中文摘要：

连续型Hopfield神经网络（CHNN）可用于优化计算,但其会遭遇较复杂的参数辨识问题.为了较好地解决这一问题,将擅长全局搜索的蚁群-粒子群混合算法用于对系统参数的最优化选取.再将此混合算法与CHNN有机结合,更好地解决参数辨识问题,且能有效避免CHNN在应用过程中陷入局部最优解.最后,将理论结果应用于求解TSP问题来验证其有效性.

英文摘要：

Continuous Hopfield Neural Network （CHNN） can be used for optimization calculation, but it will encounter the problem that the parameter identification is more complicated. In order to solve the problem, we use the ant algorithm with particle swarm algorithm to optimize the system parameters ,which are good at global search. Furthermore, combine the hybrid algorithm with the CHNN network, it can solve the parameter identification problem, and can effectively prevent falling into local optimal solution with the application of CHNN. Finally, the theoretical results are applied to solve the TSP problem to verify its effectiveness.