Abstract
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 have implemented the Iterative Annealing Optimization Method on the analog 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.
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Sevgen, S., Yucel, E., Arik, S. (2009). Cellular Neural Networks Template Training System Using Iterative Annealing Optimization Technique on ACE16k Chip. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_52
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DOI: https://doi.org/10.1007/978-3-642-10677-4_52
Publisher Name: Springer, Berlin, Heidelberg
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