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.