Optimization design based on improved ant colony algorithm for PID parameters of BP neural network

Aiming at manually carry through optimization of experiment way adopted for traditional PID controller parameter, an optimization method based on improved ant colony algorithm for PID parameters of BP neural network is presented. The improved ant colony algorithm and BP neural is organically combined by this method. Which not only overcomes effectively the shortcoming of BP algorithm on some degree such as low solving accuracy, slow search speed, easy convergence to minimum, but also has wide mapping ability of neural network. The results are shown by numerical simulation that the optimization strategy on PID parameters has stronger flexibility and adaptability, and are further verified feasibility and validity of purposed method.