Computer Implementation and Simulation of Some Neural Networks Used in Pattern Recognition and Classification
Abstract
Searchers and scientists have been studying neural networks for many years hoping to achieve human-like performance in the fields of speech and pattern recognition and classification. This study will first make an introduction to the field of artificial neural networks, then describe some of the neural nets used in the pattern recognition and classification. A computer simulation program from an algorithmic approach for each one of these networks will be constructed and used to implement the operation of the net. Its ability will be demonstrated in differentiating between different patterns and even correcting a noisy pattern and recognizing it. The Hopfield network, the Hamming network and the Carpenter/Grossberg network will be individually utilized in developing an algorithm for pattern recognition and classification. The maximum- likelihood sequence estimation function will be mapped onto a neural network structure. The application of this structure computations for data detection in digital communications receivers will be described. A computer simulation program will be constructed and used to show that neural networks offer attractive implementation alternatives for MLSE. Keywords: Neural networks; Hopfield net; Hamming net; Carpenter/Grossberg net; Pattern recognition.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 01, 1989
- Accession Number
- ADA208112
Entities
People
- Mohamed H. Khaidar
Organizations
- Naval Postgraduate School