Summary
We propose a neural network approach for global optimization with applications to nonlinear least square problems. The center idea is defined by the algorithm that is developed from neural network learning. By searching in the neighborhood of the target trajectory in the state space, the algorithm provides the best feasible solution to the optimization problem. 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 would provide a good alternative in addition to statistics methods for power regression models with large data.
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A Neural Network Approach for Global Optimization with Applications
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1. IntroductionGlobal optimization provides strategies and numerical procedures to analyze and solve linear or nonlinear optimization problems. It is the task of finding the absolutely best set of parameters to optimize an objective function. In general, there can be solutions that are locally optimal but not globally optimal. Consequently, global optimization problems are typically quite difficult to solve exactly ([2]). In this paper, we propose a neural network approach for global optimization with applications to nonlinear least square problems. More precisely, a neural network learning algorithm-the state space search algorithm (SSSA) [9] is introduced to perform the optimization procedures to solve the nonlinear least square problems of regression models. Regression models are routinely developed and used mostly in the fields of science for predictive purpose. The last few decades' study of nonlinear regression models has steadily attracted the interest of specialists in the field.Neural networks can be seen as ...See the full content of this document
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