基于结构的拟神经网络建模与优化方法研究　  

RESEARCH ON MODELING & OPTIMIZING METHOD EMPLOYING LIKE-NEURAL NETWORKS BASED ON SYSTEM ARCHITECTURE

作　　者：乔俊伟[1] 詹永麒[1] 施光林[1] 

机构地区：[1]上海交通大学机械工程学院,上海200030

出　　处：《机械工程学报》2002年第1期71-74,共4页Journal of Mechanical Engineering

摘　　要：提出一种基于系统结构的拟神经网络建模与优化原理及拓扑结构。与传统的人工神经网络建模方法不同，该方法在建立系统模型时充分利用所研究对象的已有知识，使所建网络模型的权值具有明确的物理意义。通过对其网络权值的调整，同时实现了非线性系统的结构参数优化。以直动式溢流阀为例，验证了该方法的可行性和有效性，为非线性系统建模和优化提供了一个新的方法。A novel principium and topology of modeling and optimizing is introduced, which employs like-neural networks based on system architecture. Known from traditional artificial neural networks modeling, existing knowledge about the investigated object is taken into account fully in the novel method, hence physical meanings can be found from the weights of the network. In the training phase, optimized architecture parameters of non-linear system can be obtained by adjusting the weights of networks. The feasibility and validity of the method is illustrated by modeling and optimization of direct-acting relief valve. A new method is provided for non-linear systems modeling and optimizing.

关 键 词：拟神经网络 建模 优化 拓扑结构 溢流阀 

分 类 号：TH137.521[机械工程—机械制造及自动化] TP183[自动化与计算机技术—控制理论与控制工程]