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Open AccessArticle

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

1
Engineering Faculty, University Autonomous of Chihuahua, Chihuahua 31125, Mexico
2
Division of Graduate Studies, Tijuana Institute of Technology, Tijuana 22414, Mexico
3
Chemical Faculty of Sciences and Engineering, University Autonomous of Baja California, Tijuana 14418, Mexico
*
Author to whom correspondence should be addressed.
Axioms 2019, 8(1), 14; https://doi.org/10.3390/axioms8010014
Received: 4 December 2018 / Revised: 9 January 2019 / Accepted: 9 January 2019 / Published: 24 January 2019
(This article belongs to the Special Issue Type-2 Fuzzy Logic: Theory, Algorithms and Applications)
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. View Full-Text
Keywords: particle swarm optimization; fuzzy numbers; type-2 fuzzy weights; neural networks; backpropagation; time series prediction particle swarm optimization; fuzzy numbers; type-2 fuzzy weights; neural networks; backpropagation; time series prediction
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Gaxiola, F.; Melin, P.; Valdez, F.; Castro, J.R.; Manzo-Martínez, A. PSO with Dynamic Adaptation of Parameters for Optimization in Neural Networks with Interval Type-2 Fuzzy Numbers Weights. Axioms 2019, 8, 14.

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