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NEFCLASS  A NeuroFuzzy Approach For The Classification Of Data
 Applied Computing 1995. Proc. of the 1995 ACM Symposium on Applied Computing
, 1995
"... In this paper we present NEFCLASS, a neurofuzzy system for the classification of data. This approach is based on our generic model of a fuzzy perceptron which can be used to derive fuzzy neural networks or neural fuzzy systems for specific domains. The presented model derives fuzzy rules from data ..."
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Cited by 45 (12 self)
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In this paper we present NEFCLASS, a neurofuzzy system for the classification of data. This approach is based on our generic model of a fuzzy perceptron which can be used to derive fuzzy neural networks or neural fuzzy systems for specific domains. The presented model derives fuzzy rules from data to classify patterns into a number of (crisp) classes. NEFCLASS uses a supervised learning algorithm based on fuzzy error backpropagation that is used in other derivations of the fuzzy perceptron. Introduction Combinations of neural networks and fuzzy systems are very popular (for an overview see [4, 6]), but most of the approaches are not easy to compare because they use very different architectures, activation functions, propagation and learning algorithms, etc. In [5] we presented a fuzzy perceptron as a generic model of multilayer fuzzy neural networks. It can be used as a common base for neurofuzzy architectures in order to ease the comparision of different approaches. By applying a...
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 15 (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...
NEFCONI: An XWindow Based Simulator for Neural Fuzzy Controllers
 In Proc. IEEE Int. Conf. Neural Networks 1994 at IEEE WCCI'94
, 1994
"... In this paper we present NEFCONI, a graphical simulation environment for building and training neural fuzzy controllers based on the NEFCON model [6]. NEFCONI is an XWindow based software that allows a user to specify initial fuzzy sets, fuzzy rules and a rule based fuzzy error. The neural fuzzy ..."
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Cited by 14 (6 self)
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In this paper we present NEFCONI, a graphical simulation environment for building and training neural fuzzy controllers based on the NEFCON model [6]. NEFCONI is an XWindow based software that allows a user to specify initial fuzzy sets, fuzzy rules and a rule based fuzzy error. The neural fuzzy controller is trained by a reinforcement learning procedure which is derived from the fuzzy error backpropagation algorithm for fuzzy perceptrons [7]. NEFCONI communicates with an external process where a dynamical system is simulated. NEFCONI is freely available on the internet. I. Introduction NEFCON is a model for neural fuzzy controllers based on the architecture of a fuzzy perceptron [7]. The system consists of 3 layers of units, and the connections between the layers are weighted by fuzzy sets [5, 6]. The learning algorithm defined for NEFCON is able to learn fuzzy sets as well as fuzzy rules. We present the learning algorithm that uses a rule based fuzzy error measure, and that has...
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 12 (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...
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.
New Learning Algorithms for the NeuroFuzzy Environment NEFCONI
 In Proceedings of NeuroFuzzySysteme '95
, 1995
"... NEFCONI is an XWindow based graphical simulation environment for neurofuzzy controllers, and it is freely available on the Internet. The NEFCON model is based on a generic fuzzy perceptron, and it is able to learn fuzzy sets and fuzzy rules by a reinforcement learning algorithm that uses a fuz ..."
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Cited by 7 (4 self)
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NEFCONI is an XWindow based graphical simulation environment for neurofuzzy controllers, and it is freely available on the Internet. The NEFCON model is based on a generic fuzzy perceptron, and it is able to learn fuzzy sets and fuzzy rules by a reinforcement learning algorithm that uses a fuzzy error measure. The former version of NEFCON had some restrictions on the form of the membership functions of the conclusions, and an expensive rule learning procedure. The new version of the NEFCON model incorporates new learning algorithms for both the fuzzy sets, and the fuzzy rules, and it removes the restrictions on the conclusion fuzzy sets. 1 Introduction The NEFCON model is a neurofuzzy system for control applications [3, 5, 6, 7]. The system is able to learn fuzzy sets and fuzzy rules online by reinforcement learning, i.e. it learns while trying to control a dynamic system without knowing the correct output value. The learning process is guided by a linguistic description of...
Neural fuzzy systems
 IN ADVANCES IN SOFT COMPUTING SERIES. BERLIN/HEILDELBERG: SPRINGERVERLAG, 2000, ISBN
, 1995
"... the paper presented fuzzy logics ..."
Learning Methods for Fuzzy Systems
 NonLinear Electromagnetic Systems: Advanced Techniques and Mathematical Methods
, 1998
"... . Fuzzy systems are currently being used in a wide field of industrial and scientific applications. Therefore, it is necessary to have algorithms which construct and optimize such systems automatically. Since the idea of learning is being studied in other research areas like machine learning and ..."
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Cited by 5 (1 self)
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. Fuzzy systems are currently being used in a wide field of industrial and scientific applications. Therefore, it is necessary to have algorithms which construct and optimize such systems automatically. Since the idea of learning is being studied in other research areas like machine learning and data mining, some of the developed methods have been made available and optimized for the learning process in fuzzy systems. In this paper, we present a short survey of these methods and take a closer look at a special learning approach, the neurofuzzy systems. 1. Introduction At present, fuzzy systems are being used in a wide range of industrial and scientific applications with the main application areas being fuzzy control, data analysis and knowledge based systems. Fuzzy controllers, for instance, model the control strategy of a human expert to control a system for which no mathematical or physical model exists. They employ a set of linguistic rules to describe the human behavior. A...
Interpreting Changes In The Fuzzy Sets Of A SelfAdaptive Neural Fuzzy Controller
 In Proc. Second Int. Workshop on Industrial Applications of Fuzzy Control and Intelligent Systems (IFIS'92
, 1992
"... We describe a procedure for the adaptation of membership functions in a fuzzy control environment by using neural network learning principles. The changes in the fuzzy sets can be easily interpreted. By using a fuzzy error that is propagated back through the architecture of our fuzzy controller, we ..."
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Cited by 5 (5 self)
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We describe a procedure for the adaptation of membership functions in a fuzzy control environment by using neural network learning principles. The changes in the fuzzy sets can be easily interpreted. By using a fuzzy error that is propagated back through the architecture of our fuzzy controller, we receive an unsupervised learning technique, where each rule tunes the membership functions of its antecedent and its consequence. INTRODUCTION Classical control theory is based on mathematical models that describe the behaviour of the plant under consideration. The main idea of fuzzy control [9, 10], which has proved to be a very successful method [5], is to build a model of a human control expert who is capable of controlling the plant without thinking in a mathematical model. The control expert specifies his control actions in the form of linguistic rules. These control rules are translated into the framework of fuzzy set theory providing a calculus which can simulate the behaviour of the ...
Choosing Appropriate NeuroFuzzy Models
 In Proc. Second European Congress on Fuzzy and Intelligent Technologies (EUFIT94
, 1994
"... To use fuzzy controllers for automization tasks appropriate fuzzy sets and fuzzy rules have to be defined. This can be difficult in some domains, and the resulting controller has to be tuned. Neurofuzzy models can help in this tuning process by adapting fuzzy sets and creating fuzzy rules. Combina ..."
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Cited by 4 (2 self)
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To use fuzzy controllers for automization tasks appropriate fuzzy sets and fuzzy rules have to be defined. This can be difficult in some domains, and the resulting controller has to be tuned. Neurofuzzy models can help in this tuning process by adapting fuzzy sets and creating fuzzy rules. Combinations of neural networks and fuzzy controllers are suitable if there is only partial knowledge in the form of fuzzy sets and fuzzy rules, but training data is available. To be be able to choose an appropriate model one has to know the different approaches to neural fuzzy control. In this paper we present a classification of generic neural fuzzy controllers and give some hints when to choose a certain type of model. 1 Introduction Classical control theory is based on mathematical models that describe the behavior of a plant 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 plant without thinki...