Symbiotic neuron evolution of a neural-network-aided grey model for time series prediction

Shih-Hung Yang, Yon Ping Chen

研究成果: Conference contribution

摘要

This paper introduces a symbiotic neuron evolution algorithm (SNEA) to determine the topology of a neural-network-aided grey model (NNAGM) for time series prediction problem. The SNEA uses an evolutionary approach to evolve partially connected neural networks (NNs) and determine the number of hidden neurons. To achieve symbiotic evolution, SNEA first establishes a neuron population where each neuron is randomly created, and evaluates the neurons by constructing NNs with different numbers of neurons. Each neuron shares fitness from participating NNs. This algorithm then performs evolution on the neuron population by crossover and mutation based on neuron fitness. An NNAGM designed by SNEA is applied to the prediction problems and compared with other methods. The experimental results show that SNEA can produce an NNAGM with appropriate topology and higher prediction performance than other methods.

原文English
主出版物標題FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings
頁面195-201
頁數7
DOIs
出版狀態Published - 2011 九月 27
事件2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei, Taiwan
持續時間: 2011 六月 272011 六月 30

出版系列

名字IEEE International Conference on Fuzzy Systems
ISSN(列印)1098-7584

Other

Other2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011
國家Taiwan
城市Taipei
期間11-06-2711-06-30

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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