Cellular Neural Networks-based Emulation of Analog Filters: Theory and Design Principle

摘要

We develop and validate through some illustrative examples an efficient concept based on cellular neural networks for the synthesis of analog filters. Several types of filters (i.e. Low-pass, High-pass, Band-pass, and Band-stop) are envisaged each of which is emulated in the form of the CNN mathematical model. The resulting coefficients of the CNN mathematical model (called CNN templates) are derived for each type of filters. Correspondences are established between the parameters of the CNN- mathematical model and the physical components (i.e. resistors, capacitors, inductances, op-amps, etc.) of the filters. The key contribution of this work is to demonstrate the potential of using cellular neural networks (CNN) as a universal concept for analog filters modelling and simulation. The main motivation of developing the CNN- concept for the emulation of analog filters is justified by its excellent features (e.g. high flexibility, good stability, and high accuracy). Further, the CNN-concept developed is a framework which can be efficiently used for a straightforward conversion of analog filters into digital filters.