A new modified hybrid learning algorithm for feedforward neural networks

Authors:
Fei Han;Deshuang Huang;Yiuming Cheung;Guangbin Huang
Affiliations:
Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China;Department of Computer Science, Hong Kong Baptist University, Hong Kong, China;School of Electrical and Electronic Engineering, Nanyang Technological University
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

Citing 5
Cited 2

Multilayer feedforward networks are universal approximators

Neural Networks
On the approximate realization of continuous mappings by neural networks

Neural Networks
Merging back-propagation and Hebbian learning rules for robust classifications

Neural Networks
A neural root finder of polynomials based on root moments

Neural Computation
A constructive approach for finding arbitrary roots of polynomials by neural networks

IEEE Transactions on Neural Networks

A constrained approximation algorithm by encoding second-order derivative information into feedforward neural networks

ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
A new learning algorithm for function approximation incorporating a priori information into extreme learning machine

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a new modified hybrid learning algorithm for feedforward neural networks is proposed to obtain better generalization performance. For the sake of penalizing both the input-to-output mapping sensitivity and the high frequency components in training data, the first additional cost term and the second one are selected based on the first-order derivatives of the neural activation at the hidden layers and the second-order derivatives of the neural activation at the output layer, respectively. Finally, theoretical justifications and simulation results are given to verify the efficiency and effectiveness of our proposed learning algorithm.