Implementation of on-chip training system for cellular neural networks using iterative annealing optimisation method
Selçuk Sevgen, Sabri Arik · International Journal of Reasoning-based Intelligent Systems · 2010
Cellular neural networks proved to be a useful parallel computing system for image processing applications. Cellular neural networks (CNNs) constitute a class of recurrent and locally coupled arrays of identical cells. The connectivity among the cells is determined by a set of parameters called templates. CNN templates are the key parameters to perform a desired task. One of the challenging problems in designing templates is to find the optimal template that functions appropriately for the solution of the intended problem. In this paper, we implement the iterative annealing optimisation method (IAOM) on the analogue CNN chip to find an optimum template by training a randomly selected initial template. We have been able to show that the proposed system is efficient to find the suitable template for some specific image processing applications.