Interior-point competitive learning of control agents in colony-style systems

Michael D. Lemmon; Peter T. Szymanski; Chris Bett

doi:10.1117/12.205148

6 April 1995 Interior-point competitive learning of control agents in colony-style systems

Michael D. Lemmon, Peter T. Szymanski, Chris Bett

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205148
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

This paper presents an alternating minimization (AM) algorithm used in training radial basis function (RBF) networks. The AM algorithm can also be viewed as a competitive learning paradigm. Its use is illustrated by optimizing a colony-style control system. The application arises in the context of hybrid control systems. The algorithm is a modification of a small-step interior point method used in solving primal linear programs. The algorithm has a convergence rate of 0((root)nL) iterations where n is a measure of the network size and L is a measure of the resulting solution's accuracy.

Citation Download Citation

Michael D. Lemmon, Peter T. Szymanski, and Chris Bett "Interior-point competitive learning of control agents in colony-style systems", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205148

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