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Learning in Fuzzy Neural Networks
 in Proc. IEEE Int. Conf. Neural Networks
, 1996
"... In our fuzzy neural networks fuzzy weights and fuzzy operations are used for training crisp and fuzzy data. Theoretical studies of fuzzy networks where triangular fuzzy numbers are used, show that the output behaviour of these networks can be estimated for arbitrary input data. To make use of these ..."
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Cited by 6 (0 self)
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In our fuzzy neural networks fuzzy weights and fuzzy operations are used for training crisp and fuzzy data. Theoretical studies of fuzzy networks where triangular fuzzy numbers are used, show that the output behaviour of these networks can be estimated for arbitrary input data. To make use
Fuzzy Neural Networks are Overlapping
, 1996
"... Fuzzy neural networks can be trained with crisp and fuzzy data. J. Buckley and Y. Hayashi have shown that these networks are monotonic (see [2]) when extension principle based operations are used to compute the network output. In this paper we show that these networks are also overlapping. This prop ..."
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Fuzzy neural networks can be trained with crisp and fuzzy data. J. Buckley and Y. Hayashi have shown that these networks are monotonic (see [2]) when extension principle based operations are used to compute the network output. In this paper we show that these networks are also overlapping
FUZZY GRAPHS IN FUZZY NEURAL NETWORKS
, 2009
"... In this paper we observe that, the fuzzy neural network architecture is isomorphic to the fuzzy graph model and the output of a fuzzy neural network with OR fuzzy neuron is equal to the strength of strongest path between the input layer (particular input neuron/neurons) and the out put layer(particu ..."
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In this paper we observe that, the fuzzy neural network architecture is isomorphic to the fuzzy graph model and the output of a fuzzy neural network with OR fuzzy neuron is equal to the strength of strongest path between the input layer (particular input neuron/neurons) and the out put layer
Fuzzy Neural Networks Are Universal Approximators
 World Congress 1995, Sao Paulo, Brasil
, 1995
"... . In this paper we examine the capacity of fuzzy neural networks. These networks are multilayer feedforword nets whose processing elements  the formal neurons  operate on fuzzy numbers instead of real numbers. We show that these fuzzy neural networks can approximate fuzzy continuous real functio ..."
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Cited by 7 (1 self)
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. In this paper we examine the capacity of fuzzy neural networks. These networks are multilayer feedforword nets whose processing elements  the formal neurons  operate on fuzzy numbers instead of real numbers. We show that these fuzzy neural networks can approximate fuzzy continuous real
Supervised Learning in Fuzzy Neural Networks
, 1995
"... 2 2 Basics of Fuzzy Logic 2 2.1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 2.2 Fuzzy Sets : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 2 2.3 Fuzzy Numbers : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 2 ..."
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Cited by 1 (0 self)
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2.4 The Extension Principle : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2.5 Fuzzification of the Basic Operations : : : : : : : : : : : : : : : : : : : : : : 5 3 Definition and Properties of Fuzzy Neural Networks 6 3.1 Introduction
Application of FuzzyNeural Network in Classification of . . .
, 2002
"... Errors associated with visual inspection and interpretation of radargrams often inhibits the intensive surveying of widespread areas using groundpenetrating radar (GPR). To automate the interpretive process, this paper presents an application of a fuzzyneural network (FNN) classifier for unsuperv ..."
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Errors associated with visual inspection and interpretation of radargrams often inhibits the intensive surveying of widespread areas using groundpenetrating radar (GPR). To automate the interpretive process, this paper presents an application of a fuzzyneural network (FNN) classifier
A Procedure for the Construction of the Structure of Fuzzy Neural Networks
, 1996
"... A fuzzy neural network is presented where the structure will be generated in the learning algorithm. The system recognizes node regions where new nodes have to be introduced such that the system will be able to use the offered information in the learning examples. ..."
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A fuzzy neural network is presented where the structure will be generated in the learning algorithm. The system recognizes node regions where new nodes have to be introduced such that the system will be able to use the offered information in the learning examples.
Type2 Fuzzy Neural Network Systems and Learning
 International Journal of Computational Cognition
, 2003
"... This paper presents a type2 fuzzy neural network system (type2 FNN) and its learning using genetic algorithm. The socalled type1 fuzzy neural network (FNN) has the properties of parallel computation scheme, easy to implement, fuzzy logic inference system, and parameters convergence. And, the mem ..."
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Cited by 4 (1 self)
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This paper presents a type2 fuzzy neural network system (type2 FNN) and its learning using genetic algorithm. The socalled type1 fuzzy neural network (FNN) has the properties of parallel computation scheme, easy to implement, fuzzy logic inference system, and parameters convergence. And
Face Recognition Method based on the Fuzzy Neural Network
"... Abstract: This paper discusses a face recognition method based on the fuzzy neural network (FNN). The fuzzy neural network has more advantages than artificial neural network alone. The paper firstly introduces the structure of the FNN. Than proposed the fuzzy rules and the study algorithm. Thirdly i ..."
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Abstract: This paper discusses a face recognition method based on the fuzzy neural network (FNN). The fuzzy neural network has more advantages than artificial neural network alone. The paper firstly introduces the structure of the FNN. Than proposed the fuzzy rules and the study algorithm. Thirdly
Results 1  10
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345,163