A SIMILARITY-BASED APPROACH TO INTERPRETATION OF SENSOR DATA USING ADAPTIVE RESONANCE THEORY

被引：20

作者：

WHITELEY, JR ^{[1
]}

DAVIS, JF ^{[1
]}

机构：

[1] OHIO STATE UNIV,DEPT CHEM ENGN,COLUMBUS,OH 43210

来源：

COMPUTERS & CHEMICAL ENGINEERING | 1994年 / 18卷 / 07期

关键词：

D O I：

10.1016/0098-1354(94)85003-8

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A machine methodology for generating qualitative interpretations (QIs) of 2-D sensor patterns is described. The approach enables a computer to interpret multisensor patterns under transient conditions at a level comparable to that of an experienced plant operator. Adaptive Resonance Theory introduced by Grossberg (1976a, b) is utilized with modification to provide human-like memory attributes. The methodology offers a more robust and adaptable means to interface symbolic knowledge-based systems with numeric plant operating systems. Exemplar-based, supervised learning is utilized to construct a high dimensional QI-map. During run-time, qualitative interpretations are generated for input patterns based on their location on this QI-map. Demonstration results for a dynamically simulated chemical process are presented.

引用

页码：637 / 661

页数：25

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