scholarly journals Genetic Algorithm for Fuzzy Neural Networks using Locally Crossover

2011 ◽  
Vol 6 (1) ◽  
pp. 8 ◽  
Author(s):  
Dragos Arotaritei
Keyword(s):  
Neural Networks ◽  
Fuzzy Numbers ◽  
Signal Propagation ◽  
Fuzzy Neural Networks ◽  
Fuzzy Arithmetic ◽  
Recurrent Networks ◽  
Triangular Fuzzy Numbers ◽  
Fuzzy Neural ◽  
Sinusoidal Oscillations ◽  
Real World Problems

Fuzzy feed-forward (FFNR) and fuzzy recurrent networks (FRNN) proved to be solutions for "real world problems". In the most cases, the learning algorithms are based on gradient techniques adapted for fuzzy logic with heuristic rules in the case of fuzzy numbers. In this paper we propose a learning mechanism based on genetic algorithms (GA) with locally crossover that can be applied to various topologies of fuzzy neural networks with fuzzy numbers. The mechanism is applied to FFNR and FRNN with L-R fuzzy numbers as inputs, outputs and weights and fuzzy arithmetic as forward signal propagation. The α-cuts and fuzzy biases are also taken into account. The effectiveness of the proposed method is proven in two applications: the mapping a vector of triangular fuzzy numbers into another vector of triangular fuzzy numbers for FFNR and the dynamic capture of fuzzy sinusoidal oscillations for FRNN.

THREE-PARAMETER FUZZY ARITHMETIC APPROXIMATION OF L-R FUZZY NUMBERS FOR FUZZY NEURAL NETWORKS

2006 ◽  
Vol 14 (02) ◽  
pp. 211-233 ◽  
Author(s):  
HSIAO-FAN WANG ◽  
CHING-YI KUO
Keyword(s):  
Approximation Method ◽  
Fuzzy Numbers ◽  
Learning Algorithm ◽  
Error Function ◽  
Back Propagation ◽  
Fuzzy Neural Networks ◽  
Fuzzy Arithmetic ◽  
Learning Procedure ◽  
New Learning ◽  
Fuzzy Neural

In this study, we proposed an alternative operation of fuzzy arithmetic on L-R fuzzy numbers by three parameters of mode, left spread and right spread. Then, based on this approximation method, a new learning algorithm of a fully fuzzified neural network was developed in which the L-R fuzzy numbers were considered as the fuzzy signals. While the forward operations of fuzzy signals were based on the proposed three-parameter fuzzy arithmetic approximation method, the backward learning adopted a back-propagation learning procedure with a measurable error function. The learning algorithm was illustrated by an example of the recognition of fuzzy IF-THEN rules. The simulation result showed that the proposed approximation method used in such learning model was efficient and accurate.

scholarly journals PSO with Dynamic Adaptation of Parameters for Optimization in Neural Networks with Interval Type-2 Fuzzy Numbers Weights

Axioms ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 14 ◽  
Author(s):  
Fernando Gaxiola ◽  
Patricia Melin ◽  
Fevrier Valdez ◽  
Juan Castro ◽  
Alain Manzo-Martínez
Keyword(s):  
Neural Networks ◽  
Fuzzy Numbers ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Fuzzy Neural Networks ◽  
Dynamic Adjustment ◽  
Inference System ◽  
Fuzzy Neural ◽  
Interval Type

A dynamic adjustment of parameters for the particle swarm optimization (PSO) utilizing an interval type-2 fuzzy inference system is proposed in this work. A fuzzy neural network with interval type-2 fuzzy number weights using S-norm and T-norm is optimized with the proposed method. A dynamic adjustment of the PSO allows the algorithm to behave better in the search for optimal results because the dynamic adjustment provides good synchrony between the exploration and exploitation of the algorithm. Results of experiments and a comparison between traditional neural networks and the fuzzy neural networks with interval type-2 fuzzy numbers weights using T-norms and S-norms are given to prove the performance of the proposed approach. For testing the performance of the proposed approach, some cases of time series prediction are applied, including the stock exchanges of Germany, Mexican, Dow-Jones, London, Nasdaq, Shanghai, and Taiwan.

scholarly journals Fuzzy Neural Models Based on Some New Fuzzy* Arithmetic Operations

1999 ◽  
Vol 3 (4) ◽  
pp. 245-254
Author(s):  
Petr Musilekl ◽  
◽  
Madan M. Gupta
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Theoretical Description ◽  
Fuzzy Arithmetic ◽  
Neural Processing ◽  
Neural Models ◽  
Arithmetic Operations ◽  
Neural Structure ◽  
Novel Approach ◽  
Fuzzy Neural

This paper introduces a novel approach to fuzzy arithmetic computation in fuzzy neural networks. The first part provides an overview of the standard fuzzy arithmetic operations and limitations of their use in fuzzy arithmetic based neural models. Consequently, alternative fuzzy arithmetic operations are developed and their aspects for the neural models are discussed in more detail. Originality of our approach lies in the treatment of neural inputs and weights as interactive variables which allows control of uncertainty growth in neural processing. Besides the detailed theoretical description of these operations, corresponding implementation algorithms are given as well. Combination of the alternative fuzzy arithmetic operations is briefly shown on two particular fuzzy arithmetic neurons providing fuzzy extensions of common crisp neural models. Finally, an example of a simple fuzzy neural structure for pattern classification is given.

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

Hotbox level detection of railway vehicle using fuzzy neural networks

2013 ◽  
Vol 33 (9) ◽  
pp. 2566-2569 ◽  
Author(s):  
Zhuanling CUI ◽  
Guoning LI ◽  
Sen LIN
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Railway Vehicle ◽  
Fuzzy Neural

scholarly journals Affine Memory Control for Synchronization of Delayed Fuzzy Neural Networks

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Wookyong Kwon ◽  
Yongsik Jin ◽  
Dongyeop Kang ◽  
Sangmoon Lee
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Memory Control
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