A mixture of experts network structure construction algorithm for modelling and control

Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298 and Harris, C. J. (2002) A mixture of experts network structure construction algorithm for modelling and control. Applied Intelligence, 16 (1). pp. 59-69. ISSN 1573-7497 doi: 10.1023/A:1012869427428

Abstract/Summary

This paper introduces a new fast, effective and practical model structure construction algorithm for a mixture of experts network system utilising only process data. The algorithm is based on a novel forward constrained regression procedure. Given a full set of the experts as potential model bases, the structure construction algorithm, formed on the forward constrained regression procedure, selects the most significant model base one by one so as to minimise the overall system approximation error at each iteration, while the gate parameters in the mixture of experts network system are accordingly adjusted so as to satisfy the convex constraints required in the derivation of the forward constrained regression procedure. The procedure continues until a proper system model is constructed that utilises some or all of the experts. A pruning algorithm of the consequent mixture of experts network system is also derived to generate an overall parsimonious construction algorithm. Numerical examples are provided to demonstrate the effectiveness of the new algorithms. The mixture of experts network framework can be applied to a wide variety of applications ranging from multiple model controller synthesis to multi-sensor data fusion.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/18499
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords	mixtures of experts, model selection, structure identification, forward constrained regression
Publisher	Springer
Download/View statistics	View download statistics for this item