Collaborative Research: Globally Optimal Neural Computing: Algorithms and Applications

合作研究：全局最优神经计算：算法与应用

• 批准号: 0099378
• 负责人: Theodore Trafalis
• 金额: $ 15.06万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-01 至 2004-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0099378&HistoricalAwards=false
• 关键词: Collaborative; Research; Globally; Optimal; Neural

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.

0099378Trafalis这项拨款支持全球优化社区的一名成员（Nick Sahinidis）和神经计算和优化专家（Theodore Trafalis）之间的合作，以开发新的神经网络训练算法，并证明它们在解决大规模学习问题方面的好处。随着工程师和科学家在寻求对复杂现象和系统的更深层次理解时广泛采用神经计算，神经网络在技术各个方面的应用已经升级。为一个特定的应用程序找到最好的神经网络需要以一种最小化学习误差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）。尽管神经网络受到了广泛的关注，但目前还没有一种有效的方法能够在确定时间内识别误差函数的全局最小值。当前的方法，如反向传播和随机搜索方法，可能会陷入局部最小值，从而导致较大的学习误差和次优神经网络。这可能会导致错误的推断，并使决策者陷入困境。全局最优神经计算有望成为一种使能技术，可以显著改善许多不同应用领域的学习。拟议的研究结果将在其广泛分发的全球优化软件包中实施，并将提供给研究界。