Optimal piecewise locally linear modeling

Chris J. Harris; Xia Hong; M. Feng

doi:10.1117/12.342906

22 March 1999 Optimal piecewise locally linear modeling

Chris J. Harris, Xia Hong, M. Feng

Proceedings Volume 3722, Applications and Science of Computational Intelligence II; (1999) https://doi.org/10.1117/12.342906
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

Associative memory networks such as Radial Basis Functions, Neurofuzzy and Fuzzy Logic used for modelling nonlinear processes suffer from the curse of dimensionality (COD), in that as the input dimension increases the parameterization, computation cost, training data requirements, etc. increase exponentially. Here a new algorithm is introduced for the construction of a Delaunay input space partitioned optimal piecewise locally linear models to overcome the COD as well as generate locally linear models directly amenable to linear control and estimation algorithms. The training of the model is configured as a new mixture of experts network with a new fast decision rule derived using convex set theory. A very fast simulated reannealing (VFSR) algorithm is utilized to search a global optimal solution of the Delaunay input space partition. A benchmark non-linear time series is used to demonstrate the new approach.

Citation Download Citation

Chris J. Harris, Xia Hong, and M. Feng "Optimal piecewise locally linear modeling", Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); https://doi.org/10.1117/12.342906

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