Fuzzy logic and neural networks in artificial intelligence and pattern recognition

Science.gov (United States)

Sanchez, Elie

1991-10-01

With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

Fuzzy Neural Networks for Decision Support in Negotiation

International Nuclear Information System (INIS)

Sakas, D. P.; Vlachos, D. S.; Simos, T. E.

2008-01-01

There is a large number of parameters which one can take into account when building a negotiation model. These parameters in general are uncertain, thus leading to models which represents them with fuzzy sets. On the other hand, the nature of these parameters makes them very difficult to model them with precise values. During negotiation, these parameters play an important role by altering the outcomes or changing the state of the negotiators. One reasonable way to model this procedure is to accept fuzzy relations (from theory or experience). The action of these relations to fuzzy sets, produce new fuzzy sets which describe now the new state of the system or the modified parameters. But, in the majority of these situations, the relations are multidimensional, leading to complicated models and exponentially increasing computational time. In this paper a solution to this problem is presented. The use of fuzzy neural networks is shown that it can substitute the use of fuzzy relations with comparable results. Finally a simple simulation is carried in order to test the new method.

Robust adaptive fuzzy neural tracking control for a class of unknown ...

Indian Academy of Sciences (India)

In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for ...

Numerical Solution of Fuzzy Differential Equations with Z-numbers Using Bernstein Neural Networks

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Raheleh Jafari

2017-01-01

Full Text Available The uncertain nonlinear systems can be modeled with fuzzy equations or fuzzy differential equations (FDEs by incorporating the fuzzy set theory. The solutions of them are applied to analyze many engineering problems. However, it is very difficult to obtain solutions of FDEs. In this paper, the solutions of FDEs are approximated by two types of Bernstein neural networks. Here, the uncertainties are in the sense of Z-numbers. Initially the FDE is transformed into four ordinary differential equations (ODEs with Hukuhara differentiability. Then neural models are constructed with the structure of ODEs. With modified back propagation method for Z- number variables, the neural networks are trained. The theory analysis and simulation results show that these new models, Bernstein neural networks, are effective to estimate the solutions of FDEs based on Z-numbers.

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network.

Science.gov (United States)

Guan, Hongjun; Dai, Zongli; Zhao, Aiwu; He, Jie

2018-01-01

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method.

A neural fuzzy controller learning by fuzzy error propagation

Science.gov (United States)

Nauck, Detlef; Kruse, Rudolf

1992-01-01

In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

Application and Simulation of Fuzzy Neural Network PID Controller in the Aircraft Cabin Temperature

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Ding Fang

2013-06-01

Full Text Available Considering complex factors of affecting ambient temperature in Aircraft cabin, and some shortages of traditional PID control like the parameters difficult to be tuned and control ineffective, this paper puts forward the intelligent PID algorithm that makes fuzzy logic method and neural network together, scheming out the fuzzy neural net PID controller. After the correction of the fuzzy inference and dynamic learning of neural network, PID parameters of the controller get the optimal parameters. MATLAB simulation results of the cabin temperature control model show that the performance of the fuzzy neural network PID controller has been greatly improved, with faster response, smaller overshoot and better adaptability.

A Fuzzy Neural Network Based on Non-Euclidean Distance Clustering for Quality Index Model in Slashing Process

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Yuxian Zhang

2015-01-01

Full Text Available The quality index model in slashing process is difficult to build by reason of the outliers and noise data from original data. To the above problem, a fuzzy neural network based on non-Euclidean distance clustering is proposed in which the input space is partitioned into many local regions by the fuzzy clustering based on non-Euclidean distance so that the computation complexity is decreased, and fuzzy rule number is determined by validity function based on both the separation and the compactness among clusterings. Then, the premise parameters and consequent parameters are trained by hybrid learning algorithm. The parameters identification is realized; meanwhile the convergence condition of consequent parameters is obtained by Lyapunov function. Finally, the proposed method is applied to build the quality index model in slashing process in which the experimental data come from the actual slashing process. The experiment results show that the proposed fuzzy neural network for quality index model has lower computation complexity and faster convergence time, comparing with GP-FNN, BPNN, and RBFNN.

Neuro-fuzzy system modeling based on automatic fuzzy clustering

Institute of Scientific and Technical Information of China (English)

Yuangang TANG; Fuchun SUN; Zengqi SUN

2005-01-01

A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.

Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

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M. Bazazzadeh

2011-01-01

Full Text Available This paper presents a successful approach in designing a Fuzzy Logic Controller (FLC for a specific Jet Engine. At first, a suitable mathematical model for the jet engine is presented by the aid of SIMULINK. Then by applying different reasonable fuel flow functions via the engine model, some important engine-transient operation parameters (such as thrust, compressor surge margin, turbine inlet temperature, etc. are obtained. These parameters provide a precious database, which train a neural network. At the second step, by designing and training a feedforward multilayer perceptron neural network according to this available database; a number of different reasonable fuel flow functions for various engine acceleration operations are determined. These functions are used to define the desired fuzzy fuel functions. Indeed, the neural networks are used as an effective method to define the optimum fuzzy fuel functions. At the next step, we propose a FLC by using the engine simulation model and the neural network results. The proposed control scheme is proved by computer simulation using the designed engine model. The simulation results of engine model with FLC illustrate that the proposed controller achieves the desired performance and stability.

Robust adaptive controller design for a class of uncertain nonlinear systems using online T-S fuzzy-neural modeling approach.

Science.gov (United States)

Chien, Yi-Hsing; Wang, Wei-Yen; Leu, Yih-Guang; Lee, Tsu-Tian

2011-04-01

This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

Predictive Modeling of Mechanical Properties of Welded Joints Based on Dynamic Fuzzy RBF Neural Network

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ZHANG Yongzhi

2016-10-01

Full Text Available A dynamic fuzzy RBF neural network model was built to predict the mechanical properties of welded joints, and the purpose of the model was to overcome the shortcomings of static neural networks including structural identification, dynamic sample training and learning algorithm. The structure and parameters of the model are no longer head of default, dynamic adaptive adjustment in the training, suitable for dynamic sample data for learning, learning algorithm introduces hierarchical learning and fuzzy rule pruning strategy, to accelerate the training speed of model and make the model more compact. Simulation of the model was carried out by using three kinds of thickness and different process TC4 titanium alloy TIG welding test data. The results show that the model has higher prediction accuracy, which is suitable for predicting the mechanical properties of welded joints, and has opened up a new way for the on-line control of the welding process.

A software sensor model based on hybrid fuzzy neural network for rapid estimation water quality in Guangzhou section of Pearl River, China.

Science.gov (United States)

Zhou, Chunshan; Zhang, Chao; Tian, Di; Wang, Ke; Huang, Mingzhi; Liu, Yanbiao

2018-01-02

In order to manage water resources, a software sensor model was designed to estimate water quality using a hybrid fuzzy neural network (FNN) in Guangzhou section of Pearl River, China. The software sensor system was composed of data storage module, fuzzy decision-making module, neural network module and fuzzy reasoning generator module. Fuzzy subtractive clustering was employed to capture the character of model, and optimize network architecture for enhancing network performance. The results indicate that, on basis of available on-line measured variables, the software sensor model can accurately predict water quality according to the relationship between chemical oxygen demand (COD) and dissolved oxygen (DO), pH and NH 4 + -N. Owing to its ability in recognizing time series patterns and non-linear characteristics, the software sensor-based FNN is obviously superior to the traditional neural network model, and its R (correlation coefficient), MAPE (mean absolute percentage error) and RMSE (root mean square error) are 0.8931, 10.9051 and 0.4634, respectively.

A fuzzy Hopfield neural network for medical image segmentation

International Nuclear Information System (INIS)

Lin, J.S.; Cheng, K.S.; Mao, C.W.

1996-01-01

In this paper, an unsupervised parallel segmentation approach using a fuzzy Hopfield neural network (FHNN) is proposed. The main purpose is to embed fuzzy clustering into neural networks so that on-line learning and parallel implementation for medical image segmentation are feasible. The idea is to cast a clustering problem as a minimization problem where the criteria for the optimum segmentation is chosen as the minimization of the Euclidean distance between samples to class centers. In order to generate feasible results, a fuzzy c-means clustering strategy is included in the Hopfield neural network to eliminate the need of finding weighting factors in the energy function, which is formulated and based on a basic concept commonly used in pattern classification, called the within-class scatter matrix principle. The suggested fuzzy c-means clustering strategy has also been proven to be convergent and to allow the network to learn more effectively than the conventional Hopfield neural network. The fuzzy Hopfield neural network based on the within-class scatter matrix shows the promising results in comparison with the hard c-means method

FUZZY NEURAL NETWORK FOR OBJECT IDENTIFICATION ON INTEGRATED CIRCUIT LAYOUTS

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A. A. Doudkin

2015-01-01

Full Text Available Fuzzy neural network model based on neocognitron is proposed to identify layout objects on images of topological layers of integrated circuits. Testing of the model on images of real chip layouts was showed a highеr degree of identification of the proposed neural network in comparison to base neocognitron.

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network.

Directory of Open Access Journals (Sweden)

Hongjun Guan

Full Text Available In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBPNeural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS. On this basis, the FTTS blur into fuzzy time series (FFTS based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method.

Computational intelligence synergies of fuzzy logic, neural networks and evolutionary computing

CERN Document Server

Siddique, Nazmul

2013-01-01

Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspect

Fundamentals of computational intelligence neural networks, fuzzy systems, and evolutionary computation

CERN Document Server

Keller, James M; Fogel, David B

2016-01-01

This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...

Neural Modeling of Fuzzy Controllers for Maximum Power Point Tracking in Photovoltaic Energy Systems

Science.gov (United States)

Lopez-Guede, Jose Manuel; Ramos-Hernanz, Josean; Altın, Necmi; Ozdemir, Saban; Kurt, Erol; Azkune, Gorka

2018-06-01

One field in which electronic materials have an important role is energy generation, especially within the scope of photovoltaic energy. This paper deals with one of the most relevant enabling technologies within that scope, i.e, the algorithms for maximum power point tracking implemented in the direct current to direct current converters and its modeling through artificial neural networks (ANNs). More specifically, as a proof of concept, we have addressed the problem of modeling a fuzzy logic controller that has shown its performance in previous works, and more specifically the dimensionless duty cycle signal that controls a quadratic boost converter. We achieved a very accurate model since the obtained medium squared error is 3.47 × 10-6, the maximum error is 16.32 × 10-3 and the regression coefficient R is 0.99992, all for the test dataset. This neural implementation has obvious advantages such as a higher fault tolerance and a simpler implementation, dispensing with all the complex elements needed to run a fuzzy controller (fuzzifier, defuzzifier, inference engine and knowledge base) because, ultimately, ANNs are sums and products.

Global exponential stability of uncertain fuzzy BAM neural networks with time-varying delays

International Nuclear Information System (INIS)

Syed Ali, M.; Balasubramaniam, P.

2009-01-01

In this paper, the Takagi-Sugeno (TS) fuzzy model representation is extended to the stability analysis for uncertain Bidirectional Associative Memory (BAM) neural networks with time-varying delays using linear matrix inequality (LMI) theory. A novel LMI-based stability criterion is obtained by LMI optimization algorithms to guarantee the exponential stability of uncertain BAM neural networks with time-varying delays which are represented by TS fuzzy models. Finally, the proposed stability conditions are demonstrated with numerical examples.

Type-2 fuzzy neural networks and their applications

CERN Document Server

Aliev, Rafik Aziz

2014-01-01

This book deals with the theory, design principles, and application of hybrid intelligent systems using type-2 fuzzy sets in combination with other paradigms of Soft Computing technology such as Neuro-Computing and Evolutionary Computing. It provides a self-contained exposition of the foundation of type-2 fuzzy neural networks and presents a vast compendium of its applications to control, forecasting, decision making, system identification and other real problems. Type-2 Fuzzy Neural Networks and Their Applications is helpful for teachers and students of universities and colleges, for scientis

Estimation of Collapse Moment for Wall Thinned Elbows Using Fuzzy Neural Networks

International Nuclear Information System (INIS)

Na, Man Gyun; Kim, Jin Weon; Shin, Sun Ho; Kim, Koung Suk; Kang, Ki Soo

2004-01-01

In this work, the collapse moment due to wall-thinning defects is estimated by using fuzzy neural networks. The developed fuzzy neural networks have been applied to the numerical data obtained from the finite element analysis. Principal component analysis is used to preprocess the input signals into the fuzzy neural network to reduce the sensitivity to the input change and the fuzzy neural networks are trained by using the data set prepared for training (training data) and verified by using another data set different (independent) from the training data. Also, two fuzzy neural networks are trained for two data sets divided into the two classes of extrados and intrados defects, which is because they have different characteristics. The relative 2-sigma errors of the estimated collapse moment are 3.07% for the training data and 4.12% for the test data. It is known from this result that the fuzzy neural networks are sufficiently accurate to be used in the wall-thinning monitoring of elbows

A neural-fuzzy approach to classify the ecological status in surface waters

International Nuclear Information System (INIS)

Ocampo-Duque, William; Schuhmacher, Marta; Domingo, Jose L.

2007-01-01

A methodology based on a hybrid approach that combines fuzzy inference systems and artificial neural networks has been used to classify ecological status in surface waters. This methodology has been proposed to deal efficiently with the non-linearity and highly subjective nature of variables involved in this serious problem. Ecological status has been assessed with biological, hydro-morphological, and physicochemical indicators. A data set collected from 378 sampling sites in the Ebro river basin has been used to train and validate the hybrid model. Up to 97.6% of sampling sites have been correctly classified with neural-fuzzy models. Such performance resulted very competitive when compared with other classification algorithms. With non-parametric classification-regression trees and probabilistic neural networks, the predictive capacities were 90.7% and 97.0%, respectively. The proposed methodology can support decision-makers in evaluation and classification of ecological status, as required by the EU Water Framework Directive. - Fuzzy inference systems can be used as environmental classifiers

The Neural-fuzzy Thermal Error Compensation Controller on CNC Machining Center

Science.gov (United States)

Tseng, Pai-Chung; Chen, Shen-Len

The geometric errors and structural thermal deformation are factors that influence the machining accuracy of Computer Numerical Control (CNC) machining center. Therefore, researchers pay attention to thermal error compensation technologies on CNC machine tools. Some real-time error compensation techniques have been successfully demonstrated in both laboratories and industrial sites. The compensation results still need to be enhanced. In this research, the neural-fuzzy theory has been conducted to derive a thermal prediction model. An IC-type thermometer has been used to detect the heat sources temperature variation. The thermal drifts are online measured by a touch-triggered probe with a standard bar. A thermal prediction model is then derived by neural-fuzzy theory based on the temperature variation and the thermal drifts. A Graphic User Interface (GUI) system is also built to conduct the user friendly operation interface with Insprise C++ Builder. The experimental results show that the thermal prediction model developed by neural-fuzzy theory methodology can improve machining accuracy from 80µm to 3µm. Comparison with the multi-variable linear regression analysis the compensation accuracy is increased from ±10µm to ±3µm.

Intelligent neural network and fuzzy logic control of industrial and power systems

Science.gov (United States)

Kuljaca, Ognjen

The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of

New backpropagation algorithm with type-2 fuzzy weights for neural networks

CERN Document Server

Gaxiola, Fernando; Valdez, Fevrier

2016-01-01

In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights. The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method. The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a cases of prediction for the Mackey-Glass (for ô=17) and Dow-Jones time series, and recognition of person with iris bi...

The Satellite Clock Bias Prediction Method Based on Takagi-Sugeno Fuzzy Neural Network

Science.gov (United States)

Cai, C. L.; Yu, H. G.; Wei, Z. C.; Pan, J. D.

2017-05-01

The continuous improvement of the prediction accuracy of Satellite Clock Bias (SCB) is the key problem of precision navigation. In order to improve the precision of SCB prediction and better reflect the change characteristics of SCB, this paper proposes an SCB prediction method based on the Takagi-Sugeno fuzzy neural network. Firstly, the SCB values are pre-treated based on their characteristics. Then, an accurate Takagi-Sugeno fuzzy neural network model is established based on the preprocessed data to predict SCB. This paper uses the precise SCB data with different sampling intervals provided by IGS (International Global Navigation Satellite System Service) to realize the short-time prediction experiment, and the results are compared with the ARIMA (Auto-Regressive Integrated Moving Average) model, GM(1,1) model, and the quadratic polynomial model. The results show that the Takagi-Sugeno fuzzy neural network model is feasible and effective for the SCB short-time prediction experiment, and performs well for different types of clocks. The prediction results for the proposed method are better than the conventional methods obviously.

Neural and Fuzzy Adaptive Control of Induction Motor Drives

International Nuclear Information System (INIS)

Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.

2008-01-01

This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller

A new approach to self-organizing fuzzy polynomial neural networks guided by genetic optimization

International Nuclear Information System (INIS)

Oh, Sung-Kwun; Pedrycz, Witold

2005-01-01

In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology. The underlying methodology involves mechanisms of genetic optimization, especially genetic algorithms (GAs). Let us recall that the design of the 'conventional' FPNNs uses an extended Group Method of Data Handling (GMDH) and exploits a fixed fuzzy inference type located at each FPN of the FPNN as well as considers a fixed number of input nodes at FPNs (or nodes) located in each layer. The proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in the case of the parametric optimization we proceed with a standard least square method based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. The performance of the proposed gFPNN is quantified through experimentation that exploits standard data already being used in fuzzy modeling. The results reveal superiority of the proposed networks over the existing fuzzy and neural models

Neuro-fuzzy modelling of hydro unit efficiency

International Nuclear Information System (INIS)

Iliev, Atanas; Fushtikj, Vangel

2003-01-01

This paper presents neuro-fuzzy method for modeling of the hydro unit efficiency. The proposed method uses the characteristics of the fuzzy systems as universal function approximates, as well the abilities of the neural networks to adopt the parameters of the membership's functions and rules in the consequent part of the developed fuzzy system. Developed method is practically applied for modeling of the efficiency of unit which will be installed in the hydro power plant Kozjak. Comparison of the performance of the derived neuro-fuzzy method with several classical polynomials models is also performed. (Author)

Fuzzy/Neural Software Estimates Costs of Rocket-Engine Tests

Science.gov (United States)

Douglas, Freddie; Bourgeois, Edit Kaminsky

2005-01-01

The Highly Accurate Cost Estimating Model (HACEM) is a software system for estimating the costs of testing rocket engines and components at Stennis Space Center. HACEM is built on a foundation of adaptive-network-based fuzzy inference systems (ANFIS) a hybrid software concept that combines the adaptive capabilities of neural networks with the ease of development and additional benefits of fuzzy-logic-based systems. In ANFIS, fuzzy inference systems are trained by use of neural networks. HACEM includes selectable subsystems that utilize various numbers and types of inputs, various numbers of fuzzy membership functions, and various input-preprocessing techniques. The inputs to HACEM are parameters of specific tests or series of tests. These parameters include test type (component or engine test), number and duration of tests, and thrust level(s) (in the case of engine tests). The ANFIS in HACEM are trained by use of sets of these parameters, along with costs of past tests. Thereafter, the user feeds HACEM a simple input text file that contains the parameters of a planned test or series of tests, the user selects the desired HACEM subsystem, and the subsystem processes the parameters into an estimate of cost(s).

Approximate solutions of dual fuzzy polynomials by feed-back neural networks

Directory of Open Access Journals (Sweden)

Ahmad Jafarian

2012-11-01

Full Text Available Recently, artificial neural networks (ANNs have been extensively studied and used in different areas such as pattern recognition, associative memory, combinatorial optimization, etc. In this paper, we investigate the ability of fuzzy neural networks to approximate solution of a dual fuzzy polynomial of the form $a_{1}x+ ...+a_{n}x^n =b_{1}x+ ...+b_{n}x^n+d,$ where $a_{j},b_{j},d epsilon E^1 (for j=1,...,n.$ Since the operation of fuzzy neural networks is based on Zadeh's extension principle. For this scope we train a fuzzified neural network by back-propagation-type learning algorithm which has five layer where connection weights are crisp numbers. This neural network can get a crisp input signal and then calculates its corresponding fuzzy output. Presented method can give a real approximate solution for given polynomial by using a cost function which is defined for the level sets of fuzzy output and target output. The simulation results are presented to demonstrate the efficiency and effectiveness of the proposed approach.

Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays

Science.gov (United States)

Syed Ali, M.; Balasubramaniam, P.

2008-07-01

In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB.

Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays

International Nuclear Information System (INIS)

Syed Ali, M.; Balasubramaniam, P.

2008-01-01

In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB

Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms

CERN Document Server

Siddique, Nazmul

2014-01-01

Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller.  The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...

Fuzzy logic, artificial neural network and mathematical model for prediction of white mulberry drying kinetics

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Jahedi Rad, Shahpour; Kaveh, Mohammad; Sharabiani, Vali Rasooli; Taghinezhad, Ebrahim

2018-05-01

The thin-layer convective- infrared drying behavior of white mulberry was experimentally studied at infrared power levels of 500, 1000 and 1500 W, drying air temperatures of 40, 55 and 70 °C and inlet drying air speeds of 0.4, 1 and 1.6 m/s. Drying rate raised with the rise of infrared power levels at a distinct air temperature and velocity and thus decreased the drying time. Five mathematical models describing thin-layer drying have been fitted to the drying data. Midlli et al. model could satisfactorily describe the convective-infrared drying of white mulberry fruit with the values of the correlation coefficient (R 2=0.9986) and root mean square error of (RMSE= 0.04795). Artificial neural network (ANN) and fuzzy logic methods was desirably utilized for modeling output parameters (moisture ratio (MR)) regarding input parameters. Results showed that output parameters were more accurately predicted by fuzzy model than by the ANN and mathematical models. Correlation coefficient (R 2) and RMSE generated by the fuzzy model (respectively 0.9996 and 0.01095) were higher than referred values for the ANN model (0.9990 and 0.01988 respectively).

Study on Maritime Logistics Warehousing Center Model and Precision Marketing Strategy Optimization Based on Fuzzy Method and Neural Network Model

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Xiao Kefeng

2017-08-01

Full Text Available The bulk commodity, different with the retail goods, has a uniqueness in the location selection, the chosen of transportation program and the decision objectives. How to make optimal decisions in the facility location, requirement distribution, shipping methods and the route selection and establish an effective distribution system to reduce the cost has become a burning issue for the e-commerce logistics, which is worthy to be deeply and systematically solved. In this paper, Logistics warehousing center model and precision marketing strategy optimization based on fuzzy method and neural network model is proposed to solve this problem. In addition, we have designed principles of the fuzzy method and neural network model to solve the proposed model because of its complexity. Finally, we have solved numerous examples to compare the results of lingo and Matlab, we use Matlab and lingo just to check the result and to illustrate the numerical example, we can find from the result, the multi-objective model increases logistics costs and improves the efficiency of distribution time.

New Passivity Criteria for Fuzzy Bam Neural Networks with Markovian Jumping Parameters and Time-Varying Delays

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Vadivel, P.; Sakthivel, R.; Mathiyalagan, K.; Thangaraj, P.

2013-02-01

This paper addresses the problem of passivity analysis issue for a class of fuzzy bidirectional associative memory (BAM) neural networks with Markovian jumping parameters and time varying delays. A set of sufficient conditions for the passiveness of the considered fuzzy BAM neural network model is derived in terms of linear matrix inequalities by using the delay fractioning technique together with the Lyapunov function approach. In addition, the uncertainties are inevitable in neural networks because of the existence of modeling errors and external disturbance. Further, this result is extended to study the robust passivity criteria for uncertain fuzzy BAM neural networks with time varying delays and uncertainties. These criteria are expressed in the form of linear matrix inequalities (LMIs), which can be efficiently solved via standard numerical software. Two numerical examples are provided to demonstrate the effectiveness of the obtained results.

Vector control of wind turbine on the basis of the fuzzy selective neural net*

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Engel, E. A.; Kovalev, I. V.; Engel, N. E.

2016-04-01

An article describes vector control of wind turbine based on fuzzy selective neural net. Based on the wind turbine system’s state, the fuzzy selective neural net tracks an maximum power point under random perturbations. Numerical simulations are accomplished to clarify the applicability and advantages of the proposed vector wind turbine’s control on the basis of the fuzzy selective neuronet. The simulation results show that the proposed intelligent control of wind turbine achieves real-time control speed and competitive performance, as compared to a classical control model with PID controllers based on traditional maximum torque control strategy.

PROCESSING THE INFORMATION CONTENT ON THE BASIS OF FUZZY NEURAL MODEL OF DECISION MAKING

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Nina V. Komleva

2013-01-01

Full Text Available The article is devoted to the issues of mathematical modeling of the decision-making process of information content processing based on the fuzzy neural network TSK. Integral rating assessment of the content, which is necessary for taking a decision about its further usage, is made depended on varying characteristics. Mechanism for building individual trajectory and forming individual competence is provided to make the intellectual content search.

Exponential stability of delayed fuzzy cellular neural networks with diffusion

International Nuclear Information System (INIS)

Huang Tingwen

2007-01-01

The exponential stability of delayed fuzzy cellular neural networks (FCNN) with diffusion is investigated. Exponential stability, significant for applications of neural networks, is obtained under conditions that are easily verified by a new approach. Earlier results on the exponential stability of FCNN with time-dependent delay, a special case of the model studied in this paper, are improved without using the time-varying term condition: dτ(t)/dt < μ

Identification-based chaos control via backstepping design using self-organizing fuzzy neural networks

International Nuclear Information System (INIS)

Peng Yafu; Hsu, C.-F.

2009-01-01

This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.

Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

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Ahcene Farah

2002-06-01

Full Text Available This paper proposes a Neural-Network-Based Fuzzy logic system for navigation control of intelligent vehicles. First, the use of Neural Networks and Fuzzy Logic to provide intelligent vehicles  with more autonomy and intelligence is discussed. Second, the system  for the obstacle avoidance behavior is developed. Fuzzy Logic improves Neural Networks (NN obstacle avoidance approach by handling imprecision and rule-based approximate reasoning. This system must make the vehicle able, after supervised learning, to achieve two tasks: 1- to make one’s way towards its target by a NN, and 2- to avoid static or dynamic obstacles by a Fuzzy NN capturing the behavior of a human expert. Afterwards, two association phases between each task and the appropriate actions are carried out by Trial and Error learning and their coordination allows to decide the appropriate action. Finally, the simulation results display the generalization and adaptation abilities of the system by testing it in new unexplored environments.

Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

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Biaobiao Zhang

2011-01-01

Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

Evaluation of the Application of Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems for Rainfall-Runoff Modelling in Zayandeh_rood Dam Basin

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Mohammad Taghi Dastorani

2012-01-01

Full Text Available During recent few decades, due to the importance of the availability of water, and therefore the necesity of predicting run off resulted from rain fall there has been an increase in developing and implementation of new suitable method for prediction of run off using precipitation data. One of these approaches that have been developed in several areas of sciences including water related fields, is soft computing techniques such as artificial neural networks and fuzzy logic systems. This research was designed to evaluate the applicability of artificial neural network and adaptive neuro –fuzzy inference system to model rainfall-runoff process in Zayandeh_rood dam basin. It must be mentioned that, data have been analysed using Wingamma software, to select appropriate type and number of training input data before they can be used in the models. Then, it has been tried to evaluated applicability of artificial neural networks and neuro-fuzzy techniques to predict runoff generated from daily rainfall. Finally, the accuracy of the results produced by these methods has been compared using statistical criterion. Results taken from this research show that artificial neural networks and neuro-fuzzy technique presented different outputs in different conditions in terms of type and number of inputs variables, but both method have been able to produce acceptable results when suitable input variables and network structures are used.

Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

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Xin Zhang

2014-01-01

Full Text Available In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

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Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

2017-09-01

This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network.

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Lin, Yang-Yin; Chang, Jyh-Yeong; Lin, Chin-Teng

2013-02-01

This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems. The recurrent structure in an IRSFNN is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself. The consequent part in the IRSFNN is composed of a Takagi-Sugeno-Kang (TSK) or functional-link-based type. The proposed IRSFNN employs a functional link neural network (FLNN) to the consequent part of fuzzy rules for promoting the mapping ability. Unlike a TSK-type fuzzy neural network, the FLNN in the consequent part is a nonlinear function of input variables. An IRSFNNs learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning. An on-line clustering algorithm is effective in generating fuzzy rules. The consequent update parameters are derived by a variable-dimensional Kalman filter algorithm. The premise and recurrent parameters are learned through a gradient descent algorithm. We test the IRSFNN for the prediction and identification of dynamic plants and compare it to other well-known recurrent FNNs. The proposed model obtains enhanced performance results.

Abrasive slurry jet cutting model based on fuzzy relations

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Qiang, C. H.; Guo, C. W.

2017-12-01

The cutting process of pre-mixed abrasive slurry or suspension jet (ASJ) is a complex process affected by many factors, and there is a highly nonlinear relationship between the cutting parameters and cutting quality. In this paper, guided by fuzzy theory, the fuzzy cutting model of ASJ was developed. In the modeling of surface roughness, the upper surface roughness prediction model and the lower surface roughness prediction model were established respectively. The adaptive fuzzy inference system combines the learning mechanism of neural networks and the linguistic reasoning ability of the fuzzy system, membership functions, and fuzzy rules are obtained by adaptive adjustment. Therefore, the modeling process is fast and effective. In this paper, the ANFIS module of MATLAB fuzzy logic toolbox was used to establish the fuzzy cutting model of ASJ, which is found to be quite instrumental to ASJ cutting applications.

Neuro-fuzzy modeling in bankruptcy prediction

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Vlachos D.

2003-01-01

Full Text Available For the past 30 years the problem of bankruptcy prediction had been thoroughly studied. From the paper of Altman in 1968 to the recent papers in the '90s, the progress of prediction accuracy was not satisfactory. This paper investigates an alternative modeling of the system (firm, combining neural networks and fuzzy controllers, i.e. using neuro-fuzzy models. Classical modeling is based on mathematical models that describe the behavior of the firm under consideration. The main idea of fuzzy control, on the other hand, is to build a model of a human control expert who is capable of controlling the process without thinking in a mathematical model. This control expert specifies his control action in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus, which can stimulate the behavior of the control expert and enhance its performance. The accuracy of the model is studied using datasets from previous research papers.

A Comparative Study of Neural Networks and Fuzzy Systems in Modeling of a Nonlinear Dynamic System

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Metin Demirtas

2011-07-01

Full Text Available The aim of this paper is to compare the neural networks and fuzzy modeling approaches on a nonlinear system. We have taken Permanent Magnet Brushless Direct Current (PMBDC motor data and have generated models using both approaches. The predictive performance of both methods was compared on the data set for model configurations. The paper describes the results of these tests and discusses the effects of changing model parameters on predictive and practical performance. Modeling sensitivity was used to compare for two methods.

Fuzzylot: a novel self-organising fuzzy-neural rule-based pilot system for automated vehicles.

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Pasquier, M; Quek, C; Toh, M

2001-10-01

This paper presents part of our research work concerned with the realisation of an Intelligent Vehicle and the technologies required for its routing, navigation, and control. An automated driver prototype has been developed using a self-organising fuzzy rule-based system (POPFNN-CRI(S)) to model and subsequently emulate human driving expertise. The ability of fuzzy logic to represent vague information using linguistic variables makes it a powerful tool to develop rule-based control systems when an exact working model is not available, as is the case of any vehicle-driving task. Designing a fuzzy system, however, is a complex endeavour, due to the need to define the variables and their associated fuzzy sets, and determine a suitable rule base. Many efforts have thus been devoted to automating this process, yielding the development of learning and optimisation techniques. One of them is the family of POP-FNNs, or Pseudo-Outer Product Fuzzy Neural Networks (TVR, AARS(S), AARS(NS), CRI, Yager). These generic self-organising neural networks developed at the Intelligent Systems Laboratory (ISL/NTU) are based on formal fuzzy mathematical theory and are able to objectively extract a fuzzy rule base from training data. In this application, a driving simulator has been developed, that integrates a detailed model of the car dynamics, complete with engine characteristics and environmental parameters, and an OpenGL-based 3D-simulation interface coupled with driving wheel and accelerator/ brake pedals. The simulator has been used on various road scenarios to record from a human pilot driving data consisting of steering and speed control actions associated to road features. Specifically, the POPFNN-CRI(S) system is used to cluster the data and extract a fuzzy rule base modelling the human driving behaviour. Finally, the effectiveness of the generated rule base has been validated using the simulator in autopilot mode.

Modelling self-optimised short term load forecasting for medium voltage loads using tunning fuzzy systems and Artificial Neural Networks

International Nuclear Information System (INIS)

Mahmoud, Thair S.; Habibi, Daryoush; Hassan, Mohammed Y.; Bass, Octavian

2015-01-01

Highlights: • A novel Short Term Medium Voltage (MV) Load Forecasting (STLF) model is presented. • A knowledge-based STLF error control mechanism is implemented. • An Artificial Neural Network (ANN)-based optimum tuning is applied on STLF. • The relationship between load profiles and operational conditions is analysed. - Abstract: This paper presents an intelligent mechanism for Short Term Load Forecasting (STLF) models, which allows self-adaptation with respect to the load operational conditions. Specifically, a knowledge-based FeedBack Tunning Fuzzy System (FBTFS) is proposed to instantaneously correlate the information about the demand profile and its operational conditions to make decisions for controlling the model’s forecasting error rate. To maintain minimum forecasting error under various operational scenarios, the FBTFS adaptation was optimised using a Multi-Layer Perceptron Artificial Neural Network (MLPANN), which was trained using Backpropagation algorithm, based on the information about the amount of error and the operational conditions at time of forecasting. For the sake of comparison and performance testing, this mechanism was added to the conventional forecasting methods, i.e. Nonlinear AutoRegressive eXogenous-Artificial Neural Network (NARXANN), Fuzzy Subtractive Clustering Method-based Adaptive Neuro Fuzzy Inference System (FSCMANFIS) and Gaussian-kernel Support Vector Machine (GSVM), and the measured forecasting error reduction average in a 12 month simulation period was 7.83%, 8.5% and 8.32% respectively. The 3.5 MW variable load profile of Edith Cowan University (ECU) in Joondalup, Australia, was used in the modelling and simulations of this model, and the data was provided by Western Power, the transmission and distribution company of the state of Western Australia.

Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model

International Nuclear Information System (INIS)

Xu Long; Wang Junping; Chen Quanshi

2012-01-01

Highlights: ► A novel extended Kalman Filtering SOC estimation method based on a stochastic fuzzy neural network (SFNN) battery model is proposed. ► The SFNN which has filtering effect on noisy input can model the battery nonlinear dynamic with high accuracy. ► A robust parameter learning algorithm for SFNN is studied so that the parameters can converge to its true value with noisy data. ► The maximum SOC estimation error based on the proposed method is 0.6%. - Abstract: Extended Kalman filtering is an intelligent and optimal means for estimating the state of a dynamic system. In order to use extended Kalman filtering to estimate the state of charge (SOC), we require a mathematical model that can accurately capture the dynamics of battery pack. In this paper, we propose a stochastic fuzzy neural network (SFNN) instead of the traditional neural network that has filtering effect on noisy input to model the battery nonlinear dynamic. Then, the paper studies the extended Kalman filtering SOC estimation method based on a SFNN model. The modeling test is realized on an 80 Ah Ni/MH battery pack and the Federal Urban Driving Schedule (FUDS) cycle is used to verify the SOC estimation method. The maximum SOC estimation error is 0.6% compared with the real SOC obtained from the discharging test.

A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese TTS system.

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Lin, Chin-Teng; Wu, Rui-Cheng; Chang, Jyh-Yeong; Liang, Sheng-Fu

2004-02-01

In this paper, a new technique for the Chinese text-to-speech (TTS) system is proposed. Our major effort focuses on the prosodic information generation. New methodologies for constructing fuzzy rules in a prosodic model simulating human's pronouncing rules are developed. The proposed Recurrent Fuzzy Neural Network (RFNN) is a multilayer recurrent neural network (RNN) which integrates a Self-cOnstructing Neural Fuzzy Inference Network (SONFIN) into a recurrent connectionist structure. The RFNN can be functionally divided into two parts. The first part adopts the SONFIN as a prosodic model to explore the relationship between high-level linguistic features and prosodic information based on fuzzy inference rules. As compared to conventional neural networks, the SONFIN can always construct itself with an economic network size in high learning speed. The second part employs a five-layer network to generate all prosodic parameters by directly using the prosodic fuzzy rules inferred from the first part as well as other important features of syllables. The TTS system combined with the proposed method can behave not only sandhi rules but also the other prosodic phenomena existing in the traditional TTS systems. Moreover, the proposed scheme can even find out some new rules about prosodic phrase structure. The performance of the proposed RFNN-based prosodic model is verified by imbedding it into a Chinese TTS system with a Chinese monosyllable database based on the time-domain pitch synchronous overlap add (TD-PSOLA) method. Our experimental results show that the proposed RFNN can generate proper prosodic parameters including pitch means, pitch shapes, maximum energy levels, syllable duration, and pause duration. Some synthetic sounds are online available for demonstration.

Implementation of a fuzzy logic/neural network multivariable controller

International Nuclear Information System (INIS)

Cordes, G.A.; Clark, D.E.; Johnson, J.A.; Smartt, H.B.; Wickham, K.L.; Larson, T.K.

1992-01-01

This paper describes a multivariable controller developed at the Idaho National Engineering Laboratory (INEL) that incorporates both fuzzy logic rules and a neural network. The controller was implemented in a laboratory demonstration and was robust, producing smooth temperature and water level response curves with short time constants. In the future, intelligent control systems will be a necessity for optimal operation of autonomous reactor systems located on earth or in space. Even today, there is a need for control systems that adapt to the changing environment and process. Hybrid intelligent control systems promise to provide this adaptive capability. Fuzzy logic implements our imprecise, qualitative human reasoning. The values of system variables (controller inputs) and control variables (controller outputs) are described in linguistic terms and subdivided into fully overlapping value ranges. The fuzzy rule base describes how combinations of input parameter ranges determine the output control values. Neural networks implement our human learning. In this controller, neural networks were embedded in the software to explore their potential for adding adaptability

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

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

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

Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks

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Y.-M. Chiang

2011-01-01

Full Text Available Pumping stations play an important role in flood mitigation in metropolitan areas. The existing sewerage systems, however, are facing a great challenge of fast rising peak flow resulting from urbanization and climate change. It is imperative to construct an efficient and accurate operating prediction model for pumping stations to simulate the drainage mechanism for discharging the rainwater in advance. In this study, we propose two rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system (ANFIS and counterpropagation fuzzy neural network for on-line predicting of the number of open and closed pumps of a pivotal pumping station in Taipei city up to a lead time of 20 min. The performance of ANFIS outperforms that of CFNN in terms of model efficiency, accuracy, and correctness. Furthermore, the results not only show the predictive water levels do contribute to the successfully operating pumping stations but also demonstrate the applicability and reliability of ANFIS in automatically controlling the urban sewerage systems.

Modeling of Activated Sludge Process Using Sequential Adaptive Neuro-fuzzy Inference System

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Mahsa Vajedi

2014-10-01

Full Text Available In this study, an adaptive neuro-fuzzy inference system (ANFIS has been applied to model activated sludge wastewater treatment process of Mobin petrochemical company. The correlation coefficients between the input variables and the output variable were calculated to determine the input with the highest influence on the output (the quality of the outlet flow in order to compare three neuro-fuzzy structures with different number of parameters. The predictions of the neuro-fuzzy models were compared with those of multilayer artificial neural network models with similar structure. The comparison indicated that both methods resulted in flexible, robust and effective models for the activated sludge system. Moreover, the root mean square of the error for neuro-fuzzy and neural network models were 5.14 and 6.59, respectively, which means the former is the superior method.

Comments on "The multisynapse neural network and its application to fuzzy clustering".

Science.gov (United States)

Yu, Jian; Hao, Pengwei

2005-05-01

In the above-mentioned paper, Wei and Fahn proposed a neural architecture, the multisynapse neural network, to solve constrained optimization problems including high-order, logarithmic, and sinusoidal forms, etc. As one of its main applications, a fuzzy bidirectional associative clustering network (FBACN) was proposed for fuzzy-partition clustering according to the objective-functional method. The connection between the objective-functional-based fuzzy c-partition algorithms and FBACN is the Lagrange multiplier approach. Unfortunately, the Lagrange multiplier approach was incorrectly applied so that FBACN does not equivalently minimize its corresponding constrained objective-function. Additionally, Wei and Fahn adopted traditional definition of fuzzy c-partition, which is not satisfied by FBACN. Therefore, FBACN can not solve constrained optimization problems, either.

A Lateral Control Method of Intelligent Vehicle Based on Fuzzy Neural Network

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Linhui Li

2015-01-01

Full Text Available A lateral control method is proposed for intelligent vehicle to track the desired trajectory. Firstly, a lateral control model is established based on the visual preview and dynamic characteristics of intelligent vehicle. Then, the lateral error and orientation error are melded into an integrated error. Considering the system parameter perturbation and the external interference, a sliding model control is introduced in this paper. In order to design a sliding surface, the integrated error is chosen as the parameter of the sliding mode switching function. The sliding mode switching function and its derivative are selected as two inputs of the controller, and the front wheel angle is selected as the output. Next, a fuzzy neural network is established, and the self-learning functions of neural network is utilized to construct the fuzzy rules. Finally, the simulation results demonstrate the effectiveness and robustness of the proposed method.

Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS

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Tien Bui, Dieu; Pradhan, Biswajeet; Nampak, Haleh; Bui, Quang-Thanh; Tran, Quynh-An; Nguyen, Quoc-Phi

2016-09-01

This paper proposes a new artificial intelligence approach based on neural fuzzy inference system and metaheuristic optimization for flood susceptibility modeling, namely MONF. In the new approach, the neural fuzzy inference system was used to create an initial flood susceptibility model and then the model was optimized using two metaheuristic algorithms, Evolutionary Genetic and Particle Swarm Optimization. A high-frequency tropical cyclone area of the Tuong Duong district in Central Vietnam was used as a case study. First, a GIS database for the study area was constructed. The database that includes 76 historical flood inundated areas and ten flood influencing factors was used to develop and validate the proposed model. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Receiver Operating Characteristic (ROC) curve, and area under the ROC curve (AUC) were used to assess the model performance and its prediction capability. Experimental results showed that the proposed model has high performance on both the training (RMSE = 0.306, MAE = 0.094, AUC = 0.962) and validation dataset (RMSE = 0.362, MAE = 0.130, AUC = 0.911). The usability of the proposed model was evaluated by comparing with those obtained from state-of-the art benchmark soft computing techniques such as J48 Decision Tree, Random Forest, Multi-layer Perceptron Neural Network, Support Vector Machine, and Adaptive Neuro Fuzzy Inference System. The results show that the proposed MONF model outperforms the above benchmark models; we conclude that the MONF model is a new alternative tool that should be used in flood susceptibility mapping. The result in this study is useful for planners and decision makers for sustainable management of flood-prone areas.

Now comes the time to defuzzify neuro-fuzzy models

International Nuclear Information System (INIS)

Bersini, H.; Bontempi, G.

1996-01-01

Fuzzy models present a singular Janus-faced : on one hand, they are knowledge-based software environments constructed from a collection of linguistic IF-THEN rules, and on the other hand, they realize nonlinear mappings which have interesting mathematical properties like low-order interpolation and universal function approximation. Neuro-fuzzy basically provides fuzzy models with the capacity, based on the available data, to compensate for the missing human knowledge by an automatic self-tuning of the structure and the parameters. A first consequence of this hybridization between the architectural and representational aspect of fuzzy models and the learning mechanisms of neural networks has been to progressively increase and fuzzify the contrast between the two Janus faces: readability or performance

Soft computing integrating evolutionary, neural, and fuzzy systems

CERN Document Server

Tettamanzi, Andrea

2001-01-01

Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

Directory of Open Access Journals (Sweden)

Jinjun Tang

Full Text Available Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN, two learning processes are proposed: (1 a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2 a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE, root mean square error (RMSE, and mean absolute relative error (MARE are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR, instantaneous model (IM, linear model (LM, neural network (NN, and cumulative plots (CP.

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

Science.gov (United States)

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).

Robust sliding mode control for uncertain servo system using friction observer and recurrent fuzzy neural networks

International Nuclear Information System (INIS)

Han, Seong Ik; Jeong, Chan Se; Yang, Soon Yong

2012-01-01

A robust positioning control scheme has been developed using friction parameter observer and recurrent fuzzy neural networks based on the sliding mode control. As a dynamic friction model, the LuGre model is adopted for handling friction compensation because it has been known to capture sufficiently the properties of a nonlinear dynamic friction. A developed friction parameter observer has a simple structure and also well estimates friction parameters of the LuGre friction model. In addition, an approximation method for the system uncertainty is developed using recurrent fuzzy neural networks technology to improve the precision positioning degree. Some simulation and experiment provide the verification on the performance of a proposed robust control scheme

Robust sliding mode control for uncertain servo system using friction observer and recurrent fuzzy neural networks

Energy Technology Data Exchange (ETDEWEB)

Han, Seong Ik [Pusan National University, Busan (Korea, Republic of); Jeong, Chan Se; Yang, Soon Yong [University of Ulsan, Ulsan (Korea, Republic of)

2012-04-15

A robust positioning control scheme has been developed using friction parameter observer and recurrent fuzzy neural networks based on the sliding mode control. As a dynamic friction model, the LuGre model is adopted for handling friction compensation because it has been known to capture sufficiently the properties of a nonlinear dynamic friction. A developed friction parameter observer has a simple structure and also well estimates friction parameters of the LuGre friction model. In addition, an approximation method for the system uncertainty is developed using recurrent fuzzy neural networks technology to improve the precision positioning degree. Some simulation and experiment provide the verification on the performance of a proposed robust control scheme.

Global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Wang Jian; Lu Junguo

2008-01-01

In this paper, we study the global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms. By constructing a suitable Lyapunov functional and utilizing some inequality techniques, we obtain a sufficient condition for the uniqueness and global exponential stability of the equilibrium solution for a class of fuzzy cellular neural networks with delays and reaction-diffusion terms. The result imposes constraint conditions on the network parameters independently of the delay parameter. The result is also easy to check and plays an important role in the design and application of globally exponentially stable fuzzy neural circuits

Boundedness, Mittag-Leffler stability and asymptotical ω-periodicity of fractional-order fuzzy neural networks.

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Wu, Ailong; Zeng, Zhigang

2016-02-01

We show that the ω-periodic fractional-order fuzzy neural networks cannot generate non-constant ω-periodic signals. In addition, several sufficient conditions are obtained to ascertain the boundedness and global Mittag-Leffler stability of fractional-order fuzzy neural networks. Furthermore, S-asymptotical ω-periodicity and global asymptotical ω-periodicity of fractional-order fuzzy neural networks is also characterized. The obtained criteria improve and extend the existing related results. To illustrate and compare the theoretical criteria, some numerical examples with simulation results are discussed in detail. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

Real-time flood forecasts & risk assessment using a possibility-theory based fuzzy neural network

Science.gov (United States)

Khan, U. T.

2016-12-01

Globally floods are one of the most devastating natural disasters and improved flood forecasting methods are essential for better flood protection in urban areas. Given the availability of high resolution real-time datasets for flood variables (e.g. streamflow and precipitation) in many urban areas, data-driven models have been effectively used to predict peak flow rates in river; however, the selection of input parameters for these types of models is often subjective. Additionally, the inherit uncertainty associated with data models along with errors in extreme event observations means that uncertainty quantification is essential. Addressing these concerns will enable improved flood forecasting methods and provide more accurate flood risk assessments. In this research, a new type of data-driven model, a quasi-real-time updating fuzzy neural network is developed to predict peak flow rates in urban riverine watersheds. A possibility-to-probability transformation is first used to convert observed data into fuzzy numbers. A possibility theory based training regime is them used to construct the fuzzy parameters and the outputs. A new entropy-based optimisation criterion is used to train the network. Two existing methods to select the optimum input parameters are modified to account for fuzzy number inputs, and compared. These methods are: Entropy-Wavelet-based Artificial Neural Network (EWANN) and Combined Neural Pathway Strength Analysis (CNPSA). Finally, an automated algorithm design to select the optimum structure of the neural network is implemented. The overall impact of each component of training this network is to replace the traditional ad hoc network configuration methods, with one based on objective criteria. Ten years of data from the Bow River in Calgary, Canada (including two major floods in 2005 and 2013) are used to calibrate and test the network. The EWANN method selected lagged peak flow as a candidate input, whereas the CNPSA method selected lagged

Adaptive Sliding Mode Control of Chaos in Permanent Magnet Synchronous Motor via Fuzzy Neural Networks

Directory of Open Access Journals (Sweden)

Tat-Bao-Thien Nguyen

2014-01-01

Full Text Available In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method.

Application of fuzzy neural network technologies in management of transport and logistics processes in Arctic

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Levchenko, N. G.; Glushkov, S. V.; Sobolevskaya, E. Yu; Orlov, A. P.

2018-05-01

The method of modeling the transport and logistics process using fuzzy neural network technologies has been considered. The analysis of the implemented fuzzy neural network model of the information management system of transnational multimodal transportation of the process showed the expediency of applying this method to the management of transport and logistics processes in the Arctic and Subarctic conditions. The modular architecture of this model can be expanded by incorporating additional modules, since the working conditions in the Arctic and the subarctic themselves will present more and more realistic tasks. The architecture allows increasing the information management system, without affecting the system or the method itself. The model has a wide range of application possibilities, including: analysis of the situation and behavior of interacting elements; dynamic monitoring and diagnostics of management processes; simulation of real events and processes; prediction and prevention of critical situations.

Dynamics and genetic fuzzy neural network vibration control design of a smart flexible four-bar linkage mechanism

International Nuclear Information System (INIS)

Rong Bao; Rui Xiaoting; Tao Ling

2012-01-01

In this paper, a dynamic modeling method and an active vibration control scheme for a smart flexible four-bar linkage mechanism featuring piezoelectric actuators and strain gauge sensors are presented. The dynamics of this smart mechanism is described by the Discrete Time Transfer Matrix Method of Multibody System (MS-DTTMM). Then a nonlinear fuzzy neural network control is employed to suppress the vibration of this smart mechanism. For improving the dynamic performance of the fuzzy neural network, a genetic algorithm based on the MS-DTTMM is designed offline to tune the initial parameters of the fuzzy neural network. The MS-DTTMM avoids the global dynamics equations of the system, which results in the matrices involved are always very small, so the computational efficiency of the dynamic analysis and control system optimization can be greatly improved. Formulations of the method as well as a numerical simulation are given to demonstrate the proposed dynamic method and control scheme.

Applying Fuzzy Artificial Neural Network OSPF to develop Smart ...

African Journals Online (AJOL)

pc

2018-03-05

Mar 5, 2018 ... Fuzzy Artificial Neural Network to create Smart Routing. Protocol Algorithm. ... manufactured mental aptitude strategy. The capacity to study .... Based Energy Efficiency in Wireless Sensor Networks: A Survey",. International ...

Study on pattern recognition of Raman spectrum based on fuzzy neural network

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Zheng, Xiangxiang; Lv, Xiaoyi; Mo, Jiaqing

2017-10-01

Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.

Estimation of Minimum DNBR Using Cascaded Fuzzy Neural Networks

International Nuclear Information System (INIS)

Kim, Dong Yeong; Yoo, Kwae Hwan; Na, Man Gyun

2015-01-01

This phenomenon of boiling crisis is called a departure from nucleate boiling (DNB). The DNB phenomena can influence the fuel cladding and fuel pellets. The DNB ratio (DNBR) is defined as the ratio of the expected DNB heat flux to the actual fuel rod heat flux. Since it is very important to monitor and predict the minimum DNBR in a reactor core to prevent the boiling crisis and clad melting, a number of researches have been conducted to predict DNBR values. The aim of this study is to estimate the minimum DNBR in a reactor core using the measured signals of the reactor coolant system (RCS) by applying cascaded fuzzy neural networks (CFNN) according to operating conditions. Reactor core monitoring and protection systems require minimum DNBR prediction. The CFNN can be used to optimize the minimum DNBR value through the process of adding fuzzy neural networks (FNN) repeatedly. The proposed algorithm is trained by using the data set prepared for training (development data) and verified by using another data set different (independent) from the development data. The developed CFNN models were applied to the first fuel cycle of OPR1000. The RMS errors are 0.23% and 0.12% for the positive and negative ASI, respectively

Artificial frame filling using adaptive neural fuzzy inference system for particle image velocimetry dataset

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Akdemir, Bayram; Doǧan, Sercan; Aksoy, Muharrem H.; Canli, Eyüp; Özgören, Muammer

2015-03-01

Liquid behaviors are very important for many areas especially for Mechanical Engineering. Fast camera is a way to observe and search the liquid behaviors. Camera traces the dust or colored markers travelling in the liquid and takes many pictures in a second as possible as. Every image has large data structure due to resolution. For fast liquid velocity, there is not easy to evaluate or make a fluent frame after the taken images. Artificial intelligence has much popularity in science to solve the nonlinear problems. Adaptive neural fuzzy inference system is a common artificial intelligence in literature. Any particle velocity in a liquid has two dimension speed and its derivatives. Adaptive Neural Fuzzy Inference System has been used to create an artificial frame between previous and post frames as offline. Adaptive neural fuzzy inference system uses velocities and vorticities to create a crossing point vector between previous and post points. In this study, Adaptive Neural Fuzzy Inference System has been used to fill virtual frames among the real frames in order to improve image continuity. So this evaluation makes the images much understandable at chaotic or vorticity points. After executed adaptive neural fuzzy inference system, the image dataset increase two times and has a sequence as virtual and real, respectively. The obtained success is evaluated using R2 testing and mean squared error. R2 testing has a statistical importance about similarity and 0.82, 0.81, 0.85 and 0.8 were obtained for velocities and derivatives, respectively.

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

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Kisi, Ozgur; Kim, Sungwon; Shiri, Jalal

2013-11-01

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.

Introduction to Fuzzy Set Theory

Science.gov (United States)

Kosko, Bart

1990-01-01

An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

Development of neural network driven fuzzy controller for outlet sodium temperature of DHX

International Nuclear Information System (INIS)

Okusa, Kyoichi; Endou, Akira; Yoshikawa, Shinji; Ozawa, Kenji

1996-01-01

Fuzzy controls are capable to exquisitely control non-linear dynamic systems in wide operating range, using linguistic description to define the control law. However the selection and the definition of the fuzzy rules and sets require a tedious trial and error process based on experience. As a method to overcome this limitation, a neural network driven fuzzy control (NDF), where the learning capability of the neural network (NN) is used to build the fuzzy rules and sets, is presented in this paper. In the NDF control the IF part of a fuzzy control is represented by a multilayer NN while the THEN part is represented by a series of multilayer NNs which calculate the desirable control action. In this work the usual stepwise variable reduction method, used for the selection of the input variable in the THEN part NN, is replaced with a learning algorithm with forgetting mechanism that realizes the automatic reduction of the variables and the tuning up of all the fuzzy control law i.e. the membership function. The NDF has been successfully applied to control the outlet sodium temperature of a dump heat exchanger (DHX) of a FBR plant

An input feature selection method applied to fuzzy neural networks for signal esitmation

International Nuclear Information System (INIS)

Na, Man Gyun; Sim, Young Rok

2001-01-01

It is well known that the performance of a fuzzy neural networks strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output. As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural networks and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PAC), genetic algorithms (GA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods

Clinical Outcome Prediction in Aneurysmal Subarachnoid Hemorrhage Using Bayesian Neural Networks with Fuzzy Logic Inferences

Directory of Open Access Journals (Sweden)

Benjamin W. Y. Lo

2013-01-01

Full Text Available Objective. The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH. Methods. The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients. Results. Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs. Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique denoted cut-off points for poor prognosis at greater than 2.5 clusters. Discussion. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication.

Training the Recurrent neural network by the Fuzzy Min-Max algorithm for fault prediction

International Nuclear Information System (INIS)

Zemouri, Ryad; Racoceanu, Daniel; Zerhouni, Noureddine; Minca, Eugenia; Filip, Florin

2009-01-01

In this paper, we present a training technique of a Recurrent Radial Basis Function neural network for fault prediction. We use the Fuzzy Min-Max technique to initialize the k-center of the RRBF neural network. The k-means algorithm is then applied to calculate the centers that minimize the mean square error of the prediction task. The performances of the k-means algorithm are then boosted by the Fuzzy Min-Max technique.

An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination.

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Kuo, R J; Wu, P; Wang, C P

2002-09-01

Sales forecasting plays a very prominent role in business strategy. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average (ARMA). However, sales forecasting is very complicated owing to influence by internal and external environments. Recently, artificial neural networks (ANNs) have also been applied in sales forecasting since their promising performances in the areas of control and pattern recognition. However, further improvement is still necessary since unique circumstances, e.g. promotion, cause a sudden change in the sales pattern. Thus, this study utilizes a proposed fuzzy neural network (FNN), which is able to eliminate the unimportant weights, for the sake of learning fuzzy IF-THEN rules obtained from the marketing experts with respect to promotion. The result from FNN is further integrated with the time series data through an ANN. Both the simulated and real-world problem results show that FNN with weight elimination can have lower training error compared with the regular FNN. Besides, real-world problem results also indicate that the proposed estimation system outperforms the conventional statistical method and single ANN in accuracy.

Prediction of mechanical properties of a warm compacted molybdenum prealloy using artificial neural network and adaptive neuro-fuzzy models

International Nuclear Information System (INIS)

Zare, Mansour; Vahdati Khaki, Jalil

2012-01-01

Highlights: ► ANNs and ANFIS fairly predicted UTS and YS of warm compacted molybdenum prealloy. ► Effects of composition, temperature, compaction pressure on output were studied. ► ANFIS model was in better agreement with experimental data from published article. ► Sintering temperature had the most significant effect on UTS and YS. -- Abstract: Predictive models using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were successfully developed to predict yield strength and ultimate tensile strength of warm compacted 0.85 wt.% molybdenum prealloy samples. To construct these models, 48 different experimental data were gathered from the literature. A portion of the data set was randomly chosen to train both ANN with back propagation (BP) learning algorithm and ANFIS model with Gaussian membership function and the rest was implemented to verify the performance of the trained network against the unseen data. The generalization capability of the networks was also evaluated by applying new input data within the domain covered by the training pattern. To compare the obtained results, coefficient of determination (R 2 ), root mean squared error (RMSE) and average absolute error (AAE) indexes were chosen and calculated for both of the models. The results showed that artificial neural network and adaptive neuro-fuzzy system were both potentially strong for prediction of the mechanical properties of warm compacted 0.85 wt.% molybdenum prealloy; however, the proposed ANFIS showed better performance than the ANN model. Also, the ANFIS model was subjected to a sensitivity analysis to find the significant inputs affecting mechanical properties of the samples.

Neural model of gene regulatory network: a survey on supportive meta-heuristics.

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Biswas, Surama; Acharyya, Sriyankar

2016-06-01

Gene regulatory network (GRN) is produced as a result of regulatory interactions between different genes through their coded proteins in cellular context. Having immense importance in disease detection and drug finding, GRN has been modelled through various mathematical and computational schemes and reported in survey articles. Neural and neuro-fuzzy models have been the focus of attraction in bioinformatics. Predominant use of meta-heuristic algorithms in training neural models has proved its excellence. Considering these facts, this paper is organized to survey neural modelling schemes of GRN and the efficacy of meta-heuristic algorithms towards parameter learning (i.e. weighting connections) within the model. This survey paper renders two different structure-related approaches to infer GRN which are global structure approach and substructure approach. It also describes two neural modelling schemes, such as artificial neural network/recurrent neural network based modelling and neuro-fuzzy modelling. The meta-heuristic algorithms applied so far to learn the structure and parameters of neutrally modelled GRN have been reviewed here.

Exponential stability result for discrete-time stochastic fuzzy uncertain neural networks

International Nuclear Information System (INIS)

Mathiyalagan, K.; Sakthivel, R.; Marshal Anthoni, S.

2012-01-01

This Letter addresses the stability analysis problem for a class of uncertain discrete-time stochastic fuzzy neural networks (DSFNNs) with time-varying delays. By constructing a new Lyapunov–Krasovskii functional combined with the free weighting matrix technique, a new set of delay-dependent sufficient conditions for the robust exponential stability of the considered DSFNNs is established in terms of Linear Matrix Inequalities (LMIs). Finally, numerical examples with simulation results are provided to illustrate the applicability and usefulness of the obtained theory. -- Highlights: ► Applications of neural networks require the knowledge of dynamic behaviors. ► Exponential stability of discrete-time stochastic fuzzy neural networks is studied. ► Linear matrix inequality optimization approach is used to obtain the result. ► Delay-dependent stability criterion is established in terms of LMIs. ► Examples with simulation are provided to show the effectiveness of the result.

Mixed H-Infinity and Passive Filtering for Discrete Fuzzy Neural Networks With Stochastic Jumps and Time Delays.

Science.gov (United States)

Shi, Peng; Zhang, Yingqi; Chadli, Mohammed; Agarwal, Ramesh K

2016-04-01

In this brief, the problems of the mixed H-infinity and passivity performance analysis and design are investigated for discrete time-delay neural networks with Markovian jump parameters represented by Takagi-Sugeno fuzzy model. The main purpose of this brief is to design a filter to guarantee that the augmented Markovian jump fuzzy neural networks are stable in mean-square sense and satisfy a prescribed passivity performance index by employing the Lyapunov method and the stochastic analysis technique. Applying the matrix decomposition techniques, sufficient conditions are provided for the solvability of the problems, which can be formulated in terms of linear matrix inequalities. A numerical example is also presented to illustrate the effectiveness of the proposed techniques.

Neural-fuzzy control of adept one SCARA

International Nuclear Information System (INIS)

Er, M.J.; Toh, B.H.; Toh, B.Y.

1998-01-01

This paper presents an Intelligent Control Strategy for the Adept One SCARA (Selective Compliance Assembly Robot Arm). It covers the design and simulation study of a Neural-Fuzzy Controller (NFC) for the SCARA with a view of tracking a predetermined trajectory of motion in the joint space. The SCARA was simulated as a three-axis manipulator with the dynamics of the tool (fourth link) neglected and the mass of the load incorporated into the mass of the third link. The overall performance of the control system under different conditions, namely variation in playload, variations in coefficients of static, dynamic and viscous friction and different trajectories were studied and comparison made with an existing Neural Network Controller and two Computed Torque Controllers. The NFC was shown to be robust and is able to overcome the drawback of the existing Neural Network Controller

Integration of neural networks with fuzzy reasoning for measuring operational parameters in a nuclear reactor

International Nuclear Information System (INIS)

Ikonomopoulos, A.; Tsoukalas, L.H.

1993-01-01

A novel approach is described for measuring variables with operational significance in a complex system such as a nuclear reactor. The methodology is based on the integration of artificial neural networks with fuzzy reasoning. Neural networks are used to map dynamic time series to a set of user-defined linguistic labels called fuzzy values. The process takes place in a manner analogous to that of measurement. Hence, the entire procedure is referred to as virtual measurement and its software implementation as a virtual measuring device. An optimization algorithm based on information criteria and fuzzy algebra augments the process and assists in the identification of different states of the monitored parameter. The proposed technique is applied for monitoring parameters such as performance, valve position, transient type, and reactivity. The results obtained from the application of the neural network-fuzzy reasoning integration in a high power research reactor clearly demonstrate the excellent tolerance of the virtual measuring device to faulty signals as well as its ability to accommodate noisy inputs

Study on a Biometric Authentication Model based on ECG using a Fuzzy Neural Network

Science.gov (United States)

Kim, Ho J.; Lim, Joon S.

2018-03-01

Traditional authentication methods use numbers or graphic passwords and thus involve the risk of loss or theft. Various studies are underway regarding biometric authentication because it uses the unique biometric data of a human being. Biometric authentication technology using ECG from biometric data involves signals that record electrical stimuli from the heart. It is difficult to manipulate and is advantageous in that it enables unrestrained measurements from sensors that are attached to the skin. This study is on biometric authentication methods using the neural network with weighted fuzzy membership functions (NEWFM). In the biometric authentication process, normalization and the ensemble average is applied during preprocessing, characteristics are extracted using Haar-wavelets, and a registration process called “training” is performed in the fuzzy neural network. In the experiment, biometric authentication was performed on 73 subjects in the Physionet Database. 10-40 ECG waveforms were tested for use in the registration process, and 15 ECG waveforms were deemed the appropriate number for registering ECG waveforms. 1 ECG waveforms were used during the authentication stage to conduct the biometric authentication test. Upon testing the proposed biometric authentication method based on 73 subjects from the Physionet Database, the TAR was 98.32% and FAR was 5.84%.

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

Energy Technology Data Exchange (ETDEWEB)

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

2010-12-15

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

Hierarchical modular granular neural networks with fuzzy aggregation

CERN Document Server

Sanchez, Daniela

2016-01-01

In this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus resulting in a new concept of MNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL) and hierarchical genetic algorithms (HGAs) are techniques used in this research work to improve results. These techniques are chosen because in other works have demonstrated to be a good option, and in the case of MNNs and HGAs, these techniques allow to improve the results obtained than with their conventional versions; respectively artificial neural networks and genetic algorithms.

Issues in the use of neural networks in information retrieval

CERN Document Server

Iatan, Iuliana F

2017-01-01

This book highlights the ability of neural networks (NNs) to be excellent pattern matchers and their importance in information retrieval (IR), which is based on index term matching. The book defines a new NN-based method for learning image similarity and describes how to use fuzzy Gaussian neural networks to predict personality. It introduces the fuzzy Clifford Gaussian network, and two concurrent neural models: (1) concurrent fuzzy nonlinear perceptron modules, and (2) concurrent fuzzy Gaussian neural network modules. Furthermore, it explains the design of a new model of fuzzy nonlinear perceptron based on alpha level sets and describes a recurrent fuzzy neural network model with a learning algorithm based on the improved particle swarm optimization method.

Fuzzy-neural network in the automatic detection and volumetry of the spleen on spiral CT scans

International Nuclear Information System (INIS)

Heitmann, K.R.; Mainz Univ.; Rueckert, S.; Heussel, C.P.; Thelen, M.; Kauczor, H.U.; Uthmann, T.

2000-01-01

Purpose: To assess spleen segmentation and volumetry in spiral CT scans with and without pathological changes of splenic tissue. Methods: The image analysis software HYBRIKON is based on region growing, self-organized neural nets, and fuzzy-anatomic rules. The neural nets were trained with spiral CT data from 10 patients, not used in the following evaluation on spiral CT scans from 19 patients. An experienced radiologist verified the results. The true positive and false positive areas were compared in terms to the areas marked by the radiologist. The results were compared with a standard thresholding method. Results: The neural nets achieved a higher accuracy than the thresholding method. Correlation coefficient of the fuzzy-neural nets: 0.99 (thresholding: 0.63). Mean true positive rate: 90% (thresholding: 75%), mean false positive rate: 5% (thresholding>100%). Pitfalls were caused by accessory spleens, extreme changes in the morphology (tumors, metastases, cysts), and parasplenic masses. Conclusions: Self-organizing neural nets combined with fuzzy rules are ready for use in the automatic detection and volumetry of the spleen in spiral CT scans. (orig.) [de

Study on application of adaptive fuzzy control and neural network in the automatic leveling system

Science.gov (United States)

Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

2015-04-01

This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

The fundamental of fuzzy neutral network and application in nuclear monitoring

International Nuclear Information System (INIS)

Feng Diqing; Lei Ming

1996-01-01

The authors present a fuzzy modeling method using fuzzy neural network with the back-propagation algorithm. The new method can identify the fuzzy model of a nonlinear system automatically. Fuzzy neural network is used to generate fuzzy rules and membership functions. The feasibility and inferential statistics of the method is examined by using numerical data and XOR problem. As an experimental result, the FNN improves accuracy and reliability, saves design time and minimizes system cost of fuzzy design. The FNN can be used for estimation of human injury in nuclear explosions and can be simplified to a rule neural network (RNN), which is used for pole extraction of signal. Preliminary simulation shows that FNN has vast vistas in nuclear monitoring

Performance assessment of electric power generations using an adaptive neural network algorithm and fuzzy DEA

Energy Technology Data Exchange (ETDEWEB)

Javaheri, Zahra

2010-09-15

Modeling, evaluating and analyzing performance of Iranian thermal power plants is the main goal of this study which is based on multi variant methods analysis. These methods include fuzzy DEA and adaptive neural network algorithm. At first, we determine indicators, then data is collected, next we obtained values of ranking and efficiency by Fuzzy DEA, Case study is thermal power plants In view of the fact that investment to establish on power plant is very high, and maintenance of power plant causes an expensive expenditure, moreover using fossil fuel effected environment hence optimum produce of current power plants is important.

Fuzzy Evidence in Identification, Forecasting and Diagnosis

CERN Document Server

Rotshtein, Alexander P

2012-01-01

The purpose of this book is to present a methodology for designing and tuning fuzzy expert systems in order to identify nonlinear objects; that is, to build input-output models using expert and experimental information. The results of these identifications are used for direct and inverse fuzzy evidence in forecasting and diagnosis problem solving. The book is organised as follows: Chapter 1 presents the basic knowledge about fuzzy sets, genetic algorithms and neural nets necessary for a clear understanding of the rest of this book. Chapter 2 analyzes direct fuzzy inference based on fuzzy if-then rules. Chapter 3 is devoted to the tuning of fuzzy rules for direct inference using genetic algorithms and neural nets. Chapter 4 presents models and algorithms for extracting fuzzy rules from experimental data. Chapter 5 describes a method for solving fuzzy logic equations necessary for the inverse fuzzy inference in diagnostic systems. Chapters 6 and 7 are devoted to inverse fuzzy inference based on fu...

A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

Science.gov (United States)

Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng

2009-11-01

Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.

A spatial neural fuzzy network for estimating pan evaporation at ungauged sites

Directory of Open Access Journals (Sweden)

C.-H. Chung

2012-01-01

Full Text Available Evaporation is an essential reference to the management of water resources. In this study, a hybrid model that integrates a spatial neural fuzzy network with the kringing method is developed to estimate pan evaporation at ungauged sites. The adaptive network-based fuzzy inference system (ANFIS can extract the nonlinear relationship of observations, while kriging is an excellent geostatistical interpolator. Three-year daily data collected from nineteen meteorological stations covering the whole of Taiwan are used to train and test the constructed model. The pan evaporation (E_pan at ungauged sites can be obtained through summing up the outputs of the spatially weighted ANFIS and the residuals adjusted by kriging. Results indicate that the proposed AK model (hybriding ANFIS and kriging can effectively improve the accuracy of E_pan estimation as compared with that of empirical formula. This hybrid model demonstrates its reliability in estimating the spatial distribution of E_pan and consequently provides precise E_pan estimation by taking geographical features into consideration.

Robust adaptive fuzzy neural tracking control for a class of unknown ...

Indian Academy of Sciences (India)

In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is ... The robust controller is used to guarantee the stability and to control the per- ..... From the above analysis we have the following theorem:.

A Mamdani Adaptive Neural Fuzzy Inference System for Improvement of Groundwater Vulnerability.

Science.gov (United States)

Agoubi, Belgacem; Dabbaghi, Radhia; Kharroubi, Adel

2018-01-23

Assessing groundwater vulnerability is an important procedure for sustainable water management. Various methods have been developed for effective assessment of groundwater vulnerability and protection. However, each method has its own conditions of use and, in practice; it is difficult to return the same results for the same site. The research conceptualized and developed an improved DRASTIC method using Mamdani Adaptive Neural Fuzzy Inference System (M-ANFIS-DRASTIC). DRASTIC and M-ANFIS-DRASTIC were applied in the Jorf aquifer, southeastern Tunisia, and results were compared. Results confirm that M-ANFIS-DRASTIC combined with geostatistical tools is more powerful, generated more precise vulnerability classes with very low estimation variance. Fuzzy logic has a power to produce more realistic aquifer vulnerability assessments and introduces new ways of modeling in hydrogeology using natural human language expressed by logic rules. © 2018, National Ground Water Association.

Stability analysis of impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Li Zuoan; Li Kelin

2009-01-01

In this paper, we investigate a class of impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. By employing the delay differential inequality with impulsive initial conditions and M-matrix theory, we find some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. In particular, the estimate of the exponential converging index is also provided, which depends on the system parameters. An example is given to show the effectiveness of the results obtained here.

A Hybrid Neuro-Fuzzy Model For Integrating Large Earth-Science Datasets

Science.gov (United States)

Porwal, A.; Carranza, J.; Hale, M.

2004-12-01

A GIS-based hybrid neuro-fuzzy approach to integration of large earth-science datasets for mineral prospectivity mapping is described. It implements a Takagi-Sugeno type fuzzy inference system in the framework of a four-layered feed-forward adaptive neural network. Each unique combination of the datasets is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent datasets. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location) is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a prospectivity map. The procedure is demonstrated by an application to regional-scale base metal prospectivity mapping in a study area located in the Aravalli metallogenic province (western India). A comparison of the hybrid neuro-fuzzy approach with pure knowledge-driven fuzzy and pure data-driven neural network approaches indicates that the former offers a superior method for integrating large earth-science datasets for predictive spatial mathematical modelling.

Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects.

Science.gov (United States)

Tan, Shing Chiang; Watada, Junzo; Ibrahim, Zuwairie; Khalid, Marzuki

2015-05-01

Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.

Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model

Directory of Open Access Journals (Sweden)

Bogdan Gliwa

2011-01-01

Full Text Available The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods. Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.

Neuro-fuzzy inverse model control structure of robotic manipulators utilized for physiotherapy applications

Directory of Open Access Journals (Sweden)

A.A. Fahmy

2013-12-01

Full Text Available This paper presents a new neuro-fuzzy controller for robot manipulators. First, an inductive learning technique is applied to generate the required inverse modeling rules from input/output data recorded in the off-line structure learning phase. Second, a fully differentiable fuzzy neural network is developed to construct the inverse dynamics part of the controller for the online parameter learning phase. Finally, a fuzzy-PID-like incremental controller was employed as Feedback servo controller. The proposed control system was tested using dynamic model of a six-axis industrial robot. The control system showed good results compared to the conventional PID individual joint controller.

A New Prediction Model for Transformer Winding Hotspot Temperature Fluctuation Based on Fuzzy Information Granulation and an Optimized Wavelet Neural Network

Directory of Open Access Journals (Sweden)

Li Zhang

2017-12-01

Full Text Available Winding hotspot temperature is the key factor affecting the load capacity and service life of transformers. For the early detection of transformer winding hotspot temperature anomalies, a new prediction model for the hotspot temperature fluctuation range based on fuzzy information granulation (FIG and the chaotic particle swarm optimized wavelet neural network (CPSO-WNN is proposed in this paper. The raw data are firstly processed by FIG to extract useful information from each time window. The extracted information is then used to construct a wavelet neural network (WNN prediction model. Furthermore, the structural parameters of WNN are optimized by chaotic particle swarm optimization (CPSO before it is used to predict the fluctuation range of the hotspot temperature. By analyzing the experimental data with four different prediction models, we find that the proposed method is more effective and is of guiding significance for the operation and maintenance of transformers.

Backstepping fuzzy-neural-network control design for hybrid maglev transportation system.

Science.gov (United States)

Wai, Rong-Jong; Yao, Jing-Xiang; Lee, Jeng-Dao

2015-02-01

This paper focuses on the design of a backstepping fuzzy-neural-network control (BFNNC) for the online levitated balancing and propulsive positioning of a hybrid magnetic levitation (maglev) transportation system. The dynamic model of the hybrid maglev transportation system including levitated hybrid electromagnets to reduce the suspension power loss and the friction force during linear movement and a propulsive linear induction motor based on the concepts of mechanical geometry and motion dynamics is first constructed. The ultimate goal is to design an online fuzzy neural network (FNN) control methodology to cope with the problem of the complicated control transformation and the chattering control effort in backstepping control (BSC) design, and to directly ensure the stability of the controlled system without the requirement of strict constraints, detailed system information, and auxiliary compensated controllers despite the existence of uncertainties. In the proposed BFNNC scheme, an FNN control is utilized to be the major control role by imitating the BSC strategy, and adaptation laws for network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the network convergence as well as stable control performance. The effectiveness of the proposed control strategy for the hybrid maglev transportation system is verified by experimental results, and the superiority of the BFNNC scheme is indicated in comparison with the BSC strategy and the backstepping particle-swarm-optimization control system in previous research.

Modeling, control, and simulation of grid connected intelligent hybrid battery/photovoltaic system using new hybrid fuzzy-neural method.

Science.gov (United States)

Rezvani, Alireza; Khalili, Abbas; Mazareie, Alireza; Gandomkar, Majid

2016-07-01

Nowadays, photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is its dependence on weather conditions. Therefore, battery energy storage (BES) can be considered to assist for a stable and reliable output from PV generation system for loads and improve the dynamic performance of the whole generation system in grid connected mode. In this paper, a novel topology of intelligent hybrid generation systems with PV and BES in a DC-coupled structure is presented. Each photovoltaic cell has a specific point named maximum power point on its operational curve (i.e. current-voltage or power-voltage curve) in which it can generate maximum power. Irradiance and temperature changes affect these operational curves. Therefore, the nonlinear characteristic of maximum power point to environment has caused to development of different maximum power point tracking techniques. In order to capture the maximum power point (MPP), a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. Obtained results represent the effectiveness and superiority of the proposed method, and the average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison to the conventional methods. It has the advantages of robustness, fast response and good performance. A detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

Directory of Open Access Journals (Sweden)

Leandro L. S. Linhares

2015-01-01

Full Text Available Recent researches have demonstrated that the Fuzzy Wavelet Neural Networks (FWNNs are an efficient tool to identify nonlinear systems. In these structures, features related to fuzzy logic, wavelet functions, and neural networks are combined in an architecture similar to the Adaptive Neurofuzzy Inference Systems (ANFIS. In practical applications, the experimental data set used in the identification task often contains unknown noise and outliers, which decrease the FWNN model reliability. In order to reduce the negative effects of these erroneous measurements, this work proposes the direct use of a similarity measure based on information theory in the FWNN learning procedure. The Mean Squared Error (MSE cost function is replaced by the Maximum Correntropy Criterion (MCC in the traditional error backpropagation (BP algorithm. The input-output maps of a real nonlinear system studied in this work are identified from an experimental data set corrupted by different outliers rates and additive white Gaussian noise. The results demonstrate the advantages of the proposed cost function using the MCC as compared to the MSE. This work also investigates the influence of the kernel size on the performance of the MCC in the BP algorithm, since it is the only free parameter of correntropy.

Classification and prediction of the critical heat flux using fuzzy theory and artificial neural networks

International Nuclear Information System (INIS)

Moon, Sang Ki; Chang, Soon Heung

1994-01-01

A new method to predict the critical heat flux (CHF) is proposed, based on the fuzzy clustering and artificial neural network. The fuzzy clustering classifies the experimental CHF data into a few data clusters (data groups) according to the data characteristics. After classification of the experimental data, the characteristics of the resulting clusters are discussed with emphasis on the distribution of the experimental conditions and physical mechanism. The CHF data in each group are trained in an artificial neural network to predict the CHF. The artificial neural network adjusts the weight so as to minimize the prediction error within the corresponding cluster. Application of the proposed method to the KAIST CHF data bank shows good prediction capability of the CHF, better than other existing methods. ((orig.))

Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network.

Science.gov (United States)

Liu, Yu-Ting; Lin, Yang-Yin; Wu, Shang-Lin; Chuang, Chun-Hsiang; Lin, Chin-Teng

2016-02-01

This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.

Fuzzy-Neural Controller in Service Requests Distribution Broker for SOA-Based Systems

Science.gov (United States)

Fras, Mariusz; Zatwarnicka, Anna; Zatwarnicki, Krzysztof

The evolution of software architectures led to the rising importance of the Service Oriented Architecture (SOA) concept. This architecture paradigm support building flexible distributed service systems. In the paper the architecture of service request distribution broker designed for use in SOA-based systems is proposed. The broker is built with idea of fuzzy control. The functional and non-functional request requirements in conjunction with monitoring of execution and communication links are used to distribute requests. Decisions are made with use of fuzzy-neural network.

A fuzzy art neural network based color image processing and ...

African Journals Online (AJOL)

To improve the learning process from the input data, a new learning rule was suggested. In this paper, a new method is proposed to deal with the RGB color image pixels, which enables a Fuzzy ART neural network to process the RGB color images. The application of the algorithm was implemented and tested on a set of ...

Global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Wang Xiaohu; Xu Daoyi

2009-01-01

In this paper, the global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms is considered. By establishing an integro-differential inequality with impulsive initial condition and using the properties of M-cone and eigenspace of the spectral radius of nonnegative matrices, several new sufficient conditions are obtained to ensure the global exponential stability of the equilibrium point for fuzzy cellular neural networks with delays and reaction-diffusion terms. These results extend and improve the earlier publications. Two examples are given to illustrate the efficiency of the obtained results.

A novel methodology improves reservoir characterization models using geologic fuzzy variables

Energy Technology Data Exchange (ETDEWEB)

Soto B, Rodolfo [DIGITOIL, Maracaibo (Venezuela); Soto O, David A. [Texas A and M University, College Station, TX (United States)

2004-07-01

One of the research projects carried out in Cusiana field to explain its rapid decline during the last years was to get better permeability models. The reservoir of this field has a complex layered system that it is not easy to model using conventional methods. The new technique included the development of porosity and permeability maps from cored wells following the same trend of the sand depositions for each facie or layer according to the sedimentary facie and the depositional system models. Then, we used fuzzy logic to reproduce those maps in three dimensions as geologic fuzzy variables. After multivariate statistical and factor analyses, we found independence and a good correlation coefficient between the geologic fuzzy variables and core permeability and porosity. This means, the geologic fuzzy variable could explain the fabric, the grain size and the pore geometry of the reservoir rock trough the field. Finally, we developed a neural network permeability model using porosity, gamma ray and the geologic fuzzy variable as input variables. This model has a cross-correlation coefficient of 0.873 and average absolute error of 33% compared with the actual model with a correlation coefficient of 0.511 and absolute error greater than 250%. We tested different methodologies, but this new one showed dramatically be a promiser way to get better permeability models. The use of the models have had a high impact in the explanation of well performance and workovers, and reservoir simulation models. (author)

Application of ANNs approach for solving fully fuzzy polynomials system

Directory of Open Access Journals (Sweden)

R. Novin

2017-11-01

Full Text Available In processing indecisive or unclear information, the advantages of fuzzy logic and neurocomputing disciplines should be taken into account and combined by fuzzy neural networks. The current research intends to present a fuzzy modeling method using multi-layer fuzzy neural networks for solving a fully fuzzy polynomials system. To clarify the point, it is necessary to inform that a supervised gradient descent-based learning law is employed. The feasibility of the method is examined using computer simulations on a numerical example. The experimental results obtained from the investigation of the proposed method are valid and delivers very good approximation results.

Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques

Science.gov (United States)

Kisi, Ozgur; Sanikhani, Hadi; Cobaner, Murat

2017-08-01

The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models' accuracy was also investigated. Including periodicity component in models' inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.

Stability analysis of polynomial fuzzy models via polynomial fuzzy Lyapunov functions

OpenAIRE

Bernal Reza, Miguel Ángel; Sala, Antonio; JAADARI, ABDELHAFIDH; Guerra, Thierry-Marie

2011-01-01

In this paper, the stability of continuous-time polynomial fuzzy models by means of a polynomial generalization of fuzzy Lyapunov functions is studied. Fuzzy Lyapunov functions have been fruitfully used in the literature for local analysis of Takagi-Sugeno models, a particular class of the polynomial fuzzy ones. Based on a recent Taylor-series approach which allows a polynomial fuzzy model to exactly represent a nonlinear model in a compact set of the state space, it is shown that a refinemen...

Design of fuzzy systems using neurofuzzy networks.

Science.gov (United States)

Figueiredo, M; Gomide, F

1999-01-01

This paper introduces a systematic approach for fuzzy system design based on a class of neural fuzzy networks built upon a general neuron model. The network structure is such that it encodes the knowledge learned in the form of if-then fuzzy rules and processes data following fuzzy reasoning principles. The technique provides a mechanism to obtain rules covering the whole input/output space as well as the membership functions (including their shapes) for each input variable. Such characteristics are of utmost importance in fuzzy systems design and application. In addition, after learning, it is very simple to extract fuzzy rules in the linguistic form. The network has universal approximation capability, a property very useful in, e.g., modeling and control applications. Here we focus on function approximation problems as a vehicle to illustrate its usefulness and to evaluate its performance. Comparisons with alternative approaches are also included. Both, nonnoisy and noisy data have been studied and considered in the computational experiments. The neural fuzzy network developed here and, consequently, the underlying approach, has shown to provide good results from the accuracy, complexity, and system design points of view.

Forecasting business cycle with chaotic time series based on neural network with weighted fuzzy membership functions

International Nuclear Information System (INIS)

Chai, Soo H.; Lim, Joon S.

2016-01-01

This study presents a forecasting model of cyclical fluctuations of the economy based on the time delay coordinate embedding method. The model uses a neuro-fuzzy network called neural network with weighted fuzzy membership functions (NEWFM). The preprocessed time series of the leading composite index using the time delay coordinate embedding method are used as input data to the NEWFM to forecast the business cycle. A comparative study is conducted using other methods based on wavelet transform and Principal Component Analysis for the performance comparison. The forecasting results are tested using a linear regression analysis to compare the approximation of the input data against the target class, gross domestic product (GDP). The chaos based model captures nonlinear dynamics and interactions within the system, which other two models ignore. The test results demonstrated that chaos based method significantly improved the prediction capability, thereby demonstrating superior performance to the other methods.

A neuro-fuzzy computing technique for modeling hydrological time series

Science.gov (United States)

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

2004-05-01

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proven to be efficient when applied individually to a variety of problems. Recently there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have evolved. This approach has been tested and evaluated in the field of signal processing and related areas, but researchers have only begun evaluating the potential of this neuro-fuzzy hybrid approach in hydrologic modeling studies. This paper presents the application of an adaptive neuro fuzzy inference system (ANFIS) to hydrologic time series modeling, and is illustrated by an application to model the river flow of Baitarani River in Orissa state, India. An introduction to the ANFIS modeling approach is also presented. The advantage of the method is that it does not require the model structure to be known a priori, in contrast to most of the time series modeling techniques. The results showed that the ANFIS forecasted flow series preserves the statistical properties of the original flow series. The model showed good performance in terms of various statistical indices. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANNs and other traditional time series models in terms of computational speed, forecast errors, efficiency, peak flow estimation etc. It was observed that the ANFIS model preserves the potential of the ANN approach fully, and eases the model building process.

Assessing Wheat Frost Risk with the Support of GIS: An Approach Coupling a Growing Season Meteorological Index and a Hybrid Fuzzy Neural Network Model

Directory of Open Access Journals (Sweden)

Yaojie Yue

2016-12-01

Full Text Available Crop frost, one kind of agro-meteorological disaster, often causes significant loss to agriculture. Thus, evaluating the risk of wheat frost aids scientific response to such disasters, which will ultimately promote food security. Therefore, this paper aims to propose an integrated risk assessment model of wheat frost, based on meteorological data and a hybrid fuzzy neural network model, taking China as an example. With the support of a geographic information system (GIS, a comprehensive method was put forward. Firstly, threshold temperatures of wheat frost at three growth stages were proposed, referring to phenology in different wheat growing areas and the meteorological standard of Degree of Crop Frost Damage (QX/T 88-2008. Secondly, a vulnerability curve illustrating the relationship between frost hazard intensity and wheat yield loss was worked out using hybrid fuzzy neural network model. Finally, the wheat frost risk was assessed in China. Results show that our proposed threshold temperatures are more suitable than using 0 °C in revealing the spatial pattern of frost occurrence, and hybrid fuzzy neural network model can further improve the accuracy of the vulnerability curve of wheat subject to frost with limited historical hazard records. Both these advantages ensure the precision of wheat frost risk assessment. In China, frost widely distributes in 85.00% of the total winter wheat planting area, but mainly to the north of 35°N; the southern boundary of wheat frost has moved northward, potentially because of the warming climate. There is a significant trend that suggests high risk areas will enlarge and gradually expand to the south, with the risk levels increasing from a return period of 2 years to 20 years. Among all wheat frost risk levels, the regions with loss rate ranges from 35.00% to 45.00% account for the largest area proportion, ranging from 58.60% to 63.27%. We argue that for wheat and other frost-affected crops, it is

A medical cost estimation with fuzzy neural network of acute hepatitis patients in emergency room.

Science.gov (United States)

Kuo, R J; Cheng, W C; Lien, W C; Yang, T J

2015-10-01

Taiwan is an area where chronic hepatitis is endemic. Liver cancer is so common that it has been ranked first among cancer mortality rates since the early 1980s in Taiwan. Besides, liver cirrhosis and chronic liver diseases are the sixth or seventh in the causes of death. Therefore, as shown by the active research on hepatitis, it is not only a health threat, but also a huge medical cost for the government. The estimated total number of hepatitis B carriers in the general population aged more than 20 years old is 3,067,307. Thus, a case record review was conducted from all patients with diagnosis of acute hepatitis admitted to the Emergency Department (ED) of a well-known teaching-oriented hospital in Taipei. The cost of medical resource utilization is defined as the total medical fee. In this study, a fuzzy neural network is employed to develop the cost forecasting model. A total of 110 patients met the inclusion criteria. The computational results indicate that the FNN model can provide more accurate forecasts than the support vector regression (SVR) or artificial neural network (ANN). In addition, unlike SVR and ANN, FNN can also provide fuzzy IF-THEN rules for interpretation. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Prediction of hydrogen concentration in nuclear power plant containment under severe accidents using cascaded fuzzy neural networks

Energy Technology Data Exchange (ETDEWEB)

Choi, Geon Pil; Kim, Dong Yeong; Yoo, Kwae Hwan; Na, Man Gyun, E-mail: magyna@chosun.ac.kr

2016-04-15

Highlights: • We present a hydrogen-concentration prediction method in an NPP containment. • The cascaded fuzzy neural network (CFNN) is used in this prediction model. • The CFNN model is much better than the existing FNN model. • This prediction can help prevent severe accidents in NPP due to hydrogen explosion. - Abstract: Recently, severe accidents in nuclear power plants (NPPs) have attracted worldwide interest since the Fukushima accident. If the hydrogen concentration in an NPP containment is increased above 4% in atmospheric pressure, hydrogen combustion will likely occur. Therefore, the hydrogen concentration must be kept below 4%. This study presents the prediction of hydrogen concentration using cascaded fuzzy neural network (CFNN). The CFNN model repeatedly applies FNN modules that are serially connected. The CFNN model was developed using data on severe accidents in NPPs. The data were obtained by numerically simulating the accident scenarios using the MAAP4 code for optimized power reactor 1000 (OPR1000) because real severe accident data cannot be obtained from actual NPP accidents. The root-mean-square error level predicted by the CFNN model is below approximately 5%. It was confirmed that the CFNN model could accurately predict the hydrogen concentration in the containment. If NPP operators can predict the hydrogen concentration in the containment using the CFNN model, this prediction can assist them in preventing a hydrogen explosion.

Study on intelligence fault diagnosis method for nuclear power plant equipment based on rough set and fuzzy neural network

International Nuclear Information System (INIS)

Liu Yongkuo; Xia Hong; Xie Chunli; Chen Zhihui; Chen Hongxia

2007-01-01

Rough set theory and fuzzy neural network are combined, to take full advantages of the two of them. Based on the reduction technology to knowledge of Rough set method, and by drawing the simple rule from a large number of initial data, the fuzzy neural network was set up, which was with better topological structure, improved study speed, accurate judgment, strong fault-tolerant ability, and more practical. In order to test the validity of the method, the inverted U-tubes break accident of Steam Generator and etc are used as examples, and many simulation experiments are performed. The test result shows that it is feasible to incorporate the fault intelligence diagnosis method based on rough set and fuzzy neural network in the nuclear power plant equipment, and the method is simple and convenience, with small calculation amount and reliable result. (authors)

Fuzzy portfolio model with fuzzy-input return rates and fuzzy-output proportions

Science.gov (United States)

Tsaur, Ruey-Chyn

2015-02-01

In the finance market, a short-term investment strategy is usually applied in portfolio selection in order to reduce investment risk; however, the economy is uncertain and the investment period is short. Further, an investor has incomplete information for selecting a portfolio with crisp proportions for each chosen security. In this paper we present a new method of constructing fuzzy portfolio model for the parameters of fuzzy-input return rates and fuzzy-output proportions, based on possibilistic mean-standard deviation models. Furthermore, we consider both excess or shortage of investment in different economic periods by using fuzzy constraint for the sum of the fuzzy proportions, and we also refer to risks of securities investment and vagueness of incomplete information during the period of depression economics for the portfolio selection. Finally, we present a numerical example of a portfolio selection problem to illustrate the proposed model and a sensitivity analysis is realised based on the results.

Distributed collaborative probabilistic design of multi-failure structure with fluid-structure interaction using fuzzy neural network of regression

Science.gov (United States)

Song, Lu-Kai; Wen, Jie; Fei, Cheng-Wei; Bai, Guang-Chen

2018-05-01

To improve the computing efficiency and precision of probabilistic design for multi-failure structure, a distributed collaborative probabilistic design method-based fuzzy neural network of regression (FR) (called as DCFRM) is proposed with the integration of distributed collaborative response surface method and fuzzy neural network regression model. The mathematical model of DCFRM is established and the probabilistic design idea with DCFRM is introduced. The probabilistic analysis of turbine blisk involving multi-failure modes (deformation failure, stress failure and strain failure) was investigated by considering fluid-structure interaction with the proposed method. The distribution characteristics, reliability degree, and sensitivity degree of each failure mode and overall failure mode on turbine blisk are obtained, which provides a useful reference for improving the performance and reliability of aeroengine. Through the comparison of methods shows that the DCFRM reshapes the probability of probabilistic analysis for multi-failure structure and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of multi-failure structure and thereby also enriches the theory and method of mechanical reliability design.

Estimation of LOCA break size using cascaded Fuzzy neural networks

Energy Technology Data Exchange (ETDEWEB)

Choi, Geon Pil; Yoo, Kwae Hwan; Back, Ju Hyun; Na, Man Gyun [Dept. of Nuclear Engineering, Chosun University, Gwangju (Korea, Republic of)

2017-04-15

Operators of nuclear power plants may not be equipped with sufficient information during a loss-of-coolant accident (LOCA), which can be fatal, or they may not have sufficient time to analyze the information they do have, even if this information is adequate. It is not easy to predict the progression of LOCAs in nuclear power plants. Therefore, accurate information on the LOCA break position and size should be provided to efficiently manage the accident. In this paper, the LOCA break size is predicted using a cascaded fuzzy neural network (CFNN) model. The input data of the CFNN model are the time-integrated values of each measurement signal for an initial short-time interval after a reactor scram. The training of the CFNN model is accomplished by a hybrid method combined with a genetic algorithm and a least squares method. As a result, LOCA break size is estimated exactly by the proposed CFNN model.

Fuzzy bilevel programming with multiple non-cooperative followers: model, algorithm and application

Science.gov (United States)

Ke, Hua; Huang, Hu; Ralescu, Dan A.; Wang, Lei

2016-04-01

In centralized decision problems, it is not complicated for decision-makers to make modelling technique selections under uncertainty. When a decentralized decision problem is considered, however, choosing appropriate models is no longer easy due to the difficulty in estimating the other decision-makers' inconclusive decision criteria. These decision criteria may vary with different decision-makers because of their special risk tolerances and management requirements. Considering the general differences among the decision-makers in decentralized systems, we propose a general framework of fuzzy bilevel programming including hybrid models (integrated with different modelling methods in different levels). Specially, we discuss two of these models which may have wide applications in many fields. Furthermore, we apply the proposed two models to formulate a pricing decision problem in a decentralized supply chain with fuzzy coefficients. In order to solve these models, a hybrid intelligent algorithm integrating fuzzy simulation, neural network and particle swarm optimization based on penalty function approach is designed. Some suggestions on the applications of these models are also presented.

Fuzzy Control Teaching Models

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Klaus-Dietrich Kramer

2016-05-01

Full Text Available Many degree courses at technical universities include the subject of control systems engineering. As an addition to conventional approaches Fuzzy Control can be used to easily find control solutions for systems, even if they include nonlinearities. To support further educational training, models which represent a technical system to be controlled are required. These models have to represent the system in a transparent and easy cognizable manner. Furthermore, a programming tool is required that supports an easy Fuzzy Control development process, including the option to verify the results and tune the system behavior. In order to support the development process a graphical user interface is needed to display the fuzzy terms under real time conditions, especially with a debug system and trace functionality. The experiences with such a programming tool, the Fuzzy Control Design Tool (FHFCE Tool, and four fuzzy teaching models will be presented in this paper. The methodical and didactical objective in the utilization of these teaching models is to develop solution strategies using Computational Intelligence (CI applications for Fuzzy Controllers in order to analyze different algorithms of inference or defuzzyfication and to verify and tune those systems efficiently.

Development of classification and prediction methods of critical heat flux using fuzzy theory and artificial neural networks

International Nuclear Information System (INIS)

Moon, Sang Ki

1995-02-01

This thesis applies new information techniques, artificial neural networks, (ANNs) and fuzzy theory, to the investigation of the critical heat flux (CHF) phenomenon for water flow in vertical round tubes. The work performed are (a) classification and prediction of CHF based on fuzzy clustering and ANN, (b) prediction and parametric trends analysis of CHF using ANN with the introduction of dimensionless parameters, and (c) detection of CHF occurrence using fuzzy rule and spatiotemporal neural network (STN). Fuzzy clustering and ANN are used for classification and prediction of the CHF using primary system parameters. The fuzzy clustering classifies the experimental CHF data into a few data clusters (data groups) according to the data characteristics. After classification of the experimental data, the characteristics of the resulted clusters are discussed with emphasis on the distribution of the experimental conditions and physical mechanisms. The CHF data in each group are trained in an artificial neural network to predict the CHF. The artificial neural network adjusts the weight so as to minimize the prediction error within the corresponding cluster. Application of the proposed method to the KAIST CHF data bank shows good prediction capability of the CHF, better than other existing methods. Parametric trends of the CHF are analyzed by applying artificial neural networks to a CHF data base for water flow in uniformly heated vertical round tubes. The analyses are performed from three viewpoints, i.e., for fixed inlet conditions, for fixed exit conditions, and based on local conditions hypothesis. In order to remove the necessity of data classification, Katto and Groeneveld et al.'s dimensionless parameters are introduced in training the ANNs with the experimental CHF data. The trained ANNs predict the CHF better than any other conventional correlations, showing RMS error of 8.9%, 13.1%, and 19.3% for fixed inlet conditions, for fixed exit conditions, and for local

Predicting Subcontractor Performance Using Web-Based Evolutionary Fuzzy Neural Networks

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Chien-Ho Ko

2013-01-01

Full Text Available Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs, Fuzzy Logic (FL, and Neural Networks (NNs. FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.

Predicting subcontractor performance using web-based Evolutionary Fuzzy Neural Networks.

Science.gov (United States)

Ko, Chien-Ho

2013-01-01

Subcontractor performance directly affects project success. The use of inappropriate subcontractors may result in individual work delays, cost overruns, and quality defects throughout the project. This study develops web-based Evolutionary Fuzzy Neural Networks (EFNNs) to predict subcontractor performance. EFNNs are a fusion of Genetic Algorithms (GAs), Fuzzy Logic (FL), and Neural Networks (NNs). FL is primarily used to mimic high level of decision-making processes and deal with uncertainty in the construction industry. NNs are used to identify the association between previous performance and future status when predicting subcontractor performance. GAs are optimizing parameters required in FL and NNs. EFNNs encode FL and NNs using floating numbers to shorten the length of a string. A multi-cut-point crossover operator is used to explore the parameter and retain solution legality. Finally, the applicability of the proposed EFNNs is validated using real subcontractors. The EFNNs are evolved using 22 historical patterns and tested using 12 unseen cases. Application results show that the proposed EFNNs surpass FL and NNs in predicting subcontractor performance. The proposed approach improves prediction accuracy and reduces the effort required to predict subcontractor performance, providing field operators with web-based remote access to a reliable, scientific prediction mechanism.

Four Degree Freedom Robot Arm with Fuzzy Neural Network Control

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Şinasi Arslan

2013-01-01

Full Text Available In this study, the control of four degree freedom robot arm has been realized with the computed torque control method.. It is usually required that the four jointed robot arm has high precision capability and good maneuverability for using in industrial applications. Besides, high speed working and external applied loads have been acting as important roles. For those purposes, the computed torque control method has been developed in a good manner that the robot arm can track the given trajectory, which has been able to enhance the feedback control together with fuzzy neural network control. The simulation results have proved that the computed torque control with the neural network has been so successful in robot control.

Linear matrix inequality approach for synchronization control of fuzzy cellular neural networks with mixed time delays

International Nuclear Information System (INIS)

Balasubramaniam, P.; Kalpana, M.; Rakkiyappan, R.

2012-01-01

Fuzzy cellular neural networks (FCNNs) are special kinds of cellular neural networks (CNNs). Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The design of FCNNs is based on fuzzy local rules. In this paper, a linear matrix inequality (LMI) approach for synchronization control of FCNNs with mixed delays is investigated. Mixed delays include discrete time-varying delays and unbounded distributed delays. A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network. By constructing the Lyapunov—Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs. The controller can be easily obtained by solving the derived LMIs. A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method. (interdisciplinary physics and related areas of science and technology)

Almost sure exponential stability of stochastic fuzzy cellular neural networks with delays

International Nuclear Information System (INIS)

Zhao Hongyong; Ding Nan; Chen Ling

2009-01-01

This paper is concerned with the problem of exponential stability analysis for fuzzy cellular neural network with delays. By constructing suitable Lyapunov functional and using stochastic analysis we present some sufficient conditions ensuring almost sure exponential stability for the network. Moreover, an example is given to demonstrate the advantages of our method.

Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning

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Ya’nan Wang

2016-01-01

Full Text Available Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.

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

Science.gov (United States)

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

2012-11-01

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.

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

International Nuclear Information System (INIS)

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

2012-01-01

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. (paper)

Fuzzy Neuroidal Nets and Recurrent Fuzzy Computations

Czech Academy of Sciences Publication Activity Database

Wiedermann, Jiří

2001-01-01

Roč. 11, č. 6 (2001), s. 675-686 ISSN 1210-0552. [SOFSEM 2001 Workshop on Soft Computing. Piešťany, 29.11.2001-30.11.2001] R&D Projects: GA ČR GA201/00/1489; GA AV ČR KSK1019101 Institutional research plan: AV0Z1030915 Keywords : fuzzy computing * fuzzy neural nets * fuzzy Turing machines * non-uniform computational complexity Subject RIV: BA - General Mathematics

Comparison of fuzzy logic and neural network in maximum power point tracker for PV systems

Energy Technology Data Exchange (ETDEWEB)

Ben Salah, Chokri; Ouali, Mohamed [Research Unit on Intelligent Control, Optimization, Design and Optimization of Complex Systems (ICOS), Department of Electrical Engineering, National School of Engineers of Sfax, BP. W, 3038, Sfax (Tunisia)

2011-01-15

This paper proposes two methods of maximum power point tracking using a fuzzy logic and a neural network controllers for photovoltaic systems. The two maximum power point tracking controllers receive solar radiation and photovoltaic cell temperature as inputs, and estimated the optimum duty cycle corresponding to maximum power as output. The approach is validated on a 100 Wp PVP (two parallels SM50-H panel) connected to a 24 V dc load. The new method gives a good maximum power operation of any photovoltaic array under different conditions such as changing solar radiation and PV cell temperature. From the simulation and experimental results, the fuzzy logic controller can deliver more power than the neural network controller and can give more power than other different methods in literature. (author)

Lag synchronization of unknown chaotic delayed Yang-Yang-type fuzzy neural networks with noise perturbation based on adaptive control and parameter identification.

Science.gov (United States)

Xia, Yonghui; Yang, Zijiang; Han, Maoan

2009-07-01

This paper considers the lag synchronization (LS) issue of unknown coupled chaotic delayed Yang-Yang-type fuzzy neural networks (YYFCNN) with noise perturbation. Separate research work has been published on the stability of fuzzy neural network and LS issue of unknown coupled chaotic neural networks, as well as its application in secure communication. However, there have not been any studies that integrate the two. Motivated by the achievements from both fields, we explored the benefits of integrating fuzzy logic theories into the study of LS problems and applied the findings to secure communication. Based on adaptive feedback control techniques and suitable parameter identification, several sufficient conditions are developed to guarantee the LS of coupled chaotic delayed YYFCNN with or without noise perturbation. The problem studied in this paper is more general in many aspects. Various problems studied extensively in the literature can be treated as special cases of the findings of this paper, such as complete synchronization (CS), effect of fuzzy logic, and noise perturbation. This paper presents an illustrative example and uses simulated results of this example to show the feasibility and effectiveness of the proposed adaptive scheme. This research also demonstrates the effectiveness of application of the proposed adaptive feedback scheme in secure communication by comparing chaotic masking with fuzziness with some previous studies. Chaotic signal with fuzziness is more complex, which makes unmasking more difficult due to the added fuzzy logic.

DEVELOPMENT OF FUZZY NEURAL NETWORK FOR THE INTERPRETATION OF THE RESULTS OF DISSOLVED IN OIL GASES ANALYSIS

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V.Е. Bondarenko

2017-04-01

Full Text Available Purpose. The purpose of this paper is a diagnosis of power transformers on the basis of the results of the analysis of gases dissolved in oil. Methodology. To solve this problem a fuzzy neural network has been developed, tested and trained. Results. The analysis of neural network to recognize the possibility of developing defects at an early stage of their development, or growth of gas concentrations in the healthy transformers, made after the emergency actions on the part of electric networks is made. It has been established greatest difficulty in making a diagnosis on the criterion of the boundary gas concentrations, are the results of DGA obtained for the healthy transformers in which the concentration of gases dissolved in oil exceed their limit values, as well as defective transformers at an early stage development defects. The analysis showed that the accuracy of recognition of fuzzy neural networks has its limitations, which are determined by the peculiarities of the DGA method, used diagnostic features and the selected decision rule. Originality. Unlike similar studies in the training of the neural network, the membership functions of linguistic terms were chosen taking into account the functions gas concentrations density distribution transformers with various diagnoses, allowing to consider a particular gas content of oils that are typical of a leaky transformer, and the operating conditions of the equipment. Practical value. Developed fuzzy neural network allows to perform diagnostics of power transformers on the basis of the result of the analysis of gases dissolved in oil, with a high level of reliability.

The attractor recurrent neural network based on fuzzy functions: An effective model for the classification of lung abnormalities.

Science.gov (United States)

Khodabakhshi, Mohammad Bagher; Moradi, Mohammad Hassan

2017-05-01

The respiratory system dynamic is of high significance when it comes to the detection of lung abnormalities, which highlights the importance of presenting a reliable model for it. In this paper, we introduce a novel dynamic modelling method for the characterization of the lung sounds (LS), based on the attractor recurrent neural network (ARNN). The ARNN structure allows the development of an effective LS model. Additionally, it has the capability to reproduce the distinctive features of the lung sounds using its formed attractors. Furthermore, a novel ARNN topology based on fuzzy functions (FFs-ARNN) is developed. Given the utility of the recurrent quantification analysis (RQA) as a tool to assess the nature of complex systems, it was used to evaluate the performance of both the ARNN and the FFs-ARNN models. The experimental results demonstrate the effectiveness of the proposed approaches for multichannel LS analysis. In particular, a classification accuracy of 91% was achieved using FFs-ARNN with sequences of RQA features. Copyright © 2017 Elsevier Ltd. All rights reserved.

Driver's Behavior Modeling Using Fuzzy Logic

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Sehraneh Ghaemi

2010-01-01

Full Text Available In this study, we propose a hierarchical fuzzy system for human in a driver-vehicle-environment system to model takeover by different drivers. The driver's behavior is affected by the environment. The climate, road and car conditions are included in fuzzy modeling. For obtaining fuzzy rules, experts' opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. Also the precision, age and driving individuality are used to model the driver's behavior. Three different positions are considered for driving and decision making. A fuzzy model called Model I is presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. Also we obtained two other models based on fuzzy rules called Model II and Model III by using Sugeno fuzzy inference. Model II and Model III have less linguistic terms than Model I for the steering angle and direction of car. The results of three models are compared for a driver who drives based on driving laws.

Predicting diametral creep of the pressure tubes in CANDU reactors using fuzzy neural networks

International Nuclear Information System (INIS)

Lee, Jae Yong; Na, Man Gyun; Park, Jong Ho

2011-01-01

Pressure tube (PT) creep is one of the principal aging mechanisms governing the heat transfer and hydraulic degradation of the heat transport system (HTS) in Canada deuterium uranium reactors. PT diametral creep affects the thermal hydraulic characteristics of coolant channels and the critical heat flux (CHF). CHF is a key parameter in determining the critical channel power, which is used in the trip setpoint calculations of regional overpower protection systems. This paper aims to predict PT diametral creep using the measured signals of the HTS by applying fuzzy neural networks (FNNs) according to operating conditions. The FNN model was optimized in terms of its fuzzy rules and parameters by a genetic algorithm combined with the least-squares method. Informative data that demonstrate the system's characteristic behavior were selected to train the FNN model using the subtractive clustering method. The proposed FNN model for predicting PT diametral creep was verified using the operating data of the Wolsong Unit 1 nuclear power plant in Korea. It was known that the FNN could predict the PT diametral creep accurately. Statistical and analytical uncertainty analysis methods were applied to the models and their uncertainties were evaluated using 60 sampled training and optimization data sets, as well as two fixed test data sets. (author)

Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.

Science.gov (United States)

Kamesh, Reddi; Rani, K Yamuna

2016-09-01

A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

The fuzzy bag model revisited

International Nuclear Information System (INIS)

Pilotto, F.; Vasconcellos, C.A.Z.; Coelho, H.T.

2001-01-01

In this work we develop a new version of the fuzzy bag model. Th main ideas is to include the conservation of energy and momentum in the model. This feature is not included in the original formulation of the fuzzy bag model, but is of paramount importance to interpret the model as being a bag model - that, is a model in which the outward pressure of the quarks inside the bag is balanced by the inward pressure of the non-perturbative vacuum outside the bag - as opposed to a relativistic potential model, in which there is no energy-momentum conservation. In the MT bag model, as well as in the original version of the fuzzy bag model, the non-perturbative QCD vacuum is parametrized by a constant B in the Lagrangian density. One immediate consequence of including energy-momentum conservation in the fuzzy bag model is that the bag constant B will acquire a radial dependence, B = B(r). (author)

The fuzzy bag model revisited

Energy Technology Data Exchange (ETDEWEB)

Pilotto, F.; Vasconcellos, C.A.Z. [Rio Grande do Sul Univ., Porto Alegre, RS (Brazil). Inst. de Fisica; Coelho, H.T. [Pernambuco Univ., Recife, PE (Brazil). Inst. de Fisica

2001-07-01

In this work we develop a new version of the fuzzy bag model. Th main ideas is to include the conservation of energy and momentum in the model. This feature is not included in the original formulation of the fuzzy bag model, but is of paramount importance to interpret the model as being a bag model - that, is a model in which the outward pressure of the quarks inside the bag is balanced by the inward pressure of the non-perturbative vacuum outside the bag - as opposed to a relativistic potential model, in which there is no energy-momentum conservation. In the MT bag model, as well as in the original version of the fuzzy bag model, the non-perturbative QCD vacuum is parametrized by a constant B in the Lagrangian density. One immediate consequence of including energy-momentum conservation in the fuzzy bag model is that the bag constant B will acquire a radial dependence, B = B(r). (author)

Neuro-fuzzy model for estimating race and gender from geometric distances of human face across pose

Science.gov (United States)

Nanaa, K.; Rahman, M. N. A.; Rizon, M.; Mohamad, F. S.; Mamat, M.

2018-03-01

Classifying human face based on race and gender is a vital process in face recognition. It contributes to an index database and eases 3D synthesis of the human face. Identifying race and gender based on intrinsic factor is problematic, which is more fitting to utilizing nonlinear model for estimating process. In this paper, we aim to estimate race and gender in varied head pose. For this purpose, we collect dataset from PICS and CAS-PEAL databases, detect the landmarks and rotate them to the frontal pose. After geometric distances are calculated, all of distance values will be normalized. Implementation is carried out by using Neural Network Model and Fuzzy Logic Model. These models are combined by using Adaptive Neuro-Fuzzy Model. The experimental results showed that the optimization of address fuzzy membership. Model gives a better assessment rate and found that estimating race contributing to a more accurate gender assessment.

Fuzzy GML Modeling Based on Vague Soft Sets

Directory of Open Access Journals (Sweden)

Bo Wei

2017-01-01

Full Text Available The Open Geospatial Consortium (OGC Geography Markup Language (GML explicitly represents geographical spatial knowledge in text mode. All kinds of fuzzy problems will inevitably be encountered in spatial knowledge expression. Especially for those expressions in text mode, this fuzziness will be broader. Describing and representing fuzziness in GML seems necessary. Three kinds of fuzziness in GML can be found: element fuzziness, chain fuzziness, and attribute fuzziness. Both element fuzziness and chain fuzziness belong to the reflection of the fuzziness between GML elements and, then, the representation of chain fuzziness can be replaced by the representation of element fuzziness in GML. On the basis of vague soft set theory, two kinds of modeling, vague soft set GML Document Type Definition (DTD modeling and vague soft set GML schema modeling, are proposed for fuzzy modeling in GML DTD and GML schema, respectively. Five elements or pairs, associated with vague soft sets, are introduced. Then, the DTDs and the schemas of the five elements are correspondingly designed and presented according to their different chains and different fuzzy data types. While the introduction of the five elements or pairs is the basis of vague soft set GML modeling, the corresponding DTD and schema modifications are key for implementation of modeling. The establishment of vague soft set GML enables GML to represent fuzziness and solves the problem of lack of fuzzy information expression in GML.

Applied to neuro-fuzzy models for signal validation in Angra 1 nuclear power plant

International Nuclear Information System (INIS)

Oliveira, Mauro Vitor de

1999-06-01

This work develops two models of signal validation in which the analytical redundancy of the monitored signals from an industrial plant is made by neural networks. In one model the analytical redundancy is made by only one neural network while in the other it is done by several neural networks, each one working in a specific part of the entire operation region of the plant. Four cluster techniques were tested to separate the entire region of operation in several specific regions. An additional information of systems' reliability is supplied by a fuzzy inference system. The models were implemented in C language and tested with signals acquired from Angra I nuclear power plant, from its start to 100% of power. (author)

Fuzzy Clustering Methods and their Application to Fuzzy Modeling

DEFF Research Database (Denmark)

Kroszynski, Uri; Zhou, Jianjun

1999-01-01

Fuzzy modeling techniques based upon the analysis of measured input/output data sets result in a set of rules that allow to predict system outputs from given inputs. Fuzzy clustering methods for system modeling and identification result in relatively small rule-bases, allowing fast, yet accurate....... An illustrative synthetic example is analyzed, and prediction accuracy measures are compared between the different variants...

Markowitz portfolio optimization model employing fuzzy measure

Science.gov (United States)

Ramli, Suhailywati; Jaaman, Saiful Hafizah

2017-04-01

Markowitz in 1952 introduced the mean-variance methodology for the portfolio selection problems. His pioneering research has shaped the portfolio risk-return model and become one of the most important research fields in modern finance. This paper extends the classical Markowitz's mean-variance portfolio selection model applying the fuzzy measure to determine the risk and return. In this paper, we apply the original mean-variance model as a benchmark, fuzzy mean-variance model with fuzzy return and the model with return are modeled by specific types of fuzzy number for comparison. The model with fuzzy approach gives better performance as compared to the mean-variance approach. The numerical examples are included to illustrate these models by employing Malaysian share market data.

Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

Directory of Open Access Journals (Sweden)

Seng-Chi Chen

2014-01-01

Full Text Available Studies on active magnetic bearing (AMB systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC. The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC, the parameters of which are adjusted using a radial basis function neural network (RBFNN, is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.

Global exponential stability of fuzzy BAM neural networks with time-varying delays

International Nuclear Information System (INIS)

Zhang Qianhong; Luo Wei

2009-01-01

In this paper, a class of fuzzy bidirectional associated memory (BAM) neural networks with time-varying delays are studied. Employing fixed point theorem, matrix theory and inequality analysis, some sufficient conditions are established for the existence, uniqueness and global exponential stability of equilibrium point. The sufficient conditions are easy to verify at pattern recognition and automatic control. Finally, an example is given to show feasibility and effectiveness of our results.

System identification and adaptive control theory and applications of the neurofuzzy and fuzzy cognitive network models

CERN Document Server

Boutalis, Yiannis; Kottas, Theodore; Christodoulou, Manolis A

2014-01-01

Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented.  Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model  stems  from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering s...

Extraction of Fuzzy Logic Rules from Data by Means of Artificial Neural Networks

Czech Academy of Sciences Publication Activity Database

Holeňa, Martin

2005-01-01

Roč. 41, č. 3 (2005), s. 297-314 ISSN 0023-5954 R&D Projects: GA AV ČR IAA1030004 Institutional research plan: CEZ:AV0Z10300504 Keywords : knowledge extraction from data * artificial neural networks * fuzzy logic * Lukasiewicz logic * disjunctive normal form Subject RIV: BA - General Mathematics Impact factor: 0.343, year: 2005 http://dml.cz/handle/10338.dmlcz/135657

工业PTA氧化过程中4-CBA浓度的模糊神经网络模型%Fuzzy Neural Network Model of 4-CBA Concentration for Industrial Purified Terephthalic Acid Oxidation Process

Institute of Scientific and Technical Information of China (English)

刘瑞兰; 苏宏业; 牟盛静; 贾涛; 陈渭泉; 褚健

2004-01-01

A fuzzy neural network (FNN) model is developed to predict the 4-CBA concentration of the oxidation unit in purified terephthalic acid process. Several technologies are used to deal with the process data before modeling.First, a set of preliminary input variables is selected according to prior knowledge and experience. Secondly, a method based on the maximum correlation coefficient is proposed to detect the dead time between the process variables and response variables. Finally, the fuzzy curve method is used to reduce the unimportant input variables. The simulation results based on industrial data show that the relative error range of the FNN model is narrower than that of the American Oil Company (AMOCO) model. Furthermore, the FNN model can predict the trend of the 4-CBA concentration more accurately.

Neural-Fuzzy Digital Strategy of Continuous-Time Nonlinear Systems Using Adaptive Prediction and Random-Local-Optimization Design

Directory of Open Access Journals (Sweden)

Zhi-Ren Tsai

2013-01-01

Full Text Available A tracking problem, time-delay, uncertainty and stability analysis of a predictive control system are considered. The predictive control design is based on the input and output of neural plant model (NPM, and a recursive fuzzy predictive tracker has scaling factors which limit the value zone of measured data and cause the tuned parameters to converge to obtain a robust control performance. To improve the further control performance, the proposed random-local-optimization design (RLO for a model/controller uses offline initialization to obtain a near global optimal model/controller. Other issues are the considerations of modeling error, input-delay, sampling distortion, cost, greater flexibility, and highly reliable digital products of the model-based controller for the continuous-time (CT nonlinear system. They are solved by a recommended two-stage control design with the first-stage (offline RLO and second-stage (online adaptive steps. A theorizing method is then put forward to replace the sensitivity calculation, which reduces the calculation of Jacobin matrices of the back-propagation (BP method. Finally, the feedforward input of reference signals helps the digital fuzzy controller improve the control performance, and the technique works to control the CT systems precisely.

A study on the optimal fuel loading pattern design in pressurized water reactor using the artificial neural network and the fuzzy rule based system

International Nuclear Information System (INIS)

Kim, Han Gon; Chang, Soon Heung; Lee, Byung

2004-01-01

The Optimal Fuel Shuffling System (OFSS) is developed for optimal design of PWR fuel loading pattern. In this paper, an optimal loading pattern is defined that the local power peaking factor is lower than predetermined value during one cycle and the effective multiplication factor is maximized in order to extract maximum energy. OFSS is a hybrid system that a rule based system, a fuzzy logic, and an artificial neural network are connected each other. The rule based system classifies loading patterns into two classes using several heuristic rules and a fuzzy rule. A fuzzy rule is introduced to achieve more effective and fast searching. Its membership function is automatically updated in accordance with the prediction results. The artificial neural network predicts core parameters for the patterns generated from the rule based system. The back-propagation network is used for fast prediction of core parameters. The artificial neural network and the fuzzy logic can be used as the tool for improvement of existing algorithm's capabilities. OFSS was demonstrated and validated for cycle 1 of Kori unit 1 PWR. (author)

A study on the optimal fuel loading pattern design in pressurized water reactor using the artificial neural network and the fuzzy rule based system

Energy Technology Data Exchange (ETDEWEB)

Kim, Han Gon; Chang, Soon Heung; Lee, Byung [Department of Nuclear Engineering, Korea Advanced Institute of Science and Technology, Yusong-gu, Taejon (Korea, Republic of)

2004-07-01

The Optimal Fuel Shuffling System (OFSS) is developed for optimal design of PWR fuel loading pattern. In this paper, an optimal loading pattern is defined that the local power peaking factor is lower than predetermined value during one cycle and the effective multiplication factor is maximized in order to extract maximum energy. OFSS is a hybrid system that a rule based system, a fuzzy logic, and an artificial neural network are connected each other. The rule based system classifies loading patterns into two classes using several heuristic rules and a fuzzy rule. A fuzzy rule is introduced to achieve more effective and fast searching. Its membership function is automatically updated in accordance with the prediction results. The artificial neural network predicts core parameters for the patterns generated from the rule based system. The back-propagation network is used for fast prediction of core parameters. The artificial neural network and the fuzzy logic can be used as the tool for improvement of existing algorithm's capabilities. OFSS was demonstrated and validated for cycle 1 of Kori unit 1 PWR. (author)

Estimation of Leak Flow Rate during Post-LOCA Using Cascaded Fuzzy Neural Networks

Energy Technology Data Exchange (ETDEWEB)

Kim, Dong Yeong [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Na, Man Gyun [Chosun University, Gwangju (Korea, Republic of)

2016-10-15

In this study, important parameters such as the break position, size, and leak flow rate of loss of coolant accidents (LOCAs), provide operators with essential information for recovering the cooling capability of the nuclear reactor core, for preventing the reactor core from melting down, and for managing severe accidents effectively. Leak flow rate should consist of break size, differential pressure, temperature, and so on (where differential pressure means difference between internal and external reactor vessel pressure). The leak flow rate is strongly dependent on the break size and the differential pressure, but the break size is not measured and the integrity of pressure sensors is not assured in severe circumstances. In this paper, a cascaded fuzzy neural network (CFNN) model is appropriately proposed to estimate the leak flow rate out of break, which has a direct impact on the important times (time approaching the core exit temperature that exceeds 1200 .deg. F, core uncover time, reactor vessel failure time, etc.). The CFNN is a data-based model, it requires data to develop and verify itself. Because few actual severe accident data exist, it is essential to obtain the data required in the proposed model using numerical simulations. In this study, a CFNN model was developed to predict the leak flow rate before proceeding to severe LOCAs. The simulations showed that the developed CFNN model accurately predicted the leak flow rate with less error than 0.5%. The CFNN model is much better than FNN model under the same conditions, such as the same fuzzy rules. At the result of comparison, the RMS errors of the CFNN model were reduced by approximately 82 ~ 97% of those of the FNN model.

Fuzzy time-series based on Fibonacci sequence for stock price forecasting

Science.gov (United States)

Chen, Tai-Liang; Cheng, Ching-Hsue; Jong Teoh, Hia

2007-07-01

Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. This paper employs a 5-year period TSMC (Taiwan Semiconductor Manufacturing Company) stock price data and a 13-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index data as experimental datasets. By comparing our forecasting performances with Chen's (Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst. 81 (1996) 311-319), Yu's (Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004) 609-624) and Huarng's (The application of neural networks to forecast fuzzy time series. Physica A 336 (2006) 481-491) models, we conclude that the proposed model surpasses in accuracy these conventional fuzzy time-series models.

Models for cooperative games with fuzzy relations among the agents fuzzy communication, proximity relation and fuzzy permission

CERN Document Server

Jiménez-Losada, Andrés

2017-01-01

This book offers a comprehensive introduction to cooperative game theory and a practice-oriented reference guide to new models and tools for studying bilateral fuzzy relations among several agents or players. It introduces the reader to several fuzzy models, each of which is first analyzed in the context of classical games (crisp games) and subsequently in the context of fuzzy games. Special emphasis is given to the value of Shapley, which is presented for the first time in the context of fuzzy games. Students and researchers will find here a self-contained reference guide to cooperative fuzzy games, characterized by a wealth of examples, descriptions of a wide range of possible situations, step-by-step explanations of the basic mathematical concepts involved, and easy-to-follow information on axioms and properties.

Fuzzy production planning models for an unreliable production system with fuzzy production rate and stochastic/fuzzy demand rate

Directory of Open Access Journals (Sweden)

K. A. Halim

2011-01-01

Full Text Available In this article, we consider a single-unit unreliable production system which produces a single item. During a production run, the production process may shift from the in-control state to the out-of-control state at any random time when it produces some defective items. The defective item production rate is assumed to be imprecise and is characterized by a trapezoidal fuzzy number. The production rate is proportional to the demand rate where the proportionality constant is taken to be a fuzzy number. Two production planning models are developed on the basis of fuzzy and stochastic demand patterns. The expected cost per unit time in the fuzzy sense is derived in each model and defuzzified by using the graded mean integration representation method. Numerical examples are provided to illustrate the optimal results of the proposed fuzzy models.

Model predictive control using fuzzy decision functions

NARCIS (Netherlands)

Kaymak, U.; Costa Sousa, da J.M.

2001-01-01

Fuzzy predictive control integrates conventional model predictive control with techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the

Fuzzy controllers in nuclear material accounting

International Nuclear Information System (INIS)

Zardecki, A.

1994-01-01

Fuzzy controllers are applied to predicting and modeling a time series, with particular emphasis on anomaly detection in nuclear material inventory differences. As compared to neural networks, the fuzzy controllers can operate in real time; their learning process does not require many iterations to converge. For this reason fuzzy controllers are potentially useful in time series forecasting, where the authors want to detect and identify trends in real time. They describe an object-oriented implementation of the algorithm advanced by Wang and Mendel. Numerical results are presented both for inventory data and time series corresponding to chaotic situations, such as encountered in the context of strange attractors. In the latter case, the effects of noise on the predictive power of the fuzzy controller are explored

Neural fuzzy modelization of copper removal from water by biosorption in fixed-bed columns using olive stone and pinion shell.

Science.gov (United States)

Calero, M; Iáñez-Rodríguez, I; Pérez, A; Martín-Lara, M A; Blázquez, G

2018-03-01

Continuous copper biosorption in fixed-bed column by olive stone and pinion shell was studied. The effect of three operational parameters was analyzed: feed flow rate (2-6 ml/min), inlet copper concentration (40-100 mg/L) and bed-height (4.4-13.4 cm). Artificial Neural-Fuzzy Inference System (ANFIS) was used in order to optimize the percentage of copper removal and the retention capacity in the column. The highest percentage of copper retained was achieved at 2 ml/min, 40 mg/L and 4.4 cm. However, the optimum biosorption capacity was obtained at 6 ml/min, 100 mg/L and 13.4 cm. Finally, breakthrough curves were simulated with mathematical traditional models and ANFIS model. The calculated results obtained with each model were compared with experimental data. The best results were given by ANFIS modelling that predicted copper biosorption with high accuracy. Breakthrough curves surfaces, which enable the visualization of the behavior of the system in different process conditions, were represented. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mode Choice Modeling Using Artificial Neural Networks

OpenAIRE

Edara, Praveen Kumar

2003-01-01

Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data becom...

Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure.

Science.gov (United States)

El-Nagar, Ahmad M

2018-01-01

In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Development of a neural fuzzy system for advanced prediction of dew point pressure in gas condensate reservoirs

Energy Technology Data Exchange (ETDEWEB)

Nowroozi, Saeed; Hashemipour, Hasan; Schaffie, Mahin [Department of Chemical Engineering, Shahid Bahonar University of Kerman (Iran); ERC, Shahid Bahonar University of Kerman (Iran); Ranjbar, Mohammad [Department of Mining Engineering, Shahid Bahonar University of Kerman (Iran); ERC, Shahid Bahonar University of Kerman (Iran)

2009-03-15

Dew point pressure is one of the most critical quantities for characterizing a gas condensate reservoir. So, accurate determination of this property has been the main challenge in reservoir development and management. The experimental determination of dew point pressure in PVT cell is often difficult especially in case of lean retrograde gas condensate. Empirical correlations and some equations of state can be used to calculate reservoir fluid properties. Empirical correlations do not have ability to reliable duplicate the temperature behavior of constant composition fluids. Equations of state have convergence problem and need to be tuned against some experimental data. Complexity, non-linearity and vagueness are some reservoir parameter characteristic which can be propagated simply by intelligent system. With the advantage of fuzzy sets in knowledge representation and the high capacity of neural nets (NNs) in learning knowledge expressed in data, in this paper a neural fuzzy system(NFS) is proposed to predict dew point pressure of gas condensate reservoir. The model was developed using 110 measurements of dew point pressure. The performance of the model is compared against performance of some of the most accurate and general correlations for dew point pressure calculation. From the results of this study, it can be pointed out that this novel method is more accurate and reliable with the mean square error of 0.058%, 0.074% and 0.044% for training, validation and test processes, respectively. (author)

Self-learning fuzzy controllers based on temporal back propagation

Science.gov (United States)

Jang, Jyh-Shing R.

1992-01-01

This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.

Short term load forecasting using neuro-fuzzy networks

Energy Technology Data Exchange (ETDEWEB)

Hoffman, M.; Hassan, A. [South Dakota School of Mines and Technology, Rapid City, SD (United States); Martinez, D. [Black Hills Power and Light, Rapid City, SD (United States)

2005-07-01

Details of a neuro-fuzzy network-based short term load forecasting system for power utilities were presented. The fuzzy logic controller was used to fuzzify inputs representing historical temperature and load curves. The fuzzified inputs were then used to develop the fuzzy rules matrix. Output membership function values were determined by evaluating the fuzzified inputs with the fuzzy rules. Output membership function values were used as inputs for the neural network portion of the system. The training process used a back propagation gradient descent algorithm to adjust the weight values of the neural network in order to reduce the error between the neural network output and the desired output. The neural network was then used to predict future load values. Sample data were taken from a local power company's daily load curve to validate the system. A 10 per cent forecast error was introduced in the temperature values to determine the effect on load prediction. Results of the study suggest that the combined use of fuzzy logic and neural networks provide greater accuracy than studies where either approach is used alone. 6 refs., 6 figs.

Neuro-fuzzy controller to navigate an unmanned vehicle.

Science.gov (United States)

Selma, Boumediene; Chouraqui, Samira

2013-12-01

A Neuro-fuzzy control method for an Unmanned Vehicle (UV) simulation is described. The objective is guiding an autonomous vehicle to a desired destination along a desired path in an environment characterized by a terrain and a set of distinct objects, such as obstacles like donkey traffic lights and cars circulating in the trajectory. The autonomous navigate ability and road following precision are mainly influenced by its control strategy and real-time control performance. Fuzzy Logic Controller can very well describe the desired system behavior with simple "if-then" relations owing the designer to derive "if-then" rules manually by trial and error. On the other hand, Neural Networks perform function approximation of a system but cannot interpret the solution obtained neither check if its solution is plausible. The two approaches are complementary. Combining them, Neural Networks will allow learning capability while Fuzzy-Logic will bring knowledge representation (Neuro-Fuzzy). In this paper, an artificial neural network fuzzy inference system (ANFIS) controller is described and implemented to navigate the autonomous vehicle. Results show several improvements in the control system adjusted by neuro-fuzzy techniques in comparison to the previous methods like Artificial Neural Network (ANN).

Ellipsoidal fuzzy learning for smart car platoons

Science.gov (United States)

Dickerson, Julie A.; Kosko, Bart

1993-12-01

A neural-fuzzy system combined supervised and unsupervised learning to find and tune the fuzzy-rules. An additive fuzzy system approximates a function by covering its graph with fuzzy rules. A fuzzy rule patch can take the form of an ellipsoid in the input-output space. Unsupervised competitive learning found the statistics of data clusters. The covariance matrix of each synaptic quantization vector defined on ellipsoid centered at the centroid of the data cluster. Tightly clustered data gave smaller ellipsoids or more certain rules. Sparse data gave larger ellipsoids or less certain rules. Supervised learning tuned the ellipsoids to improve the approximation. The supervised neural system used gradient descent to find the ellipsoidal fuzzy patches. It locally minimized the mean-squared error of the fuzzy approximation. Hybrid ellipsoidal learning estimated the control surface for a smart car controller.

Fuzzy logic of Aristotelian forms

Energy Technology Data Exchange (ETDEWEB)

Perlovsky, L.I. [Nichols Research Corp., Lexington, MA (United States)

1996-12-31

Model-based approaches to pattern recognition and machine vision have been proposed to overcome the exorbitant training requirements of earlier computational paradigms. However, uncertainties in data were found to lead to a combinatorial explosion of the computational complexity. This issue is related here to the roles of a priori knowledge vs. adaptive learning. What is the a-priori knowledge representation that supports learning? I introduce Modeling Field Theory (MFT), a model-based neural network whose adaptive learning is based on a priori models. These models combine deterministic, fuzzy, and statistical aspects to account for a priori knowledge, its fuzzy nature, and data uncertainties. In the process of learning, a priori fuzzy concepts converge to crisp or probabilistic concepts. The MFT is a convergent dynamical system of only linear computational complexity. Fuzzy logic turns out to be essential for reducing the combinatorial complexity to linear one. I will discuss the relationship of the new computational paradigm to two theories due to Aristotle: theory of Forms and logic. While theory of Forms argued that the mind cannot be based on ready-made a priori concepts, Aristotelian logic operated with just such concepts. I discuss an interpretation of MFT suggesting that its fuzzy logic, combining a-priority and adaptivity, implements Aristotelian theory of Forms (theory of mind). Thus, 2300 years after Aristotle, a logic is developed suitable for his theory of mind.

Fuzzy One-Class Classification Model Using Contamination Neighborhoods

Directory of Open Access Journals (Sweden)

Lev V. Utkin

2012-01-01

Full Text Available A fuzzy classification model is studied in the paper. It is based on the contaminated (robust model which produces fuzzy expected risk measures characterizing classification errors. Optimal classification parameters of the models are derived by minimizing the fuzzy expected risk. It is shown that an algorithm for computing the classification parameters is reduced to a set of standard support vector machine tasks with weighted data points. Experimental results with synthetic data illustrate the proposed fuzzy model.

Fuzzy logic and artificial neural networks for nuclear power plant applications

International Nuclear Information System (INIS)

Berkan, R.C.; Eryurek, E.; Upadhyaya, B.R.

1992-01-01

This paper discusses the feasibility of applying fuzzy logic and neural networks to plant-wide monitoring, diagnostics, and control problems. Different data sets are gathered from several sources including two commercial Pressurized Water Reactors (PWR), the Experimental Breeder Reactor-II (EBR-II), and the conceptual design of Modular Liquid-Metal Reactor (PRISM). These data sets are used to illustrate applications to operating processes, and to PRISM design. The results show that the artificial intelligence approach to a number of operational tasks can considerably improve the safety and availability of nuclear power generation

COMPARISON of FUZZY-BASED MODELS in LANDSLIDE HAZARD MAPPING

Directory of Open Access Journals (Sweden)

N. Mijani

2017-09-01

Full Text Available Landslide is one of the main geomorphic processes which effects on the development of prospect in mountainous areas and causes disastrous accidents. Landslide is an event which has different uncertain criteria such as altitude, slope, aspect, land use, vegetation density, precipitation, distance from the river and distance from the road network. This research aims to compare and evaluate different fuzzy-based models including Fuzzy Analytic Hierarchy Process (Fuzzy-AHP, Fuzzy Gamma and Fuzzy-OR. The main contribution of this paper reveals to the comprehensive criteria causing landslide hazard considering their uncertainties and comparison of different fuzzy-based models. The quantify of evaluation process are calculated by Density Ratio (DR and Quality Sum (QS. The proposed methodology implemented in Sari, one of the city of Iran which has faced multiple landslide accidents in recent years due to the particular environmental conditions. The achieved results of accuracy assessment based on the quantifier strated that Fuzzy-AHP model has higher accuracy compared to other two models in landslide hazard zonation. Accuracy of zoning obtained from Fuzzy-AHP model is respectively 0.92 and 0.45 based on method Precision (P and QS indicators. Based on obtained landslide hazard maps, Fuzzy-AHP, Fuzzy Gamma and Fuzzy-OR respectively cover 13, 26 and 35 percent of the study area with a very high risk level. Based on these findings, fuzzy-AHP model has been selected as the most appropriate method of zoning landslide in the city of Sari and the Fuzzy-gamma method with a minor difference is in the second order.

Development of a new fuzzy exposure model

International Nuclear Information System (INIS)

Vasconcelos, Wagner Eustaquio de; Lira, Carlos Alberto Brayner de Oliveira; Texeira, Marcello Goulart

2007-01-01

The main topic of this study is the development of an exposure fuzzy model to evaluate the exposure of inhabitants in an area containing uranium, which present a high natural background. In this work, a fuzzy model was created, based on some of the following main factors: activity concentration of uranium, physiological factors and characteristic customs of the exposed individuals. An inference block was created to evaluate some factors of radiation exposure. For this, AHP-fuzzy technique (Analytic Hierarchic Process) was used and its application was demonstrated for a subjected population to the radiation of the natural uranium. The Mandami type fuzzy model was also created from the opinion of specialists. The Monte Carlo method was used to generate a statistics of input data and the daily average exposure served as comparison parameter between the three techniques. The output fuzzy sets were expressed in form of linguistic variables, such as high, medium and low. In the qualitative analysis, the obtained results were satisfactory when translating the opinion of the specialists. In the quantitative analysis, the obtained values are part of the same fuzzy set as the values found in literature. The global results suggest that this type of fuzzy model is highly promising for analysis of exposure to ionizing radiation. (author)

Development of a new fuzzy exposure model

Energy Technology Data Exchange (ETDEWEB)

Vasconcelos, Wagner Eustaquio de; Lira, Carlos Alberto Brayner de Oliveira [Universidade Federal de Pernambuco (UFPE), Recife, PE (Brazil). Dept. de Energia Nuclear. Grupo de Engenharia de Reatores], E-mail: wagner@ufpe.br, E-mail: cabol@ufpe.br; Texeira, Marcello Goulart [Instituto Militar de Engenharia (IME), Rio de Janeiro, RJ (Brazil). Terrestrial Modelling Group], E-mail: marcellogt@ime.eb.br

2007-07-01

The main topic of this study is the development of an exposure fuzzy model to evaluate the exposure of inhabitants in an area containing uranium, which present a high natural background. In this work, a fuzzy model was created, based on some of the following main factors: activity concentration of uranium, physiological factors and characteristic customs of the exposed individuals. An inference block was created to evaluate some factors of radiation exposure. For this, AHP-fuzzy technique (Analytic Hierarchic Process) was used and its application was demonstrated for a subjected population to the radiation of the natural uranium. The Mandami type fuzzy model was also created from the opinion of specialists. The Monte Carlo method was used to generate a statistics of input data and the daily average exposure served as comparison parameter between the three techniques. The output fuzzy sets were expressed in form of linguistic variables, such as high, medium and low. In the qualitative analysis, the obtained results were satisfactory when translating the opinion of the specialists. In the quantitative analysis, the obtained values are part of the same fuzzy set as the values found in literature. The global results suggest that this type of fuzzy model is highly promising for analysis of exposure to ionizing radiation. (author)

Fuzzy audit risk modeling algorithm

Directory of Open Access Journals (Sweden)

Zohreh Hajihaa

2011-07-01

Full Text Available Fuzzy logic has created suitable mathematics for making decisions in uncertain environments including professional judgments. One of the situations is to assess auditee risks. During recent years, risk based audit (RBA has been regarded as one of the main tools to fight against fraud. The main issue in RBA is to determine the overall audit risk an auditor accepts, which impact the efficiency of an audit. The primary objective of this research is to redesign the audit risk model (ARM proposed by auditing standards. The proposed model of this paper uses fuzzy inference systems (FIS based on the judgments of audit experts. The implementation of proposed fuzzy technique uses triangular fuzzy numbers to express the inputs and Mamdani method along with center of gravity are incorporated for defuzzification. The proposed model uses three FISs for audit, inherent and control risks, and there are five levels of linguistic variables for outputs. FISs include 25, 25 and 81 rules of if-then respectively and officials of Iranian audit experts confirm all the rules.

Fuzzy model-based control of a nuclear reactor

International Nuclear Information System (INIS)

Van Den Durpel, L.; Ruan, D.

1994-01-01

The fuzzy model-based control of a nuclear power reactor is an emerging research topic world-wide. SCK-CEN is dealing with this research in a preliminary stage, including two aspects, namely fuzzy control and fuzzy modelling. The aim is to combine both methodologies in contrast to conventional model-based PID control techniques, and to state advantages of including fuzzy parameters as safety and operator feedback. This paper summarizes the general scheme of this new research project

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

Directory of Open Access Journals (Sweden)

Saleh Shahinfar

2012-01-01

Full Text Available 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.

Fuzzy Stochastic Optimization Theory, Models and Applications

CERN Document Server

Wang, Shuming

2012-01-01

Covering in detail both theoretical and practical perspectives, this book is a self-contained and systematic depiction of current fuzzy stochastic optimization that deploys the fuzzy random variable as a core mathematical tool to model the integrated fuzzy random uncertainty. It proceeds in an orderly fashion from the requisite theoretical aspects of the fuzzy random variable to fuzzy stochastic optimization models and their real-life case studies.   The volume reflects the fact that randomness and fuzziness (or vagueness) are two major sources of uncertainty in the real world, with significant implications in a number of settings. In industrial engineering, management and economics, the chances are high that decision makers will be confronted with information that is simultaneously probabilistically uncertain and fuzzily imprecise, and optimization in the form of a decision must be made in an environment that is doubly uncertain, characterized by a co-occurrence of randomness and fuzziness. This book begins...

Fuzziness and fuzzy modelling in Bulgaria's energy policy decision-making dilemma

International Nuclear Information System (INIS)

Wang Xingquan

2006-01-01

The decision complexity resulting from imprecision in decision variables and parameters, a major difficulty for conventional decision analysis methods, can be relevantly analysed and modelled by fuzzy logic. Bulgaria's nuclear policy decision-making process implicates such complexity of imprecise nature: stakeholders, criteria, measurement, etc. Given the suitable applicability of fuzzy logic in this case, this article tries to offer a concrete fuzzy paradigm including delimitation of decision space, quantification of imprecise variables, and, of course, parameterisation. (author)

Fuzzy algorithmic and knowledge-based decision support in nuclear engineering

International Nuclear Information System (INIS)

Zimmermann, H. J.

1996-01-01

Fuzzy Set Theory was originally conceived as a means to model non- stochastic uncertainty. In the meantime it has matured to Fuzzy Technology and - together with Neural Nets and Genetic Algorithms - to Computational Intelligence. The goals have expanded considerably. In addition to uncertainty modeling, relaxation, compactification and meaning preserving reasoning have become major objectives. Nuclear engineering is one of the areas with a large potential for applications of Fuzzy Technologies, in which, however, the development is still at the beginning. This paper tries to survey applications and point to some potential applications which have not yet been realized

Integration of Adaptive Neuro-Fuzzy Inference System, Neural Networks and Geostatistical Methods for Fracture Density Modeling

Directory of Open Access Journals (Sweden)

Ja’fari A.

2014-01-01

Full Text Available Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS and Neural Networks (NN algorithms for overall estimation of fracture density from conventional well log data. A simple averaging method was used to obtain a better result by combining results of ANFIS and NN. The algorithm applied on other wells of the field to obtain fracture density. In order to model the fracture density in the reservoir, we used variography and sequential simulation algorithms like Sequential Indicator Simulation (SIS and Truncated Gaussian Simulation (TGS. The overall algorithm applied to Asmari reservoir one of the SW Iranian oil fields. Histogram analysis applied to control the quality of the obtained models. Results of this study show that for higher number of fracture facies the TGS algorithm works better than SIS but in small number of fracture facies both algorithms provide approximately same results.

a New Model for Fuzzy Personalized Route Planning Using Fuzzy Linguistic Preference Relation

Science.gov (United States)

Nadi, S.; Houshyaripour, A. H.

2017-09-01

This paper proposes a new model for personalized route planning under uncertain condition. Personalized routing, involves different sources of uncertainty. These uncertainties can be raised from user's ambiguity about their preferences, imprecise criteria values and modelling process. The proposed model uses Fuzzy Linguistic Preference Relation Analytical Hierarchical Process (FLPRAHP) to analyse user's preferences under uncertainty. Routing is a multi-criteria task especially in transportation networks, where the users wish to optimize their routes based on different criteria. However, due to the lake of knowledge about the preferences of different users and uncertainties available in the criteria values, we propose a new personalized fuzzy routing method based on the fuzzy ranking using center of gravity. The model employed FLPRAHP method to aggregate uncertain criteria values regarding uncertain user's preferences while improve consistency with least possible comparisons. An illustrative example presents the effectiveness and capability of the proposed model to calculate best personalize route under fuzziness and uncertainty.

Artificial neural networks and neuro-fuzzy inference systems as virtual sensors for hydrogen safety prediction

Energy Technology Data Exchange (ETDEWEB)

Karri, Vishy; Ho, Tien [School of Engineering, University of Tasmania, GPO Box 252-65, Hobart, Tasmania 7001 (Australia); Madsen, Ole [Department of Production, Aalborg University, Fibigerstraede 16, DK-9220 Aalborg (Denmark)

2008-06-15

Hydrogen is increasingly investigated as an alternative fuel to petroleum products in running internal combustion engines and as powering remote area power systems using generators. The safety issues related to hydrogen gas are further exasperated by expensive instrumentation required to measure the percentage of explosive limits, flow rates and production pressure. This paper investigates the use of model based virtual sensors (rather than expensive physical sensors) in connection with hydrogen production with a Hogen 20 electrolyzer system. The virtual sensors are used to predict relevant hydrogen safety parameters, such as the percentage of lower explosive limit, hydrogen pressure and hydrogen flow rate as a function of different input conditions of power supplied (voltage and current), the feed of de-ionized water and Hogen 20 electrolyzer system parameters. The virtual sensors are developed by means of the application of various Artificial Intelligent techniques. To train and appraise the neural network models as virtual sensors, the Hogen 20 electrolyzer is instrumented with necessary sensors to gather experimental data which together with MATLAB neural networks toolbox and tailor made adaptive neuro-fuzzy inference systems (ANFIS) were used as predictive tools to estimate hydrogen safety parameters. It was shown that using the neural networks hydrogen safety parameters were predicted to less than 3% of percentage average root mean square error. The most accurate prediction was achieved by using ANFIS. (author)

Silicon microgyroscope temperature prediction and control system based on BP neural network and Fuzzy-PID control method

International Nuclear Information System (INIS)

Xia, Dunzhu; Kong, Lun; Hu, Yiwei; Ni, Peizhen

2015-01-01

We present a novel silicon microgyroscope (SMG) temperature prediction and control system in a narrow space. As the temperature of SMG is closely related to its drive mode frequency and driving voltage, a temperature prediction model can be established based on the BP neural network. The simulation results demonstrate that the established temperature prediction model can estimate the temperature in the range of −40 to 60 °C with an error of less than ±0.05 °C. Then, a temperature control system based on the combination of fuzzy logic controller and the increment PID control method is proposed. The simulation results prove that the Fuzzy-PID controller has a smaller steady state error, less rise time and better robustness than the PID controller. This is validated by experimental results that show the Fuzzy-PID control method can achieve high precision in keeping the SMG temperature stable at 55 °C with an error of less than 0.2 °C. The scale factor can be stabilized at 8.7 mV/°/s with a temperature coefficient of 33 ppm °C −1 . ZRO (zero rate output) instability is decreased from 1.10°/s (9.5 mV) to 0.08°/s (0.7 mV) when the temperature control system is implemented over an ambient temperature range of −40 to 60 °C. (paper)

Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach

International Nuclear Information System (INIS)

Kisi, Ozgur

2014-01-01

The study investigates the ability of FG (fuzzy genetic) approach in modeling solar radiation of seven cities from Mediterranean region of Anatolia, Turkey. Latitude, longitude, altitude and month of the year data from the Adana, K. Maras, Mersin, Antalya, Isparta, Burdur and Antakya cities are used as inputs to the FG model to estimate one month ahead solar radiation. FG model is compared with ANNs (artificial neural networks) and ANFIS (adaptive neruro fuzzzy inference system) models with respect to RMSE (root mean square errors), MAE (mean absolute errors) and determination coefficient (R 2 ) statistics. Comparison results indicate that the FG model performs better than the ANN and ANFIS models. It is found that the FG model can be successfully used for estimating solar radiation by using latitude, longitude, altitude and month of the year information. FG model with RMSE = 6.29 MJ/m 2 , MAE = 4.69 MJ/m 2 and R 2 = 0.905 in the test stage was found to be superior to the optimal ANN model with RMSE = 7.17 MJ/m 2 , MAE = 5.29 MJ/m 2 and R 2 = 0.876 and ANFIS model with RMSE = 6.75 MJ/m 2 , MAE = 5.10 MJ/m 2 and R 2 = 0.892 in estimating solar radiation. - Highlights: • SR (Solar radiation) of seven cities from Mediterranean region of Turkey is predicted. • FG (Fuzzy genetic) models are developed for accurately estimation of SR. • The ability of the FG models used in the study is found to be satisfactory. • FG models are compared with commonly used ANNs (artificial neural networks). • FG models are found to perform better than the ANNs models

An adaptive neuro fuzzy model for estimating the reliability of component-based software systems

Directory of Open Access Journals (Sweden)

Kirti Tyagi

2014-01-01

Full Text Available Although many algorithms and techniques have been developed for estimating the reliability of component-based software systems (CBSSs, much more research is needed. Accurate estimation of the reliability of a CBSS is difficult because it depends on two factors: component reliability and glue code reliability. Moreover, reliability is a real-world phenomenon with many associated real-time problems. Soft computing techniques can help to solve problems whose solutions are uncertain or unpredictable. A number of soft computing approaches for estimating CBSS reliability have been proposed. These techniques learn from the past and capture existing patterns in data. The two basic elements of soft computing are neural networks and fuzzy logic. In this paper, we propose a model for estimating CBSS reliability, known as an adaptive neuro fuzzy inference system (ANFIS, that is based on these two basic elements of soft computing, and we compare its performance with that of a plain FIS (fuzzy inference system for different data sets.

Using fuzzy self-organising maps for safety critical systems

International Nuclear Information System (INIS)

Kurd, Zeshan; Kelly, Tim P.

2007-01-01

This paper defines a type of constrained artificial neural network (ANN) that enables analytical certification arguments whilst retaining valuable performance characteristics. Previous work has defined a safety lifecycle for ANNs without detailing a specific neural model. Building on this previous work, the underpinning of the devised model is based upon an existing neuro-fuzzy system called the fuzzy self-organising map (FSOM). The FSOM is type of 'hybrid' ANN which allows behaviour to be described qualitatively and quantitatively using meaningful expressions. Safety of the FSOM is argued through adherence to safety requirements-derived from hazard analysis and expressed using safety constraints. The approach enables the construction of compelling (product-based) arguments for mitigation of potential failure modes associated with the FSOM. The constrained FSOM has been termed a 'safety critical artificial neural network' (SCANN). The SCANN can be used for non-linear function approximation and allows certified learning and generalisation for high criticality roles. A discussion of benefits for real-world applications is also presented

Kalman-fuzzy algorithm in short term load forecasting

International Nuclear Information System (INIS)

Shah Baki, S.R.; Saibon, H.; Lo, K.L.

1996-01-01

A combination of Kalman-Fuzzy-Neural is developed to forecast the next 24 hours load. The input data fed to neural network are presented with training data set composed of historical load data, weather, day of the week, month of the year and holidays. The load data is fed through Kalman-Fuzzy filter before being applied to Neural Network for training. With this techniques Neural Network converges faster and the mean percentage error of predicted load is reduced as compared to the classical ANN technique

Comparison of crisp and fuzzy character networks in handwritten word recognition

Science.gov (United States)

Gader, Paul; Mohamed, Magdi; Chiang, Jung-Hsien

1992-01-01

Experiments involving handwritten word recognition on words taken from images of handwritten address blocks from the United States Postal Service mailstream are described. The word recognition algorithm relies on the use of neural networks at the character level. The neural networks are trained using crisp and fuzzy desired outputs. The fuzzy outputs were defined using a fuzzy k-nearest neighbor algorithm. The crisp networks slightly outperformed the fuzzy networks at the character level but the fuzzy networks outperformed the crisp networks at the word level.

Fuzzy model-based servo and model following control for nonlinear systems.

Science.gov (United States)

Ohtake, Hiroshi; Tanaka, Kazuo; Wang, Hua O

2009-12-01

This correspondence presents servo and nonlinear model following controls for a class of nonlinear systems using the Takagi-Sugeno fuzzy model-based control approach. First, the construction method of the augmented fuzzy system for continuous-time nonlinear systems is proposed by differentiating the original nonlinear system. Second, the dynamic fuzzy servo controller and the dynamic fuzzy model following controller, which can make outputs of the nonlinear system converge to target points and to outputs of the reference system, respectively, are introduced. Finally, the servo and model following controller design conditions are given in terms of linear matrix inequalities. Design examples illustrate the utility of this approach.

Dynamic fuzzy cognitive network approach for modelling and control of PEM fuel cell for power electric bicycle system

International Nuclear Information System (INIS)

Kheirandish, Azadeh; Motlagh, Farid; Shafiabady, Niusha; Dahari, Mahidzal; Khairi Abdul Wahab, Ahmad

2017-01-01

Highlights: •Fuzzy cognitive map was proposed for the first time to describe the behaviour of fuel cell electric bicycle system. •Fuzzy rules were applied to explain the cause and effect between concepts. •To predict and analyse the cognitive map involved in the negotiation process. -- Abstract: Modelling Proton Exchange Membrane Fuel Cell (PEMFC) is the fundamental step in designing efficient systems for achieving higher performance. Among the development of new energy technologies, modelling and optimization of energy processes with pollution reduction, sufficient efficiency and low emission are considered one of the most promising areas of study. Despite affecting factors in PEMFC functionality, providing a reliable model for PEMFC is the key of performance optimization challenge. In this paper, fuzzy cognitive map has been used for modelling PEMFC system that is directed to provide a dynamic cognitive map from the affecting factors of the system. Controlling and modification of the system performance in various conditions is more practical by correlations among the performance factors of the PEMFC derived from fuzzy cognitive maps. On the other hand, the information of fuzzy cognitive map modelling is applicable for modification of neural networks structure for providing more accurate results based on the extracted knowledge from the cognitive map and visualization of the system’s performance. Finally, a rule based fuzzy cognitive map has been used that can be implemented for decision-making to control the system. This rule-based approach provides interpretability while enhancing the performance of the overall system.

Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system

Energy Technology Data Exchange (ETDEWEB)

Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)

2008-07-01

This article present a comparison of artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) applied for modelling a ground-coupled heat pump system (GCHP). The aim of this study is predicting system performance related to ground and air (condenser inlet and outlet) temperatures by using desired models. Performance forecasting is the precondition for the optimal design and energy-saving operation of air-conditioning systems. So obtained models will help the system designer to realize this precondition. The most suitable algorithm and neuron number in the hidden layer are found as Levenberg-Marquardt (LM) with seven neurons for ANN model whereas the most suitable membership function and number of membership functions are found as Gauss and two, respectively, for ANFIS model. The root-mean squared (RMS) value and the coefficient of variation in percent (cov) value are 0.0047 and 0.1363, respectively. The absolute fraction of variance (R{sup 2}) is 0.9999 which can be considered as very promising. This paper shows the appropriateness of ANFIS for the quantitative modeling of GCHP systems. (author)

Consumer Behavior Modeling: Fuzzy Logic Model for Air Purifiers Choosing

Directory of Open Access Journals (Sweden)

Oleksandr Dorokhov

2017-12-01

Full Text Available At the beginning, the article briefly describes the features of the marketing complex household goods. Also provides an overview of some aspects of the market for indoor air purifiers. The specific subject of the study was the process of consumer choice of household appliances for cleaning air in living quarters. The aim of the study was to substantiate and develop a computer model for evaluating by the potential buyers devices for air purification in conditions of vagueness and ambiguity of their consumer preferences. Accordingly, the main consumer criteria are identified, substantiated and described when buyers choose air purifiers. As methods of research, approaches based on fuzzy logic, fuzzy sets theory and fuzzy modeling were chosen. It was hypothesized that the fuzzy-multiple model allows rather accurately reflect consumer preferences and potential consumer choice in conditions of insufficient and undetermined information. Further, a computer model for estimating the consumer qualities of air cleaners by customers is developed. A proposed approach based on the application of fuzzy logic theory and practical modeling in the specialized computer software MATLAB. In this model, the necessary membership functions and their terms are constructed, as well as a set of rules for fuzzy inference to make decisions on the estimation of a specific air purifier. A numerical example of a comparative evaluation of air cleaners presented on the Ukrainian market is made and is given. Numerical simulation results confirmed the applicability of the proposed approach and the correctness of the hypothesis advanced about the possibility of modeling consumer behavior using fuzzy logic. The analysis of the obtained results is carried out and the prospects of application, development, and improvement of the developed model and the proposed approach are determined.

Forecasting of rainfall using ocean-atmospheric indices with a fuzzy neural technique

Science.gov (United States)

Srivastava, Gaurav; Panda, Sudhindra N.; Mondal, Pratap; Liu, Junguo

2010-12-01

SummaryForecasting of rainfall is imperative for rainfed agriculture of arid and semi-arid regions of the world where agriculture consumes nearly 80% of the total water demand. Fuzzy-Ranking Algorithm (FRA) is used to identify the significant input variables for rainfall forecast. A case study is carried out to forecast monthly rainfall in India with several ocean-atmospheric predictor variables. Three different scenarios of ocean-atmospheric predictor variables are used as a set of possible input variables for rainfall forecasting model: (1) two climate indices, i.e. Southern Oscillation Index (SOI) and Pacific Decadal Oscillation Index (PDOI); (2) Sea Surface Temperature anomalies (SSTa) in the 5° × 5° grid points in Indian Ocean; and (3) both the climate indices and SSTa. To generate a set of possible input variables for these scenarios, we use climatic indices and the SSTa data with different lags between 1 and 12 months. Nonlinear relationship between identified inputs and rainfall is captured with an Artificial Neural Network (ANN) technique. A new approach based on fuzzy c-mean clustering is proposed for dividing data into representative subsets for training, testing, and validation. The results show that this proposed approach overcomes the difficulty in determining optimal numbers of clusters associated with the data division technique of self-organized map. The ANN model developed with both the climate indices and SSTa shows the best performance for the forecast of the monthly August rainfall in India. Similar approach can be applied to forecast rainfall of any period at selected climatic regions of the world where significant relationship exists between the rainfall and climate indices.

Classification of mammographic masses using generalized dynamic fuzzy neural networks

International Nuclear Information System (INIS)

Lim, Wei Keat; Er, Meng Joo

2004-01-01

In this article, computer-aided classification of mammographic masses using generalized dynamic fuzzy neural networks (GDFNN) is presented. The texture parameters, derived from first-order gradient distribution and gray-level co-occurrence matrices, were computed from the regions of interest. A total of 343 images containing 180 benign masses and 163 malignant masses from the Digital Database for Screening Mammography were analyzed. A fast approach of automatically generating fuzzy rules from training samples was implemented to classify tumors. This work is novel in that it alleviates the problem of requiring a designer to examine all the input-output relationships of a training database in order to obtain the most appropriate structure for the classifier in a conventional computer-aided diagnosis. In this approach, not only the connection weights can be adjusted, but also the structure can be self-adaptive during the learning process. By virtue of the automatic generation of the classifier by the GDFNN learning algorithm, the area under the receiver-operating characteristic curve, A z , attains 0.868±0.020, which corresponds to a true-positive fraction of 95.0% at a false positive fraction of 52.8%. The corresponding accuracy is 70.0%, the positive predictive value is 62.0%, and the negative predictive value is 91.4%

Fuzzy linguistic model for interpolation

International Nuclear Information System (INIS)

Abbasbandy, S.; Adabitabar Firozja, M.

2007-01-01

In this paper, a fuzzy method for interpolating of smooth curves was represented. We present a novel approach to interpolate real data by applying the universal approximation method. In proposed method, fuzzy linguistic model (FLM) applied as universal approximation for any nonlinear continuous function. Finally, we give some numerical examples and compare the proposed method with spline method

Fuzzy model-based observers for fault detection in CSTR.

Science.gov (United States)

Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

2015-11-01

Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Spatial interpolation and radiological mapping of ambient gamma dose rate by using artificial neural networks and fuzzy logic methods.

Science.gov (United States)

Yeşilkanat, Cafer Mert; Kobya, Yaşar; Taşkın, Halim; Çevik, Uğur

2017-09-01

The aim of this study was to determine spatial risk dispersion of ambient gamma dose rate (AGDR) by using both artificial neural network (ANN) and fuzzy logic (FL) methods, compare the performances of methods, make dose estimations for intermediate stations with no previous measurements and create dose rate risk maps of the study area. In order to determine the dose distribution by using artificial neural networks, two main networks and five different network structures were used; feed forward ANN; Multi-layer perceptron (MLP), Radial basis functional neural network (RBFNN), Quantile regression neural network (QRNN) and recurrent ANN; Jordan networks (JN), Elman networks (EN). In the evaluation of estimation performance obtained for the test data, all models appear to give similar results. According to the cross-validation results obtained for explaining AGDR distribution, Pearson's r coefficients were calculated as 0.94, 0.91, 0.89, 0.91, 0.91 and 0.92 and RMSE values were calculated as 34.78, 43.28, 63.92, 44.86, 46.77 and 37.92 for MLP, RBFNN, QRNN, JN, EN and FL, respectively. In addition, spatial risk maps showing distributions of AGDR of the study area were created by all models and results were compared with geological, topological and soil structure. Copyright © 2017 Elsevier Ltd. All rights reserved.

Introduction to n-adaptive fuzzy models to analyze public opinion on AIDS

CERN Document Server

Kandasamy, D W B V; Kandasamy, Dr.W.B.Vasantha; Smarandache, Dr.Florentin

2006-01-01

There are many fuzzy models like Fuzzy matrices, Fuzzy Cognitive Maps, Fuzzy relational Maps, Fuzzy Associative Memories, Bidirectional Associative memories and so on. But almost all these models can give only one sided solution like hidden pattern or a resultant output vector dependent on the input vector depending in the problem at hand. So for the first time we have defined a n-adaptive fuzzy model which can view or analyze the problem in n ways (n >=2) Though we have defined these n- adaptive fuzzy models theorectically we are not in a position to get a n-adaptive fuzzy model for n > 2 for practical real world problems. The highlight of this model is its capacity to analyze the same problem in different ways thereby arriving at various solutions that mirror multiple perspectives. We have used the 2-adaptive fuzzy model having the two fuzzy models, fuzzy matrices model and BAMs viz. model to analyze the views of public about HIV/ AIDS disease, patient and the awareness program. This book has five chapters ...

Dependent-Chance Programming Models for Capital Budgeting in Fuzzy Environments

Institute of Scientific and Technical Information of China (English)

LIANG Rui; GAO Jinwu

2008-01-01

Capital budgeting is concerned with maximizing the total net profit subject to budget constraints by selecting an appropriate combination of projects. This paper presents chance maximizing models for capital budgeting with fuzzy input data and multiple conflicting objectives. When the decision maker sets a prospec-tive profit level and wants to maximize the chances of the total profit achieving the prospective profit level, a fuzzy dependent-chance programming model, a fuzzy multi-objective dependent-chance programming model, and a fuzzy goal dependent-chance programming model are used to formulate the fuzzy capital budgeting problem. A fuzzy simulation based genetic algorithm is used to solve these models. Numerical examples are provided to illustrate the effectiveness of the simulation-based genetic algorithm and the po-tential applications of these models.

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems.

Science.gov (United States)

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S; Agarwal, Dev P

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems

Directory of Open Access Journals (Sweden)

Vandana Sakhre

2015-01-01

Full Text Available Fuzzy Counter Propagation Neural Network (FCPN controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL. FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN and Back Propagation Network (BPN on the basis of Mean Absolute Error (MAE, Mean Square Error (MSE, Best Fit Rate (BFR, and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO and a single input and single output (SISO gas furnace Box-Jenkins time series data.

A hybrid framework for reservoir characterization using fuzzy ranking and an artificial neural network

Science.gov (United States)

Wang, Baijie; Wang, Xin; Chen, Zhangxin

2013-08-01

Reservoir characterization refers to the process of quantitatively assigning reservoir properties using all available field data. Artificial neural networks (ANN) have recently been introduced to solve reservoir characterization problems dealing with the complex underlying relationships inherent in well log data. Despite the utility of ANNs, the current limitation is that most existing applications simply focus on directly implementing existing ANN models instead of improving/customizing them to fit the specific reservoir characterization tasks at hand. In this paper, we propose a novel intelligent framework that integrates fuzzy ranking (FR) and multilayer perceptron (MLP) neural networks for reservoir characterization. FR can automatically identify a minimum subset of well log data as neural inputs, and the MLP is trained to learn the complex correlations from the selected well log data to a target reservoir property. FR guarantees the selection of the optimal subset of representative data from the overall well log data set for the characterization of a specific reservoir property; and, this implicitly improves the modeling and predication accuracy of the MLP. In addition, a growing number of industrial agencies are implementing geographic information systems (GIS) in field data management; and, we have designed the GFAR solution (GIS-based FR ANN Reservoir characterization solution) system, which integrates the proposed framework into a GIS system that provides an efficient characterization solution. Three separate petroleum wells from southwestern Alberta, Canada, were used in the presented case study of reservoir porosity characterization. Our experiments demonstrate that our method can generate reliable results.

A Novel Approach to Implement Takagi-Sugeno Fuzzy Models.

Science.gov (United States)

Chang, Chia-Wen; Tao, Chin-Wang

2017-09-01

This paper proposes new algorithms based on the fuzzy c-regressing model algorithm for Takagi-Sugeno (T-S) fuzzy modeling of the complex nonlinear systems. A fuzzy c-regression state model (FCRSM) algorithm is a T-S fuzzy model in which the functional antecedent and the state-space-model-type consequent are considered with the available input-output data. The antecedent and consequent forms of the proposed FCRSM consists mainly of two advantages: one is that the FCRSM has low computation load due to only one input variable is considered in the antecedent part; another is that the unknown system can be modeled to not only the polynomial form but also the state-space form. Moreover, the FCRSM can be extended to FCRSM-ND and FCRSM-Free algorithms. An algorithm FCRSM-ND is presented to find the T-S fuzzy state-space model of the nonlinear system when the input-output data cannot be precollected and an assumed effective controller is available. In the practical applications, the mathematical model of controller may be hard to be obtained. In this case, an online tuning algorithm, FCRSM-FREE, is designed such that the parameters of a T-S fuzzy controller and the T-S fuzzy state model of an unknown system can be online tuned simultaneously. Four numerical simulations are given to demonstrate the effectiveness of the proposed approach.

Neutral network and fuzzy logic based grate control; Roststyrning med neutrala naetverk och fuzzy logic

Energy Technology Data Exchange (ETDEWEB)

Ramstroem, Erik [TPS Termiska Processer AB, Nykoeping (Sweden)

2002-04-01

Grate-control is a complex task in many ways. The relations between controlled variables and the values they depend on are mostly unknown. Research projects are going on to create grate models based on physical laws. Those models are too complex for control implementation. The evaluation time is to long for control use. Another fundamental difficulty is that the relationships are none linear. That is, for a specific change in control value, the change in controlled value depends on the original size of control value, process disturbances and controlled values. There are extensive theories for linear process control. Non-linear control theory is used in robotic applications, but not in process and combustion control. The aim of grate control is to use as much of the grate area as possible, without having unburned material in ash. The outlined strategy is: To keep the position of the final bum out zone constant and its extension controlled. The control variables should be primary airflow, distribution of primary air, and fuel flow. Disturbances that should be measured are the fuel moisture content, the temperature of primary air and the grate temperature under the fuel bed. Technologies used are, fuzzy-logic and neural networks. A combination of booth could be used as well as any of them separately. A Fuzzy-logic controller acts as a computerised operator. Rules are specified with 'if - then' thesis. An example of that is: - if temperature is low, then close the valve The boundaries between the rules are made fuzzy. That makes it possible for the temperature to be just a bit low, which makes the valve open a bit. A lot of rules are created so that the controller knows what to do in every situation. Neural networks are sort of multi dimensional curves, with arbitrary degrees of freedom. The nets are used to predict future process values from measured ones. The model is evaluated from collected data. Parameters are adjusted for best correspondence between

Fuzzy fault diagnosis system of MCFC

Institute of Scientific and Technical Information of China (English)

Wang Zhenlei; Qian Feng; Cao Guangyi

2005-01-01

A kind of fault diagnosis system of molten carbonate fuel cell (MCFC) stack is proposed in this paper. It is composed of a fuzzy neural network (FNN) and a fault diagnosis element. FNN is able to deal with the information of the expert knowledge and the experiment data efficiently. It also has the ability to approximate any smooth system. FNN is used to identify the fault diagnosis model of MCFC stack. The fuzzy fault decision element can diagnose the state of the MCFC generating system, normal or fault, and can decide the type of the fault based on the outputs of FNN model and the MCFC system. Some simulation experiment results are demonstrated in this paper.

SEffEst: Effort estimation in software projects using fuzzy logic and neural networks

Directory of Open Access Journals (Sweden)

Israel

2012-08-01

Full Text Available Academia and practitioners confirm that software project effort prediction is crucial for an accurate software project management. However, software development effort estimation is uncertain by nature. Literature has developed methods to improve estimation correctness, using artificial intelligence techniques in many cases. Following this path, this paper presents SEffEst, a framework based on fuzzy logic and neural networks designed to increase effort estimation accuracy on software development projects. Trained using ISBSG data, SEffEst presents remarkable results in terms of prediction accuracy.

Modeling Research Project Risks with Fuzzy Maps

Science.gov (United States)

Bodea, Constanta Nicoleta; Dascalu, Mariana Iuliana

2009-01-01

The authors propose a risks evaluation model for research projects. The model is based on fuzzy inference. The knowledge base for fuzzy process is built with a causal and cognitive map of risks. The map was especially developed for research projects, taken into account their typical lifecycle. The model was applied to an e-testing research…

A reduced-form intensity-based model under fuzzy environments

Science.gov (United States)

Wu, Liang; Zhuang, Yaming

2015-05-01

The external shocks and internal contagion are the important sources of default events. However, the external shocks and internal contagion effect on the company is not observed, we cannot get the accurate size of the shocks. The information of investors relative to the default process exhibits a certain fuzziness. Therefore, using randomness and fuzziness to study such problems as derivative pricing or default probability has practical needs. But the idea of fuzzifying credit risk models is little exploited, especially in a reduced-form model. This paper proposes a new default intensity model with fuzziness and presents a fuzzy default probability and default loss rate, and puts them into default debt and credit derivative pricing. Finally, the simulation analysis verifies the rationality of the model. Using fuzzy numbers and random analysis one can consider more uncertain sources in the default process of default and investors' subjective judgment on the financial markets in a variety of fuzzy reliability so as to broaden the scope of possible credit spreads.

Fuzzy rule-based model for hydropower reservoirs operation

Energy Technology Data Exchange (ETDEWEB)

Moeini, R.; Afshar, A.; Afshar, M.H. [School of Civil Engineering, Iran University of Science and Technology, Tehran (Iran, Islamic Republic of)

2011-02-15

Real-time hydropower reservoir operation is a continuous decision-making process of determining the water level of a reservoir or the volume of water released from it. The hydropower operation is usually based on operating policies and rules defined and decided upon in strategic planning. This paper presents a fuzzy rule-based model for the operation of hydropower reservoirs. The proposed fuzzy rule-based model presents a set of suitable operating rules for release from the reservoir based on ideal or target storage levels. The model operates on an 'if-then' principle, in which the 'if' is a vector of fuzzy premises and the 'then' is a vector of fuzzy consequences. In this paper, reservoir storage, inflow, and period are used as premises and the release as the consequence. The steps involved in the development of the model include, construction of membership functions for the inflow, storage and the release, formulation of fuzzy rules, implication, aggregation and defuzzification. The required knowledge bases for the formulation of the fuzzy rules is obtained form a stochastic dynamic programming (SDP) model with a steady state policy. The proposed model is applied to the hydropower operation of ''Dez'' reservoir in Iran and the results are presented and compared with those of the SDP model. The results indicate the ability of the method to solve hydropower reservoir operation problems. (author)

Monitoring nuclear reactor systems using neural networks and fuzzy logic

International Nuclear Information System (INIS)

Ikonomopoulos, A.; Tsoukalas, L.H.; Uhrig, R.E.; Mullens, J.A.

1992-01-01

A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such virtual measurements the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up-or performance can be determined. In the methodology presented the output of virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems

A Fuzzy Cognitive Model of aeolian instability across the South Texas Sandsheet

Science.gov (United States)

Houser, C.; Bishop, M. P.; Barrineau, C. P.

2014-12-01

Characterization of aeolian systems is complicated by rapidly changing surface-process regimes, spatio-temporal scale dependencies, and subjective interpretation of imagery and spatial data. This paper describes the development and application of analytical reasoning to quantify instability of an aeolian environment using scale-dependent information coupled with conceptual knowledge of process and feedback mechanisms. Specifically, a simple Fuzzy Cognitive Model (FCM) for aeolian landscape instability was developed that represents conceptual knowledge of key biophysical processes and feedbacks. Model inputs include satellite-derived surface biophysical and geomorphometric parameters. FCMs are a knowledge-based Artificial Intelligence (AI) technique that merges fuzzy logic and neural computing in which knowledge or concepts are structured as a web of relationships that is similar to both human reasoning and the human decision-making process. Given simple process-form relationships, the analytical reasoning model is able to map the influence of land management practices and the geomorphology of the inherited surface on aeolian instability within the South Texas Sandsheet. Results suggest that FCMs can be used to formalize process-form relationships and information integration analogous to human cognition with future iterations accounting for the spatial interactions and temporal lags across the sand sheets.

Type-2 fuzzy logic uncertain systems’ modeling and control

CERN Document Server

Antão, Rómulo

2017-01-01

This book focuses on a particular domain of Type-2 Fuzzy Logic, related to process modeling and control applications. It deepens readers’understanding of Type-2 Fuzzy Logic with regard to the following three topics: using simpler methods to train a Type-2 Takagi-Sugeno Fuzzy Model; using the principles of Type-2 Fuzzy Logic to reduce the influence of modeling uncertainties on a locally linear n-step ahead predictor; and developing model-based control algorithms according to the Generalized Predictive Control principles using Type-2 Fuzzy Sets. Throughout the book, theory is always complemented with practical applications and readers are invited to take their learning process one step farther and implement their own applications using the algorithms’ source codes (provided). As such, the book offers avaluable referenceguide for allengineers and researchers in the field ofcomputer science who are interested in intelligent systems, rule-based systems and modeling uncertainty.

A generic methodology for developing fuzzy decision models

NARCIS (Netherlands)

Bosma, R.; Berg, van den J.; Kaymak, U.; Udo, H.; Verreth, J.

2012-01-01

An important paradigm in decision-making models is utility-maximization where most models do not include actors’ motives. Fuzzy set theory on the other hand offers a method to simulate human decisionmaking. However, the literature describing expert-driven fuzzy logic models, rarely gives precise

A generic methodology for developing fuzzy decision models

NARCIS (Netherlands)

Bosma, R.H.; Berg, van den J.; Kaymak, Uzay; Udo, H.M.J.; Verreth, J.A.J.

2012-01-01

An important paradigm in decision-making models is utility-maximization where most models do not include actors’ motives. Fuzzy set theory on the other hand offers a method to simulate human decision-making. However, the literature describing expert-driven fuzzy logic models, rarely gives precise

AUTOMOTIVE APPLICATIONS OF EVOLVING TAKAGI-SUGENO-KANG FUZZY MODELS

Directory of Open Access Journals (Sweden)

Radu-Emil Precup

2017-08-01

Full Text Available This paper presents theoretical and application results concerning the development of evolving Takagi-Sugeno-Kang fuzzy models for two dynamic systems, which will be viewed as controlled processes, in the field of automotive applications. The two dynamic systems models are nonlinear dynamics of the longitudinal slip in the Anti-lock Braking Systems (ABS and the vehicle speed in vehicles with the Continuously Variable Transmission (CVT systems. The evolving Takagi-Sugeno-Kang fuzzy models are obtained as discrete-time fuzzy models by incremental online identification algorithms. The fuzzy models are validated against experimental results in the case of the ABS and the first principles simulation results in the case of the vehicle with the CVT.

Extended Traffic Crash Modelling through Precision and Response Time Using Fuzzy Clustering Algorithms Compared with Multi-layer Perceptron

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Iman Aghayan

2012-11-01

Full Text Available This paper compares two fuzzy clustering algorithms – fuzzy subtractive clustering and fuzzy C-means clustering – to a multi-layer perceptron neural network for their ability to predict the severity of crash injuries and to estimate the response time on the traffic crash data. Four clustering algorithms – hierarchical, K-means, subtractive clustering, and fuzzy C-means clustering – were used to obtain the optimum number of clusters based on the mean silhouette coefficient and R-value before applying the fuzzy clustering algorithms. The best-fit algorithms were selected according to two criteria: precision (root mean square, R-value, mean absolute errors, and sum of square error and response time (t. The highest R-value was obtained for the multi-layer perceptron (0.89, demonstrating that the multi-layer perceptron had a high precision in traffic crash prediction among the prediction models, and that it was stable even in the presence of outliers and overlapping data. Meanwhile, in comparison with other prediction models, fuzzy subtractive clustering provided the lowest value for response time (0.284 second, 9.28 times faster than the time of multi-layer perceptron, meaning that it could lead to developing an on-line system for processing data from detectors and/or a real-time traffic database. The model can be extended through improvements based on additional data through induction procedure.

Adaptive fuzzy-neural-network control for maglev transportation system.

Science.gov (United States)

Wai, Rong-Jong; Lee, Jeng-Dao

2008-01-01

A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.

Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT) for Aquaculture.

Science.gov (United States)

Chen, Yingyi; Zhen, Zhumi; Yu, Huihui; Xu, Jing

2017-01-14

In the Internet of Things (IoT) equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT.

Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT for Aquaculture

Directory of Open Access Journals (Sweden)

Yingyi Chen

2017-01-01

Full Text Available In the Internet of Things (IoT equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT.

Fuzzy model investic do High-tech projektů

Directory of Open Access Journals (Sweden)

Alžběta Kubíčková

2013-10-01

Full Text Available Purpose of the article: Relations among parameters of High-tech projects are very complex, vague, partially inconsistent and multidimensional. Optimal decisions to invest into High-tech companies require top field experts and knowledgeable investors. Therefore the conventional methods of investments analysis are not relevant. Therefore fuzzy logic is introduced. Methodology/methods: A fuzzy knowledge base is a flexible framework for acquisition of vague inconsistent knowledge items which are typical for knowledge economics and consequently for High-tech projects. The pooling of the records and / or observations represents a trade-off between minimal modification of the original data and elimination of inconsistencies among available sets of data. Scientific aim: The paper presents a detailed description of fuzzy model of investment decision making into High-tech firm’s projects. A set of conditional statements was used to formalize the effects of selected variables on investment feasibility of High-tech projects. The main aim is to quantify feasibilities of High-tech projects risk investors make good /not bad decisions. Findings: A set of 50 observations of High-tech companies was transformed into a set of 50 conditional statements using 14 variables. The result is the fuzzy model, which can be used to answer investors’ queries. Two queries are answered and presented in details as an example and as a nucleus of a fuzzy dialogue investor – computer. Conclusions: The main problem is the sparseness of the fuzzy model. Many fuzzy similarities are relatively low and the decision process is therefore often problematic. A much more complex set of variables must be applied to specify the fuzzy model to increase reliability of predictions and decisions.

Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems

Science.gov (United States)

El-Sebakhy, Emad A.

2009-09-01

Pressure-volume-temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson-Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.

5th International Conference on Fuzzy and Neuro Computing

CERN Document Server

Panigrahi, Bijaya; Das, Swagatam; Suganthan, Ponnuthurai

2015-01-01

This proceedings bring together contributions from researchers from academia and industry to report the latest cutting edge research made in the areas of Fuzzy Computing, Neuro Computing and hybrid Neuro-Fuzzy Computing in the paradigm of Soft Computing. The FANCCO 2015 conference explored new application areas, design novel hybrid algorithms for solving different real world application problems. After a rigorous review of the 68 submissions from all over the world, the referees panel selected 27 papers to be presented at the Conference. The accepted papers have a good, balanced mix of theory and applications. The techniques ranged from fuzzy neural networks, decision trees, spiking neural networks, self organizing feature map, support vector regression, adaptive neuro fuzzy inference system, extreme learning machine, fuzzy multi criteria decision making, machine learning, web usage mining, Takagi-Sugeno Inference system, extended Kalman filter, Goedel type logic, fuzzy formal concept analysis, biclustering e...

A Fuzzy Knowledge Representation Model for Student Performance Assessment

DEFF Research Database (Denmark)

Badie, Farshad

Knowledge representation models based on Fuzzy Description Logics (DLs) can provide a foundation for reasoning in intelligent learning environments. While basic DLs are suitable for expressing crisp concepts and binary relationships, Fuzzy DLs are capable of processing degrees of truth/completene......Knowledge representation models based on Fuzzy Description Logics (DLs) can provide a foundation for reasoning in intelligent learning environments. While basic DLs are suitable for expressing crisp concepts and binary relationships, Fuzzy DLs are capable of processing degrees of truth....../completeness about vague or imprecise information. This paper tackles the issue of representing fuzzy classes using OWL2 in a dataset describing Performance Assessment Results of Students (PARS)....

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR END MILLING

Directory of Open Access Journals (Sweden)

ANGELOS P. MARKOPOULOS

2016-09-01

Full Text Available Soft computing is commonly used as a modelling method in various technological areas. Methods such as Artificial Neural Networks and Fuzzy Logic have found application in manufacturing technology as well. NeuroFuzzy systems, aimed to combine the benefits of both the aforementioned Artificial Intelligence methods, are a subject of research lately as have proven to be superior compared to other methods. In this paper an adaptive neuro-fuzzy inference system for the prediction of surface roughness in end milling is presented. Spindle speed, feed rate, depth of cut and vibrations were used as independent input variables, while roughness parameter Ra as dependent output variable. Several variations are tested and the results of the optimum system are presented. Final results indicate that the proposed model can accurately predict surface roughness, even for input that was not used in training.

Fuzzy-cellular neural network for face recognition HCI Authentication

Science.gov (United States)

Hoomod, Haider K.; ali, Ahmed abd

2018-05-01

Because of the rapid development of mobile devices technology, ease of use and interact with humans. May have found a mobile device most uses in our communications. Mobile devices can carry large amounts of personal and sensitive data, but often left not guaranteed (pin) locks are inconvenient to use and thus have seen low adoption while biometrics is more convenient and less susceptible to fraud and manipulation. Were propose in this paper authentication technique for using a mobile face recognition based on cellular neural networks [1] and fuzzy rules control. The good speed and get recognition rate from applied the proposed system in Android system. The images obtained in real time for 60 persons each person has 20 t0 60 different shot face images (about 3600 images), were the results for (FAR = 0), (FRR = 1.66%), (FER = 1.66) and accuracy = 98.34

Research on Fault Diagnosis Method Based on Rule Base Neural Network

Directory of Open Access Journals (Sweden)

Zheng Ni

2017-01-01

Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

A fuzzy mathematical model of West Java population with logistic growth model

Science.gov (United States)

Nurkholipah, N. S.; Amarti, Z.; Anggriani, N.; Supriatna, A. K.

2018-03-01

In this paper we develop a mathematics model of population growth in the West Java Province Indonesia. The model takes the form as a logistic differential equation. We parameterize the model using several triples of data, and choose the best triple which has the smallest Mean Absolute Percentage Error (MAPE). The resulting model is able to predict the historical data with a high accuracy and it also able to predict the future of population number. Predicting the future population is among the important factors that affect the consideration is preparing a good management for the population. Several experiment are done to look at the effect of impreciseness in the data. This is done by considering a fuzzy initial value to the crisp model assuming that the model propagates the fuzziness of the independent variable to the dependent variable. We assume here a triangle fuzzy number representing the impreciseness in the data. We found that the fuzziness may disappear in the long-term. Other scenarios also investigated, such as the effect of fuzzy parameters to the crisp initial value of the population. The solution of the model is obtained numerically using the fourth-order Runge-Kutta scheme.

Fuzzy model predictive control algorithm applied in nuclear power plant

International Nuclear Information System (INIS)

Zuheir, Ahmad

2006-01-01

The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)

Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

Science.gov (United States)

Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

2015-11-01

In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

Application of a fuzzy neural network model in predicting polycyclic aromatic hydrocarbon-mediated perturbations of the Cyp1b1 transcriptional regulatory network in mouse skin

Energy Technology Data Exchange (ETDEWEB)

Larkin, Andrew [Department of Environmental and Molecular Toxicology, Oregon State University (United States); Department of Statistics, Oregon State University (United States); Superfund Research Center, Oregon State University (United States); Siddens, Lisbeth K. [Department of Environmental and Molecular Toxicology, Oregon State University (United States); Superfund Research Center, Oregon State University (United States); Krueger, Sharon K. [Superfund Research Center, Oregon State University (United States); Linus Pauling Institute, Oregon State University (United States); Tilton, Susan C.; Waters, Katrina M. [Superfund Research Center, Oregon State University (United States); Computational Biology and Bioinformatics Group, Pacific Northwest National Laboratory, Richland, WA 99352 (United States); Williams, David E., E-mail: david.williams@oregonstate.edu [Department of Environmental and Molecular Toxicology, Oregon State University (United States); Superfund Research Center, Oregon State University (United States); Linus Pauling Institute, Oregon State University (United States); Environmental Health Sciences Center, Oregon State University, Corvallis, OR 97331 (United States); Baird, William M. [Department of Environmental and Molecular Toxicology, Oregon State University (United States); Superfund Research Center, Oregon State University (United States); Environmental Health Sciences Center, Oregon State University, Corvallis, OR 97331 (United States)

2013-03-01

Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdani logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave-one-out cross-validation. Predictions were within 1 log{sub 2} fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. - Highlights: ► Tested a model to predict PAH mixture-mediated changes in Cyp1b1 expression ► Quantitative predictions in agreement with microarrays for Cyp1b1 induction ► Unexpected difference in expression between DBC and other treatments predicted ► Model predictions

Polynomial fuzzy model-based approach for underactuated surface vessels

DEFF Research Database (Denmark)

Khooban, Mohammad Hassan; Vafamand, Navid; Dragicevic, Tomislav

2018-01-01

The main goal of this study is to introduce a new polynomial fuzzy model-based structure for a class of marine systems with non-linear and polynomial dynamics. The suggested technique relies on a polynomial Takagi–Sugeno (T–S) fuzzy modelling, a polynomial dynamic parallel distributed compensation...... surface vessel (USV). Additionally, in order to overcome the USV control challenges, including the USV un-modelled dynamics, complex nonlinear dynamics, external disturbances and parameter uncertainties, the polynomial fuzzy model representation is adopted. Moreover, the USV-based control structure...... and a sum-of-squares (SOS) decomposition. The new proposed approach is a generalisation of the standard T–S fuzzy models and linear matrix inequality which indicated its effectiveness in decreasing the tracking time and increasing the efficiency of the robust tracking control problem for an underactuated...

Streamflow Forecasting Using Nuero-Fuzzy Inference System

Science.gov (United States)

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

2005-12-01

The prediction of flow into a reservoir is fundamental in water resources planning and management. The need for timely and accurate streamflow forecasting is widely recognized and emphasized by many in water resources fraternity. Real-time forecasts of natural inflows to reservoirs are of particular interest for operation and scheduling. The physical system of the river basin that takes the rainfall as an input and produces the runoff is highly nonlinear, complicated and very difficult to fully comprehend. The system is influenced by large number of factors and variables. The large spatial extent of the systems forces the uncertainty into the hydrologic information. A variety of methods have been proposed for forecasting reservoir inflows including conceptual (physical) and empirical (statistical) models (WMO 1994), but none of them can be considered as unique superior model (Shamseldin 1997). Owing to difficulties of formulating reasonable non-linear watershed models, recent attempts have resorted to Neural Network (NN) approach for complex hydrologic modeling. In recent years the use of soft computing in the field of hydrological forecasting is gaining ground. The relatively new soft computing technique of Adaptive Neuro-Fuzzy Inference System (ANFIS), developed by Jang (1993) is able to take care of the non-linearity, uncertainty, and vagueness embedded in the system. It is a judicious combination of the Neural Networks and fuzzy systems. It can learn and generalize highly nonlinear and uncertain phenomena due to the embedded neural network (NN). NN is efficient in learning and generalization, and the fuzzy system mimics the cognitive capability of human brain. Hence, ANFIS can learn the complicated processes involved in the basin and correlate the precipitation to the corresponding discharge. In the present study, one step ahead forecasts are made for ten-daily flows, which are mostly required for short term operational planning of multipurpose reservoirs. A

Precision position control of servo systems using adaptive back-stepping and recurrent fuzzy neural networks

International Nuclear Information System (INIS)

Kim, Han Me; Kim, Jong Shik; Han, Seong Ik

2009-01-01

To improve position tracking performance of servo systems, a position tracking control using adaptive back-stepping control(ABSC) scheme and recurrent fuzzy neural networks(RFNN) is proposed. An adaptive rule of the ABSC based on system dynamics and dynamic friction model is also suggested to compensate nonlinear dynamic friction characteristics. However, it is difficult to reduce the position tracking error of servo systems by using only the ABSC scheme because of the system uncertainties which cannot be exactly identified during the modeling of servo systems. Therefore, in order to overcome system uncertainties and then to improve position tracking performance of servo systems, the RFNN technique is additionally applied to the servo system. The feasibility of the proposed control scheme for a servo system is validated through experiments. Experimental results show that the servo system with ABS controller based on the dual friction observer and RFNN including the reconstruction error estimator can achieve desired tracking performance and robustness

Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems

Energy Technology Data Exchange (ETDEWEB)

Jassar, S.; Zhao, L. [Department of Electrical and Computer Engineering, Ryerson University, 350 Victoria Street, Toronto, ON (Canada); Liao, Z. [Department of Architectural Science, Ryerson University (Canada)

2009-08-15

The heating systems are conventionally controlled by open-loop control systems because of the absence of practical methods for estimating average air temperature in the built environment. An inferential sensor model, based on adaptive neuro-fuzzy inference system modeling, for estimating the average air temperature in multi-zone space heating systems is developed. This modeling technique has the advantage of expert knowledge of fuzzy inference systems (FISs) and learning capability of artificial neural networks (ANNs). A hybrid learning algorithm, which combines the least-square method and the back-propagation algorithm, is used to identify the parameters of the network. This paper describes an adaptive network based inferential sensor that can be used to design closed-loop control for space heating systems. The research aims to improve the overall performance of heating systems, in terms of energy efficiency and thermal comfort. The average air temperature results estimated by using the developed model are strongly in agreement with the experimental results. (author)

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

Science.gov (United States)

Tahmasebi, Pejman; Hezarkhani, Ardeshir

2012-05-01

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

Modelling hourly dissolved oxygen concentration (DO) using dynamic evolving neural-fuzzy inference system (DENFIS)-based approach: case study of Klamath River at Miller Island Boat Ramp, OR, USA.

Science.gov (United States)

Heddam, Salim

2014-01-01

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

NEURAL NETWORKS, FUZZY LOGIC AND GENETIC ALGORITHMS: APPLICATIONS AND POSSIBILITIES IN FINANCE AND ACCOUNTING

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José Alonso Borba

2010-04-01

Full Text Available There are problems in Finance and Accounting that can not be easily solved by means of traditional techniques (e.g. bankruptcy prediction and strategies for investing in common stock. In these situations, it is possible to use methods of Artificial Intelligence. This paper analyzes empirical works published in international journals between 2000 and 2007 that present studies about the application of Neural Networks, Fuzzy Logic and Genetic Algorithms to problems in Finance and Accounting. The objective is to identify and quantify the relationships established between the available techniques and the problems studied by the researchers. Analyzing 258 papers, it was noticed that the most used technique is the Artificial Neural Network. The most researched applications are from the field of Finance, especially those related to stock exchanges (forecasting of common stock and indices prices.

Creating Clinical Fuzzy Automata with Fuzzy Arden Syntax.

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de Bruin, Jeroen S; Steltzer, Heinz; Rappelsberger, Andrea; Adlassnig, Klaus-Peter

2017-01-01

Formal constructs for fuzzy sets and fuzzy logic are incorporated into Arden Syntax version 2.9 (Fuzzy Arden Syntax). With fuzzy sets, the relationships between measured or observed data and linguistic terms are expressed as degrees of compatibility that model the unsharpness of the boundaries of linguistic terms. Propositional uncertainty due to incomplete knowledge of relationships between clinical linguistic concepts is modeled with fuzzy logic. Fuzzy Arden Syntax also supports the construction of fuzzy state monitors. The latter are defined as monitors that employ fuzzy automata to observe gradual transitions between different stages of disease. As a use case, we re-implemented FuzzyARDS, a previously published clinical monitoring system for patients suffering from acute respiratory distress syndrome (ARDS). Using the re-implementation as an example, we show how key concepts of fuzzy automata, i.e., fuzzy states and parallel fuzzy state transitions, can be implemented in Fuzzy Arden Syntax. The results showed that fuzzy state monitors can be implemented in a straightforward manner.

An integrated supply chain model for the perishable items with fuzzy production rate and fuzzy demand rate

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Singh Chaman

2011-01-01

Full Text Available In the changing market scenario, supply chain management is getting phenomenal importance amongst researchers. Studies on supply chain management have emphasized the importance of a long-term strategic relationship between the manufacturer, distributor and retailer. In the present paper, a model has been developed by assuming that the demand rate and production rate as triangular fuzzy numbers and items deteriorate at a constant rate. The expressions for the average inventory cost are obtained both in crisp and fuzzy sense. The fuzzy model is defuzzified using the fuzzy extension principle, and its optimization with respect to the decision variable is also carried out. Finally, an example is given to illustrate the model and sensitivity analysis is performed to study the effect of parameters.

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

Science.gov (United States)

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

2013-02-01

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

D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

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Shu-zhi Gao

2014-01-01

Full Text Available PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature. Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.

Novel stability criteria for fuzzy Hopfield neural networks based on an improved homogeneous matrix polynomials technique

International Nuclear Information System (INIS)

Feng Yi-Fu; Zhang Qing-Ling; Feng De-Zhi

2012-01-01

The global stability problem of Takagi—Sugeno (T—S) fuzzy Hopfield neural networks (FHNNs) with time delays is investigated. Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism. Firstly, using both Finsler's lemma and an improved homogeneous matrix polynomial technique, and applying an affine parameter-dependent Lyapunov—Krasovskii functional, we obtain the convergent LMI-based stability criteria. Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique. Secondly, to further reduce the conservatism, a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs, which is suitable to the homogeneous matrix polynomials setting. Finally, two illustrative examples are given to show the efficiency of the proposed approaches

Bonissone CIDU Presentation: Design of Local Fuzzy Models

Data.gov (United States)

National Aeronautics and Space Administration — After reviewing key background concepts in fuzzy systems and evolutionary computing, we will focus on the use of local fuzzy models, which are related to both kernel...

A version of Stone-Weierstrass theorem in Fuzzy Analysis

Energy Technology Data Exchange (ETDEWEB)

Font, J.J.; Sanchis, D.; Sanchis, M.

2017-07-01

Fuzzy numbers provide formalized tools to deal with non-precise quantities. They are indeed fuzzy sets in the real line and were introduced in 1978 by Dubois and Prade , who also defined their basic operations. Since then, Fuzzy Analysis has developed based on the notion of fuzzy number just as much as classical Real Analysis did based on the concept of real number. Such development was eased by a characterization of fuzzy numbers provided in 1986 by Goetschel and Voxman leaning on their level sets. As in the classical setting, continuous fuzzy-valued functions (fuzzy functions) are the central core of the theory. The principal difference with regard to real-valued continuous functions is the fact that the fuzzy numbers do not form a vectorial space, which determines all the results, and, especially, the proofs. The study of fuzzy functions has developed, principally, about two lines of investigation: - Differential fuzzy equations, which have turned out to be the natural way of modelling physical and engineering problems in contexts where the parameters are vague or incomplete. - The problem of approximation of fuzzy functions, basically using the approximation capability of fuzzy neural networks. We will focus on this second line of investigation, though our approach will be more general and based on an adaptation of the famous Stone-Weierstrass Theorem to the fuzzy context. This way so, we introduce the concept of “multiplier” of a set of fuzzy functions and use it to give a constructive proof of a Stone-Weiestrass type theorem for fuzzy functions. (Author)

Application of a Self-recurrent Wavelet Neural Network in the Modeling and Control of an AC Servo System

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Run Min HOU

2014-05-01

Full Text Available To control the nonlinearity, widespread variations in loads and time varying characteristic of the high power ac servo system, the modeling and control techniques are studied here. A self-recurrent wavelet neural network (SRWNN modeling scheme is proposed, which successfully addresses the issue of the traditional wavelet neural network easily falling into local optimum, and significantly improves the network approximation capability and convergence rate. The control scheme of a SRWNN based on fuzzy compensation is expected. Gradient information is provided in real time for the controller by using a SRWNN identifier, so as to ensure that the learning and adjusting function of the controller of the SRWNN operate well, and fuzzy compensation control is applied to improve rapidity and accuracy of the entire system. Then the Lyapunov function is utilized to judge the stability of the system. The experimental analysis and comparisons with other modeling and control methods, it is clearly shown that the validities of the proposed modeling scheme and control scheme are effective.

Progress in the prediction of disruptions in ASDEX-Upgrade via neural and fuzzy-neural techniques

International Nuclear Information System (INIS)

Versaci, M.; Morabito, F.C.; Tichmann, C.; Pautasso, G.

2001-01-01

The paper addresses the problem of predicting the onset of a disruption on the basis of some known precursors possibly announcing the event. The availability in real time of a large set of diagnostic signals allows us to collectively interpret the data in order to decide whether we are near a disruption or during a normal operation scenario. As a relevant experimental example, a database of disruptive discharges in ASDEX-Upgrade has been analysed in this work. Both Neural Networks (NN's) and Fuzzy Inference Systems (FIS) have been investigated as suitable tools to cope with the prediction problem. The experimental database has been exploited aiming to gain information about the mechanisms which drive the plasma column to a disruption. The proposed processor will operate by implementing a classification of the shot type, and outputting a real number that indicates the time left before the disruption will effectively take place (ttd). (author)

T-S Fuzzy Modelling and H∞ Attitude Control for Hypersonic Gliding Vehicles

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Weidong Zhang

2017-01-01

Full Text Available This paper addresses the T-S fuzzy modelling and H∞ attitude control in three channels for hypersonic gliding vehicles (HGVs. First, the control-oriented affine nonlinear model has been established which is transformed from the reentry dynamics. Then, based on Taylor’s expansion approach and the fuzzy linearization approach, the homogeneous T-S local modelling technique for HGVs is proposed. Given the approximation accuracy and controller design complexity, appropriate fuzzy premise variables and operating points of interest are selected to construct the T-S homogeneous submodels. With so-called fuzzy blending, the original plant is transformed into the overall T-S fuzzy model with disturbance. By utilizing Lyapunov functional approach, a state feedback fuzzy controller has been designed based on relaxed linear matrix inequality (LMI conditions to stable the original plants with a prescribed H∞ performance of disturbance. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed H∞ T-S fuzzy controller for the original attitude dynamics; the superiority of the designed T-S fuzzy controller compared with other local controllers based on the constructed fuzzy model is shown as well.

Blind Source Separation and Dynamic Fuzzy Neural Network for Fault Diagnosis in Machines

International Nuclear Information System (INIS)

Huang, Haifeng; Ouyang, Huajiang; Gao, Hongli

2015-01-01

Many assessment and detection methods are used to diagnose faults in machines. High accuracy in fault detection and diagnosis can be achieved by using numerical methods with noise-resistant properties. However, to some extent, noise always exists in measured data on real machines, which affects the identification results, especially in the diagnosis of early- stage faults. In view of this situation, a damage assessment method based on blind source separation and dynamic fuzzy neural network (DFNN) is presented to diagnose the early-stage machinery faults in this paper. In the processing of measurement signals, blind source separation is adopted to reduce noise. Then sensitive features of these faults are obtained by extracting low dimensional manifold characteristics from the signals. The model for fault diagnosis is established based on DFNN. Furthermore, on-line computation is accelerated by means of compressed sensing. Numerical vibration signals of ball screw fault modes are processed on the model for mechanical fault diagnosis and the results are in good agreement with the actual condition even at the early stage of fault development. This detection method is very useful in practice and feasible for early-stage fault diagnosis. (paper)

Evolving RBF neural networks for adaptive soft-sensor design.

Science.gov (United States)

Alexandridis, Alex

2013-12-01

This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

A hybrid modeling approach for option pricing

Science.gov (United States)

Hajizadeh, Ehsan; Seifi, Abbas

2011-11-01

The complexity of option pricing has led many researchers to develop sophisticated models for such purposes. The commonly used Black-Scholes model suffers from a number of limitations. One of these limitations is the assumption that the underlying probability distribution is lognormal and this is so controversial. We propose a couple of hybrid models to reduce these limitations and enhance the ability of option pricing. The key input to option pricing model is volatility. In this paper, we use three popular GARCH type model for estimating volatility. Then, we develop two non-parametric models based on neural networks and neuro-fuzzy networks to price call options for S&P 500 index. We compare the results with those of Black-Scholes model and show that both neural network and neuro-fuzzy network models outperform Black-Scholes model. Furthermore, comparing the neural network and neuro-fuzzy approaches, we observe that for at-the-money options, neural network model performs better and for both in-the-money and an out-of-the money option, neuro-fuzzy model provides better results.

Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks

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Erdal Kayacan

2017-01-01

Full Text Available A learning control strategy is preferred for the control and guidance of a fixed-wing unmanned aerial vehicle to deal with lack of modeling and flight uncertainties. For learning the plant model as well as changing working conditions online, a fuzzy neural network (FNN is used in parallel with a conventional P (proportional controller. Among the learning algorithms in the literature, a derivative-free one, sliding mode control (SMC theory-based learning algorithm, is preferred as it has been proved to be computationally efficient in real-time applications. Its proven robustness and finite time converging nature make the learning algorithm appropriate for controlling an unmanned aerial vehicle as the computational power is always limited in unmanned aerial vehicles (UAVs. The parameter update rules and stability conditions of the learning are derived, and the proof of the stability of the learning algorithm is shown by using a candidate Lyapunov function. Intensive simulations are performed to illustrate the applicability of the proposed controller which includes the tracking of a three-dimensional trajectory by the UAV subject to time-varying wind conditions. The simulation results show the efficiency of the proposed control algorithm, especially in real-time control systems because of its computational efficiency.

Mathematical Modelling for EOQ Inventory System with Advance Payment and Fuzzy Parameters

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S Priyan

2014-11-01

Full Text Available This study considers an EOQ inventory model with advance payment policy in a fuzzy situation by employing two types of fuzzy numbers that are trapezoidal and triangular. Two fuzzy models are developed here. In the first model the cost parameters are fuzzified, but the demand rate is treated as crisp constant. In the second model, the demand rate is fuzzified but the cost parameters are treated as crisp constants. For each fuzzy model, we use signed distance method to defuzzify the fuzzy total cost and obtain an estimate of the total cost in the fuzzy sense. Numerical example is provided to ascertain the sensitiveness in the decision variables about fuzziness in the components. In practical situations, costs may be dependent on some foreign monetary unit. In such a case, due to a change in the exchange rates, the costs are often not known precisely. The first model can be used in this situation. In actual applications, demand is uncertain and must be predicted. Accordingly, the decision maker faces a fuzzy environment rather than a stochastic one in these cases. The second model can be used in this situation. Moreover, the proposed models can be expended for imperfect production process.

Construction of fuzzy spaces and their applications to matrix models

Science.gov (United States)

Abe, Yasuhiro

Quantization of spacetime by means of finite dimensional matrices is the basic idea of fuzzy spaces. There remains an issue of quantizing time, however, the idea is simple and it provides an interesting interplay of various ideas in mathematics and physics. Shedding some light on such an interplay is the main theme of this dissertation. The dissertation roughly separates into two parts. In the first part, we consider rather mathematical aspects of fuzzy spaces, namely, their construction. We begin with a review of construction of fuzzy complex projective spaces CP k (k = 1, 2, · · ·) in relation to geometric quantization. This construction facilitates defining symbols and star products on fuzzy CPk. Algebraic construction of fuzzy CPk is also discussed. We then present construction of fuzzy S 4, utilizing the fact that CP3 is an S2 bundle over S4. Fuzzy S4 is obtained by imposing an additional algebraic constraint on fuzzy CP3. Consequently it is proposed that coordinates on fuzzy S4 are described by certain block-diagonal matrices. It is also found that fuzzy S8 can analogously be constructed. In the second part of this dissertation, we consider applications of fuzzy spaces to physics. We first consider theories of gravity on fuzzy spaces, anticipating that they may offer a novel way of regularizing spacetime dynamics. We obtain actions for gravity on fuzzy S2 and on fuzzy CP3 in terms of finite dimensional matrices. Application to M(atrix) theory is also discussed. With an introduction of extra potentials to the theory, we show that it also has new brane solutions whose transverse directions are described by fuzzy S 4 and fuzzy CP3. The extra potentials can be considered as fuzzy versions of differential forms or fluxes, which enable us to discuss compactification models of M(atrix) theory. In particular, compactification down to fuzzy S4 is discussed and a realistic matrix model of M-theory in four-dimensions is proposed.

Exponential stability of fuzzy cellular neural networks with constant and time-varying delays

International Nuclear Information System (INIS)

Liu Yanqing; Tang Wansheng

2004-01-01

In this Letter, the global stability of delayed fuzzy cellular neural networks (FCNN) with either constant delays or time varying delays is proposed. Firstly, we give the existence and uniqueness of the equilibrium point by using the theory of topological degree and the properties of nonsingular M-matrix and the sufficient conditions for ascertaining the global exponential stability by constructing a suitable Lyapunov functional. Secondly, the criteria for guaranteeing the global exponential stability of FCNN with time varying delays are given and the estimation of exponential convergence rate with regard to speed of vary of delays is presented by constructing a suitable Lyapunov functional

Novel Fuzzy-Modeling-Based Adaptive Synchronization of Nonlinear Dynamic Systems

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Shih-Yu Li

2017-01-01

Full Text Available In this paper, a novel fuzzy-model-based adaptive synchronization scheme and its fuzzy update laws of parameters are proposed to address the adaptive synchronization problem. The proposed fuzzy controller does not share the same premise of fuzzy system, and the numbers of fuzzy controllers is reduced effectively through the novel modeling strategy. In addition, based on the adaptive synchronization scheme, the error dynamic system can be guaranteed to be asymptotically stable and the true values of unknown parameters can be obtained. Two identical complicated dynamic systems, Mathieu-Van der pol system (M-V system with uncertainties, are illustrated for numerical simulation example to show the effectiveness and feasibility of the proposed novel adaptive control strategy.

Fuzzy object models for newborn brain MR image segmentation

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Kobashi, Syoji; Udupa, Jayaram K.

2013-03-01

Newborn brain MR image segmentation is a challenging problem because of variety of size, shape and MR signal although it is the fundamental study for quantitative radiology in brain MR images. Because of the large difference between the adult brain and the newborn brain, it is difficult to directly apply the conventional methods for the newborn brain. Inspired by the original fuzzy object model introduced by Udupa et al. at SPIE Medical Imaging 2011, called fuzzy shape object model (FSOM) here, this paper introduces fuzzy intensity object model (FIOM), and proposes a new image segmentation method which combines the FSOM and FIOM into fuzzy connected (FC) image segmentation. The fuzzy object models are built from training datasets in which the cerebral parenchyma is delineated by experts. After registering FSOM with the evaluating image, the proposed method roughly recognizes the cerebral parenchyma region based on a prior knowledge of location, shape, and the MR signal given by the registered FSOM and FIOM. Then, FC image segmentation delineates the cerebral parenchyma using the fuzzy object models. The proposed method has been evaluated using 9 newborn brain MR images using the leave-one-out strategy. The revised age was between -1 and 2 months. Quantitative evaluation using false positive volume fraction (FPVF) and false negative volume fraction (FNVF) has been conducted. Using the evaluation data, a FPVF of 0.75% and FNVF of 3.75% were achieved. More data collection and testing are underway.

Fuzzy expert systems models for operations research and management science

Science.gov (United States)

Turksen, I. B.

1993-12-01

Fuzzy expert systems can be developed for the effective use of management within the domains of concern associated with Operations Research and Management Science. These models are designed with: (1) expressive powers of representation embedded in linguistic variables and their linguistic values in natural language expressions, and (2) improved methods of interference based on fuzzy logic which is a generalization of multi-valued logic with fuzzy quantifiers. The results of these fuzzy expert system models are either (1) approximately good in comparison with their classical counterparts, or (2) much better than their counterparts. Moreover, for fuzzy expert systems models, it is only necessary to obtain ordinal scale data. Whereas for their classical counterparts, it is generally required that data be at least on ratio and absolute scale in order to guarantee the additivity and multiplicativity assumptions.

Fuzzy logic applications to control engineering

Science.gov (United States)

Langari, Reza

1993-12-01

This paper presents the results of a project presently under way at Texas A&M which focuses on the use of fuzzy logic in integrated control of manufacturing systems. The specific problems investigated here include diagnosis of critical tool wear in machining of metals via a neuro-fuzzy algorithm, as well as compensation of friction in mechanical positioning systems via an adaptive fuzzy logic algorithm. The results indicate that fuzzy logic in conjunction with conventional algorithmic based approaches or neural nets can prove useful in dealing with the intricacies of control/monitoring of manufacturing systems and can potentially play an active role in multi-modal integrated control systems of the future.

Simulation of neuro-fuzzy model for optimization of combine header setting

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S Zareei

2016-09-01

Full Text Available Introduction The noticeable proportion of producing wheat losses occur during production and consumption steps and the loss due to harvesting with combine harvester is regarded as one of the main factors. A grain combines harvester consists of different sets of equipment and one of the most important parts is the header which comprises more than 50% of the entire harvesting losses. Some researchers have presented regression equation to estimate grain loss of combine harvester. The results of their study indicated that grain moisture content, reel index, cutter bar speed, service life of cutter bar, tine spacing, tine clearance over cutter bar, stem length were the major parameters affecting the losses. On the other hand, there are several researchswhich have used the variety of artificial intelligence methods in the different aspects of combine harvester. In neuro-fuzzy control systems, membership functions and if-then rules were defined through neural networks. Sugeno- type fuzzy inference model was applied to generate fuzzy rules from a given input-output data set due to its less time-consuming and mathematically tractable defuzzification operation for sample data-based fuzzy modeling. In this study, neuro-fuzzy model was applied to develop forecasting models which can predict the combine header loss for each set of the header parameter adjustments related to site-specific information and therefore can minimize the header loss. Materials and Methods The field experiment was conducted during the harvesting season of 2011 at the research station of the Faulty of Agriculture, Shiraz University, Shiraz, Iran. The wheat field (CV. Shiraz was harvested with a Claas Lexion-510 combine harvester. The factors which were selected as main factors influenced the header performance were three levels of reel index (RI (forward speed of combine harvester divided by peripheral speed of reel (1, 1.2, 1.5, three levels of cutting height (CH(25, 30, 35 cm, three

Performance Modelling of Steam Turbine Performance using Fuzzy ...

African Journals Online (AJOL)

Performance Modelling of Steam Turbine Performance using Fuzzy Logic ... AFRICAN JOURNALS ONLINE (AJOL) · Journals · Advanced Search · USING AJOL · RESOURCES. Journal of Applied Sciences and Environmental Management ... A Fuzzy Inference System for predicting the performance of steam turbine

Innovative neuro-fuzzy system of smart transport infrastructure for road traffic safety

Science.gov (United States)

Beinarovica, Anna; Gorobetz, Mikhail; Levchenkov, Anatoly

2017-09-01

The proposed study describes applying of neural network and fuzzy logic in transport control for safety improvement by evaluation of accidents’ risk by intelligent infrastructure devices. Risk evaluation is made by following multiple-criteria: danger, changeability and influence of changes for risk increasing. Neuro-fuzzy algorithms are described and proposed for task solution. The novelty of the proposed system is proved by deep analysis of known studies in the field. The structure of neuro-fuzzy system for risk evaluation and mathematical model is described in the paper. The simulation model of the intelligent devices for transport infrastructure is proposed to simulate different situations, assess the risks and propose the possible actions for infrastructure or vehicles to minimize the risk of possible accidents.

Fuzzy modeling to predict chicken egg hatchability in commercial hatchery.

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Peruzzi, N J; Scala, N L; Macari, M; Furlan, R L; Meyer, A D; Fernandez-Alarcon, M F; Kroetz Neto, F L; Souza, F A

2012-10-01

Experimental studies have shown that hatching rate depends, among other factors, on the main physical characteristics of the eggs. The physical parameters used in our work were egg weight, eggshell thickness, egg sphericity, and yolk per albumen ratio. The relationships of these parameters in the incubation process were modeled by Fuzzy logic. The rules of the Fuzzy modeling were based on the analysis of the physical characteristics of the hatching eggs and the respective hatching rate using a commercial hatchery by applying a trapezoidal membership function into the modeling process. The implementations were performed in software. Aiming to compare the Fuzzy with a statistical modeling, the same data obtained in the commercial hatchery were analyzed using multiple linear regression. The estimated parameters of multiple linear regressions were based on a backward selection procedure. The results showed that the determination coefficient and the mean square error were higher using the Fuzzy method when compared with the statistical modeling. Furthermore, the predicted hatchability rates by Fuzzy Logic agreed with hatching rates obtained in the commercial hatchery.

Improved Fuzzy Logic based DTC of Induction machine for wide range of speed control using AI based controllers

Directory of Open Access Journals (Sweden)

H. Sudheer

2016-06-01

Full Text Available This paper presents improvements in Direct Torque control of induction motor using Fuzzy logic switching controller (FDTC. The conventional DTC (CDTC and FDTC drive performance is compared using Conventional PI, Fuzzy controller and Neural Network controllers. The major disadvantages of CDTC are high torque and flux ripples in steady state operation of the drive, inferior performance at low speed operation and variable switching frequency. The presence of hysteresis bands is the major reason for high torque and flux ripples in CDTC. In FDTC the hysteresis band and switching table are replaced by Fuzzy logic switching controller. Using fuzzy logic torque, stator flux space are divided into smaller subsections which results in precise and optimal selection of switching state to meet load torque. In high performance drives accurate tuning of PI speed controller is required. The conventional PI controller cannot adapt to the variation in model parameters. Artificial intelligence based fuzzy controller and neural network controller are compared with PI controller for both CDTC and FDTC of Induction machine. The proposed schemes are developed in Matlab/Simulink environment. Simulation results shows reduction in torque and flux ripples in FDTC and dynamic performance of the drive at low speeds and sudden change in load torque can be improved using Fuzzy logic controller compared to PI and neural network controller.

Monitoring nuclear reactor systems using neural networks and fuzzy logic

International Nuclear Information System (INIS)

Ikonomopoulos, A.; Tsoukalas, L.H.; Uhrig, R.E.; Mullens, J.A.

1991-01-01

A new approach is presented that demonstrates the potential of trained artificial neural networks (ANNs) as generators of membership functions for the purpose of monitoring nuclear reactor systems. ANN's provide a complex-to-simple mapping of reactor parameters in a process analogous to that of measurement. Through such ''virtual measurements'' the value of parameters with operational significance, e.g., control-valve-disk-position, valve-line-up or performance can be determined. In the methodology presented the output of a virtual measuring device is a set of membership functions which independently represent different states of the system. Utilizing a fuzzy logic representation offers the advantage of describing the state of the system in a condensed form, developed through linguistic descriptions and convenient for application in monitoring, diagnostics and generally control algorithms. The developed methodology is applied to the problem of measuring the disk position of the secondary flow control valve of an experimental reactor using data obtained during a start-up. The enhanced noise tolerance of the methodology is clearly demonstrated as well as a method for selecting the actual output. The results suggest that it is possible to construct virtual measuring devices through artificial neural networks mapping dynamic time series to a set of membership functions and thus enhance the capability of monitoring systems. 8 refs., 11 figs., 1 tab

Identification of a Typical CSTR Using Optimal Focused Time Lagged Recurrent Neural Network Model with Gamma Memory Filter

OpenAIRE

Naikwad, S. N.; Dudul, S. V.

2009-01-01

A focused time lagged recurrent neural network (FTLR NN) with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. As CSTR process includes tempora...

Adaptive time-variant models for fuzzy-time-series forecasting.

Science.gov (United States)

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

A fuzzy inventory model with acceptable shortage using graded mean integration value method

Science.gov (United States)

Saranya, R.; Varadarajan, R.

2018-04-01

In many inventory models uncertainty is due to fuzziness and fuzziness is the closed possible approach to reality. In this paper, we proposed a fuzzy inventory model with acceptable shortage which is completely backlogged. We fuzzily the carrying cost, backorder cost and ordering cost using Triangular and Trapezoidal fuzzy numbers to obtain the fuzzy total cost. The purpose of our study is to defuzzify the total profit function by Graded Mean Integration Value Method. Further a numerical example is also given to demonstrate the developed crisp and fuzzy models.

Adaptive complementary fuzzy self-recurrent wavelet neural network controller for the electric load simulator system

Directory of Open Access Journals (Sweden)

Wang Chao

2016-03-01

Full Text Available Due to the complexities existing in the electric load simulator, this article develops a high-performance nonlinear adaptive controller to improve the torque tracking performance of the electric load simulator, which mainly consists of an adaptive fuzzy self-recurrent wavelet neural network controller with variable structure (VSFSWC and a complementary controller. The VSFSWC is clearly and easily used for real-time systems and greatly improves the convergence rate and control precision. The complementary controller is designed to eliminate the effect of the approximation error between the proposed neural network controller and the ideal feedback controller without chattering phenomena. Moreover, adaptive learning laws are derived to guarantee the system stability in the sense of the Lyapunov theory. Finally, the hardware-in-the-loop simulations are carried out to verify the feasibility and effectiveness of the proposed algorithms in different working styles.

Using Adaptive Neural-Fuzzy Inference Systems (ANFIS for Demand Forecasting and an Application

Directory of Open Access Journals (Sweden)

Onur Doğan

2016-06-01

Full Text Available Due to the rapid increase in global competition among organizations and companies, rational approaches in decision making have become indispensable for organizations in today’s world. Establishing a safe and robust path through uncertainties and risks depends on the decision units’ ability of using scientific methods as well as technology. Demand forecasting is known to be one of the most critical problems in organizations.  A company which supports its demand forecasting mechanism with scientific methodologies could increase its productivity and efficiency in all other functions. New methods, such as fuzzy logic and artificial neural networks are frequently being used as a decision-making mechanism in organizations and companies recently.  In this study, it is aimed to solve a critical demand forecasting problem with ANFIS. In the first phase of the study, the factors which impact demand forecasting are determined, and then a database of the model is established using these factors. It has been shown that ANFIS could be used for demand forecasting.

Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization

International Nuclear Information System (INIS)

Yong, Li; Ying-Gan, Tang

2010-01-01

A fuzzy Wiener model is proposed to identify chaotic systems. The proposed fuzzy Wiener model consists of two parts, one is a linear dynamic subsystem and the other is a static nonlinear part, which is represented by the Takagi–Sugeno fuzzy model. Identification of chaotic systems is converted to find optimal parameters of the fuzzy Wiener model by minimizing the state error between the original chaotic system and the fuzzy Wiener model. Particle swarm optimization algorithm, a global optimizer, is used to search the optimal parameter of the fuzzy Wiener model. The proposed method can identify the parameters of the linear part and nonlinear part simultaneously. Numerical simulations for Henón and Lozi chaotic system identification show the effectiveness of the proposed method

Stabilization of nonlinear systems using sampled-data output-feedback fuzzy controller based on polynomial-fuzzy-model-based control approach.

Science.gov (United States)

Lam, H K

2012-02-01

This paper investigates the stability of sampled-data output-feedback (SDOF) polynomial-fuzzy-model-based control systems. Representing the nonlinear plant using a polynomial fuzzy model, an SDOF fuzzy controller is proposed to perform the control process using the system output information. As only the system output is available for feedback compensation, it is more challenging for the controller design and system analysis compared to the full-state-feedback case. Furthermore, because of the sampling activity, the control signal is kept constant by the zero-order hold during the sampling period, which complicates the system dynamics and makes the stability analysis more difficult. In this paper, two cases of SDOF fuzzy controllers, which either share the same number of fuzzy rules or not, are considered. The system stability is investigated based on the Lyapunov stability theory using the sum-of-squares (SOS) approach. SOS-based stability conditions are obtained to guarantee the system stability and synthesize the SDOF fuzzy controller. Simulation examples are given to demonstrate the merits of the proposed SDOF fuzzy control approach.

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

Science.gov (United States)

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

2014-10-01

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

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

Directory of Open Access Journals (Sweden)

P. Akhavan

2014-10-01

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

Fuzzy Optimization of Option Pricing Model and Its Application in Land Expropriation

Directory of Open Access Journals (Sweden)

Aimin Heng

2014-01-01

Full Text Available Option pricing is irreversible, fuzzy, and flexible. The fuzzy measure which is used for real option pricing is a useful supplement to the traditional real option pricing method. Based on the review of the concepts of the mean and variance of trapezoidal fuzzy number and the combination with the Carlsson-Fuller model, the trapezoidal fuzzy variable can be used to represent the current price of land expropriation and the sale price of land on the option day. Fuzzy Black-Scholes option pricing model can be constructed under fuzzy environment and problems also can be solved and discussed through numerical examples.

Predictive models for PEM-electrolyzer performance using adaptive neuro-fuzzy inference systems

Energy Technology Data Exchange (ETDEWEB)

Becker, Steffen [University of Tasmania, Hobart 7001, Tasmania (Australia); Karri, Vishy [Australian College of Kuwait (Kuwait)

2010-09-15

Predictive models were built using neural network based Adaptive Neuro-Fuzzy Inference Systems for hydrogen flow rate, electrolyzer system-efficiency and stack-efficiency respectively. A comprehensive experimental database forms the foundation for the predictive models. It is argued that, due to the high costs associated with the hydrogen measuring equipment; these reliable predictive models can be implemented as virtual sensors. These models can also be used on-line for monitoring and safety of hydrogen equipment. The quantitative accuracy of the predictive models is appraised using statistical techniques. These mathematical models are found to be reliable predictive tools with an excellent accuracy of {+-}3% compared with experimental values. The predictive nature of these models did not show any significant bias to either over prediction or under prediction. These predictive models, built on a sound mathematical and quantitative basis, can be seen as a step towards establishing hydrogen performance prediction models as generic virtual sensors for wider safety and monitoring applications. (author)

Robust fuzzy output feedback controller for affine nonlinear systems via T-S fuzzy bilinear model: CSTR benchmark.

Science.gov (United States)

Hamdy, M; Hamdan, I

2015-07-01

In this paper, a robust H∞ fuzzy output feedback controller is designed for a class of affine nonlinear systems with disturbance via Takagi-Sugeno (T-S) fuzzy bilinear model. The parallel distributed compensation (PDC) technique is utilized to design a fuzzy controller. The stability conditions of the overall closed loop T-S fuzzy bilinear model are formulated in terms of Lyapunov function via linear matrix inequality (LMI). The control law is robustified by H∞ sense to attenuate external disturbance. Moreover, the desired controller gains can be obtained by solving a set of LMI. A continuous stirred tank reactor (CSTR), which is a benchmark problem in nonlinear process control, is discussed in detail to verify the effectiveness of the proposed approach with a comparative study. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Multitask TSK fuzzy system modeling by mining intertask common hidden structure.

Science.gov (United States)

Jiang, Yizhang; Chung, Fu-Lai; Ishibuchi, Hisao; Deng, Zhaohong; Wang, Shitong

2015-03-01

The classical fuzzy system modeling methods implicitly assume data generated from a single task, which is essentially not in accordance with many practical scenarios where data can be acquired from the perspective of multiple tasks. Although one can build an individual fuzzy system model for each task, the result indeed tells us that the individual modeling approach will get poor generalization ability due to ignoring the intertask hidden correlation. In order to circumvent this shortcoming, we consider a general framework for preserving the independent information among different tasks and mining hidden correlation information among all tasks in multitask fuzzy modeling. In this framework, a low-dimensional subspace (structure) is assumed to be shared among all tasks and hence be the hidden correlation information among all tasks. Under this framework, a multitask Takagi-Sugeno-Kang (TSK) fuzzy system model called MTCS-TSK-FS (TSK-FS for multiple tasks with common hidden structure), based on the classical L2-norm TSK fuzzy system, is proposed in this paper. The proposed model can not only take advantage of independent sample information from the original space for each task, but also effectively use the intertask common hidden structure among multiple tasks to enhance the generalization performance of the built fuzzy systems. Experiments on synthetic and real-world datasets demonstrate the applicability and distinctive performance of the proposed multitask fuzzy system model in multitask regression learning scenarios.

Type-2 fuzzy graphical models for pattern recognition

CERN Document Server

Zeng, Jia

2015-01-01

This book discusses how to combine type-2 fuzzy sets and graphical models to solve a range of real-world pattern recognition problems such as speech recognition, handwritten Chinese character recognition, topic modeling as well as human action recognition. It covers these recent developments while also providing a comprehensive introduction to the fields of type-2 fuzzy sets and graphical models. Though primarily intended for graduate students, researchers and practitioners in fuzzy logic and pattern recognition, the book can also serve as a valuable reference work for researchers without any previous knowledge of these fields. Dr. Jia Zeng is a Professor at the School of Computer Science and Technology, Soochow University, China. Dr. Zhi-Qiang Liu is a Professor at the School of Creative Media, City University of Hong Kong, China.

Fuzzy-neural approaches to the prediction of disruptions in ASDEX Upgrade

International Nuclear Information System (INIS)

Morabito, F.C.; Versaci, M.; Pautasso, G.; Tichmann, C.

2001-01-01

Disruption is a sudden loss of magnetic confinement that can cause damage to the machine walls and support structures. For this reason, it is of practical interest to be able to detect the onset of such an event early. A novel technique is presented of early prediction of plasma disruption in tokamak reactors which uses neural networks and 'fuzzy' inference. The studies carried out in the work make use of an experimental database of disruptive shots made available by the ASDEX Upgrade Team. The main result of the work is that, in the limit of the available database, it is possible to predict the onset of the disruptive event sufficiently in advance in order to put the control system into action. The proposed system is a modular scheme that exploits a decomposition of the original database carried out in a proper way. (author)

ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection

Directory of Open Access Journals (Sweden)

SARACOGLU, O. G.

2016-08-01

Full Text Available This paper presents a modeling approach based on the use of fuzzy reasoning mechanism to define a measured data set obtained from an optical sensing circuit. For this purpose, we implemented a simple but effective an in vitro optical sensor to measure glucose content of an aqueous solution. Measured data contain analog voltages representing the absorbance values of three wavelengths measured from an RGB LED in different glucose concentrations. To achieve a desired model performance, the parameters of the fuzzy models are optimized by using the artificial bee colony (ABC algorithm. The modeling results presented in this paper indicate that the fuzzy model optimized by the algorithm provide a successful modeling performance having the minimum mean squared error (MSE of 0.0013 which are in clearly good agreement with the measurements.

Applying fuzzy analytic network process in quality function deployment model

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Mohammad Ali Afsharkazemi

2012-08-01

Full Text Available In this paper, we propose an empirical study of QFD implementation when fuzzy numbers are used to handle the uncertainty associated with different components of the proposed model. We implement fuzzy analytical network to find the relative importance of various criteria and using fuzzy numbers we calculate the relative importance of these factors. The proposed model of this paper uses fuzzy matrix and house of quality to study the products development in QFD and also the second phase i.e. part deployment. In most researches, the primary objective is only on CRs to implement the quality function deployment and some other criteria such as production costs, manufacturing costs etc were disregarded. The results of using fuzzy analysis network process based on the QFD model in Daroupat packaging company to develop PVDC show that the most important indexes are being waterproof, resistant pill packages, and production cost. In addition, the PVDC coating is the most important index in terms of company experts’ point of view.

Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

Science.gov (United States)

Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

2016-03-01

In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

Non-linear sigma model on the fuzzy supersphere

International Nuclear Information System (INIS)

Kurkcuoglu, Seckin

2004-01-01

In this note we develop fuzzy versions of the supersymmetric non-linear sigma model on the supersphere S (2,2) . In hep-th/0212133 Bott projectors have been used to obtain the fuzzy C P 1 model. Our approach utilizes the use of supersymmetric extensions of these projectors. Here we obtain these (super)-projectors and quantize them in a fashion similar to the one given in hep-th/0212133. We discuss the interpretation of the resulting model as a finite dimensional matrix model. (author)

Application of Fuzzy Clustering in Modeling of a Water Hydraulics System

DEFF Research Database (Denmark)

Zhou, Jianjun; Kroszynski, Uri

2000-01-01

This article presents a case study of applying fuzzy modeling techniques for a water hydraulics system. The obtained model is intended to provide a basis for model-based control of the system. Fuzzy clustering is used for classifying measured input-output data points into partitions. The fuzzy...... model is extracted from the obtained partitions. The identified model has been evaluated by comparing measurements with simulation results. The evaluation shows that the identified model is capable of describing the system dynamics over a reasonably wide frequency range....

An Evaluation Model for Tailings Storage Facilities Using Improved Neural Networks and Fuzzy Mathematics

Directory of Open Access Journals (Sweden)

Sen Tian

2014-01-01

Full Text Available With the development of mine industry, tailings storage facility (TSF, as the important facility of mining, has attracted increasing attention for its safety problems. However, the problems of low accuracy and slow operation rate often occur in current TSF safety evaluation models. This paper establishes a reasonable TSF safety evaluation index system and puts forward a new TSF safety evaluation model by combining the theories for the analytic hierarchy process (AHP and improved back-propagation (BP neural network algorithm. The varying proportions of cross validation were calculated, demonstrating that this method has better evaluation performance with higher learning efficiency and faster convergence speed and avoids the oscillation in the training process in traditional BP neural network method and other primary neural network methods. The entire analysis shows the combination of the two methods increases the accuracy and reliability of the safety evaluation, and it can be well applied in the TSF safety evaluation.

Aphasia Classification Using Neural Networks

DEFF Research Database (Denmark)

Axer, H.; Jantzen, Jan; Berks, G.

2000-01-01

A web-based software model (http://fuzzy.iau.dtu.dk/aphasia.nsf) was developed as an example for classification of aphasia using neural networks. Two multilayer perceptrons were used to classify the type of aphasia (Broca, Wernicke, anomic, global) according to the results in some subtests...

Fuzzy modelling of Atlantic salmon physical habitat

Science.gov (United States)

St-Hilaire, André; Mocq, Julien; Cunjak, Richard

2015-04-01

Fish habitat models typically attempt to quantify the amount of available river habitat for a given fish species for various flow and hydraulic conditions. To achieve this, information on the preferred range of values of key physical habitat variables (e.g. water level, velocity, substrate diameter) for the targeted fishs pecies need to be modelled. In this context, we developed several habitat suitability indices sets for three Atlantic salmon life stages (young-of-the-year (YOY), parr, spawning adults) with the help of fuzzy logic modeling. Using the knowledge of twenty-seven experts, from both sides of the Atlantic Ocean, we defined fuzzy sets of four variables (depth, substrate size, velocity and Habitat Suitability Index, or HSI) and associated fuzzy rules. When applied to the Romaine River (Canada), median curves of standardized Weighted Usable Area (WUA) were calculated and a confidence interval was obtained by bootstrap resampling. Despite the large range of WUA covered by the expert WUA curves, confidence intervals were relatively narrow: an average width of 0.095 (on a scale of 0 to 1) for spawning habitat, 0.155 for parr rearing habitat and 0.160 for YOY rearing habitat. When considering an environmental flow value corresponding to 90% of the maximum reached by WUA curve, results seem acceptable for the Romaine River. Generally, this proposed fuzzy logic method seems suitable to model habitat availability for the three life stages, while also providing an estimate of uncertainty in salmon preferences.

A variable structure fuzzy neural network model of squamous dysplasia and esophageal squamous cell carcinoma based on a global chaotic optimization algorithm.

Science.gov (United States)

Moghtadaei, Motahareh; Hashemi Golpayegani, Mohammad Reza; Malekzadeh, Reza

2013-02-07

Identification of squamous dysplasia and esophageal squamous cell carcinoma (ESCC) is of great importance in prevention of cancer incidence. Computer aided algorithms can be very useful for identification of people with higher risks of squamous dysplasia, and ESCC. Such method can limit the clinical screenings to people with higher risks. Different regression methods have been used to predict ESCC and dysplasia. In this paper, a Fuzzy Neural Network (FNN) model is selected for ESCC and dysplasia prediction. The inputs to the classifier are the risk factors. Since the relation between risk factors in the tumor system has a complex nonlinear behavior, in comparison to most of ordinary data, the cost function of its model can have more local optimums. Thus the need for global optimization methods is more highlighted. The proposed method in this paper is a Chaotic Optimization Algorithm (COA) proceeding by the common Error Back Propagation (EBP) local method. Since the model has many parameters, we use a strategy to reduce the dependency among parameters caused by the chaotic series generator. This dependency was not considered in the previous COA methods. The algorithm is compared with logistic regression model as the latest successful methods of ESCC and dysplasia prediction. The results represent a more precise prediction with less mean and variance of error. Copyright © 2012 Elsevier Ltd. All rights reserved.

Classification of behavior using unsupervised temporal neural networks

International Nuclear Information System (INIS)

Adair, K.L.

1998-03-01

Adding recurrent connections to unsupervised neural networks used for clustering creates a temporal neural network which clusters a sequence of inputs as they appear over time. The model presented combines the Jordan architecture with the unsupervised learning technique Adaptive Resonance Theory, Fuzzy ART. The combination yields a neural network capable of quickly clustering sequential pattern sequences as the sequences are generated. The applicability of the architecture is illustrated through a facility monitoring problem

Optimization of Neuro-Fuzzy System Using Genetic Algorithm for Chromosome Classification

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M. Sarosa

2013-09-01

Full Text Available Neuro-fuzzy system has been shown to provide a good performance on chromosome classification but does not offer a simple method to obtain the accurate parameter values required to yield the best recognition rate. This paper presents a neuro-fuzzy system where its parameters can be automatically adjusted using genetic algorithms. The approach combines the advantages of fuzzy logic theory, neural networks, and genetic algorithms. The structure consists of a four layer feed-forward neural network that uses a GBell membership function as the output function. The proposed methodology has been applied and tested on banded chromosome classification from the Copenhagen Chromosome Database. Simulation result showed that the proposed neuro-fuzzy system optimized by genetic algorithms offers advantages in setting the parameter values, improves the recognition rate significantly and decreases the training/testing time which makes genetic neuro-fuzzy system suitable for chromosome classification.

A fuzzy approach for modelling radionuclide in lake system

International Nuclear Information System (INIS)

Desai, H.K.; Christian, R.A.; Banerjee, J.; Patra, A.K.

2013-01-01

Radioactive liquid waste is generated during operation and maintenance of Pressurised Heavy Water Reactors (PHWRs). Generally low level liquid waste is diluted and then discharged into the near by water-body through blowdown water discharge line as per the standard waste management practice. The effluents from nuclear installations are treated adequately and then released in a controlled manner under strict compliance of discharge criteria. An attempt was made to predict the concentration of 3 H released from Kakrapar Atomic Power Station at Ratania Regulator, about 2.5 km away from the discharge point, where human exposure is expected. Scarcity of data and complex geometry of the lake prompted the use of Heuristic approach. Under this condition, Fuzzy rule based approach was adopted to develop a model, which could predict 3 H concentration at Ratania Regulator. Three hundred data were generated for developing the fuzzy rules, in which input parameters were water flow from lake and 3 H concentration at discharge point. The Output was 3 H concentration at Ratania Regulator. These data points were generated by multiple regression analysis of the original data. Again by using same methodology hundred data were generated for the validation of the model, which were compared against the predicted output generated by using Fuzzy Rule based approach. Root Mean Square Error of the model came out to be 1.95, which showed good agreement by Fuzzy model of natural ecosystem. -- Highlights: • Uncommon approach (Fuzzy Rule Base) of modelling radionuclide dispersion in Lake. • Predicts 3 H released from Kakrapar Atomic Power Station at a point of human exposure. • RMSE of fuzzy model is 1.95, which means, it has well imitated natural ecosystem

Drought Forecasting by SPI Index and ANFIS Model Using Fuzzy C-mean Clustering

Directory of Open Access Journals (Sweden)

mehdi Komasi

2013-08-01

Full Text Available Drought is the interaction between environment and water cycle in the world and affects natural environment of an area when it persists for a longer period. So, developing a suitable index to forecast the spatial and temporal distribution of drought plays an important role in the planning and management of natural resources and water resource systems. In this article, firstly, the drought concept and drought indexes were introduced and then the fuzzy neural networks and fuzzy C-mean clustering were applied to forecast drought via standardized precipitation index (SPI. The results of this research indicate that the SPI index is more capable than the other indexes such as PDSI (Palmer Drought Severity Index, PAI (Palfai Aridity Index and etc. in drought forecasting process. Moreover, application of adaptive nero-fuzzy network accomplished by C-mean clustering has high efficiency in the drought forecasting.

Multi-Model Adaptive Fuzzy Controller for a CSTR Process

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Shubham Gogoria

2015-09-01

Full Text Available Continuous Stirred Tank Reactors are intensively used to control exothermic reactions in chemical industries. It is a very complex multi-variable system with non-linear characteristics. This paper deals with linearization of the mathematical model of a CSTR Process. Multi model adaptive fuzzy controller has been designed to control the reactor concentration and temperature of CSTR process. This method combines the output of multiple Fuzzy controllers, which are operated at various operating points. The proposed solution is a straightforward implementation of Fuzzy controller with gain scheduler to control the linearly inseparable parameters of a highly non-linear process.

Design of a heart rate controller for treadmill exercise using a recurrent fuzzy neural network.

Science.gov (United States)

Lu, Chun-Hao; Wang, Wei-Cheng; Tai, Cheng-Chi; Chen, Tien-Chi

2016-05-01

In this study, we developed a computer controlled treadmill system using a recurrent fuzzy neural network heart rate controller (RFNNHRC). Treadmill speeds and inclines were controlled by corresponding control servo motors. The RFNNHRC was used to generate the control signals to automatically control treadmill speed and incline to minimize the user heart rate deviations from a preset profile. The RFNNHRC combines a fuzzy reasoning capability to accommodate uncertain information and an artificial recurrent neural network learning process that corrects for treadmill system nonlinearities and uncertainties. Treadmill speeds and inclines are controlled by the RFNNHRC to achieve minimal heart rate deviation from a pre-set profile using adjustable parameters and an on-line learning algorithm that provides robust performance against parameter variations. The on-line learning algorithm of RFNNHRC was developed and implemented using a dsPIC 30F4011 DSP. Application of the proposed control scheme to heart rate responses of runners resulted in smaller fluctuations than those produced by using proportional integra control, and treadmill speeds and inclines were smoother. The present experiments demonstrate improved heart rate tracking performance with the proposed control scheme. The RFNNHRC scheme with adjustable parameters and an on-line learning algorithm was applied to a computer controlled treadmill system with heart rate control during treadmill exercise. Novel RFNNHRC structure and controller stability analyses were introduced. The RFNNHRC were tuned using a Lyapunov function to ensure system stability. The superior heart rate control with the proposed RFNNHRC scheme was demonstrated with various pre-set heart rates. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Hybrid intelligence systems and artificial neural network (ANN approach for modeling of surface roughness in drilling

Directory of Open Access Journals (Sweden)

Ch. Sanjay

2014-12-01

Full Text Available In machining processes, drilling operation is material removal process that has been widely used in manufacturing since industrial revolution. The useful life of cutting tool and its operating conditions largely controls the economics of machining operations. Drilling is most frequently performed material removing process and is used as a preliminary step for many operations, such as reaming, tapping, and boring. Drill wear has a bad effect on the surface finish and dimensional accuracy of the work piece. The surface finish of a machined part is one of the most important quality characteristics in manufacturing industries. The primary objective of this research is the prediction of suitable parameters for surface roughness in drilling. Cutting speed, cutting force, and machining time were given as inputs to the adaptive fuzzy neural network and neuro-fuzzy analysis for estimating the values of surface roughness by using 2, 3, 4, and 5 membership functions. The best structures were selected based on minimum of summation of square with the actual values with the estimated values by artificial neural fuzzy inference system (ANFIS and neuro-fuzzy systems. For artificial neural network (ANN analysis, the number of neurons was selected from 1, 2, 3, … , 20. The learning rate was selected as .5 and .5 smoothing factor was used. The inputs were selected as cutting speed, feed, machining time, and thrust force. The best structures of neural networks were selected based on the criteria as the minimum of summation of square with the actual value of surface roughness. Drilling experiments with 10 mm size were performed at two cutting speeds and feeds. Comparative analysis has been done between the actual values and the estimated values obtained by ANFIS, neuro-fuzzy, and ANN analysis.

Modeling and control of an unstable system using probabilistic fuzzy inference system

Directory of Open Access Journals (Sweden)

Sozhamadevi N.

2015-09-01

Full Text Available A new type Fuzzy Inference System is proposed, a Probabilistic Fuzzy Inference system which model and minimizes the effects of statistical uncertainties. The blend of two different concepts, degree of truth and probability of truth in a unique framework leads to this new concept. This combination is carried out both in Fuzzy sets and Fuzzy rules, which gives rise to Probabilistic Fuzzy Sets and Probabilistic Fuzzy Rules. Introducing these probabilistic elements, a distinctive probabilistic fuzzy inference system is developed and this involves fuzzification, inference and output processing. This integrated approach accounts for all of the uncertainty like rule uncertainties and measurement uncertainties present in the systems and has led to the design which performs optimally after training. In this paper a Probabilistic Fuzzy Inference System is applied for modeling and control of a highly nonlinear, unstable system and also proved its effectiveness.

Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

Science.gov (United States)

Sun, Kaioqiong; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.

2014-03-01

This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

A Comparative Study of the Neural Network, Fuzzy Logic, and Nero-fuzzy Systems in Seismic Reservoir Characterization: An Example from Arab (Surmeh Reservoir as an Iranian Gas Field, Persian Gulf Basin

Directory of Open Access Journals (Sweden)

Reza Mohebian

2017-10-01

Full Text Available Intelligent reservoir characterization using seismic attributes and hydraulic flow units has a vital role in the description of oil and gas traps. The predicted model allows an accurate understanding of the reservoir quality, especially at the un-cored well location. This study was conducted in two major steps. In the first step, the survey compared different intelligent techniques to discover an optimum relationship between well logs and seismic data. For this purpose, three intelligent systems, including probabilistic neural network (PNN,fuzzy logic (FL, and adaptive neuro-fuzzy inference systems (ANFISwere usedto predict flow zone index (FZI. Well derived FZI logs from three wells were employed to estimate intelligent models in the Arab (Surmeh reservoir. The validation of the produced models was examined by another well. Optimal seismic attributes for the estimation of FZI include acoustic impedance, integrated absolute amplitude, and average frequency. The results revealed that the ANFIS method performed better than the other systems and showed a remarkable reduction in the measured errors. In the second part of the study, the FZI 3D model was created by using the ANFIS system.The integrated approach introduced in the current survey illustrated that the extracted flow units from intelligent models compromise well with well-logs. Based on the results obtained, the intelligent systems are powerful techniques to predict flow units from seismic data (seismic attributes for distant well location. Finally, it was shown that ANFIS method was efficient in highlighting high and low-quality flow units in the Arab (Surmeh reservoir, the Iranian offshore gas field.

A concurrent optimization model for supplier selection with fuzzy quality loss

International Nuclear Information System (INIS)

Rosyidi, C.; Murtisari, R.; Jauhari, W.

2017-01-01

The purpose of this research is to develop a concurrent supplier selection model to minimize the purchasing cost and fuzzy quality loss considering process capability and assembled product specification. Design/methodology/approach: This research integrates fuzzy quality loss in the model to concurrently solve the decision making in detailed design stage and manufacturing stage. Findings: The resulted model can be used to concurrently select the optimal supplier and determine the tolerance of the components. The model balances the purchasing cost and fuzzy quality loss. Originality/value: An assembled product consists of many components which must be purchased from the suppliers. Fuzzy quality loss is integrated in the supplier selection model to allow the vagueness in final assembly by grouping the assembly into several grades according to the resulted assembly tolerance.

A concurrent optimization model for supplier selection with fuzzy quality loss

Energy Technology Data Exchange (ETDEWEB)

Rosyidi, C.; Murtisari, R.; Jauhari, W.

2017-07-01

The purpose of this research is to develop a concurrent supplier selection model to minimize the purchasing cost and fuzzy quality loss considering process capability and assembled product specification. Design/methodology/approach: This research integrates fuzzy quality loss in the model to concurrently solve the decision making in detailed design stage and manufacturing stage. Findings: The resulted model can be used to concurrently select the optimal supplier and determine the tolerance of the components. The model balances the purchasing cost and fuzzy quality loss. Originality/value: An assembled product consists of many components which must be purchased from the suppliers. Fuzzy quality loss is integrated in the supplier selection model to allow the vagueness in final assembly by grouping the assembly into several grades according to the resulted assembly tolerance.

Fuzzy delay model based fault simulator for crosstalk delay fault test ...

Indian Academy of Sciences (India)

In this paper, a fuzzy delay model based crosstalk delay fault simulator is proposed. As design .... To find the quality of non-robust tests, a fuzzy delay ..... Dubois D and Prade H 1989 Processing Fuzzy temporal knowledge. IEEE Transactions ...

Takagi-Sugeno fuzzy model identification for turbofan aero-engines with guaranteed stability

Directory of Open Access Journals (Sweden)

Ruichao LI

2018-06-01

Full Text Available This paper is concerned with identifying a Takagi-Sugeno (TS fuzzy model for turbofan aero-engines working under the maximum power status (non-afterburning. To establish the fuzzy system, theoretical contributions are made as follows. First, by fixing antecedent parameters, the estimation of consequent parameters in state-space representations is formulated as minimizing a quadratic cost function. Second, to avoid obtaining unstable identified models, a new theorem is proposed to transform the prior-knowledge of stability into constraints. Then based on the aforementioned work, the identification problem is synthesized as a constrained quadratic optimization. By solving the constrained optimization, a TS fuzzy system is identified with guaranteed stability. Finally, the proposed method is applied to the turbofan aero-engine using simulation data generated from an aerothermodynamics component-level model. Results show the identified fuzzy model achieves a high fitting accuracy while stabilities of the overall fuzzy system and all its local models are also guaranteed. Keywords: Constrained optimization, Fuzzy system, Stability, System identification, Turbofan engine

Reactive Power Control of Single-Stage Three-Phase Photovoltaic System during Grid Faults Using Recurrent Fuzzy Cerebellar Model Articulation Neural Network

Directory of Open Access Journals (Sweden)

Faa-Jeng Lin

2014-01-01

Full Text Available This study presents a new active and reactive power control scheme for a single-stage three-phase grid-connected photovoltaic (PV system during grid faults. The presented PV system utilizes a single-stage three-phase current-controlled voltage-source inverter to achieve the maximum power point tracking (MPPT control of the PV panel with the function of low voltage ride through (LVRT. Moreover, a formula based on positive sequence voltage for evaluating the percentage of voltage sag is derived to determine the ratio of the injected reactive current to satisfy the LVRT regulations. To reduce the risk of overcurrent during LVRT operation, a current limit is predefined for the injection of reactive current. Furthermore, the control of active and reactive power is designed using a two-dimensional recurrent fuzzy cerebellar model articulation neural network (2D-RFCMANN. In addition, the online learning laws of 2D-RFCMANN are derived according to gradient descent method with varied learning-rate coefficients for network parameters to assure the convergence of the tracking error. Finally, some experimental tests are realized to validate the effectiveness of the proposed control scheme.

Design of robust reliable control for T-S fuzzy Markovian jumping delayed neutral type neural networks with probabilistic actuator faults and leakage delays: An event-triggered communication scheme.

Science.gov (United States)

Syed Ali, M; Vadivel, R; Saravanakumar, R

2018-06-01

This study examines the problem of robust reliable control for Takagi-Sugeno (T-S) fuzzy Markovian jumping delayed neural networks with probabilistic actuator faults and leakage terms. An event-triggered communication scheme. First, the randomly occurring actuator faults and their failures rates are governed by two sets of unrelated random variables satisfying certain probabilistic failures of every actuator, new type of distribution based event triggered fault model is proposed, which utilize the effect of transmission delay. Second, Takagi-Sugeno (T-S) fuzzy model is adopted for the neural networks and the randomness of actuators failures is modeled in a Markov jump model framework. Third, to guarantee the considered closed-loop system is exponential mean square stable with a prescribed reliable control performance, a Markov jump event-triggered scheme is designed in this paper, which is the main purpose of our study. Fourth, by constructing appropriate Lyapunov-Krasovskii functional, employing Newton-Leibniz formulation and integral inequalities, several delay-dependent criteria for the solvability of the addressed problem are derived. The obtained stability criteria are stated in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. Finally, numerical examples are given to illustrate the effectiveness and reduced conservatism of the proposed results over the existing ones, among them one example was supported by real-life application of the benchmark problem. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Fuzzy multiobjective models for optimal operation of a hydropower system

Science.gov (United States)

Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.

2013-06-01

Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.

A fuzzy chance-constrained programming model with type 1 and type 2 fuzzy sets for solid waste management under uncertainty

Science.gov (United States)

Ma, Xiaolin; Ma, Chi; Wan, Zhifang; Wang, Kewei

2017-06-01

Effective management of municipal solid waste (MSW) is critical for urban planning and development. This study aims to develop an integrated type 1 and type 2 fuzzy sets chance-constrained programming (ITFCCP) model for tackling regional MSW management problem under a fuzzy environment, where waste generation amounts are supposed to be type 2 fuzzy variables and treated capacities of facilities are assumed to be type 1 fuzzy variables. The evaluation and expression of uncertainty overcome the drawbacks in describing fuzzy possibility distributions as oversimplified forms. The fuzzy constraints are converted to their crisp equivalents through chance-constrained programming under the same or different confidence levels. Regional waste management of the City of Dalian, China, was used as a case study for demonstration. The solutions under various confidence levels reflect the trade-off between system economy and reliability. It is concluded that the ITFCCP model is capable of helping decision makers to generate reasonable waste-allocation alternatives under uncertainties.

A Fuzzy Knowledge Representation Model for Student Performance Assessment

DEFF Research Database (Denmark)

Badie, Farshad

Knowledge representation models based on Fuzzy Description Logics (DLs) can provide a foundation for reasoning in intelligent learning environments. While basic DLs are suitable for expressing crisp concepts and binary relationships, Fuzzy DLs are capable of processing degrees of truth/completene...

Fuzzy modeling of electrical impedance tomography images of the lungs

International Nuclear Information System (INIS)

Tanaka, Harki; Ortega, Neli Regina Siqueira; Galizia, Mauricio Stanzione; Borges, Joao Batista; Amato, Marcelo Britto Passos

2008-01-01

Objectives: Aiming to improve the anatomical resolution of electrical impedance tomography images, we developed a fuzzy model based on electrical impedance tomography's high temporal resolution and on the functional pulmonary signals of perfusion and ventilation. Introduction: Electrical impedance tomography images carry information about both ventilation and perfusion. However, these images are difficult to interpret because of insufficient anatomical resolution, such that it becomes almost impossible to distinguish the heart from the lungs. Methods: Electrical impedance tomography data from an experimental animal model were collected during normal ventilation and apnoea while an injection of hypertonic saline was administered. The fuzzy model was elaborated in three parts: a modeling of the heart, the pulmonary ventilation map and the pulmonary perfusion map. Image segmentation was performed using a threshold method, and a ventilation/perfusion map was generated. Results: Electrical impedance tomography images treated by the fuzzy model were compared with the hypertonic saline injection method and computed tomography scan images, presenting good results. The average accuracy index was 0.80 when comparing the fuzzy modeled lung maps and the computed tomography scan lung mask. The average ROC curve area comparing a saline injection image and a fuzzy modeled pulmonary perfusion image was 0.77. Discussion: The innovative aspects of our work are the use of temporal information for the delineation of the heart structure and the use of two pulmonary functions for lung structure delineation. However, robustness of the method should be tested for the imaging of abnormal lung conditions. Conclusions: These results showed the adequacy of the fuzzy approach in treating the anatomical resolution uncertainties in electrical impedance tomography images. (author)

An experimental methodology for a fuzzy set preference model

Science.gov (United States)

Turksen, I. B.; Willson, Ian A.

1992-01-01

A flexible fuzzy set preference model first requires approximate methodologies for implementation. Fuzzy sets must be defined for each individual consumer using computer software, requiring a minimum of time and expertise on the part of the consumer. The amount of information needed in defining sets must also be established. The model itself must adapt fully to the subject's choice of attributes (vague or precise), attribute levels, and importance weights. The resulting individual-level model should be fully adapted to each consumer. The methodologies needed to develop this model will be equally useful in a new generation of intelligent systems which interact with ordinary consumers, controlling electronic devices through fuzzy expert systems or making recommendations based on a variety of inputs. The power of personal computers and their acceptance by consumers has yet to be fully utilized to create interactive knowledge systems that fully adapt their function to the user. Understanding individual consumer preferences is critical to the design of new products and the estimation of demand (market share) for existing products, which in turn is an input to management systems concerned with production and distribution. The question of what to make, for whom to make it and how much to make requires an understanding of the customer's preferences and the trade-offs that exist between alternatives. Conjoint analysis is a widely used methodology which de-composes an overall preference for an object into a combination of preferences for its constituent parts (attributes such as taste and price), which are combined using an appropriate combination function. Preferences are often expressed using linguistic terms which cannot be represented in conjoint models. Current models are also not implemented an individual level, making it difficult to reach meaningful conclusions about the cause of an individual's behavior from an aggregate model. The combination of complex aggregate

Quantitative modeling of gene networks of biological systems using fuzzy Petri nets and fuzzy sets

Directory of Open Access Journals (Sweden)

Raed I. Hamed

2018-01-01

Full Text Available Quantitative demonstrating of organic frameworks has turned into an essential computational methodology in the configuration of novel and investigation of existing natural frameworks. Be that as it may, active information that portrays the framework's elements should be known keeping in mind the end goal to get pertinent results with the routine displaying strategies. This information is frequently robust or even difficult to get. Here, we exhibit a model of quantitative fuzzy rational demonstrating approach that can adapt to obscure motor information and hence deliver applicable results despite the fact that dynamic information is fragmented or just dubiously characterized. Besides, the methodology can be utilized as a part of the blend with the current cutting edge quantitative demonstrating strategies just in specific parts of the framework, i.e., where the data are absent. The contextual analysis of the methodology suggested in this paper is performed on the model of nine-quality genes. We propose a kind of FPN model in light of fuzzy sets to manage the quantitative modeling of biological systems. The tests of our model appear that the model is practical and entirely powerful for information impersonation and thinking of fuzzy expert frameworks.

Modeling minimum temperature using adaptive neuro-fuzzy inference system based on spectral analysis of climate indices: A case study in Iran

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Hojatollah Daneshmand

2015-01-01

Full Text Available Nowadays, a lot of attention is paid to the application of intelligent systems in predicting natural phenomena. Artificial neural network systems, fuzzy logic, and adaptive neuro-fuzzy inference are used in this field. Daily minimum temperature of the meteorology station of the city of Mashhad, in northeast of Iran, in a 42-year statistical period, 1966-2008, has been received from the Iranian meteorological organization. Adaptive neuro-fuzzy inference system is used for modeling and forecasting the monthly minimum temperature. To find appropriate inputs, three approaches, i.e. spectral analysis, correlation coefficient, and the knowledge of experts,are used. By applying fast Fourier transform to the parameter of monthly minimum temperature and climate indices, and by using correlation coefficient and the knowledge of experts, 3 indices, Nino 1 + 2, NP, and PNA, are selected as model inputs. A hybrid training algorithm is used to train the system. According to simulation results, a correlation coefficient of 0.987 between the observed values and the predicted values, as well as amean absolute percentage deviations of 27.6% indicate an acceptable estimation of the model.

Neuro-Fuzzy Computational Technique to Control Load Frequency in Hydro-Thermal Interconnected Power System

Science.gov (United States)

Prakash, S.; Sinha, S. K.

2015-09-01

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

Chance-constrained programming models for capital budgeting with NPV as fuzzy parameters

Science.gov (United States)

Huang, Xiaoxia

2007-01-01

In an uncertain economic environment, experts' knowledge about outlays and cash inflows of available projects consists of much vagueness instead of randomness. Investment outlays and annual net cash flows of a project are usually predicted by using experts' knowledge. Fuzzy variables can overcome the difficulties in predicting these parameters. In this paper, capital budgeting problem with fuzzy investment outlays and fuzzy annual net cash flows is studied based on credibility measure. Net present value (NPV) method is employed, and two fuzzy chance-constrained programming models for capital budgeting problem are provided. A fuzzy simulation-based genetic algorithm is provided for solving the proposed model problems. Two numerical examples are also presented to illustrate the modelling idea and the effectiveness of the proposed algorithm.

Fuzzy Simulation-Optimization Model for Waste Load Allocation

Directory of Open Access Journals (Sweden)

Motahhare Saadatpour

2006-01-01

Full Text Available This paper present simulation-optimization models for waste load allocation from multiple point sources which include uncertainty due to vagueness of the parameters and goals. This model employs fuzzy sets with appropriate membership functions to deal with uncertainties due to vagueness. The fuzzy waste load allocation model (FWLAM incorporate QUAL2E as a water quality simulation model and Genetic Algorithm (GA as an optimization tool to find the optimal combination of the fraction removal level to the dischargers and pollution control agency (PCA. Penalty functions are employed to control the violations in the system.  The results demonstrate that the goal of PCA to achieve the best water quality and the goal of the dischargers to use the full assimilative capacity of the river have not been satisfied completely and a compromise solution between these goals is provided. This fuzzy optimization model with genetic algorithm has been used for a hypothetical problem. Results demonstrate a very suitable convergence of proposed optimization algorithm to the global optima.

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

Science.gov (United States)

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

2012-04-01

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

Using adaptive network based fuzzy inference system to forecast regional electricity loads

International Nuclear Information System (INIS)

Ying, L.-C.; Pan, M.-C.

2008-01-01

Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads

Using adaptive network based fuzzy inference system to forecast regional electricity loads

Energy Technology Data Exchange (ETDEWEB)

Ying, Li-Chih [Department of Marketing Management, Central Taiwan University of Science and Technology, 11, Pu-tzu Lane, Peitun, Taichung City 406 (China); Pan, Mei-Chiu [Graduate Institute of Management Sciences, Nanhua University, 32, Chung Keng Li, Dalin, Chiayi 622 (China)

2008-02-15

Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads. (author)

Fuzzy logic, neural networks, and soft computing

Science.gov (United States)

Zadeh, Lofti A.

1994-01-01

The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial

Fuzzy model-based adaptive synchronization of time-delayed chaotic systems

International Nuclear Information System (INIS)

Vasegh, Nastaran; Majd, Vahid Johari

2009-01-01

In this paper, fuzzy model-based synchronization of a class of first order chaotic systems described by delayed-differential equations is addressed. To design the fuzzy controller, the chaotic system is modeled by Takagi-Sugeno fuzzy system considering the properties of the nonlinear part of the system. Assuming that the parameters of the chaotic system are unknown, an adaptive law is derived to estimate these unknown parameters, and the stability of error dynamics is guaranteed by Lyapunov theory. Numerical examples are given to demonstrate the validity of the proposed adaptive synchronization approach.

Designing of fuzzy expert heuristic models with cost management ...

Indian Academy of Sciences (India)

In genuine industrial case, problems are inescapable and pose enormous challenges to incorporate accurate sustainability factors into supplier selection. In this present study, three different primarily based multicriteria decision making fuzzy models have been compared with their deterministic version so as to resolve fuzzy ...

Estimation of collapse moment for the wall-thinned pipe bends using fuzzy model identification

International Nuclear Information System (INIS)

Na, Man Gyun; Kim, Jin Weon; Hwang, In Joon

2006-01-01

In this work, the collapse moment due to wall-thinned defects is estimated through fuzzy model identification. A subtractive clustering method is used as the basis of a fast and robust algorithm for identifying the fuzzy model. The fuzzy model is optimized by a genetic algorithm combined with a least squares method. The developed fuzzy model has been applied to the numerical data obtained from the finite element analysis. Principal component analysis is used to preprocess the input signals into the fuzzy model to reduce the sensitivity to the input change and the fuzzy model are trained by using the data set prepared for training (training data) and verified by using another data set different (independent) from the training data. Also, three fuzzy models are trained, respectively, for three data sets divided into the three classes of extrados, intrados, and crown defects, which is because they have different characteristics. The relative root mean square (RMS) errors of the estimated collapse moment are 0.5397% for the training data and 0.8673% for the test data. It is known from this result that the fuzzy models are sufficiently accurate to be used in the integrity evaluation of wall-thinned pipe bends and elbows

A fuzzy logic approach to modeling the underground economy in Taiwan

Science.gov (United States)

Yu, Tiffany Hui-Kuang; Wang, David Han-Min; Chen, Su-Jane

2006-04-01

The size of the ‘underground economy’ (UE) is valuable information in the formulation of macroeconomic and fiscal policy. This study applies fuzzy set theory and fuzzy logic to model Taiwan's UE over the period from 1960 to 2003. Two major factors affecting the size of the UE, the effective tax rate and the degree of government regulation, are used. The size of Taiwan's UE is scaled and compared with those of other models. Although our approach yields different estimates, similar patterns and leading are exhibited throughout the period. The advantage of applying fuzzy logic is twofold. First, it can avoid the complex calculations in conventional econometric models. Second, fuzzy rules with linguistic terms are easy for human to understand.

Type-2 fuzzy elliptic membership functions for modeling uncertainty

DEFF Research Database (Denmark)

Kayacan, Erdal; Sarabakha, Andriy; Coupland, Simon

2018-01-01

Whereas type-1 and type-2 membership functions (MFs) are the core of any fuzzy logic system, there are no performance criteria available to evaluate the goodness or correctness of the fuzzy MFs. In this paper, we make extensive analysis in terms of the capability of type-2 elliptic fuzzy MFs...... in modeling uncertainty. Having decoupled parameters for its support and width, elliptic MFs are unique amongst existing type-2 fuzzy MFs. In this investigation, the uncertainty distribution along the elliptic MF support is studied, and a detailed analysis is given to compare and contrast its performance...... advantages mentioned above, elliptic MFs have comparable prediction results when compared to Gaussian and triangular MFs. Finally, in order to test the performance of fuzzy logic controller with elliptic interval type-2 MFs, extensive real-time experiments are conducted for the 3D trajectory tracking problem...

Hybrid ellipsoidal fuzzy systems in forecasting regional electricity loads

Energy Technology Data Exchange (ETDEWEB)

Pai, Ping-Feng [Department of Information Management, National Chi Nan University, 1 University Road, Puli, Nantou 545, Taiwan (China)

2006-09-15

Because of the privatization of electricity in many countries, load forecasting has become one of the most crucial issues in the planning and operations of electric utilities. In addition, accurate regional load forecasting can provide the transmission and distribution operators with more information. The hybrid ellipsoidal fuzzy system was originally designed to solve control and pattern recognition problems. The main objective of this investigation is to develop a hybrid ellipsoidal fuzzy system for time series forecasting (HEFST) and apply the proposed model to forecast regional electricity loads in Taiwan. Additionally, a scaled conjugate gradient learning method is employed in the supervised learning phase of the HEFST model. Subsequently, numerical data taken from the existing literature is used to demonstrate the forecasting performance of the HEFST model. Simulation results reveal that the proposed model has better forecasting performance than the artificial neural network model and the regression model. Thus, the HEFST model is a valid and promising alternative for forecasting regional electricity loads. (author)

[Study on building index system of risk assessment of post-marketing Chinese patent medicine based on AHP-fuzzy neural network].

Science.gov (United States)

Li, Yuanyuan; Xie, Yanming; Fu, Yingkun

2011-10-01

Currently massive researches have been launched about the safety, efficiency and economy of post-marketing Chinese patent medicine (CPM) proprietary Chinese medicine, but it was lack of a comprehensive interpretation. Establishing the risk evaluation index system and risk assessment model of CPM is the key to solve drug safety problems and protect people's health. The clinical risk factors of CPM exist similarities with the Western medicine, can draw lessons from foreign experience, but also have itself multi-factor multivariate multi-level complex features. Drug safety risk assessment for the uncertainty and complexity, using analytic hierarchy process (AHP) to empower the index weights, AHP-based fuzzy neural network to build post-marketing CPM risk evaluation index system and risk assessment model and constantly improving the application of traditional Chinese medicine characteristic is accord with the road and feasible beneficial exploration.

Determining factors influencing survival of breast cancer by fuzzy logistic regression model.

Science.gov (United States)

Nikbakht, Roya; Bahrampour, Abbas

2017-01-01

Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.

Neural manufacturing: a novel concept for processing modeling, monitoring, and control

Science.gov (United States)

Fu, Chi Y.; Petrich, Loren; Law, Benjamin

1995-09-01

Semiconductor fabrication lines have become extremely costly, and achieving a good return from such a high capital investment requires efficient utilization of these expensive facilities. It is highly desirable to shorten processing development time, increase fabrication yield, enhance flexibility, improve quality, and minimize downtime. We propose that these ends can be achieved by applying recent advances in the areas of artificial neural networks, fuzzy logic, machine learning, and genetic algorithms. We use the term neural manufacturing to describe such applications. This paper describes our use of artificial neural networks to improve the monitoring and control of semiconductor process.

A network security situation prediction model based on wavelet neural network with optimized parameters

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Haibo Zhang

2016-08-01

Full Text Available The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN with optimized parameters by the Improved Niche Genetic Algorithm (INGA. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN and WNN.

Fuzzy delay model based fault simulator for crosstalk delay fault test ...

Indian Academy of Sciences (India)

In this paper, a fuzzy delay model based crosstalk delay fault simulator is proposed. As design trends move towards nanometer technologies, more number of new parameters affects the delay of the component. Fuzzy delay models are ideal for modelling the uncertainty found in the design and manufacturing steps.

Fuzzy logic

CERN Document Server

Smets, P

1995-01-01

We start by describing the nature of imperfect data, and giving an overview of the various models that have been proposed. Fuzzy sets theory is shown to be an extension of classical set theory, and as such has a proeminent role or modelling imperfect data. The mathematic of fuzzy sets theory is detailled, in particular the role of the triangular norms. The use of fuzzy sets theory in fuzzy logic and possibility theory,the nature of the generalized modus ponens and of the implication operator for approximate reasoning are analysed. The use of fuzzy logic is detailled for application oriented towards process control and database problems.

Intuitionistic fuzzy-based model for failure detection.

Science.gov (United States)

Aikhuele, Daniel O; Turan, Faiz B M

2016-01-01

In identifying to-be-improved product component(s), the customer/user requirements which are mainly considered, and achieved through customer surveys using the quality function deployment (QFD) tool, often fail to guarantee or cover aspects of the product reliability. Even when they do, there are always many misunderstandings. To improve the product reliability and quality during product redesigning phase and to create that novel product(s) for the customers, the failure information of the existing product, and its component(s) should ordinarily be analyzed and converted to appropriate design knowledge for the design engineer. In this paper, a new intuitionistic fuzzy multi-criteria decision-making method has been proposed. The new approach which is based on an intuitionistic fuzzy TOPSIS model uses an exponential-related function for the computation of the separation measures from the intuitionistic fuzzy positive ideal solution (IFPIS) and intuitionistic fuzzy negative ideal solution (IFNIS) of alternatives. The proposed method has been applied to two practical case studies, and the result from the different cases has been compared with some similar computational approaches in the literature.

Fuzzy Modeling and Synchronization of a New Hyperchaotic Complex System with Uncertainties

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Hadi Delavari

2015-07-01

Full Text Available In this paper, the synchronization of a new hyperchaotic complex system based on T-S fuzzy model is proposed. First the considered hyperchaotic system is represented by T-S fuzzy model equivalently. Then by using the parallel distributed compensation (PDC method and by applying linear system theory and exact linearization (EL technique, a fuzzy controller is designed to realize the synchronization. Finally, simulation results are carried out to demonstrate the performance of our proposed control scheme, and also the robustness of the designed fuzzy controller to uncertainties.

A fuzzy Bi-linear management model in reverse logistic chains

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Tadić Danijela

2016-01-01

Full Text Available The management of the electrical and electronic waste (WEEE problem in the uncertain environment has a critical effect on the economy and environmental protection of each region. The considered problem can be stated as a fuzzy non-convex optimization problem with linear objective function and a set of linear and non-linear constraints. The original problem is reformulated by using linear relaxation into a fuzzy linear programming problem. The fuzzy rating of collecting point capacities and fix costs of recycling centers are modeled by triangular fuzzy numbers. The optimal solution of the reformulation model is found by using optimality concept. The proposed model is verified through an illustrative example with real-life data. The obtained results represent an input for future research which should include a good benchmark base for tested reverse logistic chains and their continuous improvement. [Projekat Ministarstva nauke Republike Srbije, br. 035033: Sustainable development technology and equipment for the recycling of motor vehicles

Analysis of inventory difference using fuzzy controllers

International Nuclear Information System (INIS)

Zardecki, A.

1994-01-01

The principal objectives of an accounting system for safeguarding nuclear materials are as follows: (a) to provide assurance that all material quantities are present in the correct amount; (b) to provide timely detection of material loss; and (c) to estimate the amount of any loss and its location. In fuzzy control, expert knowledge is encoded in the form of fuzzy rules, which describe recommended actions for different classes of situations represented by fuzzy sets. The concept of a fuzzy controller is applied to the forecasting problem in a time series, specifically, to forecasting and detecting anomalies in inventory differences. This paper reviews the basic notion underlying the fuzzy control systems and provides examples of application. The well-known material-unaccounted-for diffusion plant data of Jaech are analyzed using both feedforward neural networks and fuzzy controllers. By forming a deference between the forecasted and observed signals, an efficient method to detect small signals in background noise is implemented

Analytic Model Predictive Control of Uncertain Nonlinear Systems: A Fuzzy Adaptive Approach

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Xiuyan Peng

2015-01-01

Full Text Available A fuzzy adaptive analytic model predictive control method is proposed in this paper for a class of uncertain nonlinear systems. Specifically, invoking the standard results from the Moore-Penrose inverse of matrix, the unmatched problem which exists commonly in input and output dimensions of systems is firstly solved. Then, recurring to analytic model predictive control law, combined with fuzzy adaptive approach, the fuzzy adaptive predictive controller synthesis for the underlying systems is developed. To further reduce the impact of fuzzy approximation error on the system and improve the robustness of the system, the robust compensation term is introduced. It is shown that by applying the fuzzy adaptive analytic model predictive controller the rudder roll stabilization system is ultimately uniformly bounded stabilized in the H-infinity sense. Finally, simulation results demonstrate the effectiveness of the proposed method.

The Prediction of the Gas Utilization Ratio based on TS Fuzzy Neural Network and Particle Swarm Optimization.

Science.gov (United States)

Zhang, Sen; Jiang, Haihe; Yin, Yixin; Xiao, Wendong; Zhao, Baoyong

2018-02-20

Gas utilization ratio (GUR) is an important indicator that is used to evaluate the energy consumption of blast furnaces (BFs). Currently, the existing methods cannot predict the GUR accurately. In this paper, we present a novel data-driven model for predicting the GUR. The proposed approach utilized both the TS fuzzy neural network (TS-FNN) and the particle swarm algorithm (PSO) to predict the GUR. The particle swarm algorithm (PSO) is applied to optimize the parameters of the TS-FNN in order to decrease the error caused by the inaccurate initial parameter. This paper also applied the box graph (Box-plot) method to eliminate the abnormal value of the raw data during the data preprocessing. This method can deal with the data which does not obey the normal distribution which is caused by the complex industrial environments. The prediction results demonstrate that the optimization model based on PSO and the TS-FNN approach achieves higher prediction accuracy compared with the TS-FNN model and SVM model and the proposed approach can accurately predict the GUR of the blast furnace, providing an effective way for the on-line blast furnace distribution control.

Uncertainty analysis of flexible rotors considering fuzzy parameters and fuzzy-random parameters

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Fabian Andres Lara-Molina

Full Text Available Abstract The components of flexible rotors are subjected to uncertainties. The main sources of uncertainties include the variation of mechanical properties. This contribution aims at analyzing the dynamics of flexible rotors under uncertain parameters modeled as fuzzy and fuzzy random variables. The uncertainty analysis encompasses the modeling of uncertain parameters and the numerical simulation of the corresponding flexible rotor model by using an approach based on fuzzy dynamic analysis. The numerical simulation is accomplished by mapping the fuzzy parameters of the deterministic flexible rotor model. Thereby, the flexible rotor is modeled by using both the Fuzzy Finite Element Method and the Fuzzy Stochastic Finite Element Method. Numerical simulations illustrate the methodology conveyed in terms of orbits and frequency response functions subject to uncertain parameters.

Neural networks and statistical learning

CERN Document Server

Du, Ke-Lin

2014-01-01

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

An Interval-Parameter Fuzzy Linear Programming with Stochastic Vertices Model for Water Resources Management under Uncertainty

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Yan Han

2013-01-01

Full Text Available An interval-parameter fuzzy linear programming with stochastic vertices (IFLPSV method is developed for water resources management under uncertainty by coupling interval-parameter fuzzy linear programming (IFLP with stochastic programming (SP. As an extension of existing interval parameter fuzzy linear programming, the developed IFLPSV approach has advantages in dealing with dual uncertainty optimization problems, which uncertainty presents as interval parameter with stochastic vertices in both of the objective functions and constraints. The developed IFLPSV method improves upon the IFLP method by allowing dual uncertainty parameters to be incorporated into the optimization processes. A hybrid intelligent algorithm based on genetic algorithm and artificial neural network is used to solve the developed model. The developed method is then applied to water resources allocation in Beijing city of China in 2020, where water resources shortage is a challenging issue. The results indicate that reasonable solutions have been obtained, which are helpful and useful for decision makers. Although the amount of water supply from Guanting and Miyun reservoirs is declining with rainfall reduction, water supply from the South-to-North Water Transfer project will have important impact on water supply structure of Beijing city, particularly in dry year and extraordinary dry year.

A fuzzy-logic-based approach to qualitative safety modelling for marine systems

International Nuclear Information System (INIS)

Sii, H.S.; Ruxton, Tom; Wang Jin

2001-01-01

Safety assessment based on conventional tools (e.g. probability risk assessment (PRA)) may not be well suited for dealing with systems having a high level of uncertainty, particularly in the feasibility and concept design stages of a maritime or offshore system. By contrast, a safety model using fuzzy logic approach employing fuzzy IF-THEN rules can model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analyses. A fuzzy-logic-based approach may be more appropriately used to carry out risk analysis in the initial design stages. This provides a tool for working directly with the linguistic terms commonly used in carrying out safety assessment. This research focuses on the development and representation of linguistic variables to model risk levels subjectively. These variables are then quantified using fuzzy sets. In this paper, the development of a safety model using fuzzy logic approach for modelling various design variables for maritime and offshore safety based decision making in the concept design stage is presented. An example is used to illustrate the proposed approach

Fuzzy Adaptation Algorithms’ Control for Robot Manipulators with Uncertainty Modelling Errors

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Yongqing Fan

2018-01-01

Full Text Available A novel fuzzy control scheme with adaptation algorithms is developed for robot manipulators’ system. At the beginning, one adjustable parameter is introduced in the fuzzy logic system, the robot manipulators system with uncertain nonlinear terms as the master device and a reference model dynamic system as the slave robot system. To overcome the limitations such as online learning computation burden and logic structure in conventional fuzzy logic systems, a parameter should be used in fuzzy logic system, which composes fuzzy logic system with updated parameter laws, and can be formed for a new fashioned adaptation algorithms controller. The error closed-loop dynamical system can be stabilized based on Lyapunov analysis, the number of online learning computation burdens can be reduced greatly, and the different kinds of fuzzy logic systems with fuzzy rules or without any fuzzy rules are also suited. Finally, effectiveness of the proposed approach has been shown in simulation example.

A location-routing problem model with multiple periods and fuzzy demands

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Ali Nadizadeh

2014-08-01

Full Text Available This paper puts forward a dynamic capacitated location-routing problem with fuzzy demands (DCLRP-FD. It is given on input a set of identical vehicles (each having a capacity, a fixed cost and availability level, a set of depots with restricted capacities and opening costs, a set of customers with fuzzy demands, and a planning horizon with multiple periods. The problem consists of determining the depots to be opened only in the first period of the planning horizon, the customers and the vehicles to be assigned to each opened depot, and performing the routes that may be changed in each time period due to fuzzy demands. A fuzzy chance-constrained programming (FCCP model has been designed using credibility theory and a hybrid heuristic algorithm with four phases is presented in order to solve the problem. To obtain the best value of the fuzzy parameters of the model and show the influence of the availability level of vehicles on final solution, some computational experiments are carried out. The validity of the model is then evaluated in contrast with CLRP-FD's models in the literature. The results indicate that the model and the proposed algorithm are robust and could be used in real world problems.

Improved Polynomial Fuzzy Modeling and Controller with Stability Analysis for Nonlinear Dynamical Systems

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Hamed Kharrati

2012-01-01

Full Text Available This study presents an improved model and controller for nonlinear plants using polynomial fuzzy model-based (FMB systems. To minimize mismatch between the polynomial fuzzy model and nonlinear plant, the suitable parameters of membership functions are determined in a systematic way. Defining an appropriate fitness function and utilizing Taylor series expansion, a genetic algorithm (GA is used to form the shape of membership functions in polynomial forms, which are afterwards used in fuzzy modeling. To validate the model, a controller based on proposed polynomial fuzzy systems is designed and then applied to both original nonlinear plant and fuzzy model for comparison. Additionally, stability analysis for the proposed polynomial FMB control system is investigated employing Lyapunov theory and a sum of squares (SOS approach. Moreover, the form of the membership functions is considered in stability analysis. The SOS-based stability conditions are attained using SOSTOOLS. Simulation results are also given to demonstrate the effectiveness of the proposed method.

Adaptive Neuro-Fuzzy Modeling of UH-60A Pilot Vibration

Science.gov (United States)

Kottapalli, Sesi; Malki, Heidar A.; Langari, Reza

2003-01-01

Adaptive neuro-fuzzy relationships have been developed to model the UH-60A Black Hawk pilot floor vertical vibration. A 200 point database that approximates the entire UH-60A helicopter flight envelope is used for training and testing purposes. The NASA/Army Airloads Program flight test database was the source of the 200 point database. The present study is conducted in two parts. The first part involves level flight conditions and the second part involves the entire (200 point) database including maneuver conditions. The results show that a neuro-fuzzy model can successfully predict the pilot vibration. Also, it is found that the training phase of this neuro-fuzzy model takes only two or three iterations to converge for most cases. Thus, the proposed approach produces a potentially viable model for real-time implementation.