复杂工业过程的遗传模糊神经网络控制

本文提出一种基于遗传算法和监督学习方法的有效模糊神经网络控制，这种控制器采用并行处理的推理网络，具有两个重要特点：自适应和学习性，所提方法经过仿真和温控验证表明控制性能良好。

Genetic-Based Neurofuzzy Control for Complex Industrial Process*

This paper proposes an effective fuzzy neural network controller based on genetic algorithm (GA) and supervised gradient descent learning.The fuzzy network control processing can be viewed as a parallel neural network where each neuron represents a fuzzy membership function and each link represents the weight of a fuzzy rule,and it has two important characteristics of adaptation and learning.The effectiveness of the proposed scheme is illustrated through simulation and temperature control processes.