Journal
NEUROCOMPUTING
Volume 81, Issue -, Pages 108-112Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2011.12.002
Keywords
Gradient descent; Load forecasting; Multi-Agent System; Online Sequential Extreme Learning; Machine; Weighted average
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Funding
- Ministry of Higher Education (MOHE) of Malaysia [01101026-FRGS]
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In the previous research, a Multi-Agent System based on Online Sequential Extreme Learning Machine (OSELM) neural network and Bayesian Formalism (MAS-OSELM-BF) has been introduced for solving pattern classification problems. However this model is incapable of handling regression tasks. In this article, a new OSELM-based multi-agent system with weighted average strategy (MAS-OSELM-WA) is introduced for solving data regression tasks. A MAS-OSELM-WA consists of several individual OSELM (individual agent) and the final decision (parent agent). The outputs of the individual agents are sent to the parent agent for a final decision whereby the coefficients of parent agent are computed by a gradient descent method. The effectiveness of the MAS-OSELM-WA is evaluated by an electrical load forecasting problem in Malaysia for a month with consequent national holidays (i.e., during the month of Hari Raya-Malay New Year of Malaysia). The results demonstrated that the MAS-OSELM-WA is able to produce good performance as compared with the other approaches. (C) 2011 Elsevier B.V. All rights reserved.
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