Extended B-polynomial neural network for time-delayed system modeling using sampled data
| Autor: | Prabin Kumar Padhy, Sudeep Sharma |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
0209 industrial biotechnology 020901 industrial engineering & automation Time delayed Artificial Intelligence Computer science 0202 electrical engineering electronic engineering information engineering General Engineering 020201 artificial intelligence & image processing 02 engineering and technology Polynomial neural network Systems modeling Algorithm |
| Zdroj: | Journal of Intelligent & Fuzzy Systems. 41:3277-3288 |
| ISSN: | 1875-8967 1064-1246 |
| DOI: | 10.3233/jifs-210580 |
| Popis: | The combination of machine learning and artificial intelligent has already proved its potential in achieving remarkable results for modeling unknown systems. These techniques commonly use enough data samples to train and optimize their architectures. In the present era, with the availability of enough storage and computation power, the machine learning based data-driven system modeling approaches are getting popular as they do not interrupt the normal system operations and work solely on collected data. This work proposes a data-driven parametric neural network technique for modeling time-delayed systems, which is demanding but challenging area of research and comes under nonlinear optimization problem. The key contribution of this work is the inclusion of an extended B-polynomial into the network structure for estimating time-delayed first and second order system models. These type of models extensively used for addressing simulations, predictions, controlling and monitoring related issues. Also, an adaptive learning based convergence of the proposed algorithm is proved with the help of the Lyapunov stability theory. The proposed algorithm compared with existing techniques on some well-known example problems. A real practical system plant is also included for validating the proposed concept. |
| Databáze: | OpenAIRE |
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