采用遗传网络算法实现非线性系统的神经网络控制(英文)

NEURON-CONTROL OF NONLINEAR SYSTEMS USING GENETIC ALGORITHMS

提出一种对于非线性系统遗传算法的神经网络控制模型 ,并给出了新的神经网络训练模型 .该模型的主要优点是 ,优化网络连接权重 。

In this thesis,we present a genetic algorithm neuron control scheme for nonlinear systems.Our method is different from those using supervised learning algorithms,such as the backpropagation(BP) algorithm,that needs training information in each step.The contributions of this thesis are the new approach to constructing neural network architecture and its training.These improvements include: Optimizing connection weights and Optimizing network topology.