Abstract
A new neural network model is proposed for solving nonlinear optimization problems with a general form of linear constraints. Linear constraints, which may include equality, inequality and bound constraints, are considered to cover the need for engineering applications. By employing this new model in image fusion algorithm, an optimal fusion vector is exploited to enhance the quality of fused images efficiently. The stability and convergence analysis of the novel model are proved in details. The simulation examples are used to demonstrate the validity of the proposed model.
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Malek, A., Yashtini, M. (2009). A Neural Network Model for Solving Nonlinear Optimization Problems with Real-Time Applications. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_11
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DOI: https://doi.org/10.1007/978-3-642-01513-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01512-0
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