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This paper proposes the concept of function correlation, which takes the concept of linear correlation and concept of non-linear correlation together with a unified. On this basis, a BP neural network model was constructed to approximate the correlation function. Experimental results show that the correlation function determined by BP neural network can be a higher correlation coefficient.
The intent of this study is to provide an initial exploration of the metamodeling capabilities of two methods, i.e. neural network (NN) and Kriging approximation, in the context of simulation optimization. A total of four performance measures are adopted, and they describe different kinds of metamodel performance, such as ability to provide good starting points for gradient-based search, accuracy...
A multilayer perceptron (MLP) based detector is proposed for approximating the average likelihood ratio (ALR) detector in composite hypothesis-testing problems. The case of detecting colored gaussian targets with Gaussian autocorrelation function (ACF) and unknown one-lag correlation coefficient (rhos), in colored Gaussian interference with Gaussian ACF is considered. The robustness of the likelihood...
In this paper, we proposed a method which incorporated multi-scale analysis into neural nets to solve the problem that fractal coding allows fast decoding but suffers from long encoding times. This method can reduce the computational load of fractal image coding significantly though efficient classification of image improve speed of image scan. Furthermore this paper also incorporates gray relational...
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