Volume 8, Issue Supplement 1, December 2015, Pages 3 - 14
Adaptive Input Selection and Evolving Neural Fuzzy Networks Modeling
Alisson Marques Silva, Walmir Caminhas, Andre Lemos, Fernando Gomide
Alisson Marques Silva
Received 11 February 2015, Accepted 26 October 2015, Available Online 1 December 2015.
- https://doi.org/10.1080/18756891.2015.1129574How to use a DOI?
- Evolving Neural Fuzzy Network, Input Selection, Neo-Fuzzy Neuron, Adaptive Modeling, Prediction, Nonlinear System Identification
- This paper suggests an evolving approach to develop neural fuzzy networks for system modeling. The approach uses an incremental learning procedure to simultaneously select the model inputs, to choose the neural network structure, and to update the network weights. models with larger and smaller number of input variables than the model are constructed and tested concurrently. The procedure employs a statistical test in each learning step to choose the best model amongst the and models. Membership functions can be added or deleted to adjust input space granulation and the neural network structure. Granulation and structure adaptation depend of the modeling error. The weights of the neural networks are updated using a gradient-descent algorithm with optimal learning rate. Prediction and nonlinear system identification examples illustrate the usefulness of the approach. Comparisons with state of the art evolving fuzzy modeling alternatives are performed to evaluate performance from the point of view of modeling error. Simulation results show that the evolving adaptive input selection modeling neural network approach achieves as high as, or higher performance than the remaining evolving modeling methods.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Alisson Marques Silva AU - Walmir Caminhas AU - Andre Lemos AU - Fernando Gomide PY - 2015 DA - 2015/12 TI - Adaptive Input Selection and Evolving Neural Fuzzy Networks Modeling JO - International Journal of Computational Intelligence Systems SP - 3 EP - 14 VL - 8 IS - Supplement 1 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2015.1129574 DO - https://doi.org/10.1080/18756891.2015.1129574 ID - Silva2015 ER -