Optimizing a Higher Order Neural Network Through Teaching Learning Based Optimization Algorithm

Higher order neural networks pay more attention due to greater computational capabilities with good learning and storage capacity than the existing traditional neural networks. In this work, a novel attempt has been made for effective optimization of the performance of a higher order neural network (in particular Pi-Sigma neural network) for classification purpose. A newly developed population based teaching learning based optimization algorithm has been used for efficient training of the neural network. The performance of the model has been benchmarked against some well recognized optimized models and they have tested by five well recognized real world bench mark datasets. The simulating results demonstrated favorable classification accuracies towards the proposed model as compared to others. Also from the statistical test, the results of the proposed model are quite interesting than others, which analyzes for fast training with stable and reliable results.