1 February 1994 Behavioral circuit modeling using neural networks
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Proceedings Volume 2093, Substance Identification Analytics; (1994) https://doi.org/10.1117/12.172495
Event: Substance Identification Technologies, 1993, Innsbruck, Austria
Abstract
A technique is presented for automatic generation of Analog Behavioral circuit models using feed-forward neural networks in static and dynamic configurations. These models are generated, by using the data output from an accurate SPICE simulation to train a neural network to model a particular circuit function. Results are given using two types of neural networks, a static neural network to model an analog multiplier, and a recurrent neural network for modeling the dynamics of a bandlimited circuit. Simulations show that neural networks are able to learn the essential nonlinear and dynamic properties found in these circuits using the training technique described.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stephen V. Kosonocky, Stephen V. Kosonocky, } "Behavioral circuit modeling using neural networks", Proc. SPIE 2093, Substance Identification Analytics, (1 February 1994); doi: 10.1117/12.172495; https://doi.org/10.1117/12.172495
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