基于神经网络模型的混沌优化及其应用

研究一种新型优化算法－混沌优化，提出加快解的疏敛速度和精度新方法，并与精确不可微罚函数结合来求解非线性约束优化问题。对不能用数学解析式精确表达的优化问题利用神经网络建模，在此基础上进行混沌搜索寻优。该方法应用于甲醛生产过程的稳态优化，获得较好的经济效益。

Chaos Optimization Based on Neural Network Model and Its Application

Chaos optimization method combined with exact nondifferentiable penalty function is proposed for solving nonlinear constraint optimization problems,and linear search is applied to speed the rate of convergence and improve the accuracy of solution.To solve the problem of establishing process staticstate model,artificial neural network is proposed.This method is applied to formaldehyde production process online optimization.Results show that ,the method is simple and easy to implement,robust in versatility,and it has high accuracy and reliability,so it is effective for chemical process optimization.