Development of adaptive p-step RBF network model with recursive orthogonal least squares training
| Autor: | Lei Gu, D. K. Tok, Dingli Yu |
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| Rok vydání: | 2016 |
| Předmět: |
Structure (mathematical logic)
T1 Computer science business.industry TK 020208 electrical & electronic engineering Internal model 02 engineering and technology Nonlinear system Model predictive control Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Radial basis function Artificial intelligence business Algorithm Software Hierarchical RBF Network model |
| Zdroj: | Neural Computing and Applications. 29:1445-1454 |
| ISSN: | 1433-3058 0941-0643 |
| DOI: | 10.1007/s00521-016-2669-x |
| Popis: | An adaptive p-step prediction model for nonlinear dynamic processes is developed in this paper and implemented with a radial basis function (RBF) network. The model can predict output for multi-step-ahead with no need for the unknown future process output. Therefore, the long-range prediction accuracy is significantly enhanced and consequently is especially useful as the internal model in a model predictive control framework. An improved network structure adaptation is also developed with the recursive orthogonal least squares algorithm. The developed model is online updated to adapt both its structure and parameters, so that a compact model structure and consequently a less computing cost are achieved with the developed adaptation algorithm applied. Two nonlinear dynamic systems are employed to evaluate the long-range prediction performance and minimum model structure and compared with an existing PSC model and a non-adaptive RBF model. The simulation results confirm the effectiveness of the developed model and superior over the existing models. |
| Databáze: | OpenAIRE |
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