Report Number: CS-TR-95-1550
Institution: Stanford University, Department of Computer Science
Title: Theory and Design of a Hybrid Pattern Recognition System
Author: Drakopoulos, John A.
Date: May 1995
Abstract: 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. Furthermore, 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 networks 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.
The membership functions of the fuzzy sets that represent
pattern classes are modeled in three different ways. Simple
sigmoidal configurations are used for simple patterns, a
structural pattern recognition method is used for dynamic
patterns, and multi-sigmoidal neural networks are used for
pattern classes for which is difficult to obtain a formal
Although efficiency is a very important consideration in
tFPR, the main issues are knowledge acquisition and knowledge
representation (in terms of pattern class descriptions).