Abstract
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.
Highlights
In time series prediction problems, the main objective is to obtain results that approximate the real data as closely as possible with minimum error
We have reached the conclusion that the interval type-2 fuzzy numbers weights neural network with S-Norms and T-norms Hamacher (IT2FNWNNH) and Frank (IT2FNWNNF) optimized using particle swarm optimization (PSO) with dynamic adjustment achieves better results than the traditional neural network and the interval type-2 fuzzy numbers weights neural network without optimization, in almost all the cases of study, for the stock exchanges time series used in this work
This assertion is based on the comparison of prediction errors for all stock exchange time series shown in Table 10 and the data of the statistical test of T-student for best results against the traditional neural network (TNN)
Summary
In time series prediction problems, the main objective is to obtain results that approximate the real data as closely as possible with minimum error. To improve the optimization and search problems, a set of methods of computational intelligence was the focus of some recent work in improving the solving of optimization and search problems [5,6,7]. These methods can achieve competitive results, but not the best solutions. Heuristic algorithms can be used like alternative methods These algorithms are not guaranteed to find the best solution, they are able to obtain an optimal solution in a reasonable time
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