C++ Neural Networks and Fuzzy Logic: Preface

ART−EMAP and Object Recognition

A neural network architecture called ART−EMAP (Gail Carpenter and William Ross) integrates Adaptive

Resonance Theory (ART) with spatial and temporal evidence integration for predictive mapping (EMAP).

The result is a system capable of complex 3−D object recognition. A vision system that samples

two−dimensional perspectives of a three−dimensional object is created that results in 98% correct recognition

with an average of 9.2 views presented on noiseless test data, and 92% recognition with an average of 11.2

views presented on noisy test data. The ART−EMAP system is an extension of ARTMAP, which is a neural

network architecture that performs supervised learning of recognition categories and multidimensional maps

in response to input vectors. A fuzzy logic extension of ARTMAP is called Fuzzy ARTMAP, which

incorporates two fuzzy modules in the ART system.

Summary

A sampling of current research and commercial applications with neural networks and fuzzy logic technology

is presented. Neural networks are applied toward a wide variety of problems, from aiding medical diagnosis to

detecting circuit faults in printed circuit board manufacturing. Some of the problem areas where neural

networks and fuzzy logic have been successfully applied are: