C++ Neural Networks and Fuzzy Logic: Preface

A Simplified Diagram of Network Layout

Figure 10.1

  simplified diagram of the neural network for an ART1 model.

Processing in ART1

The ART1 paradigm, just like the Kohonen Self−Organizing Map to be introduced in Chapter 11, performs

data clustering on input data; like inputs are clustered together into a category. As an example, you can use a

data clustering algorithm such as ART1 for Optical Character Recognition (OCR), where you try to match

different samples of a letter to its ASCII equivalent. Particular attention is made in the ART1 paradigm to

ensure that old information is not thrown away while new information is assimilated.

An input vector, when applied to an ART1 system, is first compared to existing patterns in the system. If there

is a close enough match within a specified tolerance (as indicated by a vigilance parameter), then that stored

pattern is made to resemble the input pattern further and the classification operation is complete. If the input

pattern does not resemble any of the stored patterns in the system, then a new category is created with a new

stored pattern that resembles the input pattern.