Abstract
Analog neural systems that can automatically find the minimum value of the outputs of unknown analog systems, described by convex functions, are studied. When information about derivative or gradient are not used, these systems are called analog nonderivative optimizers. An electronic circuit for the analog neural nonderivative optimizer proposed by Teixeira and Żak, and its simulation with software PSPICE, is presented. With the simulation results and hardware implementation of the system, the validity of the proposed optimizer can be verified. These results are original, from the best of the authors knowledge.
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Cardim, R., Teixeira, M.C.M., Assunção, E., Oki, N., de Carvalho, A.A., Covacic, M.R. (2006). Hardware Implementation of an Analog Neural Nonderivative Optimizer. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_125
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DOI: https://doi.org/10.1007/11893295_125
Publisher Name: Springer, Berlin, Heidelberg
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