Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Development of adaptive p-step RBF network model with recursive orthogonal least squares training

Gu, L, Tok, DKS and Yu, DL (2016) Development of adaptive p-step RBF network model with recursive orthogonal least squares training. Neural Computing and Applications, 29 (5). pp. 1445-1454. ISSN 0941-0643

Development of Adaptive p-Step RBF Network Model.pdf - Accepted Version

Download (777kB) | Preview


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.

Item Type: Article
Additional Information: This is a post-peer-review, pre-copyedit version of an article published in Neural Computing and Applications . The final authenticated version is available online at: http://dx.doi.org/10.1007/s00521-016-2669-x
Uncontrolled Keywords: 0801 Artificial Intelligence And Image Processing, 1702 Cognitive Science
Subjects: T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions: Electronics & Electrical Engineering (merged with Engineering 10 Aug 20)
Publisher: Springer
Related URLs:
Date Deposited: 20 May 2019 10:42
Last Modified: 04 Sep 2021 09:57
DOI or ID number: 10.1007/s00521-016-2669-x
URI: https://researchonline.ljmu.ac.uk/id/eprint/9611
View Item View Item