Piecewise-linear modeling of analog circuits using trained feed-forward neural networks and adaptive clustering of hidden neurons

This paper presents a new technique for automatically creating analog circuit models. The method extracts piecewise linear models from trained neural networks. A model is a set of linear dependencies between circuit performances and design parameters. The paper illustrates the technique for an OTA circuit - an amplifier circuit widely used in filters and A/D converters for which models for gain and bandwidth were automatically generated. As experiments show, the obtained models have simple form that accurately fits the sampled points and the behavior of the trained neural networks. These models are useful for fast simulation of systems with non-linear behavior and performances.