2 edition of **Dual-orthogonal radial basis function networks for nonlinear time series prediction** found in the catalog.

Dual-orthogonal radial basis function networks for nonlinear time series prediction

S. A. Billings

- 13 Want to read
- 24 Currently reading

Published
**1996**
by University of Sheffield, Dept. of Automatic Control and Systems Engineering in Sheffield
.

Written in English

**Edition Notes**

Statement | S.A. Billings and X. Hong. |

Series | Research report / University of Sheffield. Department of Automatic Control and Systems Engineering -- no.672, Research report (University of Sheffield. Department of Automatic Control and Systems Engineering) -- no.672. |

Contributions | Hong, X. |

ID Numbers | |
---|---|

Open Library | OL17430628M |

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The controller is designed by incorporating the high-gain observer and radial basis function (RBF) neural networks in vectorial backstepping method. The high-gain observer provides the estimations of the ship position and heading as well as velocities. The RBF neural networks are employed to compensate for the uncertainties of ship dynamics. Neural Networks in a Softcomputing Framework K.-L. Du and M.N.S. Swamy Neural Networks in a Softcomputing Framework With Figures K.-L. Du, PhD M.N.S. Swamy, PhD, (Eng) Centre for Signal Processing and Communications Department of Electrical and Computer Engineering Concordia University Montreal, Quebec H3G 1M8 Canada.

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A new structure of Radial Basis Function (RBF) neural network called the Dual-orthogonal RBF Network (DRBF) is introduced for nonlinear time series prediction.

The hidden nodes of a conventional RBF network compare the Euclidean distance between the network input vector and the centres, and the node responses are radially by: Dual-orthogonal Radial Basis Function Networks For Nonlinear Time Series Prediction S.

Billings and Department of Automatic Control and Systems Engineerina University of Mappin Street, Sheffield Sl 3JD Abstract A new structure of Radial Basis Function (RBF) neural network called the Dual-orthogonal RBF Network (DRBF) is introduced for.

Billings, S. and Hong, X. Dual-orthogonal radial basis function networks for nonlinear time series prediction. Neural Networks, 11(3)– CrossRef Google ScholarCited by: 4.

Dual-Orthogonal Radial Basis Function Networks for Nonlinear Time Series Prediction May Neural networks: the official journal of the International Neural Network Society X. Hong. Inferential Historical Survey of Time Series Prediction using Artificial Neural Network Marcelo C. Medeiros and Timo Teräsvirta, Building neural network models for time series.

Berthold, M. A time delay radial basis function network for phoneme recognition. In Proceedings of IEEE International Conference on Neural Networks Dual-orthogonal radial basis function networks for nonlinear Cited by: The dual-orthogonal RBF network algorithm overcomes most of these limitations for nonlinear time-series prediction.

Motivated by the linear discriminant analysis (LDA) technique, a distance metric is defined based on a classification function of the set of input vectors in order to achieve improved by: X Hong, S A Billings: "Time series multistep ahead predictability estimation and ranking", Journal of Forecasting, 18 p () S A Billings, X Hong: "Dual-orthogonal radial basis function networks for nonlinear time series prediction", Neural Networks, 11 p ().

Chen, S., Hong, X., Luk, B. and Harris, C.J. () A tunable radial basis function model for nonlinear system identification using particle swarm optimisation. In: 48th IEEE Conference on Decision and Control, held jointly with the 28th Chinese Control Conference (CDC/CCC ), Shanghai, China, pp.

Dual-orthogonal radial basis function networks for nonlinear time series prediction - S.A Billings and X Hong Type: Article | | Item not available on this server.

Bevölkerungswachstum, Binnenmigration und Waldvernichtung in Indonesien [Population growth, migration and deforestation in Indonesia] - H. Birg, J. Brüß, E.-J. Flöthmann. Billings, S. and Hong, X.

() Dual-orthogonal radial basis function networks for nonlinear time series prediction. Neural Networks, 11 (3). a journal of Scottish bibliography and book history, pp.

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“Control design and closed-loop analysis of a switched-capacitor DC-to-DC. The paper by [24] applies a radial basis functions collocation method to perform a static and free vibration analysis of cylindrical and spherical laminated panels. Although the approximation of the problem is different, the discretization procedure is quite similar to what presented here.

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