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ANFIS: AdaptiveNetworkBased Fuzzy Inference System
, 1993
"... This paper presents the architecture and learning procedure underlying ANFIS (AdaptiveNetwork based Fuzzy Inference System), a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an inputoutput mapping bas ..."
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Cited by 432 (5 self)
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This paper presents the architecture and learning procedure underlying ANFIS (AdaptiveNetwork based Fuzzy Inference System), a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an inputoutput mapping based on both human knowledge (in the form of fuzzy ifthen rules) and stipulated inputoutput data pairs. In our simulation, we employ the ANFIS architecture to model nonlinear functions, identify nonlinear components onlinely in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificail neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. 1 Introduction System modeling based on conventional mathematical tools (e.g., differential equations) is not well suited for dealing with illdefine...
Neurofuzzy modeling and control
 IEEE Proceedings
, 1995
"... Abstract  Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framew ..."
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Cited by 147 (1 self)
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Abstract  Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called ANFIS (AdaptiveNetworkbased Fuzzy Inference System), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neurofuzzy approaches are also addressed. KeywordsFuzzy logic, neural networks, fuzzy modeling, neurofuzzy modeling, neurofuzzy control, ANFIS. I.
Input Selection for ANFIS Learning
 IN PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS
, 1996
"... We present a quick and straightfoward way of input selection for neurofuzzy modeling using ANFIS. The method is tested on two realworld problems: the nonlinear regression problem of automobile MPG (miles per gallon) prediction, and the nonlinear system identification using the Box and Jenkins gas ..."
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Cited by 14 (0 self)
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We present a quick and straightfoward way of input selection for neurofuzzy modeling using ANFIS. The method is tested on two realworld problems: the nonlinear regression problem of automobile MPG (miles per gallon) prediction, and the nonlinear system identification using the Box and Jenkins gas furnace data [1].
Neurofuzzy control based on the NEFCONmodel: recent developments
, 1999
"... Fuzzy systems are currently being used in a wide field of industrial and scientific applications. Since the design and especially the optimization process of fuzzy systems can be very time consuming, it is convenient to have algorithms which construct and optimize them automatically. One popular app ..."
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Cited by 9 (2 self)
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Fuzzy systems are currently being used in a wide field of industrial and scientific applications. Since the design and especially the optimization process of fuzzy systems can be very time consuming, it is convenient to have algorithms which construct and optimize them automatically. One popular approach is to combine fuzzy systems with learning techniques derived from neural networks. Such approaches are usually called neurofuzzy systems. In this paper we present our view of neurofuzzy systems and an implementation in the area of control theory: the NEFCONModel. This model is able to learn and optimize the rule base of a Mamdani like fuzzy controller online by a reinforcement learning algorithm that uses a fuzzy error measure. Therefore, we also describe some methods to determine a fuzzy error measure for a dynamic system. In addition we present some implementations of the model and an application example. The presented implementations are available free of charge for noncommercial purposes.
A Soft Computing Approach for Modelling the Supervisor of Manufacturing Systems
 Journal of Intelligent and Robotics Systems
, 1999
"... . The development of a novel soft computing approach to model the supervisor of manufacturing systems is described, it is named Fuzzy Cognitive Maps (FCMs) and it is used to model the behaviour of complex systems. Fuzzy cognitive maps combine characteristics of both fuzzy logic and neural networks. ..."
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Cited by 6 (0 self)
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. The development of a novel soft computing approach to model the supervisor of manufacturing systems is described, it is named Fuzzy Cognitive Maps (FCMs) and it is used to model the behaviour of complex systems. Fuzzy cognitive maps combine characteristics of both fuzzy logic and neural networks. The description and the construction of fuzzy cognitive maps are examined, a new methodology for developing fuzzy cognitive maps is proposed here and as an example the fuzzy cognitive map for a simple plant is developed. A hierarchical twolevel structure for supervision of manufacturing systems is presented, where the supervisor is modelled as a fuzzy cognitive map. The fuzzy cognitive map model for the failure diagnosis part of the supervisor for a simple chemical process is constructed.
Multiobjective Optimization with Linguistic Variables
, 1998
"... Generalizing our earlier results on optimization with linguistic variables [3, 6, 7] we introduce a novel statement of fuzzy multiobjective mathematical programming problems and provide a method for findig a fair solution to these problems. Suppose we are given a multiobjective mathematical programm ..."
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Cited by 5 (2 self)
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Generalizing our earlier results on optimization with linguistic variables [3, 6, 7] we introduce a novel statement of fuzzy multiobjective mathematical programming problems and provide a method for findig a fair solution to these problems. Suppose we are given a multiobjective mathematical programming problem in which the functional relationship between the decision variables and the objective functions is not completely known. Our knowledgebase consists of a block of fuzzy ifthen rules, where the antecedent part of the rules contains some linguistic values of the decision variables, and the consequence part consists of a linguistic value of the objective functions. We suggest the use of Tsukamoto's fuzzy reasoning method to determine the crisp functional relationship between the decision variables and objective functions. We model the anding of the objective functions by a tnorm and solve the resulting (usually nonlinear) programming problem to find a fair optimal solution to the ...
A Trainable Transparent Universal Approximator for Defuzzification in Mamdani
"... A novel technique of designing application specific defuzzification strategies with neural learning is presented. The proposed neural architecture considered as a universal defuzzification approximator is validated by showing the convergence when approximating several existing defuzzification strat ..."
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Cited by 3 (1 self)
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A novel technique of designing application specific defuzzification strategies with neural learning is presented. The proposed neural architecture considered as a universal defuzzification approximator is validated by showing the convergence when approximating several existing defuzzification strategies. The method is successfully tested with fuzzy controlled reverse driving of a model truck. The transparent structure of the universal defuzzification approximator allows to analyse the generated customised defuzzification method using the existing theories of defuzzification. The integration of universal defuzzification approximator instead of traditional methods in Mamdani type fuzzy controllers can also be considered as an addition of trainable non linear noise to the output of the fuzzy rule inference before calculating the defuzzified crisp output. Therefore, non linear noise trained specifically for a given application shows a grade of confidence on the rule base, providing an additional opportunity to measure the quality of the fuzzy rule base. The possibility of modelling Mamdani type fuzzy controllers as a feed forward neural network with the ability of gradient descent training of the universal defuzzification approximator and antecedent membership functions fulfill the requirement known from Multilayer Preceptrons in finding solutions to non linear separable problems.
Inference via Fuzzy Belief Networks
"... The power of belief networks lies in its connective edges where the influences are bidirectional. While Bayesian methods capture bidirectional influences, we propose a simpler and faster method of inferencing from nodal observations that uses bidirectional fuzzy influences that are propagated via fu ..."
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Cited by 2 (1 self)
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The power of belief networks lies in its connective edges where the influences are bidirectional. While Bayesian methods capture bidirectional influences, we propose a simpler and faster method of inferencing from nodal observations that uses bidirectional fuzzy influences that are propagated via fuzzy set membership functions. We need neither the conditional probability tables nor constraining mathematical structure that make inferencing NPhard.
Fuzzy Sets in Approximate Reasoning: A Personal View
"... Fuzzy setbased methods contribute to the formalization of different types of approximate reasoning, mainly in two ways. First, when modeling classes and properties, fuzzy sets naturally encode gradual properties, such as, e.g., 'large', whose satisfaction is a matter of degree and may be only parti ..."
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Cited by 2 (0 self)
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Fuzzy setbased methods contribute to the formalization of different types of approximate reasoning, mainly in two ways. First, when modeling classes and properties, fuzzy sets naturally encode gradual properties, such as, e.g., 'large', whose satisfaction is a matter of degree and may be only partial for particular instances. Due to this ability, fuzzy sets are naturally entitled to capture intermediary situations and to be instrumental in the formalization of interpolative reasoning. More generally, similaritybased approximate reasoning can greatly benefit from fuzzy set approaches since similarity is usually a matter of degree. Second, fuzzy sets can also represent incomplete information pervaded with uncertainty. They are then viewed as possibility distributions and give birth to possibility and necessity measures to assess the degrees to which a statement is possible or is certain taking into account the available (incomplete) information. Fuzzy rules whose conclusion part are un...