in Texas, particularly in the southwestern portion where 29% of wells exceed the USEPA maximum contaminant level of 10 mug\\/L for drinking water. While the dominant source is generally thought to be geogenic rather than anthropogenic, little is known about the hydrologic\\/geochemical mechanisms affecting occurrence in groundwater.

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

2008-01-01

358

RULE-BASED INFERENCE SYSTEM FOR PREDICTING LINER/WASTE COMPATIBILITY  

EPA Science Inventory

Determining the chemical compatibility of a liner material for containment of wastes rests mainly on the application of expert opinion to interpret the results of short-term immersion tests. A methodology known as a production system is employed to encode such expert opinion into...

359

Nebulon: a system for the inference of functional relationships of gene products from the rearrangement of predicted operons  

PubMed Central

Since operons are unstable across Prokaryotes, it has been suggested that perhaps they re-combine in a conservative manner. Thus, genes belonging to a given operon in one genome might re-associate in other genomes revealing functional relationships among gene products. We developed a system to build networks of functional relationships of gene products based on their organization into operons in any available genome. The operon predictions are based on inter-genic distances. Our system can use different kinds of thresholds to accept a functional relationship, either related to the prediction of operons, or to the number of non-redundant genomes that support the associations. We also work by shells, meaning that we decide on the number of linking iterations to allow for the complementation of related gene sets. The method shows high reliability benchmarked against knowledge-bases of functional interactions. We also illustrate the use of Nebulon in finding new members of regulons, and of other functional groups of genes. Operon rearrangements produce thousands of high-quality new interactions per prokaryotic genome, and thousands of confirmations per genome to other predictions, making it another important tool for the inference of functional interactions from genomic context.

Janga, Sarath Chandra; Collado-Vides, Julio; Moreno-Hagelsieb, Gabriel

2005-01-01

360

Genetic diversity in the Homosporous Fern Ophioglossum vulgatum (Ophioglossaceae) from South Korea: inference of mating system and population history.  

PubMed

It is generally believed that the members of Ophioglossaceae have subterranean, potentially bisexual gametophytes, which favor intragametophytic selfing. In Ophioglossaceae, previous allozyme studies revealed substantial inbreeding within Botrychium species and Mankyua chejuense. However, little is known about the mating system in species of the genus Ophioglossum. Molecular marker analyses can provide insights into the relative occurrence of selfing versus cross-fertilization in the species of Ophioglossum. We investigated allozyme variation in 8 Korean populations of the homosporous fern Ophioglossum vulgatum to infer its mating system and to get some insight into the population-establishment history in South Korea. We detected homozygous genotypes for alternative alleles at several loci, which suggest the occurrence of intragametophytic self-fertilization. Populations harbor low within-population variation (% P = 7.2, A = 1.08, and H (e) = 0.026) and a high among-population differentiation (F (ST) = 0.733). This, together with the finding that alternative alleles were fixed at several loci, suggests that the number and size of populations of O. vulgatum might have been severely reduced during the last glaciation (i.e., due to its in situ persistence in small, isolated refugia). The combined effects of severe random genetic drift and high rates of intragametophytic selfing are likely responsible for the genetic structure displayed by this homosporous fern. Its low levels of genetic diversity in South Korea justify the implementation of some conservation measures to ensure its long-term preservation. PMID:23109721

Chung, Mi Yoon; López-Pujol, Jordi; Chung, Jae Min; Moon, Myung-Ok; Chung, Myong Gi

2012-10-29

361

A Generalization of Bayesian Inference  

Microsoft Academic Search

Procedures of statistical inference are described which generalize Bayesian inference in specific ways. Probability is used\\u000a in such a way that in general only bounds may be placed on the probabilities of given events, and probability systems of this\\u000a kind are suggested both for sample information and for prior information. These systems are then combined using a specified\\u000a rule. Illustrations

Arthur P. Dempster

2008-01-01

362

Inferring complex networks from time series of dynamical systems: Pitfalls, misinterpretations, and possible solutions  

NASA Astrophysics Data System (ADS)

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.

Bearden, Kathryn

363

Velocity field in Asia inferred from Quaternary fault slip rates and Global Positioning System observations  

Microsoft Academic Search

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

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

2000-01-01

364

Bayesian Hierarchical\\/Multilevel Models for Inference and Prediction Using Cross-System Lake Data  

Microsoft Academic Search

\\u000a Cross-system data have been extensively used to estimate models for predicting lake responses to management actions. Using\\u000a data from many lakes for model estimation is based on an implicit assumption that all lakes in the data set behave similarly.\\u000a A common strategy to help meet this assumption is to group the data by common lake features, such as geography, landscape

Craig A. Stow; E. Conrad Lamon; Song S. Qian; Patricia A. Soranno; Kenneth H. Recltbow

365

Parameter estimation and model selection for a class of hysteretic systems using Bayesian inference  

NASA Astrophysics Data System (ADS)

The aim of this paper is to provide an overview of the possible advantages of adopting a Bayesian approach to nonlinear system identification in structural dynamics. In contrast to identification schemes which estimate maximum likelihood values (or other point estimates) for parameters, the Bayesian scheme discussed here provides information about the complete probability density functions of parameter estimates without adopting restrictive assumptions about their nature. Among other advantages of the Bayesian viewpoint are the abilities to make informed decisions about model selection and also to effectively make predictions over entire classes of models, with each individual model weighted according to its ability to explain the observed data.The approach is illustrated using data from simulated systems, first a Duffing oscillator and then a new application to hysteretic system of the Bouc-Wen type. The modelling and identification of the latter type of system has long presented problems due to the fact that commonly used model structures like the Bouc-Wen model are nonlinear in the parameters, or have unmeasured states, etc. These issues have been dealt with in the past by adopting an optimisation-based approach to the problem; in particular, the differential evolution algorithm has proved very effective. An objective of the current paper is to illustrate how the Bayesian approach provides the same information and more as the optimisation approach; it yields parameter estimates and their associated confidence intervals, but can also provide confidence bounds on model predictions and evidence measures which can be used to select the most appropriate model from a candidate set. A new model selection criterion in this context - the Deviance Information Criterion (DIC) - is presented here.

Worden, K.; Hensman, J. J.

2012-10-01

366

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

Microsoft Academic Search

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

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

2003-01-01

367

Inferring bulk self-assembly properties from simulations of small systems with multiple constituent species and small systems in the grand canonical ensemble  

NASA Astrophysics Data System (ADS)

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

Ouldridge, Thomas E.

2012-10-01

368

Phylogenetic relationships, chromosome and breeding system evolution in Turnera (Turneraceae): inferences from its sequence data.  

PubMed

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

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

2005-10-01

369

Taal volcanic hydrothermal system (Philippines) inferred by electromagnetic and other geophysical methods  

NASA Astrophysics Data System (ADS)

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

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

2010-05-01

370

From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.  

PubMed

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

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

2013-09-12

371

From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems  

PubMed Central

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

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

2013-01-01

372

Elucidating the origin of the ExbBD components of the TonB system through Bayesian inference and maximum-likelihood phylogenies.  

PubMed

Uptake of ferric siderophores, vitamin B12, and other molecules in gram-negative bacteria is mediated by a multi-protein complex known as the TonB system. The ExbB and ExbD protein components of the TonB system play key energizing roles and are homologous with the flagellar motor proteins MotA and MotB. Here, the phylogenetic relationships of ExbBD and MotAB were investigated using Bayesian inference and the maximum-likelihood method. Phylogenetic trees of these proteins suggest that they are separated into distinct monophyletic groups and have originated from a common ancestral system. Several horizontal gene transfer events for ExbB-ExbD are also inferred, and a model for the evolution of the TonB system is proposed. PMID:23891663

Marmon, Livingstone

2013-07-23

373

Physical Properties of the Saturnian Ring System Inferred from Cassini VIMS Opposition Observations  

NASA Astrophysics Data System (ADS)

Much can be learned about the nature of Saturn's ring particles and their regoliths by studying the wavelength dependence of their reflectance as a function of phase angle. At small phase angles the reflectance of the rings exhibits the opposition effect (OE) a significant increase in reflectance as phase angle approaches zero degrees. The wavelength dependence of the width and the peak of the OE are indicators of important physical properties of the regoliths of the ring particles such as particle size, particle shape, packing density and albedo. The Cassini VIMS multi spectral imaging spectrometer obtained low phase observations of the Saturnian ring system from 0.4-5.2 microns during 2005. These data clearly show a pronounced (OE). Cassini VIMS opposition surge data indicate a wavelength dependence of the OE that relates to the size and separation of the scattering centers on the surface of the ring particles. Laboratory studies and theoretical models of the OE relate the size and shape of the reflectance increase to physical properties of the medium (Nelson et al, 2002; Spilker et al. 1995; Hapke et al., 1993)). The OE arises from two processes, shadow hiding (SH) and coherent backscattering (CB). The SHOE is observed because shadows cast by the particulate grains on one another are eliminated as phase angle approaches zero degrees. The CBOE is due to constructive interference between light rays traveling in opposite paths through the medium as the path length decreases with decreasing phase angle. The VIMS data at 1.9 microns, where the rings are highly reflective, indicate a strong CBOE effect, however, at 2.1 microns, where the rings are very absorbing, the shape of the phase curve is consistent with SHOE. Hapke et al. 1993,Science, 260, 509-511 Nelson, R. M. et al., 2002. Planetary and Space Science, 50, 849-856 Spilker aka Horn, L.J et al., 1995. IAU Colloquium #150 This work done at JPL under contract with NASA

Hapke, B.; Nelson, R. M.; Brown, R. H.; Spilker, L. J.; Smythe, W. D.; Kamp, L.; Boryta, M.; Leader, F.; Matson, D. L.; Edgington, S.; Nicholson, P. D.; Filacchione, G.; Clark, R. N.; Bibring, J.; Baines, K. H.; Buratti, B. J.; Bellucci, G.; Capaccioni, F.; Cerroni, P.; Combes, M.; Coradini, A.; Cruikshank, D. P.; Drossart, P.; Formisano, V.; Jaumann, R.; Langevin, Y.; McCord, T.; Menella, V.; Sicardy, B.

2005-12-01

374

A numerical procedure for inferring from experimental data the optimization cost functions using a multibody model of the neuro-musculoskeletal system  

Microsoft Academic Search

We propose a computational procedure for inferring the cost functions that, according to the Principle of Optimality, underlie experimentally observed motor strategies. In the current use of optimization-based mathematical models of neuro-musculoskeletal systems, the cost functions are not known a-priori, since they can not be directly observed or measured on the real bio-system. Consequently, cost functions need to be hypothesized

Carlo L. Bottasso; Boris I. Prilutsky; Alessandro Croce; Enrico Imberti; Stefano Sartirana

2006-01-01

375

Hydrodynamic processes, velocity structure and stratification in natural turbidity currents: Results inferred from field data in the Var Turbidite System  

NASA Astrophysics Data System (ADS)

The Var Turbidite System (NW Mediterranean Sea) is fed during the present-day highstand sea level by large earthquake-induced ignitive turbidity currents, low-density turbidity currents resulting from retrogressive failures triggered on the upper continental slope, and hyperpycnal flows related to the Var River floods. Using a large dataset including bathymetric data, side-scan sonar images, seismic-reflection profiles, cores and photographs of the seafloor, this paper attempts to better constrain the hydrodynamic behaviour of debris flows and turbidity currents along the Upper and Middle Valley of the Var Turbidite System. The drastic change of the seafloor morphology between the Upper and the Middle Valley suggests that gravity flows undergo rapid transformation from cohesive to fully turbulent behaviour. This transformation is related to a hydraulic jump caused by an abrupt decrease in slope angle at the transition between the Upper and the Middle Valley and is associated with en masse deposition and elevation of the seafloor. Strong seafloor erosion prevails in the Middle Valley, suggesting that, for a low and constant slope angle, turbulent flows must regain a balance between concentration and flow thickness rapidly after they experience hydraulic jump. The internal stratification and vertical grain-size distribution within turbulent flows are inferred from the distribution of fine- to coarse-grained turbidites found in cores located along the crest of the Var Sedimentary Ridge with a decreasing elevation above the floor of the Middle Valley. The theoretical vertical velocity profile deduced from the vertical grain-size distribution exhibits a general trend and an inflection of the gradient curve different from those of the velocity profiles classically obtained using numerical modelling.

Migeon, Sébastien; Mulder, Thierry; Savoye, Bruno; Sage, Françoise

2012-03-01

376

Performance Evaluation of an Adaptive-Network-Based Fuzzy Inference System Approach for Location of Faults on Transmission Lines Using Monte Carlo Simulation  

Microsoft Academic Search

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

M. Jayabharata Reddy; Dusmanta Kumar Mohanta

2008-01-01

377

An adaptive network based fuzzy inference system-fuzzy data envelopment analysis for gas consumption forecasting and analysis: The case of South America  

Microsoft Academic Search

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

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

2010-01-01

378

Adaptive network-based fuzzy inference system analysis of mixed convection in a two-sided lid-driven cavity filled with a nanofluid  

Microsoft Academic Search

A numerical study of laminar mixed convection in a two-sided lid-driven cavity filled with a water–Al2O3 nanofluid is presented. The top and bottom walls of the cavity are kept at different temperatures and can slide in the same or opposite direction. The vertical walls are thermally insulated. An Adaptive Network-based Fuzzy Inference System (ANFIS) approach is developed, trained and validated

S. M. Aminossadati; A. Kargar; B. Ghasemi

379

The uses of irresistible inference : Protecting the system from criminal penetration through more effective prosecution of money laundering offences  

Microsoft Academic Search

Purpose – The purpose of this paper is to assert that the exclusive use of predicate offence as a means of proving money laundering is an inadequate response to the level of threat presented by the crime. It aims to promote the concept of “irresistible inference” from UK case law as a basis for establishing international consensus that this provides

Kenneth Murray

2011-01-01

380

Numerical simulation of magma plumbing system associated with the eruption at the Showa crater of Sakurajima inferred from ground deformation  

NASA Astrophysics Data System (ADS)

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.

Minami, S.; Iguchi, M.; Mikada, H.; Goto, T.; Takekawa, J.

2010-12-01

381

On Quantum Statistical Inference, II  

Microsoft Academic Search

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, theoretical 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

O. E. Barndorff-Nielsen; R. D. Gill; P. E. Jupp

2003-01-01

382

Adaptation of a Mamdani Fuzzy Inference System Using NeuroGenetic Approach for Tactical Air Combat Decision Support System  

Microsoft Academic Search

Abstract. Normally a decision ,support system ,is build to solve ,problem ,where ,multicriteria decisions are involved. The knowledge ,base is the vital part of the ,decision support containing the information or data ,that is used in decision-making process. This is the field where,engineers and scientists have applied several intelligent techniques and heuristics to obtain optimal decisions from imprecise information.In this

Cong Tran; Ajith Abraham; Lakhmi C. Jain

2002-01-01

383

Frequentist Statistical Inference  

Microsoft Academic Search

The relative-frequency view of probability leads to statistical inferences using hypothesis tests and confidence intervals. Parametric tests target inference on distribution parameters, whereas nonparametric tests may relate to any sample statistic of interest. Special problems arise for correlated data, and for multiple simultaneous inferences (“field significance”).

D. S. Wilks

2011-01-01

384

Common-Sense Rule Inference  

NASA Astrophysics Data System (ADS)

In the paper we show how rule-based inference can be made more flexible by exploiting semantic information associated with the concepts involved in the rules. We introduce flexible forms of common sense reasoning in which whenever no rule applies to a given situation, the inference engine can fire rules that apply to more general or to similar situations. This can be obtained by defining new forms of match between rules and the facts in the working memory and new forms of conflict resolution. We claim that in this way we can overcome some of the brittleness problems that are common in rule-based systems.

Lombardi, Ilaria; Console, Luca

385

Aggregation and Inference: Facts and Fallacies  

Microsoft Academic Search

The author examines inference and aggregation problems that can arise in multilevel relational database systems and points out some fallacies in current thinking about these problems that may hinder real progress from being made toward their solution. She distinguishes several different types of aggregation and inference problems and shows that the different types of problems are best addressed by different

Teresa F. Lunt

1989-01-01

386

Application of Transformations in Parametric Inference  

ERIC Educational Resources Information Center

|The objective of the present paper is to provide a simple approach to statistical inference using the method of transformations of variables. We demonstrate performance of this powerful tool on examples of constructions of various estimation procedures, hypothesis testing, Bayes analysis and statistical inference for the stress-strength systems.…

Brownstein, Naomi; Pensky, Marianna

2008-01-01

387

Inference in belief networks: A procedural guide  

Microsoft Academic Search

Belief networks are popular tools for encoding uncertainty in expert systems. These networks rely on inference algorithms to compute beliefs in the context of observed evidence. One established method for exact inference on belief networks is the probability propagation in trees of clusters (PPTC) algorithm, as developed by Lauritzen and Spiegelhalter and refined by Jensen et al. PPTC converts the

Cecil Huang; Adnan Darwiche

1996-01-01

388

Incremental Locally Linear Fuzzy Classifier  

Microsoft Academic Search

\\u000a Optimizing the antecedent part of neuro-fuzzy system is investigated in a number of documents. Current approaches typically\\u000a suffer from high computational complexity or lack of ability to extract knowledge from a given set of training data. In this\\u000a paper, we introduce a novel incremental training algorithm for the class of neuro-fuzzy systems that are structured based\\u000a on local linear classifiers.

Armin Eftekhari; Mojtaba Ahmadieh Khanesar; Mohamad Forouzanfar; Mohammad Teshnehlab

389

Impact of Sampling Schemes on Demographic Inference: An Empirical Study in Two Species with Different Mating Systems and Demographic Histories  

PubMed Central

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.

St. Onge, K. R.; Palme, A. E.; Wright, S. I.; Lascoux, M.

2012-01-01

390

Inference or Observation?  

NSDL National Science Digital Library

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

Finson, Kevin D.

2010-10-01

391

Inferno: A Cautious Approach to Uncertain Inference.  

National Technical Information Service (NTIS)

Expert systems commonly employ some means of drawing inferences from domain and problem knowledge, where both the knowledge and its implications are less than certain. Methods used include subjective Bayesian reasoning, measures of belief and disbelief, a...

J. R. Quinlan

1982-01-01

392

Supervised fuzzy inference network for invariant pattern recognition  

Microsoft Academic Search

A supervised fuzzy inference network (FIN) model and its learning algorithm for invariant pattern recognition are presented in this paper. This fuzzy inference network is suitable for 2-D visual pattern recognition problems and has been tested with letter patterns of black and white pixel values. In contrast to most of the conventional pattern recognition systems, the proposed fuzzy inference network

H. K. Kwan; L. Y. Cai

2000-01-01

393

Inferring Consequences in Story Comprehension.  

ERIC Educational Resources Information Center

|A study examining readers' ability to infer consequences backward and forward from events described in stories is described. Results show that backward consequence inferences are more reliably drawn during the course of reading than forward consequence inferences. (MSE)|

Singer, Murray; Ferreira, Fernanda

1983-01-01

394

Inferring signalling networks from images.  

PubMed

The mapping of signalling networks is one of biology's most important goals. However, given their size, complexity and dynamic nature, obtaining comprehensive descriptions of these networks has proven extremely challenging. A fast and cost-effective means to infer connectivity between genes on a systems-level is by quantifying the similarity between high-dimensional cellular phenotypes following systematic gene depletion. This review describes the methodology used to map signalling networks using data generated in the context of RNAi screens. PMID:23841886

Evans, L; Sailem, H; Vargas, P Pascual; Bakal, C

2013-07-11

395

Higher order cycles, inferred chronostratigraphy, and impact of multiple lowstand systems tracts on hydrocarbon exploration, Pletmos basin, southern offshore, South Africa  

SciTech Connect

Development of 67 middle Valanginian to middle Campanian cyclic depositional sequences is interpreted to be a response to the interplay of unique tectonics and higher order eustatic sea level cycles capable of imposing type 1 unconformities. Direct correlation of 16 sequences, within available paleontological age constraints, with Exxon's global third-order cycles encompasses the remaining 51 sequences, which are inferred to be fourth-order and fifth-order cycles. These unconformity bound sequences were grouped into genetic megasequences bound by major type 1 unconformities (third-order or fourth-order falls at the trough of a third-order cycle) typically displaying evidence of extensive erosion of thick highstand systems tracts.

Brink, G.J.

1989-03-01

396

Stereotypes and tacit inference.  

PubMed

To judge another person's behavior, one often has to come to an understanding of what that behavior was in its detail. Five studies demonstrated that stereotypes influence the tacit inferences people make about the unspecified details and ambiguities of social behavior (e.g., what the behavior specifically was, what stimulus the individual reacted to, what caused the individual to act) and that these inferences occur when people encode the relevant information. One study found that participants who scored low on a measure of modern sexism were just as likely to make tacit inferences based on gender stereotypes as were those who scored high. Discussion centers on the implications of these findings for identification processes in social judgment, as well as whether stereotypes influence tacit inferences at an implicit level. PMID:9294897

Dunning, D; Sherman, D A

1997-09-01

397

Probability and Statistical Inference  

Microsoft Academic Search

These lectures introduce key concepts in probability and statistical inference at a level suitable for graduate students in particle physics. Our goal is to paint as vivid a picture as possible of the concepts covered.

Harrison B. Prosper

2006-01-01

398

Investigations of Probabilistic Inference.  

National Technical Information Service (NTIS)

Reports first-year results of project, The Use of Protocol Analysis and Process Tracing Techniques to Investigate Probabilistic Inference. Research indicates that the most recently presented information is given more attention in situations that require t...

R. M. Hamm

1990-01-01

399

Analogic: Inference beyond logic  

Microsoft Academic Search

Analogic is the unique class of many-valued, non-monotonic logics which preserves the richness of inferences in (Boolean) logic and the manipulability of (Boolean) algebra underlying logic, and, in addition, contains a number of unexpected, emergent properties which extend inferentiability in non-trivial ways beyond the limits of logic. For example, one such inference is rada (reductio ad absurdum, reasoning by contradiction,

Karvel K. Thornber

1996-01-01

400

Social Inference Through Technology  

NASA Astrophysics Data System (ADS)

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

Oulasvirta, Antti

401

Hybrid optical inference machines - Architectural considerations  

NASA Astrophysics Data System (ADS)

A class of optical computing systems is introduced for solving symbolic logic problems that are characterized by a set of data objects and a set of relationships describing the data objects. The data objects and relationships are arranged into sets of facts and rules to form a knowledge base. The solutions to symbolic logic problems involve inferring conclusions to queries by applying logical inference to the facts and rules. The general structure of an inference machine is discussed in terms of rule-driven and query-driven control flows. As examples of a query-driven inference machine, two hybrid optical system architectures are presented which use matched-filter and mapped-template logic, respectively.

Warde, C.; Kottas, J.

1986-03-01

402

Temporal order of evolution of DNA replication systems inferred by comparison of cellular and viral DNA polymerases  

Microsoft Academic Search

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

Eugene V Koonin

2006-01-01

403

Inferring genetic networks from microarray data.  

SciTech Connect

In theory, it should be possible to infer realistic genetic networks from time series microarray data. In practice, however, network discovery has proved problematic. The three major challenges are: (1) inferring the network; (2) estimating the stability of the inferred network; and (3) making the network visually accessible to the user. Here we describe a method, tested on publicly available time series microarray data, which addresses these concerns. The inference of genetic networks from genome-wide experimental data is an important biological problem which has received much attention. Approaches to this problem have typically included application of clustering algorithms [6]; the use of Boolean networks [12, 1, 10]; the use of Bayesian networks [8, 11]; and the use of continuous models [21, 14, 19]. Overviews of the problem and general approaches to network inference can be found in [4, 3]. Our approach to network inference is similar to earlier methods in that we use both clustering and Boolean network inference. However, we have attempted to extend the process to better serve the end-user, the biologist. In particular, we have incorporated a system to assess the reliability of our network, and we have developed tools which allow interactive visualization of the proposed network.

May, Elebeoba Eni; Davidson, George S.; Martin, Shawn Bryan; Werner-Washburne, Margaret C. (University of New Mexico, Albuquerque, NM); Faulon, Jean-Loup Michel

2004-06-01

404

Cortical circuits for perceptual inference  

PubMed Central

This paper assumes that cortical circuits have evolved to enable inference about the causes of sensory input received by the brain. This provides a principled specification of what neural circuits have to achieve. Here, we attempt to address how the brain makes inferences by casting inference as an optimisation problem. We look at how the ensuing recognition dynamics could be supported by directed connections and message-passing among neuronal populations, given our knowledge of intrinsic and extrinsic neuronal connections. We assume that the brain models the world as a dynamic system, which imposes causal structure on the sensorium. Perception is equated with the optimisation or inversion of this internal model, to explain sensory input. Given a model of how sensory data are generated, we use a generic variational approach to model inversion to furnish equations that prescribe recognition; i.e., the dynamics of neuronal activity that represents the causes of sensory input. Here, we focus on a model whose hierarchical and dynamical structure enables simulated brains to recognise and predict sequences of sensory states. We first review these models and their inversion under a variational free-energy formulation. We then show that the brain has the necessary infrastructure to implement this inversion and present stimulations using synthetic birds that generate and recognise birdsongs.

Friston, Karl; Kiebel, Stefan

2009-01-01

405

Bayesian inference in FMRI.  

PubMed

Bayesian inference has taken FMRI methods research into areas that frequentist statistics have struggled to reach. In this article we will consider some of the early forays into Bayes and what motivated its use. We shall see the impact that Bayes has had on haemodynamic modelling, spatial modelling, group analysis, model selection and brain connectivity analysis; and consider how these advancements have spun-off into related areas of neuroscience and some of the challenges that remain. Bayes has brought to the table inference flexibility, incorporation of prior information, adaptive regularisation and model selection. But perhaps more important than these things, is the ability of Bayes to empower the methods researcher with a mathematically principled framework for inferring on any model. PMID:22063092

Woolrich, Mark W

2011-10-20

406

Quantifying Land Cover Features of the Sabinal River Watershed Using ANFIS  

NASA Astrophysics Data System (ADS)

Appraisals of ecological services require quantitative assessments for assigning value to specific contributions. Within ecosystems where resources are limited, such as the semi-arid Edwards Aquifer region of Texas, a quantified interpretation of ecological services change is necessary for effective resource management. The focus of this paper is the development of a supervised neuro-fuzzy sub-pixel classification method to create land cover and land use geographical information datasets. The method has been applied to the Sabinal River watershed, which is located centrally in the Texas counties of Bandera, Real and Uvalde. Through the use of digital elevation model (DEM) data files, an approximate boundary of the Sabinal River watershed was located. Landsat Thematic Mapper (TM) images of the watershed taken in spring and winter of both 1999 and 2000 have been superimposed onto the delineated watershed. Select data from the extracted TM image subset have been coupled with corresponding ground-truth data in order to create an Adaptive Neuro-Fuzzy Inference System (ANFIS) model. After training of the FIS was accomplished, model validation through further ground-truth data comparison was done. Once a reasonable prediction model was developed, the corresponding feature maps were imported into a geographical information system (ArcView) for further analysis.

Peschel, J. M.; Lacey, R. E.; Haan, P. K.; Kreuter, U. P.

2002-12-01

407

Evolution and connectivity in the world-wide migration system of the mallard: Inferences from mitochondrial DNA  

Microsoft Academic Search

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

R. H. S. Kraus; A. Zeddeman; Hooft van W. F; D. Sartakov; S. A. Soloviev; R. Ydenberg; H. H. T. Prins

2011-01-01

408

The Bayes Inference Engine  

SciTech Connect

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

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

1996-04-01

409

A neuro-fuzzy approach for segmentation of human objects in image sequences  

Microsoft Academic Search

We propose a novel approach for segmentation of human objects, including face and body, in image sequences. Object segmentation is important for achieving a high compression ratio in modern video coding techniques, e.g., MPEG-4 and MPEG-7, and human objects are usually the main parts in the video streams of multimedia applications. Existing segmentation methods apply simple criteria to detect human

Shie-jue Lee; Chen-sen Ouyang; Shih-huai Du

2003-01-01

410

A Car-Steering Model Based on an Adaptive Neuro-Fuzzy Controller  

NASA Astrophysics Data System (ADS)

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.

Amor, Mohamed Anis Ben; Oda, Takeshi; Watanabe, Shigeyoshi

411

Intelligent control of a stepping motor drive using a hybrid neuro-fuzzy approach  

Microsoft Academic Search

Stepping motors are widely used in robotics and in the numerical control of machine tools where they have to perform high-precision positioning operations. However, the variations of the mechanical configuration of the drive, which are common to these two applications, can lead to a loss of synchronism for high stepping rates. Moreover, the classical open-loop speed control is weak and

Patricia Melin; Oscar Castillo

2004-01-01

412

Adaptive Control of a Stepping Motor Drive Using a Hybrid Neuro-Fuzzy Approach  

Microsoft Academic Search

Stepping motors are widely used in robotics and in the numerical control of machine tools, where they have to perform high-precision positioning operations. However, the variations of the mechanical configuration of the drive, which are common to these two applications, can lead to a loss of synchronism for high stepping rates. Moreover, the classical open-loop speed control is weak and

Patricia Melin; Oscar Castillo

2001-01-01

413

Intelligent control of a stepping motor drive using a hybrid neuro-fuzzy ANFIS approach  

Microsoft Academic Search

Stepping motors are widely used in robotics and in the numerical control of machine tools where they have to perform high-precision positioning operations. However, the variations of the mechanical configuration of the drive, which are common to these two applications, can lead to a loss of synchronism for high stepping rates. Moreover, the classical open-loop speed control is weak and

Leocundo Aguilar; Patricia Melin; Oscar Castillo

2003-01-01

414

Determining turbulent flow friction coefficient using adaptive neuro-fuzzy computing technique  

Microsoft Academic Search

In the analysis of water distribution networks, the main required design parameters are the lengths, diameters, and friction coefficients of rough-pipes, as well as nodal demands and water levels in the reservoirs. Although some of these parameters such as the pipe lengths are precisely known and would remain the same at different points of the networks whereas some parameters such

Mehmet Özger; Gürol Yildirim

2009-01-01

415

Soil liquefaction modeling by Genetic Expression Programming and Neuro-Fuzzy  

Microsoft Academic Search

Liquefaction of soils induced by the earthquake is one of the major complex problems for the geotechnical engineering. It is generally determined from in situ tests and laboratory test of which application is very difficult, expensive and time consuming. They also require extreme cautions and labor. Hence the development of new models for the prediction of liquefaction potential of soils

C. Kayadelen

2011-01-01

416

A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam  

Microsoft Academic Search

River flow forecasting is an essential procedure that is necessary for proper reservoir operation. Accurate forecasting results\\u000a in good control of water availability, refined operation of reservoirs and improved hydropower generation. Therefore, it becomes\\u000a crucial to develop forecasting models for river inflow. Several approaches have been proposed over the past few years based\\u000a on stochastic modeling or artificial intelligence (AI)

Ahmed El-Shafie; Mahmoud Reda Taha; Aboelmagd Noureldin

2007-01-01

417

Design of neuro-fuzzy based modular architecture for pattern recognition  

Microsoft Academic Search

Many pattern recognition methodologies and design techniques have been developed over the years and new approaches continue to emerge. For the solution of complex problems in pattern recognition and more generally machine intelligence, involving heterogeneous data sources of both numeric and symbolic information, the fundamental design philosophy is to employ hybrid methodologies rather than attempting to produce the solution using

S. Mercy Shalinie

2002-01-01

418

A NEURO-FUZZY APPROACH TO MODEL DISPATCHING RULES FOR THE PARALLEL MACHINE SCHEDULING PROBLEM  

Microsoft Academic Search

This paper deals with the modelling of four different dispatching rules applied to the identical parallel machine scheduling problem with the objective of minimizing makespan, by using Artificial Neural Networks (ANNs). The dispatching rules considered in this study are: Earliest Due Date (EDD), Minimum Slack (MS), Shortest Processing Time (SPT) and Critical Ratio (CR). The main purpose of the study

Derya Eren Akyol; G. Mirac Bayhan

419

Application of neuro-fuzzy techniques in oil pipeline ultrasonic nondestructive testing  

Microsoft Academic Search

This paper presents a novel approach to the problem of nondestructive pipeline testing using ultrasonic imaging. The identification of the flaw type and its dimensions are the most important problems in the pipeline inspection. Unlike typical methods, a decision based neural network is used for the detection of flaws. We train a generalized regression neural network to determine the dimensions

Hossein Ravanbod

2005-01-01

420

Neuro-Fuzzy Method for Automated Defect Detection in Aluminium Castings  

Microsoft Academic Search

The automated ?aw detection in aluminium castings consists of two steps: a) identiflcation of potential defects using image process- ing techniques, and b) classiflcation of potential defects into defects and regular structures (false alarms) using pattern recognition techniques. In the second step, since several features can be extracted from the po- tential defects, a feature selection must be performed. In

Sergio Hernández; Doris Saez; Domingo Mery

2004-01-01

421

From neural network to neuro-fuzzy modeling: Applications to the carbon dioxide capture process  

Microsoft Academic Search

Research on improving efficiency of the amine-based post combustion carbon dioxide (CO2) capture process has been ongoing during the past decade. A good understanding of the intricate relationships among parameters involved in the CO2 capture process is important for process optimization. The objective of this study is to uncover relationships among the significant parameters impacting CO2 production by modeling the

Qing Zhou; Yuxiang Wu; Christine W. Chan; Paitoon Tontiwachwuthikul

2011-01-01

422

Neuro-fuzzy control of a robotic exoskeleton with EMG signals  

Microsoft Academic Search

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,

Kazuo Kiguchi; Takakazu Tanaka; Toshio Fukuda

2004-01-01

423

The Design and Implementation of Sugarcane Intelligence Expert System Based on Eos\\/Modis Data Inference Model  

Microsoft Academic Search

One of the major problems in the real time decision of agricultural intelligence expert system is how to be obtained the real\\u000a time information of crops growth and its close relation environment data. As result, the extraction of crops planting areas\\u000a and their spatial distribution and their growth variety, especially when the natural disaster arises, such as drought, its\\u000a spatial

Zongkun Tan; Meihua Ding; Xin Yang; Zhaorong Ou; Yan He; Zhaomin Kuang; Huilin Chen; Xiaohua Mo; Zhongyan Huang

2007-01-01

424

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

NASA Astrophysics Data System (ADS)

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

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

2010-02-01

425

A temperature- and strain-rate-dependent model of NiTi shape memory alloys for seismic control of bridges  

NASA Astrophysics Data System (ADS)

This paper proposes a neuro-fuzzy model of NiTi shape memory alloy (SMA) wires that is capable of capturing behavior of superelastic SMAs at different temperatures and at various loading rates while remaining simple enough to realize numerical simulations. First, in order to collect data, uniaxial tensile tests are conducted on superelastic wires in the temperature range of 0 ºC to 40 ºC, and at the loading frequencies of 0.05 Hz to 2 Hz that is the range of interest for seismic applications. Then, an adaptive neuro-fuzzy inference system (ANFIS) is employed to construct a model of SMAs based on experimental input-output data pairs. The fuzzy model obtained from ANFIS training is validated by using an experimental data set that is not used during training. Upon having a model that can represent behavior of superelastic SMAs at various ambient temperature and loading-rates, nonlinear simulation of a multi-span continuous bridge isolated by rubber bearings that is equipped with SMA dampers is carried out. Response of the bridge to a historical earthquake record is presented at different ambient temperatures in order to evaluate the effect of temperature on the performance of the structure. It is shown that SMA damping elements can effectively decrease peak deck displacement and the relative displacement between piers and superstructure in an isolated bridge while recovering all the deformations to their original position.

Ozbulut, Osman E.; Hurlebaus, Stefan

2009-03-01

426

Stress-induced anisotropy of partially molten media inferred from experimental deformation of a simple binary system under acoustic monitoring  

NASA Astrophysics Data System (ADS)

Microstructural changes of partially molten media under deviatoric stress were investigated in a newly developed apparatus by deforming a large sample (a 70-mm cube) under a uniform pure shear stress. Borneol + melt system having a moderate dihedral angle and texturally equilibrated under hydrostatic stress was used as a partially molten rock analogue. The applied stress was small enough not to involve cataclastic-plastic deformation of the solid grains. Shear strain rate was about 10-8 s-1, and a stress exponent indicative of diffusion creep was obtained. During the deformation, sample microstructure was observed in situ by means of ultrasonic shear waves. The development of stress-induced anisotropy was successfully detected by shear wave splitting. The results obtained indicate that grain boundary contiguity in the direction of the least compressive stress (?3) was reduced with respect to the equilibrium texture and also that the relative values of liquid pressure and ?3 play an essential role for development of anisotropy. The developed anisotropy persisted as long as deviatoric stress was applied, but the initial isotropic structure was recovered by releasing this stress. Several interesting phenomena were involved in the structural change; these include shear creep-induced dilatancy, strong dependence of the timescale of structural recovery on the amount of deformation (memory effect), and relaxation creep after releasing stress. Scaling considerations using the Griffith theory shows that the structural changes observed in the present experimental system are expected to occur in the Earth as well.

Takei, Yasuko

2001-01-01

427

In Defence of Inference  

Microsoft Academic Search

It is argued that the process of inference from a set of sample data is an important part of educational research. While there may be some abuse of statistical significance testing, it does not follow that educational research should become concerned mainly with so called ‘descriptive’ statistics.

H. Goldstein

1977-01-01

428

Rational argument, rational inference  

Microsoft Academic Search

Reasoning researchers within cognitive psychology have spent decades examining the extent to which human inference measures up to normative standards. Work here has been dominated by logic, but logic has little to say about most everyday, informal arguments. Empirical work on argumentation within psychology and education has studied the development and improvement of argumentation skills, but has been theoretically limited

Ulrike Hahn; Adam J. L. Harris; Mike Oaksford

2012-01-01

429

Representation, Coherence and Inference  

Microsoft Academic Search

Approaches to story comprehension within several fields (computational linguis- tics, cognitive psychology, and artificial intelligence) are compared. Central to this comparison is an overview of much recent research in cognitive psychology, which is often not incorpor- ated into simulations of comprehension (particularly in artificial intelligence). The theoretical core of this experimental work is the establishment of coherence via inference-making. The

Elliot Smith; Peter Hancox

2001-01-01

430

Stereotypes and Tacit Inference  

Microsoft Academic Search

To judge another person's behavior, one often has to come to an understanding of what that behavior was in its detail. Five studies demonstrated that stereotypes influence the tacit inferences people make about the unspecified details and ambiguities of social behavior (e.g., what the behavior specifically was, what stimulus the individual reacted to, what caused the individual to act) and

David Dunning; David A. Sherman

1997-01-01

431

Representation, Coherence and Inference  

Microsoft Academic Search

Approaches to story comprehension within several fields (computational linguistics, cognitive psychology, and artificial intelligence) are compared. Central to this comparison is an overview of much recent research in cognitive psychology, which is often not incorporated into simulations of comprehension (particularly in artificial intelligence). The theoretical core of this experimental work is the establishment of coherence via inference-making.

Elliot Smith; Peter Hancox

2001-01-01

432

Finite Dimensional Statistical Inference  

Microsoft Academic Search

In this paper, we derive the explicit series expansion of the eigenvalue distribution of various models, namely the case of non-central Wishart distributions as well as one sided correlated zero mean Wishart distributions. The tools used are borrowed from the free probability framework which have been quite successful for high dimensional statistical inference (when the size of the matrices tends

Øyvind Ryan; A. Masucci; Sheng Yang; Mérouane Debbah

2009-01-01

433

Research in Statistical Inference.  

National Technical Information Service (NTIS)

During this period of research a substantial research program continued in statistical inference, one that has seen the publication of nearly 100 papers and a book since the initiation of support. The papers written or revised in 1991-1992 are: (1) An Ass...

R. J. Carroll

1991-01-01

434

Plutonium: Facts and Inferences.  

National Technical Information Service (NTIS)

This report reviews the knowledge that we have about plutonium from the point of view of the inferences that can be drawn from such knowledge relative to the implications for society of the creation of this element in a nuclear power industry. It represen...

C. L. Comar W. B. Seefeldt W. J. Mecham M. J. Steindler B. L. Cohen

1976-01-01

435

Iterative Multiagent Probabilistic Inference  

Microsoft Academic Search

Multiply sectioned Bayesian networks (MSBNs) support multiagent probabilistic inference in distributed large problem domains, where agents are organized in a tree structure (called hypertree). In earlier work, agents need to follow an order of the depth-first traversal of the hypertree to update their belief. Hence, agents need some synchronization with each other and belief updating can only be done in

An Xiangdong; Nick Cercone

2006-01-01

436

Inference in Linear Regression  

NSDL National Science Digital Library

This site, created by Michelle Lacey of Yale University, gives an explanation, a definition and an example of inference in linear regression. Topics include: confidence intervals for intercept and slope, significance tests, mean response, and prediction intervals. While brief, this is still a wonderful resource for any classroom studying statistics.

Lacey, Michelle

2009-11-27

437

Inference for Categorical Data  

NSDL National Science Digital Library

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.

Lacey, Michelle

2008-12-23

438

Tectonic history of the north portion of the San Andreas fault system, California, inferred from gravity and magnetic anomalies  

USGS Publications Warehouse

Geologic and geophysical data for the San Andreas fault system north of San Francisco suggest that the eastern boundary of the Pacific plate migrated eastward from its presumed original position at the base of the continental slope to its present position along the San Andreas transform fault by means of a series of eastward jumps of the Mendocino triple junction. These eastward jumps total a distance of about 150 km since 29 Ma. Correlation of right-laterally displaced gravity and magnetic anomalies that now have components at San Francisco and on the shelf north of Point Arena indicates that the presently active strand of the San Andreas fault north of the San Francisco peninsula formed recently at about 5 Ma when the triple junction jumped eastward a minimum of 100 km to its present location at the north end of the San Andreas fault. -from Authors

Griscom, A.; Jachens, R. C.

1989-01-01

439

Spatial and temporal geochemical trends in the hydrothermal system of Yellowstone National Park: Inferences from river solute fluxes  

NASA Astrophysics Data System (ADS)

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

Hurwitz, Shaul; Lowenstern, Jacob B.; Heasler, Henry

2007-05-01

440

Sulfur Isotopic Inferences of the Controls on Porewater Sulfate Profiles in the Northern Cascadia Margin Gas Hydrate System  

NASA Astrophysics Data System (ADS)

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

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

2010-12-01

441

A comparison of modern and fossil ostracods from Frasassi Cave system (northeastern Apennines, Italy) to infer past environmental conditions  

NASA Astrophysics Data System (ADS)

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.

Iepure, S.; Namiotko, T.; Montanari, A.; Brugiapaglia, E.; Mainiero, M.; Mariani, S.; Fiebig, M.

2012-04-01

442

Reductionistic Inferences in Modern Biology.  

National Technical Information Service (NTIS)

The examples of inferences that are considered span classical and molecular genetics. The principal aim of these inferences is to correlate the phenotypic and genotypic properties, functions, and structures of organisms to their molecular machinery. The i...

L. Chiaraviglio

1969-01-01

443

Type inclusion constraints and type inference  

Microsoft Academic Search

We present a general algorithm for solving systems of inclusion constraints over type expressions.The constraint language includes function types, constructor types, and liberal intersection and uniontypes. We illustrate the application of our constraint solving algorithm with a type inference systemfor the lambda calculus with constants. In this system, every pure lambda term has a (computable)type and every term typable in

Alexander Aiken; Edward L. Wimmers

1993-01-01

444

Bayesian Statistical Inference  

Microsoft Academic Search

\\u000a \\u000a \\u000a \\u000a \\u000a \\u000a \\u000a Bayesian statistics is much more easily connected to the inferential problem of Schema (1.1) than \\u000a \\u000a \\u000a \\u000a \\u000a classical statistics. The feature that distinguishes \\u000a \\u000a \\u000a \\u000a \\u000a Bayesian statistical inference from \\u000a \\u000a \\u000a \\u000a \\u000a classical statistics is that it also employs probability assignments over \\u000a \\u000a \\u000a \\u000a \\u000a statistical hypotheses. It is therefore possible to present a \\u000a \\u000a \\u000a \\u000a \\u000a Bayesian statistical procedure as an inference concerning probability assignments over hypotheses. Recall that we called\\u000a the

Rolf Haenni; Jan-Willem Romeijn; Gregory Wheeler; Jon Williamson

445

Optimal inference of sameness  

PubMed Central

Deciding whether a set of objects are the same or different is a cornerstone of perception and cognition. Surprisingly, no principled quantitative model of sameness judgment exists. We tested whether human sameness judgment under sensory noise can be modeled as a form of probabilistically optimal inference. An optimal observer would compare the reliability-weighted variance of the sensory measurements with a set size-dependent criterion. We conducted two experiments, in which we varied set size and individual stimulus reliabilities. We found that the optimal-observer model accurately describes human behavior, outperforms plausible alternatives in a rigorous model comparison, and accounts for three key findings in the animal cognition literature. Our results provide a normative footing for the study of sameness judgment and indicate that the notion of perception as near-optimal inference extends to abstract relations.

van den Berg, Ronald; Vogel, Michael; Josic, Kresimir; Ma, Wei Ji

2012-01-01

446

INFERENCES FROM ROSSI TRACES  

SciTech Connect

The authors an uncertainty analysis of data taken using the Rossi technique, in which the horizontal oscilloscope sweep is driven sinusoidally in time ,while the vertical axis follows the signal amplitude. The analysis is done within a Bayesian framework. Complete inferences are obtained by tilting the Markov chain Monte Carlo technique, which produces random samples from the posterior probability distribution expressed in terms of the parameters.

KENNETH M. HANSON; JANE M. BOOKER

2000-09-08

447

Multimodel inference and adaptive management  

USGS Publications Warehouse

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

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

2011-01-01

448

Hybrid Inference for Sensor Network Localization Using a Mobile Robot  

Microsoft Academic Search

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

Dimitri Marinakis; David Meger; Ioannis M. Rekleitis; Gregory Dudek

2007-01-01

449

Water quality analysis in rivers with non-parametric probability distributions and fuzzy inference systems: application to the Cauca River, Colombia.  

PubMed

The integration of water quality monitoring variables is essential in environmental decision making. Nowadays, advanced techniques to manage subjectivity, imprecision, uncertainty, vagueness, and variability are required in such complex evaluation process. We here propose a probabilistic fuzzy hybrid model to assess river water quality. Fuzzy logic reasoning has been used to compute a water quality integrative index. By applying a Monte Carlo technique, based on non-parametric probability distributions, the randomness of model inputs was estimated. Annual histograms of nine water quality variables were built with monitoring data systematically collected in the Colombian Cauca River, and probability density estimations using the kernel smoothing method were applied to fit data. Several years were assessed, and river sectors upstream and downstream the city of Santiago de Cali, a big city with basic wastewater treatment and high industrial activity, were analyzed. The probabilistic fuzzy water quality index was able to explain the reduction in water quality, as the river receives a larger number of agriculture, domestic, and industrial effluents. The results of the hybrid model were compared to traditional water quality indexes. The main advantage of the proposed method is that it considers flexible boundaries between the linguistic qualifiers used to define the water status, being the belongingness of water quality to the diverse output fuzzy sets or classes provided with percentiles and histograms, which allows classify better the real water condition. The results of this study show that fuzzy inference systems integrated to stochastic non-parametric techniques may be used as complementary tools in water quality indexing methodologies. PMID:23266912

Ocampo-Duque, William; Osorio, Carolina; Piamba, Christian; Schuhmacher, Marta; Domingo, José L

2012-12-23

450

Multisensory Oddity Detection as Bayesian Inference  

PubMed Central

A key goal for the perceptual system is to optimally combine information from all the senses that may be available in order to develop the most accurate and unified picture possible of the outside world. The contemporary theoretical framework of ideal observer maximum likelihood integration (MLI) has been highly successful in modelling how the human brain combines information from a variety of different sensory modalities. However, in various recent experiments involving multisensory stimuli of uncertain correspondence, MLI breaks down as a successful model of sensory combination. Within the paradigm of direct stimulus estimation, perceptual models which use Bayesian inference to resolve correspondence have recently been shown to generalize successfully to these cases where MLI fails. This approach has been known variously as model inference, causal inference or structure inference. In this paper, we examine causal uncertainty in another important class of multi-sensory perception paradigm – that of oddity detection and demonstrate how a Bayesian ideal observer also treats oddity detection as a structure inference problem. We validate this approach by showing that it provides an intuitive and quantitative explanation of an important pair of multi-sensory oddity detection experiments – involving cues across and within modalities – for which MLI previously failed dramatically, allowing a novel unifying treatment of within and cross modal multisensory perception. Our successful application of structure inference models to the new ‘oddity detection’ paradigm, and the resultant unified explanation of across and within modality cases provide further evidence to suggest that structure inference may be a commonly evolved principle for combining perceptual information in the brain.

Hospedales, Timothy; Vijayakumar, Sethu

2009-01-01

451

SYMBOLIC INFERENCE OF XENOBIOTIC METABOLISM  

PubMed Central

We present a new symbolic computational approach to elucidate the biochemical networks of living systems de novo and we apply it to an important biomedical problem: xenobiotic metabolism. A crucial issue in analyzing and modeling a living organism is understanding its biochemical network beyond what is already known. Our objective is to use the available metabolic information in a representational framework that enables the inference of novel biochemical knowledge and whose results can be validated experimentally. We describe a symbolic computational approach consisting of two parts. First, biotransformation rules are inferred from the molecular graphs of compounds in enzyme-catalyzed reactions. Second, these rules are recursively applied to different compounds to generate novel metabolic networks, containing new biotransformations and new metabolites. Using data for 456 generic reactions and 825 generic compounds from KEGG we were able to extract 110 biotransformation rules, which generalize a subset of known biocatalytic functions. We tested our approach by applying these rules to ethanol, a common substance of abuse and to furfuryl alcohol, a xenobiotic organic solvent, which is absent in metabolic databases. In both cases our predictions on the fate of ethanol and furfuryl alcohol are consistent with the literature on the metabolism of these compounds.

MCSHAN, D.C.; UPDADHAYAYA, M.; SHAH, I.

2009-01-01

452

Validate High Stakes Inferences by Designing Good Experiments, Not Audit Items: A Comment on "Self-Monitoring Assessments Educational Accountability Systems"  

ERIC Educational Resources Information Center

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

Briggs, Derek C.

2010-01-01

453

A committee machine with intelligent systems for estimation of total organic carbon content from petrophysical data: An example from Kangan and Dalan reservoirs in South Pars Gas Field, Iran  

NASA Astrophysics Data System (ADS)

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

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

2009-03-01

454

Gene-network inference by message passing  

NASA Astrophysics Data System (ADS)

The inference of gene-regulatory processes from gene-expression data belongs to the major challenges of computational systems biology. Here we address the problem from a statistical-physics perspective and develop a message-passing algorithm which is able to infer sparse, directed and combinatorial regulatory mechanisms. Using the replica technique, the algorithmic performance can be characterized analytically for artificially generated data. The algorithm is applied to genome-wide expression data of baker's yeast under various environmental conditions. We find clear cases of combinatorial control, and enrichment in common functional annotations of regulated genes and their regulators.

Braunstein, A.; Pagnani, A.; Weigt, M.; Zecchina, R.

2008-01-01

455

Network topologies: inference, modeling, and generation  

Microsoft Academic Search

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.

Hamed Haddadi; Miguel Rio; Gianluca Iannaccone; ANDREW MOORE; Richard Mortier

2008-01-01

456

Subsumption and Refinement in Model Inference  

Microsoft Academic Search

In his famous Model Inference System, Shapiro [10] uses socalled refinement operators to replace too general hypotheses by logically weaker ones. One of these refinement operators works in the search space of reduced first order sentences. In this article we show that this operator is not complete for reduced sentences, as he claims. We investigate the relations between subsumption and

Patrick R. J. Van Der Laag; Shan-hwei Nienhuys-cheng

1993-01-01

457

Satellite Inferred Surface Albedo Over Northwestern Africa  

Microsoft Academic Search

A technique has been developed from simultaneous satellite and aircraft data that allows the magnitude and gradient of the earth's surface albedo to be inferred from satellite measurements of the earth-atmosphere system brightness. The technique uses the visible brightness observations from the SMS-1 geosynchronous satellite made during the GARP Atlantic Tropical Experiment (GATE) in 1974. Direct albedo measurements from aircraft

A. A. Rockwood; S. K. Cox

1978-01-01

458

Circular inferences in schizophrenia.  

PubMed

A considerable number of recent experimental and computational studies suggest that subtle impairments of excitatory to inhibitory balance or regulation are involved in many neurological and psychiatric conditions. The current paper aims to relate, specifically and quantitatively, excitatory to inhibitory imbalance with psychotic symptoms in schizophrenia. Considering that the brain constructs hierarchical causal models of the external world, we show that the failure to maintain the excitatory to inhibitory balance results in hallucinations as well as in the formation and subsequent consolidation of delusional beliefs. Indeed, the consequence of excitatory to inhibitory imbalance in a hierarchical neural network is equated to a pathological form of causal inference called 'circular belief propagation'. In circular belief propagation, bottom-up sensory information and top-down predictions are reverberated, i.e. prior beliefs are misinterpreted as sensory observations and vice versa. As a result, these predictions are counted multiple times. Circular inference explains the emergence of erroneous percepts, the patient's overconfidence when facing probabilistic choices, the learning of 'unshakable' causal relationships between unrelated events and a paradoxical immunity to perceptual illusions, which are all known to be associated with schizophrenia. PMID:24065721

Jardri, Renaud; Denève, Sophie

2013-09-24

459

Moment inference from tomograms  

USGS Publications Warehouse

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

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

2007-01-01

460

Emotional State Inference Using Face Related Features  

Microsoft Academic Search

Obtaining reliable and complete systems able to extract human emotional status from streaming videos is of paramount importance\\u000a to Human Machine Interaction (HMI) applications. Side views, unnatural postures and context are challenges. This paper presents\\u000a a semi-supervised fuzzy emotional classification system based on Russell’s circumplex model. This emotional inference system\\u000a relies only on face related features codified with the Facial

Marco Anisetti; Valerio Bellandi

461

Frame Activated Inferences in a Story Understanding Program  

Microsoft Academic Search

An effective story undcrstander must be able to reason about characters in the story, their affects, actions, plans, and goals, as well as the settings and important points of the story. In many systems this has been done with separate inference mechanisms for each class of knowledge structure. This paper proposes a story understander with a unified frame-based inference component

Peter Norvig

1983-01-01

462

F-OWL: An Inference Engine for Semantic Web.  

National Technical Information Service (NTIS)

Understanding and using the data and knowledge encoded in semantic web documents requires an inference engine. F-OWL is an inference engine for the semantic web language OWL language based on F-logic, an approach to defining kame-based systems in logic. F...

Y. Zou T. Finin H. Chen

2005-01-01

463

Causal Inference in Multisensory Perception  

PubMed Central

Perceptual events derive their significance to an animal from their meaning about the world, that is from the information they carry about their causes. The brain should thus be able to efficiently infer the causes underlying our sensory events. Here we use multisensory cue combination to study causal inference in perception. We formulate an ideal-observer model that infers whether two sensory cues originate from the same location and that also estimates their location(s). This model accurately predicts the nonlinear integration of cues by human subjects in two auditory-visual localization tasks. The results show that indeed humans can efficiently infer the causal structure as well as the location of causes. By combining insights from the study of causal inference with the ideal-observer approach to sensory cue combination, we show that the capacity to infer causal structure is not limited to conscious, high-level cognition; it is also performed continually and effortlessly in perception.

Quartz, Steven; Tenenbaum, Joshua B.; Shams, Ladan

2007-01-01

464

Distributed generation system using wind/photovoltaic/fuel cell  

NASA Astrophysics Data System (ADS)

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

Buasri, Panhathai

465

Inferno: a cautious approach to uncertain inference  

SciTech Connect

Expert systems commonly employ some means of drawing inferences from domain and problem knowledge, where both the knowledge and its implications are less than certain. Methods used include subjective Bayesian reasoning, measures of belief and disbelief, and the Dempster-Shafer theory of evidence. Analysis of systems based on these methods reveals important deficiencies in areas such as the reliability of deductions and the ability to detect inconsistencies in the knowledge from which deductions were made. A new system call INFERNO addresses some of these points. Its approach is probabilistic but makes no assumptions whatsoever about the joint probability distributions of pieces of knowledge, so the correctness of inferences can be guaranteed. INFERNO informs the user of inconsistencies that may be present in the information presented to it, and can make suggestions about changing the information to make it consistent. An example from a Bayesian system is reworked, and the conclusions reached by that system and INFERNO are compared.

Quinlan, J.R.

1982-09-01

466

What Is an Inference Rule?  

Microsoft Academic Search

What is an inference rule? This question does not have a unique ansu'er. One usually finds two distinct standard answers in the literature: validity inference (u k> I+O if for every substitution T. the validity of T(U) entails the validity of ~(rp)). and truth inference lo I-, rp if for every substitution T. the truth of ~(o) entails the truth

Ronald Fagin; Joseph Y. Halpern; Moshe Y. Vardi

1992-01-01

467

Bayes factors and multimodel inference  

USGS Publications Warehouse

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

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

2009-01-01

468

Learning to Observe "and" Infer  

ERIC Educational Resources Information Center

|Researchers describe the need for students to have multiple opportunities and social interaction to learn about the differences between observation and inference and their role in developing scientific explanations (Harlen 2001; Simpson 2000). Helping children develop their skills of observation and inference in science while emphasizing the…

Hanuscin, Deborah L.; Park Rogers, Meredith A.

2008-01-01

469

Causal Inference in Retrospective Studies.  

ERIC Educational Resources Information Center

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…

Holland, Paul W.; Rubin, Donald B.

1988-01-01

470

Causal Inference and Developmental Psychology  

ERIC Educational Resources Information Center

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…

Foster, E. Michael

2010-01-01

471

Feature Inference Learning and Eyetracking  

ERIC Educational Resources Information Center

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

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

2009-01-01

472

Improving Inferences from Multiple Methods.  

ERIC Educational Resources Information Center

|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.…

Shotland, R. Lance; Mark, Melvin M.

1987-01-01

473

Statistical Inference as Default Reasoning  

Microsoft Academic Search

Classical statistical inference is nonmonotonic in na- ture. We show how it can be form~ized in the default logic framework. The structure of statistical inference is the same as that represented by default rules. In particular, the prerequisite corresponds to the sample statistics, the justifications require that we do not have any reason to believe that the sample is misleading,

Henry E. Kyburg Jr.; Choh-man Teng

1998-01-01

474

Causal Inference and Developmental Psychology  

ERIC Educational Resources Information Center

|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…

Foster, E. Michael

2010-01-01

475

Anfis Approach for Sssc Controller Design for the Improvement of Transient Stability Performance  

NASA Astrophysics Data System (ADS)

In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) method based on the Artificial Neural Network (ANN) is applied to design a Static Synchronous Series Compensator (SSSC)-based controller for improvement of transient stability. The proposed ANFIS controller combines the advantages of fuzzy controller and quick response and adaptability nature of ANN. The ANFIS structures were trained using the generated database by fuzzy controller of SSSC. It is observed that the proposed SSSC controller improves greatly the voltage profile of the system under severe disturbances. The results prove that the proposed SSSC-based ANFIS controller is found to be robust to fault location and change in operating conditions. Further, the results obtained are compared with the conventional lead-lag controllers for SSSC.

Khuntia, Swasti R.; Panda, Sidhartha

2011-06-01

476

Inferring the Eccentricity Distribution  

NASA Astrophysics Data System (ADS)

Standard maximum-likelihood estimators for binary-star and exoplanet eccentricities are biased high, in the sense that the estimated eccentricity tends to be larger than the true eccentricity. As with most non-trivial observables, a simple histogram of estimated eccentricities is not a good estimate of the true eccentricity distribution. Here, we develop and test a hierarchical probabilistic method for performing the relevant meta-analysis, that is, inferring the true eccentricity distribution, taking as input the likelihood functions for the individual star eccentricities, or samplings of the posterior probability distributions for the eccentricities (under a given, uninformative prior). The method is a simple implementation of a hierarchical Bayesian model; it can also be seen as a kind of heteroscedastic deconvolution. It can be applied to any quantity measured with finite precision—other orbital parameters, or indeed any astronomical measurements of any kind, including magnitudes, distances, or photometric redshifts—so long as the measurements have been communicated as a likelihood function or a posterior sampling.

Hogg, David W.; Myers, Adam D.; Bovy, Jo

2010-12-01

477

Inferring agency from sound.  

PubMed

In three experiments we investigated how people determine whether or not they are in control of sounds they hear. The sounds were either triggered by participants' taps or controlled by a computer. The task was to distinguish between self-control and external control during active tapping, and during passive listening to a playback of the sounds recorded during the active condition. Experiment 1 required detection of a change in control mode within trials. Experiments 2 and 3 introduced a simple rhythm reproduction task that requires discrimination of control modes between trials. The results demonstrate that both sensorimotor cues and perceptual cues are used to infer agency. In addition, there may be further influences of cognitive expectation and/or multimodal integration. In accordance with hierarchical models of intention [e.g., Pacherie, E. (2008). The phenomenology of action: A conceptual framework. Cognition, 107, 179-217] this suggests that the sense of agency is not situated on one specific level of action control but subject to multiple influences. PMID:19306996

Knoblich, Günther; Repp, Bruno H

2009-05-01

478

INFERRING THE ECCENTRICITY DISTRIBUTION  

SciTech Connect

Standard maximum-likelihood estimators for binary-star and exoplanet eccentricities are biased high, in the sense that the estimated eccentricity tends to be larger than the true eccentricity. As with most non-trivial observables, a simple histogram of estimated eccentricities is not a good estimate of the true eccentricity distribution. Here, we develop and test a hierarchical probabilistic method for performing the relevant meta-analysis, that is, inferring the true eccentricity distribution, taking as input the likelihood functions for the individual star eccentricities, or samplings of the posterior probability distributions for the eccentricities (under a given, uninformative prior). The method is a simple implementation of a hierarchical Bayesian model; it can also be seen as a kind of heteroscedastic deconvolution. It can be applied to any quantity measured with finite precision-other orbital parameters, or indeed any astronomical measurements of any kind, including magnitudes, distances