基于神经网络遗传算法求非线性系统的最优解

Optimal Solution to Nonlinear System Based on the Neural Network Genetic Algorithm

应用神经网络对非线性系统进行拟合,将训练好的神经网络作为非线性系统模型,并用遗传算法寻找非线性系统模型的最优解。通过多次重复仿真实验表明,提出的非线性系统寻优方法有效,均能以较快的收敛速度找到近似最优解,说明用RBF神经网络和遗传算法寻求非线性系统最优解的方法是有效的。

The paper fits nonlinear system by the neural networks, which takes the trained neural networks as nonlinear system model, and finds the optimal solution for nonlinear system model. The simulation results show that the optimization method of nonlinear systems can be of faster convergence rate to find the approximate optimal solution, which indicates that it is effective to find the optimal solution of nonlinear systems with the RBF neural network and genetic algorithm.