Abstract
This paper presents an architecture that has been developed to implement neural networks for control and signal processing applications. This architecture offers a single chip solution that can be used standalone in small and medium sized systems, or operate as a preprocessor in larger applications.
The constraints imposed on this architecture are discussed, and the methods used to satisfy these are presented. The design of a fully integrated neural processor using this architecture is proposed, and the application development tools are discussed.
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© 1993 Springer-Verlag Berlin Heidelberg
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Palmer, R.P., Rounce, P.A. (1993). An architecture for implementing control and signal processing neural networks. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_224
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DOI: https://doi.org/10.1007/3-540-56798-4_224
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