A Neural Network Approach for Global Optimization with Applications to Nonlinear Least Square Problems
Document Type
Conference Proceeding
Publication Date
8-2006
Publication Title
Proceedings of Neural, Parallel and Scientific Computations
Abstract
We propose a neural network approach for global optimization with applications to nonlinear least square problems. A state space search algorithm is introduced to perform global optimization procedures to solve the nonlinear problem. The center idea is defined by the algorithm that is developed from neural network learning. The convergence analysis shows that the convergence of the algorithm to the desired solution is guaranteed. Our examples show that the method is effective and accurate. The simplicity of this new approach, especially with the algorithm given in this paper, would provide a good alternative in addition to statistics methods for power regression models with large data.
Repository Citation
Leong Kwan Li & Sally S. L. Shao. (2006). A Neural Network Approach for Global Optimization with Applications to Nonlinear Least Square Problems. Proceedings of Neural, Parallel and Scientific Computations, August 9-12, 2006, 130-134.
Original Citation
Leong Kwan Li & Sally S. L. Shao. (2006). A Neural Network Approach for Global Optimization with Applications to Nonlinear Least Square Problems. Proceedings of Neural, Parallel and Scientific Computations, August 9-12, 2006, 130-134.
