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Neural network technology has been a curiosity since the early days of computing. Research in the area went into a near dormant state for a number of years, but recently there has been a new increased interest in the subject. This has been due to a number of factors: interest in the military, apparent ease of implementation, and the ability of the technology to develop computers which are able to learn from experience.
This report summarizes the topic, providing the reader with an overview of the field and its potential direction. Included is an introduction to the technology and its future directions, as well as a set of examples of possible applications and potential implementation technologies.
Overview. Introduction. Neurobiology. Mathematical theory. Theoretical foundations of neural networks. A biological inspiration. Artificial neurons. How a neural network learns. Layering of neural networks. Neural networks and other information processing approaches. Overview of brain physiology. Recent results in neurobiology. Map representations. Self–organisation. Network circuitry. Network dynamics. Learning. Attention. Organizing principles. Tasks neural networks perform and representative models. Tasks that networks perform. Illustrative neural network models. Available implementation tools. A simulated world. Typical applications: the Kanji character recogniser, multi–layer. Vowel classifier. Detailed approaches: single and multi layer perceptrons. Recurrent networks. Global issues. Neural networks and other disciplines. Toward a theory of neural networks. Technologies and tools for implementing neural networks. Issues in neural network research. References.
- No. of pages:
- © Elsevier Science 1991
- 18th October 1991
- Elsevier Science
- eBook ISBN:
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