Improved Type2-NPCM Fuzzy Clustering Algorithm Based on Adaptive Particle Swarm Optimization for Takagi–Sugeno Fuzzy Modeling Identification
Copyright policy )Improved Type2-NPCM Fuzzy Clustering Algorithm Based on Adaptive Particle Swarm Optimization for Takagi–Sugeno Fuzzy Modeling Identification
In this paper, an improved Type2-NPCM clustering algorithm based on improved adaptive particle swarm optimization called Type2-NPCM-IAPSO is proposed. First, a new clustering algorithm called Type2-NPCM is proposed. The Type2-NPCM algorithm can solve the problems encountered by the algorithms FCM, G-K, PCM and NPCM (sensitivity to noise or aberrant points and local minimal sensitivity), etc. Second, we combined our Type2-NPCM algorithm with the improved adaptive particle swarm optimization IAPSO algorithm to ensure proper convergence to a local minimum of the objective function. The effectiveness of the proposed Type2-NPCM-IAPSO algorithm was tested on the electro-hydraulic system, convection system and other nonlinear systems described by differential equation.
- Tunisia Private University Tunisia
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