In this paper we apply normalized radial basis function networks to function learning and in nonparametric classification. A simple parameter learning technique is proposed and convergence and the rates of convergence of the empirically trained networks are studied theoretically and in computer experiments.

in this paper, an effective and simple numerical method is proposed for solving systems of integral equations using radial basis functions (rbfs). we present an algorithm based on interpolation by radial basis functions including multiquadratics (mqs), using legendre-gauss-lobatto nodes and weights. also a theorem is proved for convergence of the algorithm. some numerical examples are presented...

A connectionist model made up of a combination of RBF networks is proposed the model decomposes multivalued dependencies into local single valued functions theory and applications are presented

On page 8 of the paper (page 3209 of the volume), in the seventh line of the second-to-last paragraph of section 5.

A necessary condition for monitoring and control of a Power System (PS) is possessing a credible model of this system. The PS model for a need of dispatchers in national control centre is created in real time. An important element of such a model is a topology model. PS Topology Verification (PSTV) is an important problem in PS engineering. Often this problem is solved together with PS state es...