Improving recognition and generalization capability of back-propagation NN using a self-organized network inspired by immune algorithm (SONIA)

To improve recognition and generalization capability of back-propagation neural networks (BPNN), a hidden layer self-organization inspired by immune algorithm called SONIA, is proposed. B cell construction mechanism of immune algorithm inspires a creation of hidden units having local data recognition ability that improves recognition capability. B cell mutation mechanism inspires a creation of hidden units having diverse data representation characteristics that improves generalization capability. Experiments on a sinusoidal benchmark problem show that the approximation error of the proposed network is 1/17 times lower than that of BP-NN. Experiments on real time–temperature-based food quality prediction data shows that the recognition capability is 18% improved comparing to that of BP-NN. The development of the world first time–temperature-based food quality prediction demonstrates the real applicability of the proposed method in the field of food industry.