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Relevant bibliographies by topics / Fuzzy systems; Neural networks

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Journal articles

Academic literature on the topic 'Fuzzy systems; Neural networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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Journal articles on the topic "Fuzzy systems; Neural networks"

1

Virgil Negoita, Constantin. "Neural Networks as Fuzzy Systems." Kybernetes 23, no. 3 (1994): 7–9. http://dx.doi.org/10.1108/03684929410059000.

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Any fuzzy system is a knowledge‐based system which implies an inference engine. Proposes neural networks as a means of performing the inference. Using the Theorem of Representation proposes an encoding scheme that allows the neural network to be trained to perform modus ponens.

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2

Rutkowska, Danuta, and Yoichi Hayashi. "Neuro-Fuzzy Systems Approaches." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 3 (1999): 177–85. http://dx.doi.org/10.20965/jaciii.1999.p0177.

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Two major approaches to neuro-fuzzy systems are distinguished in the paper. The previous one refers to fuzzy neural networks, which are neural networks with fuzzy signals, and/or fuzzy weights, as well as fuzzy transfer functions. The latter approach concerns neuro-fuzzy systems in the form of multilayer feed-forward networks, which differ from standard neural networks, because elements of particular layers conduct different operations than standard neurons. These structures are neural network representations of fuzzy systems and they are also called connectionist models of fuzzy systems, adap

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3

Godjevac, Jelena, and Nigel Steele. "Fuzzy Systems and Neural Networks." Intelligent Automation & Soft Computing 4, no. 1 (1998): 27–37. http://dx.doi.org/10.1080/10798587.1998.10750719.

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4

Kosko, Bart, and John C. Burgess. "Neural Networks and Fuzzy Systems." Journal of the Acoustical Society of America 103, no. 6 (1998): 3131. http://dx.doi.org/10.1121/1.423096.

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5

Zambelli, Stefano. "Neural networks and fuzzy systems." Journal of Economic Dynamics and Control 17, no. 3 (1993): 523–29. http://dx.doi.org/10.1016/0165-1889(93)90010-p.

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6

Dvorak, V. "Neural networks and fuzzy systems." Knowledge-Based Systems 6, no. 3 (1993): 179. http://dx.doi.org/10.1016/0950-7051(93)90043-s.

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7

Thakur, Amey. "Neuro-Fuzzy: Artificial Neural Networks & Fuzzy Logic." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (2021): 128–35. http://dx.doi.org/10.22214/ijraset.2021.37930.

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Abstract: Neuro Fuzzy is a hybrid system that combines Artificial Neural Networks with Fuzzy Logic. Provides a great deal of freedom when it comes to thinking. This phrase, on the other hand, is frequently used to describe a system that combines both approaches. There are two basic streams of neural network and fuzzy system study. Modelling several elements of the human brain (structure, reasoning, learning, perception, and so on) as well as artificial systems and data: pattern clustering and recognition, function approximation, system parameter estimate, and so on. In general, neural networks

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8

Blake, J. "The implementation of fuzzy systems, neural networks and fuzzy neural networks using FPGAs." Information Sciences 112, no. 1-4 (1998): 151–68. http://dx.doi.org/10.1016/s0020-0255(98)10029-4.

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9

NASRABADI, EBRAHIM, and S. MEHDI HASHEMI. "ROBUST FUZZY REGRESSION ANALYSIS USING NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 04 (2008): 579–98. http://dx.doi.org/10.1142/s021848850800542x.

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Some neural network related methods have been applied to nonlinear fuzzy regression analysis by several investigators. The performance of these methods will significantly worsen when the outliers exist in the training data set. In this paper, we propose a training algorithm for fuzzy neural networks with general fuzzy number weights, biases, inputs and outputs for computation of nonlinear fuzzy regression models. First, we define a cost function that is based on the concept of possibility of fuzzy equality between the fuzzy output of fuzzy neural network and the corresponding fuzzy target. Nex

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10

OH, SUNG-KWUN, DONG-WON KIM, and WITOLD PEDRYCZ. "HYBRID FUZZY POLYNOMIAL NEURAL NETWORKS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, no. 03 (2002): 257–80. http://dx.doi.org/10.1142/s0218488502001478.

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We propose a hybrid architecture based on a combination of fuzzy systems and polynomial neural networks. The resulting Hybrid Fuzzy Polynomial Neural Networks (HFPNN) dwells on the ideas of fuzzy rule-based computing and polynomial neural networks. The structure of the network comprises of fuzzy polynomial neurons (FPNs) forming the nodes of the first (input) layer of the HFPNN and polynomial neurons (PNs) that are located in the consecutive layers of the network. In the FPN (that forms a fuzzy inference system), the generic rules assume the form "if A then y = P(x) " where A is fuzzy relation

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