An efficient adaptive fuzzy neural network (EAFNN) approach for short term load forecasting

Authors:
Juan Du;Meng Joo Er;Leszek Rutkowski
Affiliations:
School of EEE, Nanyang Technological University, Singapore;School of EEE, Nanyang Technological University, Singapore;Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland and Academy of Management, SWSPiZ, Institute of Information Technology, Lodz, Poland
Venue:
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Year:
2010

Citing 2
Cited 0

Second Order Derivatives for Network Pruning: Optimal Brain Surgeon

Advances in Neural Information Processing Systems 5, [NIPS Conference]
Fuzzy neural networks for classification and detection of anomalies

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, an Efficient Adaptive Fuzzy Neural Network (EAFNN) model is proposed for electric load forecasting. The proposed approach is based on an ellipsoidal basis function (EBF) neural network, which is functionally equivalent to the TSK model-based fuzzy system. EAFNN uses the combined pruning algorithm where both Error Reduction Ratio (ERR) method and a modified Optimal Brain Surgeon (OBS) technology are used to remove the unneeded hidden units. It can not only reduce the complexity of the network but also accelerate the learning speed. The proposed EAFNN method is tested on the actual electrical load data from well-known EUNITE competition data. Results show the proposed approach provides the superior forecasting accuracy when applying in the real data.