A Methodology for Modelling of Takagi-Sugeno Fuzzy Model based on Multi-Particle Swarm Optimization: Application to Gas Furnace system

In this paper, an identification problem for nonlinear models is explored and an improved fuzzy identification method based on the heterogeneous Multi-swarm PSO (MsPSO) algorithm is proposed in order to obtain an optimal T-S fuzzy model. However, this simple homogeneous search behavior is not always optimal to find the potential solution to a special problem, and it may trap the individuals into local regions leading to premature convergence. To improve the performance of MsPSO, the particles should be able to adaptively changing their original trajectories to explore new search space. In fact, a new multiswarm particle swarm optimization algorithm using an adaptive inertia weight, denoted AIMsPSO, has been presented in order to improve the performance of constructing the T-S fuzzy system.