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Combined the advantage of Hopfield neural network and filled function method, a dynamic filled algorithm will be presented for constrainted global optimization of nonlinear programming. The algorithm contains two phases. The dynamic minimizing phase in which the dynamic minimizing system is used to find the local minimizer of the global optimization. And in the dynamic filled phase, a new initial...
In this paper a neural network-based approximate dynamic programming method, namely direct heuristic dynamic programming (direct HDP), is applied to power system stability control. Direct HDP makes use of learning and approximation to address nonlinear system control problems under uncertainty. The contribution of the paper includes a convergence proof of the direct HDP algorithm using an LQR framework...
For the quadratic programming problems with both equality and inequality constraints, an improved neural network is proposed based on the Lagrange function reconstructed based on the saddle point theorem of optimization theory. The proposed neural network has less neuron quantity than the traditional method with slack variables does. The stability and convergency of the proposed neural network is...
This paper proposes a new approach of human face recognition using partial least square (PLS) and covering algorithm. PLS constructs the component so that the correlation between the class variable and themselves maximized. Covering algorithm is a kind of neural network method. The approach first processes the image with wavelet transform, and then eliminates the redundant features with PLS. It reduces...
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