The present work utilizes the framework Clonart (Clonal Adaptive Resonance Theory) that employs many different techniques such as intelligent operators, clonal selection principle, local search, memory antibodies and ART clusterization in order to increase the performance of the algorithm. The approach uses a mechanism similar to the ART 1 network for storing a population of memory antibodies that will be responsible for the acquired knowledge of the algorithm. This characteristic allows the algorithm a self-organization of the antibodies in accordance with the complexity of the database used. A face recognition test case was applied to estimate the performance of this framework with different problem domains.
Wavelet functions have been successfully used in many problems as the activation function of feedforward neural networks [ZB92],{STK92], [PK93]. In this paper, a family of polynomial wavelets generated from powers of sigmoids is described which provides a robust way for designing neural network architectures. It is shown, through experimentation, that function members of this family can present a very good adaptation capability which make them attractive for applications of function approximation. In the experiments carried out, it is observed that only a small number of daughter wavelets is usually necessary to provide good approximation characteristics.
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