Xiong Zhihua

Nonlinear model predictive control based on multiple neural networks

Improved predictions can be obtained by using multiple neural networks instead of trying to find a single optimal network as usual. All the component networks of MNN model are selected using generalized information entropy, and the accuracy and reliability of overall model are significantly improved. Based on such an MNN model, a new nonlinear model predictive control algorithm is proposed. Simulation results of a pH CSTR demonstrates that the method is effective and practical.

A Modified PSO Algorithm with the Concept of Intrinsic Repulsive Field

An improved Particle Swarm Optimization Algorithm is proposed in this article. Inspired by the existence of Intermolecular Forces in physics, we bring particles that are influenced by repulsion in this Algorithm to keep the population active at all the stages. Meanwhile, the convergence speed can be assured because of the existence of particles that implement Standard Particle Swarm Optimization. Experimental results show that this improved algorithm can effectively solve the local optimal problems.