Synthesis of Intelligent Learning Control Systems by Evolutionary Neural Networks

点击翻译按钮获取中文摘要

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.