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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. In this study, we are first going to make an introduction to the field of artificial neural networks, then we are going to 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.

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.

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.

Thesis advisor, Tri T. Ha