Abstract
This paper describes a silicon synapsis designed to implement Weighted Radial Basis Functions. The synapsis is based on Pulse Stream computation principles, which offer interesting performance, especially for what power dissipation and computation speed concerns. Weighted Radial Basis Functions integrate the advantages of Multi-Layer Perceptrons and Radial Basis Functions alone, therefore the silicon neural networks which results may find applications in several pattern recognition and classification tasks, especially in low power environments. Furthermore it can also be used as a method to map Fuzzy Inference Systems on silicon Artificial Neural Networks.
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© 1995 Springer-Verlag Berlin Heidelberg
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Miranda, E., Reyneri, L.M. (1995). A CPWM synapsis for Weighted Radial Basis Functions. In: Mira, J., Sandoval, F. (eds) From Natural to Artificial Neural Computation. IWANN 1995. Lecture Notes in Computer Science, vol 930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59497-3_257
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DOI: https://doi.org/10.1007/3-540-59497-3_257
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