【摘要】 本文研究并实现了一个基于人工神经元网络的控制系统模型简化的专家系统(简称为ESOMRT)。该系统适用于专家和非专家用户,能够针对更体的连续和离散时间的高阶控制系统模型和简化要求选择合适的简化方法,并可对简化质量从时域和频域方面进行评估。在构造这个系统的过程中,作者提出了智能数据库的概念,使用了过程型和人工神经元网络方法相结合的知识表达方式,并利用神经元网络的再学习机制实现了斗自动知识获取,该系统具有三种工作模式和友好的人机界面,使系统的智能水平比较高并有实用价值,现已在IBM-PC/XT和386机上运行。更多还原

【Abstract】 An expert system of model reduction techniques (ESOMRT) based on artificial neural networks is studied and implemented. This expert ststem is appropriate for both expert users and non-expert users. It can choose proper reduction method based on specific high order model of continuous-time or discrete-time control systems and reduction requirements. It can also evaluate the reduction quality in time domain and/or frequency domain. We have tried to use the knowledge representation of combining procedural method with artificial neural networks(ANN) method and realize semi-automatic knowledge acquisition by using the relearning mechanism of ANN. There are three kinds of working patterns in this system and friendly man-machine interactive interface, its intelligence level is comparatively high and it has a certain practical value. The expert system runs on IBM-PC/XT and 386 machines.更多还原