Abstract
In the previous chapters, we developed our theory assuming a digital model of a neuron. This is useful as it allowed us to solve the test generation problem for digital circuits. In later chapters, we will research several applications of the discrete models. However, the real neurons are analog elements and we must examine how well the present solution will work if actual neural network hardware was available. In this chapter, we present a solution to the test generation problem using a neurocomputer that contains special hardware to perform energy minimization for analog neural networks [4]. We also wrote a computer program on a SUN 3/50 workstation to simulate the network. Both results are in agreement and demonstrate the feasibility of our approach.
“The original question, ’Can machines think?’ ,I believe to be too meaningless to deserve discussion. Nevertheless I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will beable to speak of machines thinking without expecting to be contradicted.” — A.M. Turing in “Computing Machinery and intelligence”,Mind, Vol. LIX, No. 236 (1950).
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Chakradhar, S.T., Agrawal, V.D., Bushneil, M.L. (1991). Neural Computers. In: Neural Models and Algorithms for Digital Testing. The Springer International Series in Engineering and Computer Science, vol 140. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-3958-2_8
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DOI: https://doi.org/10.1007/978-1-4615-3958-2_8
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