Abstract
Finding the weights of a Polynomial Cellular Neural/Nonlinear Network performing a given task is not straightforward. Several approaches have been proposed so far, but they are often computationally expensive. Here, we prove that quadratic programming can solve this problem efficiently and effectively in the particular case of a totalistic network. Besides the theoretical treatment, we present several examples in which our method is employed successfully for any complexity index.
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Rubi-Velez, A., Gomez-Ramirez, E., Pazienza, G.E. (2009). Computing the Weights of Polynomial Cellular Neural Networks Using Quadratic Programming. In: Bayro-Corrochano, E., Eklundh, JO. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2009. Lecture Notes in Computer Science, vol 5856. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10268-4_76
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DOI: https://doi.org/10.1007/978-3-642-10268-4_76
Publisher Name: Springer, Berlin, Heidelberg
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