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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Silva, A.M. Matos Caminhas, W. Paim Lemos, A. Gomide, F. |
| Copyright Year | 2013 |
| Description | Author affiliation: Sch. of Electr. & Comput. Eng., Univ. of Campinas (UNICAMP), Campinas, Brazil (Gomide, F.) || Fed. Center of Technol. Educ. of Minas Gerais, CEFET-MG, Divinopolis, Brazil (Silva, A.M.) || Grad. Program in Electr. Eng., Fed. Univ. of Minas Gerais, Belo Horizonte, Brazil (Matos Caminhas, W.; Paim Lemos, A.) |
| Abstract | This paper suggests an approach to develop a class of evolving neural fuzzy networks with adaptive feature selection. The approach uses the neo-fuzzy neuron structure in conjunction with an incremental learning scheme that, simultaneously, selects the input variables, evolves the network structure, and updates the neural network weights. The mechanism of the adaptive feature selection uses statistical tests and information about the current model performance to decide if a new variable should be added, or if an existing variable should be excluded or kept as an input. The network structure evolves by adding or deleting membership functions and adapting its parameters depending of the input data and modeling error. The performance of the evolving neural fuzzy network with adaptive feature selection is evaluated considering instances of times series forecasting problems. Computational experiments and comparisons show that the proposed approach is competitive and achieves higher or as high performance as alternatives reported in the literature. |
| Starting Page | 341 |
| Ending Page | 349 |
| File Size | 718571 |
| Page Count | 9 |
| File Format | |
| ISBN | 9781479931941 |
| DOI | 10.1109/BRICS-CCI-CBIC.2013.64 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-09-08 |
| Publisher Place | Brazil |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Adaptation models Adaptive systems Input variables Computational modeling Adaptive Modeling Complexity theory Evolving Neural Fuzzy System Forecasting Feature Selection Neural networks Data models Non-stationary Systems Neo-Fuzzy Neuron |
| Content Type | Text |
| Resource Type | Article |
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