Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Control Perspectives on Numerical Algorithms And Matrix Problems (Advances in Design and Control) (Advances in Design and Control 10)
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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This paper presents a unified control Liapunov function (CLF) approach to the design of heavy ball with friction (HBF) and conjugate gradient (CG) neural networks that aim to minimize scalar nonconvex functions that have continuous first- and second-order derivatives and a unique global minimum. This approach leads naturally to the design of second-order differential equations which are the mathematical models of the corresponding implementations as neural networks. Preliminary numerical simulations indicate that, on a small suite of benchmark test problems, a continuous version of the well known conjugate gradient algorithm, designed by the proposed CLF method, has better performance than its HBF competitor.