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• W1986898129 abstract "In this paper, we provide some background material on artificial neural networks (ANN’s) and show how a modification of the Hopfleld type network structure can be used to solve the minimum-variance deconvolution (MVD) problem. One central issue involved ln the development and the specification of ANN focuses on the stability and the convergence of the entire network. Using the fact that an MVD result is the optimal solution of some quadratic objective function, the global Lyapunov stability function of ANN is used for the automatic mapping from a specific algorithm into the neural network configuration. Both the functional and structural equivalences are considered in the mapping. We have demonstrated that such mapping strategy guarantees the convergence. In our work, the uniqueness and existence of the solution are also investigated, Examples of the synthetic data are given as the validation of the theory." @default.
• W1986898129 created "2016-06-24" @default.
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• W1986898129 date "1988-01-01" @default.
• W1986898129 modified "2023-09-23" @default.
• W1986898129 title "Minimum‐variance deconvolution using artificial neural networks" @default.
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• W1986898129 doi "https://doi.org/10.1190/1.1892433" @default.
• W1986898129 hasPublicationYear "1988" @default.
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