@inproceedings{4d53b9b53f85456d9503c914e3ba9117,
title = "Time series prediction by using negatively correlated neural networks",
abstract = "Negatively correlated neural networks (NCNNs) have been proposed to design neural network (NN) ensembles [1]. The idea of NC-NNs is to encourage different individual NNs in the ensemble to learn different parts or aspects of a training data so that the ensemble can learn the whole training data better. The cooperation and specialisation among different individual NNs are considered during the individual NN design. This provides an opportunity for different NNs to interact with each other and to specialise. In this paper, NCNNs are applied to two time series prediction problems (i.e., the Mackey-Glass differential equation and the chlorophyll-a prediction in Lake Kasumigaura). The experimental results show that NCNNs can produce NN ensembles with good generalisation ability. {\textcopyright} Springer-Verlag Berlin Heidelberg 1999.",
keywords = "Time Series Prediction, Individual Network, Neural Network Ensemble, Good Generalisation Ability, Negative Correlation Learning",
author = "Yong LIU and Xin YAO",
year = "1999",
doi = "10.1007/3-540-48873-1_43",
language = "English",
isbn = "9783540659075",
series = "Lecture Notes in Computer Science",
publisher = "Springer Berlin Heidelberg",
pages = "333--340",
editor = "Bob MCKAY and Xin YAO and NEWTON, {Charles S.} and Jong-Hwan KIM and Takeshi FURUHASHI",
booktitle = "Simulated Evolution and Learning : Second Asia-Pacific Conference on Simulated Evolution and Learning, SEAL'98, Canberra, Australia, November 24-27, 1998 Selected Papers",
note = "2nd Asia-Pacific Conference on Simulated Evolution and Learning, SEAL 1998 ; Conference date: 24-11-1998 Through 27-11-1998",
}