一种基于混沌遗传与粒子群混合优化的过程神经网络训练算法

针对时变输入/输出过程神经网络的训练问题，提出一种基于混沌遗传与带有动态惯性因子的粒子群优化相结合的学习方法。综合利用粒子群算法的经验记忆、信息共享和混沌遗传算法的混沌轨道遍历搜索性质，基于PNN训练目标函数，构建两种算法相混合的进化寻优机制，通过适应度评估和优化效率分析自适应调节混沌遗传与粒子群算法的切换，实现网络参数在可行解空间的全局优化求解。实验结果表明，该算法较大提高了PNN的训练效率。

A training algorithm of process neural networks based on CGA combinedwith PSO

Aiming at the training problem of time-varying input-output process neural networks(PNN), a learning algorithm
based on chaos genetic algorithm(CGA) combined with particle swarm optimization(PSO) whose inertial factor is dynamic
is proposed in the paper. With the application of the experience memory and sharing information of PSO algorithm, and
chaos track traverse searching of CGA, the hybrid evolutionary optimization mechanism of CGA and PSO algorithm is built
based on the PNN’s training objective function. The adaptive switching of two algorithms is implemented through estimating
the fitness and optimization efficiency, and the global optimal solution is obtained in feasible solution space. Experimental
results show that the algorithm considerably improves the training efficiency of PNN.