Abstract
This paper presents the structure and the learning algorithm of a multi-input
multi-output (MIMO) Neo-fuzzy neural network for nonlinear system modeling. The applied approach lies on the idea of Neo-fuzzy neuron whose dynamics depend on its own temporal behavior, while his output is generated as a singleton function. To demonstrate efficiency of the proposed modeling structure, a simulation experiments in Matlab environment modeling a nonlinear MIMO process dynamics are performed.
multi-output (MIMO) Neo-fuzzy neural network for nonlinear system modeling. The applied approach lies on the idea of Neo-fuzzy neuron whose dynamics depend on its own temporal behavior, while his output is generated as a singleton function. To demonstrate efficiency of the proposed modeling structure, a simulation experiments in Matlab environment modeling a nonlinear MIMO process dynamics are performed.
| Original language | English |
|---|---|
| Pages (from-to) | 65 |
| Number of pages | 70 |
| Journal | Journal of the Technical University – Sofia. Plovdiv branch, Bulgaria. Fundamental Sciences and Applications |
| Volume | 21 |
| Issue number | book 1 |
| Publication status | Published - 2015 |
| MoE publication type | A1 Journal article-refereed |
Keywords
- control systems
- optimization
- modeling
- artificial intelligence
- Neural networks