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Title: Configuration of continuous piecewise-linear neural networks. Author: Wang S, Huang X, Junaid KM. Journal: IEEE Trans Neural Netw; 2008 Aug; 19(8):1431-45. PubMed ID: 18701372. Abstract: The problem of constructing a general continuous piecewise-linear neural network is considered in this paper. It is shown that every projection domain of an arbitrary continuous piecewise-linear function can be partitioned into convex polyhedra by using difference functions of its local linear functions. Based on these convex polyhedra, a group of continuous piecewise-linear basis functions are formulated. It is proven that a linear combination of these basis functions plus a constant, which we call a standard continuous piecewise-linear neural network, can represent all continuous piecewise-linear functions. In addition, the proposed standard continuous piecewise-linear neural network is applied to solve some function approximation problems. A number of numerical experiments are presented to illustrate that the standard continuous piecewise-linear neural network can be a promising tool for function approximation.[Abstract] [Full Text] [Related] [New Search]