Pattern Recognition and Feedback via Parallel Distributed Computation

通过并行分布式计算进行模式识别和反馈

• 批准号: 8802268
• 负责人: Erik Ydstie
• 金额: $ 2.87万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-02-01 至 1989-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8802268&HistoricalAwards=false
• 关键词: Pattern; Recognition; Feedback; via; Parallel

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.

该项目的目的是确定 应用并行分布处理（PDP）方法， 非线性化学过程的反馈控制 PDP理论假设 许多复杂的认知过程可以用一个大的 一组相互连接的单个简单单元， 交互. 平行是指单位激发和 分布式同时在网络中相互抑制 表明知识是由知识之间的联系强度组成的 分布在整个网络中的成对单元。 这 方法“学习”，因为它沿着，即它是一种技术，模仿 收集人类情报 自适应控制器通过响应过程变化而变化来工作。 这对于线性系统是可行的，但对于 非线性系统 在本研究项目中，PDP网络模型 将用于发展敏感性和地方 线性化，可用于控制器设计的非线性 化学过程。 PI计划对开发的算法进行实验验证 实验室甲醇蒸馏塔。 这将允许 与自调整和其他自适应控制器的比较。