Adaptive particle swarm optimization for CNN associative memories design

Authors:
Girolamo Fornarelli;Antonio Giaquinto
Affiliations:
Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, Via E. Orabona, 4 70125 Bari, Italy;Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, Via E. Orabona, 4 70125 Bari, Italy
Venue:
Neurocomputing
Year:
2009

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Abstract

In this paper particle swarm optimization is used to implement a synthesis procedure for cellular neural networks autoassociative memories. The use of this optimization technique allows a global search for computing the model parameters that identify designed memories, providing a synthesis procedure that takes into account the robustness of the solution. In particular, the design parameters can be modified during the convergence in order to guarantee minimum recall performances of the network in terms of robustness to noise overlapped to input patterns. Numerical results confirm the good performances of the designed networks when patterns are affected by different kinds of noise.