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Empirical Study on Generalization of NN in Respect to Network Complexity

Problem statement: Generalization ability in mapping inputs to rational outputs occupies the primary interest in most of neural networks researches. The main trends for generalization denote training modulation, pattern analysis and structural design. Generalization feature recognition enhancement of neural networks especially for feed forward structural model has limited progress. Approach: A new approach to promote the generalization ability of neural networks is presented. It is based on the strategy of incorporating classification and association behaviour of neural networks. This approach is achieved through the involvement of two networks, an auxiliary net and main. The auxiliary net with traditional architecture performs dynamic adjustment to the threshold value of the main net, which is constructed in dual-layer architecture. Results: Experiment results and analysis of the proposed multiple network technique shows effective and acceptable level of recognition although it is achieved at the price of network complexity and computation time. Conclusions: Despite all of the presented concepts and methodologies in NN literature, it could be deduced that the problem of neural networks generalization still needs much attention and efforts to enhance the performance and upgrade these artificial issues for imitating the natural intelligence of human conduct in which generalization is a trainable conduct rather than an instinct.