Generalization Aspect of Neural Networks on Upgrading Assimilation Structure into Accommodating Scheme

Abstract

Problem statement: Generalization feature enhancement of neural networks, especially feed forward structural model has limited progress. The major reason behind such limitation is attributed to the principal definition and the inability to interpret it into convenient structure. Traditional schemes, unfortunately have regarded generalization as an innate outcome of the simple association, referred to by Pavlov and had been modeled by piaget as the basis of assimilating conduct. Approach: A new generalization approach based on the addition of a supportive layer to the traditional neural network scheme (atomic scheme) was presented. This approach extended the signal propagation of the whole net in order to generate the output in two modes, one deals with the required output of trained patterns with predefined settings, while the other tolerates output generation dynamically with tuning capability for any newly applied input. Results: Experiments and analysis showed that the new approach is not only simpler and easier, but also is very effective as the proportions promoting the generalization ability of neural networks have reached over 90% for some cases. Conclusion: Expanding neuron as the generalization essential construction denoted the accommodating capabilities involving all the innate structures in conjugation with Intelligence abilities and with the needs of further advanced learning phases. Cogent results were attained in comparison with that of the traditional schemes.