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Efficient algorithms for function approximation with piecewise linear sigmoidal networks

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2 Author(s)
Hush, D.R. ; Dept. of Electr. & Comput. Eng., New Mexico Univ., Albuquerque, NM, USA ; Horne, B.

This paper presents a computationally efficient algorithm for function approximation with piecewise linear sigmoidal nodes. A one hidden layer network is constructed one node at a time using the well-known method of fitting the residual. The task of fitting an individual node is accomplished using a new algorithm that searches for the best fit by solving a sequence of quadratic programming problems. This approach offers significant advantages over derivative-based search algorithms (e.g., backpropagation and its extensions). Unique characteristics of this algorithm include: finite step convergence, a simple stopping criterion, solutions that are independent of initial conditions, good scaling properties and a robust numerical implementation. Empirical results are included to illustrate these characteristics

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Neural Networks, IEEE Transactions on  (Volume:9 ,  Issue: 6 )