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According to the basic optimization principle of artificial neural networks, a novel kind of neural network model for solving the quadratic programming problem is presented. The methodology is based on the Lagrange multiplier theory in optimization and seeks to provide solutions satisfying the necessary conditions of optimality. The equilibrium point of the network satisfies the Kuhn-Tucker condition for the problem. The stability and convergency of the neural network is investigated and the strategy of the neural optimization is discussed. The feasibility of the neural network method is verified with computation examples. Results of the simulation of the neural network to solve optimum problems are presented to illustrate the computational power of the neural network method.

In this paper, the problem of edge preserving smoothing in image processing is tackled by combining a noise corruption model and a region and edge image model. The derivation of the probability model for the first order difference in the gray levels of the region pixels and edge pixels leads to a non-linear filter with coefficients as functions of the estimated noise variance and edge intensity. Such a model-based approach allows the design of improved filters for noise filtering and image compression. Experimental results demonstrate the improved performance of the filter for both synthetic and natural images.