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A new RBF neural network with boundary value constraints

Hong, X. and Chen, S. (2009) A new RBF neural network with boundary value constraints. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 39 (1). pp. 298-303. ISSN 1083-4419

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To link to this item DOI: 10.1109/tsmcb.2008.2005124


We present a novel topology of the radial basis function (RBF) neural network, referred to as the boundary value constraints (BVC)-RBF, which is able to automatically satisfy a set of BVC. Unlike most existing neural networks whereby the model is identified via learning from observational data only, the proposed BVC-RBF offers a generic framework by taking into account both the deterministic prior knowledge and the stochastic data in an intelligent manner. Like a conventional RBF, the proposed BVC-RBF has a linear-in-the-parameter structure, such that it is advantageous that many of the existing algorithms for linear-in-the-parameters models are directly applicable. The BVC satisfaction properties of the proposed BVC-RBF are discussed. Finally, numerical examples based on the combined D-optimality-based orthogonal least squares algorithm are utilized to illustrate the performance of the proposed BVC-RBF for completeness.

Item Type:Article
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:15272
Uncontrolled Keywords:Boundary value constraints (BVC), D-optimality, forward regression, radial basis function (RBF), system identification, ORTHOGONAL LEAST-SQUARES, OPTIMALITY EXPERIMENTAL-DESIGN, SYSTEM-IDENTIFICATION, MODEL CONSTRUCTION, REGRESSION, ALGORITHM

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