Stochastic global optimization methods. part 11: multi level methods
Mathematical Programming: Series A and B
Multilayer feedforward networks are universal approximators
Neural Networks
Trace-Based Methods for Solving Nonlinear Global Optimization and Satisfiability Problems
Journal of Global Optimization
Global Feedforward Neural Network Learning for Classification and Regression
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Global Feedforward Neural Network Learning for Classification and Regression
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
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This paper addresses the problem of minimizing an energy function by means of a monotonic transformation. With an observation on global optimality of functions under such a transformation, we show that a simple and effective algorithm can be derived to search within possible regions containing the global optima. Numerical experiments are performed to compare this algorithm with one that does not incorporate transformed information using several benchmark problems. These results are also compared to best known global search algorithms in the literature. In addition, the algorithm is shown to be useful for a class of neural network learning problems, which possess much larger parameter spaces.