An improved Mamdani Fuzzy Neural Networks Based on PSO Algorithm and New Parameter Optimization

Lei Meng, Shoulin Yin, Xinyuan Hu

Abstract


As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization(PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to optimize model's parameters. At the end, we use gradient descent method to make a further optimization for parameters. Therefore, we can realize the automatic adjustment, modification and perfection under the fuzzy rule. The experimental results show that the new algorithm improves the approximation ability of Mamdani Fuzzy neural networks.

Keywords


PSO algorithm, Mamdani Fuzzy neural networks, Fuzzy clustering, Gradient descent method

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DOI: http://doi.org/10.11591/ijeecs.v1.i1.pp201-206

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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