Junaid M. Khan

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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(More)
Due to the inherent nonlinear nature of real world systems, one of the most popular ways to deal with nonlinear systems is to find a feedback linearizing input and then deal with the system by using the rich literature on linear control methods. As an alternative, the nonlinear terms in the system model can be piecewise linearized and then the system can be(More)
The main difficulty for standard continuous piecewise linear neural networks (SCPLNN) approximation is how to partition the definitional domain into several simplices, which is called a triangulation. In this paper, we firstly propose a method of triangulation to perform SCPLNN approximation. Our scheme starts with an initial, coarse triangulation of the(More)
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