Theory and Design of a Hybrid Pattern Recognition System

摘要

Pattern recognition methods can be divided into four different categories:statistical or probabilistic structural, possibilistic or fuzzy, and neural methods. A formal analysis shows that there is a computational complexity versus representational power trade-off between probabilistic and possibilistic or fuzzy set measures, in general. Futhermore, sigmoidal theory shows that fuzzy set membership can be represented effectively by sigmoidal functions. Those results and the formalization of sigmoidal functions and subsequently multi-sigmoidal functions and neural ntworks led to the development of a hybrid pattern recognition system called tFPR. tFPR is a hybrid fuzzy, neural, and structural pattern recognition system that uses fuzzy sets to represent multi-variate pattern classes that can be either static or dynamic depending on time or some other parameter space. Given a set of input data on a pattern class specification, tFPR estimates the degree of membership of the data in the fuzzy set that corresponds to the current pattern class. The input data may be a number of time-dependent signals whose past values may influence the evaluation of the pattern class. Although efficiency is a very important consideration in tFPR, the main issues are knowledge acquistion and knowledge representation (in terms of pattern class descriptions). The fuzzy and structural components of tFPR have been implemented in Lisp, while the neural component has been implemented in C using the SNNS simulator. tFPR has been embedded in the BB1 blackboard architecture but it can also run as a stand-alone system. It is currently being applied in a system for medical monitoring.