Twice-Pruning Aided WASD Neuronet of Bernoulli-Polynomial Type with Extension to Robust Classification

This paper proposes a novel multi-input Bernoulli-polynomial neuronet (MIBPN) on the basis of function approximation theory. The MIBPN is trained by a weights-and-structure-determination (WASD) algorithm with twice pruning (TP). The WASD algorithm can obtain the optimal weights and structure for the MIBPN, and overcome the weaknesses of conventional BP (back-propagation) neuronets such as slow training speed and local minima. With the TP technique, the neurons of less importance in the MIBPN are pruned for less computational complexity. Furthermore, this MIBPN can be extended to a multiple input multiple output Bernoulli-polynomial neuronet (MIMOBPN), which can be applied as an important tool for classification. Numerical experiment results show that the MIBPN has outstanding performance in data approximation and generalization. Besides, experiment results based on the real-world classification data-sets substantiate the high accuracy and strong robustness of the MIMOBPN equipped with the proposed WASD algorithm for classification. Finally, the twice-pruning aided WASD neuronet of Bernoulli-polynomial type in the forms of MIBPN and MIMOBPN is established, together with the effective extension to robust classification.