Pattern Recognition Using Finite-Iteration Cellular Systems

This work proposes to use a special kind of cellular neural networks (CNNs) which operates in finite iteration discrete-time mode and mimics the processing of visual perception in biological systems for digit recognition.

Abstract

Cellular systems are defined by cells that have an internal state and local interactions between cells that govern the dynamics of the system. We propose to use a special kind of cellular neural networks (CNNs) which operates in finite iteration discrete-time mode and mimics the processing of visual perception in biological systems for digit recognition. We propose also a solution to another type of pattern recognition problem using a non-standard cellular neural networks called molecular graph networks (MGNs) which offer direct mapping from compound to property of interest such as physico-chemical, toxicity, logP, inhibitory activity MGNs translate molecular topology to network topology. We show how to design/train by backpropagation CNNs and MGNs in their discrete-time and finite-iteration versions to perform classification on real-world data sets.