Neural network fundamentals with graphs, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications
Advanced Engineering Mathematics: Maple Computer Guide
Advanced Engineering Mathematics: Maple Computer Guide
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Interactive Pattern Recognition
Interactive Pattern Recognition
Hybridization of gradient descent algorithms with dynamic tunnelingmethods for global optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Dynamic tunneling technique for efficient training of multilayer perceptrons
IEEE Transactions on Neural Networks
On Generalization and K-Fold Cross Validation Performance of MLP Trained with EBPDT
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
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This paper presents a new regularization method based on dynamic tunneling for enhancing generalization capability of multilayered neural networks. The proposed method enables escape through undesired sub-optimal solutions on the composite error surface by means of dynamic tunneling. Undesired sub-optimal solutions may be increased or introduced from regularized objective function. Hence, the proposed method is capable of enhancing the regularization property without getting stuck at sub-optimal values in search space. The regularization property and escape from the sub-optimal values have been demonstrated through computer simulations on two examples.