ABSTRACT
Neural networks have emerged as a field of study within AI and engineering via the collaborative efforts of engineers, physicists, mathematicians, computer scientists, and neuroscientists. Although the strands of research are many, there is a basic underlying focus on pattern recognition and pattern generation, embedded within an overall focus on network architectures. Many neural network methods can be viewed as generalizations of classical pattern-oriented techniques in statistics and the engineering areas of signal processing, system identification, optimization, and control theory. There are also ties to parallel processing, VLSI design, and numerical analysis.
A neural network is first and foremost a graph, with patterns represented in terms of numerical values attached to the nodes of the graph and transformations between patterns achieved via simple message-passing algorithms. Certain of the nodes in the graph are generally distinguished as being input nodes or output nodes, and the graph as a whole can be viewed as a representation of a multivariate function linking inputs to outputs. Numerical values (weights) are attached to the links of the graph, parameterizing the input/output function and allowing it to be adjusted via a learning algorithm.
TABLE OF CONTENTS
TITLE PAGE
CERTIFICATION PAGE
DEDICATION
ACKNOWLEDGEMENTS
ABSTRACT
TABLE OF CONTENTS
CHAPTER ONE
1.0INTRODUCTION
1.1STATEMENT OF PROBLEM
1.2PURPOSE OF THE STUDY
1.3IMPORTANCE OF THE STUDY
1.4DEFINITION OF TERMS
1.5ASSUMPTION OF THE STUDY
CHAPTER TWO
2.0LITERATURE REVIEW
2.1THE BRAIN NEURAL NETWORKS AND THE COMPUTER
CHAPTER THREE
3.0APPLICATIONS OF NATURAL AND OF ARTIFICIAL NEURAL NETWORKS
3.2TYPES OF MODELS OF NEUROLOGY
CHAPTER FOUR
4.0ARCHITECTURE OF NEUROLOGY
CHAPTER FIVE: CONCLUSION
5.1 LIMITATIONS OF THE STUDY
5.2 SUGGESTIONS FOR FURTHER RESEARCH
REFERENCES
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