Pattern Recognition and Feedback via Parallel Distributed Computation

本项目的目的是确定将并行分布式处理(PDP)方法应用于非线性化工过程的在线反馈控制的可行性。PDP理论认为，认知中的许多复杂过程可以由一大组具有复杂相互作用的单独简单的相互联系的单元来表示。并行是指网络中的单元同时相互兴奋和抑制，而分布表示知识是由分布在整个网络中的单元对之间的连接强度组成的。这种方法是在进行过程中“学习”的，也就是说，它是一种模仿人类情报收集的技术。自适应控制器的工作原理是随着过程的变化而改变。这对于线性系统是可行的，但对于非线性系统要复杂得多。在这个研究项目中，PDP网络模型将用于开发灵敏度和局部线性化，可用于非线性化工过程的控制器设计。PI计划对在实验室甲醇精馏塔上开发的算法进行实验验证。这将允许与自校正和其他自适应控制器进行比较。

The purpose of this project is to determine the feasibility of applying the Parallel Distributed Processing (PDP) approach to on-line feedback control of nonlinear chemical processes. PDP theory assumes that many complex processes in cognition can be represented by a large set of individually simple interconnected units with complex interactions. Parallel refers to the fact that units excite and inhibit each other simultaneously in the network while distributed indicates that knowledge consists of connection strengths between pairs of units that are distributed throughout the network. This approach "learns" as it goes along, i.e. it is a technique that mimics human intelligence gathering. Adaptive controllers work by changing in response to process changes. This is feasible for linear systems but much more complex for nonlinear systems. In this research project, the PDP network model will be used for the development of sensitivities and local linearizations that can be used for controller design for nonlinear chemical processes. The PI plans experimental verification of the algorithms developed on a laboratory methanol distillation column. This will permit comparisons with self-turning and other adaptive controllers.