TY - GEN
T1 - Noise robustness enhancement using fourth-order cumulants cost function
AU - Leung, C. T.
AU - Chow, T. W S
PY - 1996
Y1 - 1996
N2 - A novel robust fourth-order cumulants cost function is introduced to enhance the fitting to underlying function in small data sets with high noise level of Gaussian noise. The neural network learns based on gradient descent optimization method by introducing a constraint term in the cost function. The proposed cost function was applied to benchmark sunspot series prediction and nonlinear system identification. Excellent results are obtained. The neural network can provide lower training error and excellent generalization property. Our proposed cost function enables the network to provide, at most, 73% reduction of normalized test error in the benchmark test.
AB - A novel robust fourth-order cumulants cost function is introduced to enhance the fitting to underlying function in small data sets with high noise level of Gaussian noise. The neural network learns based on gradient descent optimization method by introducing a constraint term in the cost function. The proposed cost function was applied to benchmark sunspot series prediction and nonlinear system identification. Excellent results are obtained. The neural network can provide lower training error and excellent generalization property. Our proposed cost function enables the network to provide, at most, 73% reduction of normalized test error in the benchmark test.
UR - http://www.scopus.com/inward/record.url?scp=0029768208&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0029768208&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
VL - 4
SP - 1918
EP - 1923
BT - IEEE International Conference on Neural Networks - Conference Proceedings
PB - IEEE
T2 - Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)
Y2 - 3 June 1996 through 6 June 1996
ER -