Research on a New Hybrid Optimization Algorithm based on QPSO and FNN

Fuzzy neural network(FNN) is a neural network based on combining the advantages of the fuzzy theory and neural network. It has the characteristics of dealing with the nonlinear and fuzziness and so on. Particle swarm optimization(PSO) algorithm is a population-based search algorithm by simulating the social behavior of birds within a flock. So the quantum PSO(QPSO) algorithm is proposed for optimizing the parameters of FNN in order to construct a new hybrid optimization(QPSO-FNN) algorithm in this paper. In the proposed QPSO-FNN algorithm, the quantum theory is used to improve the PSO algorithm, then the global optimization ability of QPSO algorithm is optimize the parameters of FNN model by putting these parameters in the particle encoding. The found optimal values are regarded as the parameters of FNN model to obtain the final QPSOFNN method. Finally, the QPSO-FNN algorithm is used to solve the complex problem, the experimental results show that the QPSO-FNN algorithm takes on the shorter response time and higher solving accuracy.

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