Interior-point competitive learning of control agents in colony-style systems
Michael D. Lemmon, Peter T. Szymanski, Christopher J. Bett · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
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.