Synthesis of Intelligent Learning Control Systems by Evolutionary Neural Networks

在本计画中，我们提出一种新的方法来实现以类神经网路为基础的智慧型控制系统。神经网络有几个特定的属性，如并行处理，分布式存储器，学习等传统的信息处理，本项目是利用这些属性的智能控制系统的综合以及进化计算，如遗传算法和进化规划，然后将它们应用到真实的工厂控制问题，以显示所提出的方法的有效性。为了完成本课题的研究，我们采用了以下方法对这些智能控制系统进行综合：（1）神经网络的学习能力我们利用通信信道的信息测度，建立了神经网络的学习能力公式。(2)为了提高考虑（1）的神经网络的学习能力，我们引入了遗传算法并找到学习曲线的全局最小值。(3)用神经网络构建图像理解为了实现智能化，我们不仅使用数字数据，而且还使用图像信息处理作为多媒体数据处理。(4)基于图像信息和动作行为的规则获取系统及其应用在未知环境下，利用图像信息和动作行为实现了智能控制系统。这些方法已应用于倒立摆的镇定和加热炉温度控制等真实的控制问题。从这些实验结果来看，本研究所提出的方法具有良好的控制效果，并可应用于其他控制问题。

In this project, we have proposed a new approach to realize the intelligent control systems based on neural networks. The neural networks have several specific properties over conventional information processing such as parallel processing, distributed memory, learning, etc. The present project is to utilize these properties for synthesis of intelligent control systems as well as evolution computation such as genetic algorithms and evolution programming and then apply them to real plant control problem to show the effectiveness of the proposed methods. To complete the project study, we have adopted the following approach to synthesize these intelligent control systems :(1) Learning Ability of the Neural networkWe have formulated the learning ability of neural networks by using information measure of the communication channel.(2) Emergence of Evolution Mechanism by Genetic AlgorithmsTo improve the learning ability of neural networks considered (1), we have introduced the genetic algorithms and find the global minimum of the learning curves.(3) Construction of Image Understanding by Neural NetworksTo realize the intelligence we have used not only numerical data but also image information processing as a multi-media data processing.(4) Rule Acquisition System Based on Image Information and Action Behaviors and their ApplicationsUnder the unknown environment we have realized the intelligent control systems using the image information and action behaviors. These approaches have been applied to real control problems such as stabilization of inverted pendulum and temperature control of a heating furnace. From these experimental results the proposed approach in this study are good control results and could be applied to the other control problems.