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ANFIS: AdaptiveNetworkBased Fuzzy Inference System”,
 IEEE Trans. on System, Man and Cybernetics,
, 1993
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Neurofuzzy modeling and control
 IEEE PROCEEDINGS
, 1995
"... 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 ad ..."
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Cited by 239 (1 self)
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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.
Functional Equivalence between Radial Basis Function Networks and Fuzzy Inference Systems
, 1993
"... This short article shows that under some minor restrictions, the functional behavior of radial basis function networks and fuzzy inference systems are actually equivalent. This functional equivalence implies that advances in each literature, such as new learning rules or analysis on representational ..."
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Cited by 169 (4 self)
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This short article shows that under some minor restrictions, the functional behavior of radial basis function networks and fuzzy inference systems are actually equivalent. This functional equivalence implies that advances in each literature, such as new learning rules or analysis on representational power, etc., can be applied to both models directly. It is of interest to observe that twomodels stemming from different origins turn out to be functional equivalent.
SelfLearning Fuzzy Controllers Based on Temporal Back Propagation
, 1992
"... This paper presents a generalized control strategy that enhances fuzzy controllers with selflearning capability for achieving prescribed control objectives in a nearoptimal manner. This methodology, termed temporal back propagation, is modelinsensitive in the sense that it can deal with plants tha ..."
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Cited by 98 (3 self)
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This paper presents a generalized control strategy that enhances fuzzy controllers with selflearning capability for achieving prescribed control objectives in a nearoptimal manner. This methodology, termed temporal back propagation, is modelinsensitive 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, fuzzy models, etc. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy ifthen rules obtained from human experts, or automatically derive the fuzzy ifthen rules if human experts are not available. The inverted pendulum system is employed as a testbed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller. 1 Introduction Fuzzy controllers (FC's) have recently found various applications in industry as well as in household appliances. For com...
Rulebase structure identification in an adaptivenetworkbased fuzzy inference system
 IEEE Trans. Fuzzy Syst
, 1994
"... AbstructFuzzy rulebase modeling is the task of identifying the structure and the parameters of a fuzzy IFTHEN rule base so that a desired input/output mapping is achieved. Recently, using adaptive networks to finetune membership functions in a fuzzy rule base has received more and more attention ..."
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Cited by 33 (0 self)
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AbstructFuzzy rulebase modeling is the task of identifying the structure and the parameters of a fuzzy IFTHEN rule base so that a desired input/output mapping is achieved. Recently, using adaptive networks to finetune membership functions in a fuzzy rule base has received more and more attention. In this paper we summarize Jang’s architecture of employing an adaptive network and the Kalman filtering algorithm to identify the system parameters. Given a surface structure, the adaptively adjusted inference system performs well on a number of interpolation problems. We generalize Jang’s basic model so that it can be used to solve classification problems by employing parameterized tnorms. We also enhance the model to include weights of importance so that feature selection becomes a component of the modeling scheme. Next, we discuss two ways of identifying system structures based on Jang’s architecture. For the topdown approach, we summarize several ways of partitioning the feature space and propose a method of using clustering objective functions to evaluate possible partitions. We analyze the overall learning and operation complexity. In particular, we pinpoint the dilemma between two desired properties: modeling accuracy and pattern matching efficiency. Based on the analysis, we suggest a bottomup approach of using rule organization to meet the conflicting requirements. We introduce a data structure, called a fuzzy binary boxtree, to organize rules so that the rule base can be matched against input signals with logarithmic efficiency. To preserve the advantage of parallel processing assumed in fuzzy rulebased inference systems, we give a parallel algorithm for pattern matching with a linear speedup. Moreover, as we consider the communication and storage cost of an interpolation model, it is important to extract the essential components of the modeled system and use the rest as a backup. We propose a rule combination mechanism to build a simplified version of the original rule base according to a given focus set. This scheme can be used in various situations of pattern representation or data compression, such as in image coding or in hierarchical pattern recognition.
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 27 (1 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].
Structure Determination in Fuzzy Modeling: A Fuzzy CART Approach
, 1994
"... This paper presents an innovative approach to the structure determination problem in fuzzy modeling. By using the wellknown CART (classification and regression tree) algorithm as a quick preprocess, the proposed method can roughly estimate the structure (numbers of membership functions and number o ..."
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Cited by 21 (2 self)
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This paper presents an innovative approach to the structure determination problem in fuzzy modeling. By using the wellknown CART (classification and regression tree) algorithm as a quick preprocess, the proposed method can roughly estimate the structure (numbers of membership functions and number of fuzzy rules, etc.) of a fuzzy inference system; then the parameter identification is carried out by the hybrid learning scheme developed in our previous work [3, 2, 5]. Morevoer, the identified fuzzy inference system has the property that the total of firing strengths is always equal to one; this speeds up learning processes and reduces roundoff errors. 1 Introduction Fuzzy modeling [11, 10] is a new branch of system identification which concerns with the construction of a fuzzy inference system (or fuzzy model) that can predict and hopefully explain the behavior of an unknown system described by a set of sample data. Two primary tasks of fuzzy modeling are structure determination...
Combining Neural Networks and Fuzzy Controllers
 Fuzzy Logic in Artificial Intelligence (FLAI93
, 1993
"... . Fuzzy controllers are designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set theory depends on the choice of certain parameters, for which no formal method is known. The optimization of these parameters ..."
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Cited by 20 (5 self)
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. Fuzzy controllers are designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set theory depends on the choice of certain parameters, for which no formal method is known. The optimization of these parameters can be carried out by neural networks, which are designed to learn from training data, but which are in general not able to profit from structural knowledge. In this paper we discuss approaches which combine fuzzy controllers and neural networks, and present our own hybrid architecture where principles from fuzzy control theory and from neural networks are integrated into one system. 1 Introduction Classical control theory is based on mathematical models that describe the behavior of the plant under consideration. The main idea of fuzzy control [11, 14], which has proved to be a very successful method [7], is to build a model of a human control expert who is capable of controlling the plan...
A Fuzzy Perceptron as a Generic Model for NeuroFuzzy Approaches
 In Proc. of the 2nd German GIWorkshop FuzzySysteme '94, München
, 1994
"... This paper presents a fuzzy perceptron as a generic model of multilayer fuzzy neural networks, or neural fuzzy systems, respectively. This model is suggested to ease the comparision of different neurofuzzy approaches that are known from the literature. A fuzzy perceptron is not a fuzzification of ..."
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Cited by 14 (4 self)
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This paper presents a fuzzy perceptron as a generic model of multilayer fuzzy neural networks, or neural fuzzy systems, respectively. This model is suggested to ease the comparision of different neurofuzzy approaches that are known from the literature. A fuzzy perceptron is not a fuzzification of a common neural network architecture, and it is not our intention to enhance neural learning algorithms by fuzzy methods. The idea of the fuzzy perceptron is to provide an architecture that can be initialized with prior knowledge, and that can be trained using neural learning methods. The training is carried out in such a way that the learning result is interpretable in the form of linguistic fuzzy ifthen rules. Next to the advantage of having a generic model to compare neurofuzzy models, the fuzzy perceptron can be specialized e.g. for data analysis and control tasks. 1 Introduction Combinations of neural networks and fuzzy systems have become very popular during the last two years [Be...
Techniques for learning and tuning fuzzy rulebased systems for linguistic modeling and their application
 In C. Leondes (Ed.), Knowledge
, 1999
"... Nowadays, Linguistic Modeling is considered to be one of the most important areas of application for Fuzzy Logic. Linguistic Mamdanitype Fuzzy RuleBased Systems (FRBSs), the ones used to perform this task, provide a humanreadable description of the model in the form of linguistic rules, which is ..."
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Cited by 6 (3 self)
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Nowadays, Linguistic Modeling is considered to be one of the most important areas of application for Fuzzy Logic. Linguistic Mamdanitype Fuzzy RuleBased Systems (FRBSs), the ones used to perform this task, provide a humanreadable description of the model in the form of linguistic rules, which is a desirable characteristic in many problems. In this Chapter we are going to accomplish a short revision of the FRBSs where we shall see the different types that currently exist, along with their structures and characteristics, centering our attention on linguistic Mamdanitype FRBS. The performance of a linguistic FRBS depends on its Rule Base and the membership functions associated to the fuzzy partitions. Due to the complexity in the design of these components, a large quantity of automatic techniques has been proposed to put it into effect. Thereafter, we are going to review several learning (when it sets the Rule Base and sometimes the Data Base as well) and tuning (when it only sets the Data Base) methods. These methods are inspired in the three most well known approaches: ad hoc data covering, neural networks, and genetic algorithms. We shall introduce a brief description of these techniques and their synergy with FRBSs. The accuracy of the reviewed methods will be compared when solving two realworld applications. Some interesting conclusions will be obtained about the behavior of the methods, approaches, and techniques. I