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Cascade Training Multilayer Fuzzy Model for Nonlinear Uncertain System Identification Optimized by Differential Evolution Algorithm
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DOI：10.1007/s40815-017-0431-x.png)
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Abstract
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This paper proposes a new cascade training multilayer fuzzy logic for identifying forward model of multiple-inputs multiple-outputs (MIMO) nonlinear double-coupled fluid tank system based on experiment platform. The novel multilayer fuzzy model consists of multiple MISO model; for each MISO model, it composes of multiple single fuzzy Takagi-Sugeno (T-S) models. The cascade training using optimization algorithms optimally trained multilayer fuzzy model one by one. All parameters of multilayer fuzzy model were optimally and comparatively identified using DE, GA and PSO optimization algorithms. Then, the proposed method results are compared with normal training method results. The experimental results show that proposed method gives better performance than the normal training. Hence, the novel proposed optimized multilayer fuzzy model is efficiently applied for identifying MISO system. The experiment cascade training is clearly presented. It proves more accurate and takes less time to compute than the normal training, and it seems promisingly scalable as a simple and efficient method to successfully identify and control various uncertain nonlinear large-scale MIMO systems.
Keywords:
Multilayer fuzzy model
Cascade training
Differential evolution (DE) algorithm
Nonlinear double-coupled fluid tank system
Multiple-inputs multiple-outputs (MIMO) system
Fuzzy Takagi-Sugeno (T-S) model
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