Collaborative Research: Globally Optimal Neural Computing: Algorithms and Applications

0099378 Trafalis该基金支持全球优化社区成员之间的合作（Nick Sahinwei）和神经计算和优化专家（西奥多特拉法利斯）开发新的神经网络训练算法，并证明其在解决大规模学习的好处）神经网络在技术的各个方面的应用最近已经升级，因为工程师和科学家广泛接受神经网络。为特定应用寻找最佳的神经网络需要以最小化学习错误的方式选择网络参数。即使对于简单的学习问题，误差函数也具有大量的局部极小值（孤立谷）。尽管大量的注意力投入到神经网络，目前还没有有效的方法，可以确定时间的误差函数的全局最小值。目前的方法，如反向传播和随机搜索方法，可能会陷入局部极小值对应于大的学习误差和次优神经网络。这可能会导致不正确的推理和错误的决策制定者。全局优化的神经计算有望成为一种使能技术，可以显着改善许多不同应用领域的学习。 拟议研究的结果将在其广泛分布的全球优化软件包中实施，并将提供给研究界。

0099378TrafalisThis grant supports a collaboration between a member of the global optimization community (Nick Sahinidis) and an expert in neural computation and optimization (Theodore Trafalis) to develop novel neural network training algorithms and demonstrate their benefits in solving large-scale learning) problems.The application of neural networks to all aspects of technology has escalated recently as engineers and scientists have widely embraced neural computing in their quest for deeper understanding of complex phenomena and systems.Finding the best possible neural network for a particular application requires choosing the network parameters in a way that minimizes learning errors. Even for simple learning problems, the error function possesses a large number of local minima (isolated valleys). Despite the enormous amount of attention devoted to neural networks, there is currently no efficient method that can identify with certainty time global minimum of the error function. Current approaches, such as back-propagation and stochastic search methods, may get trapped at local minima corresponding to large learning errors and suboptimal neural networks. This may lead to incorrect inferences and devastate decision makers.Globally optimal neural computing holds the promise of an enabling technology that could significantly improve learning in many diverse application domains. The results of the proposed research will be implemented in the their widely distributed global optimization software package and will be made available to the research community.