SBIR Phase II: Improving Neural Network Reliability for Dynamic System Modeling and Control Optimization Through the use of Confidence Measures

SBIR 第二阶段：通过使用置信度措施提高动态系统建模和控制优化的神经网络可靠性

• 批准号: 9625725
• 负责人: Yuchun Lee
• 金额: $ 23.41万
• 依托单位: Unica Technologies, Incorporated
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-07-15 至 1998-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9625725&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Improving; Neural

9625725 Lee This Small Business Innovation Research (SBIR) Phase II project will develop a prototype confidence-estimator module that can be integrated with real-world neural-network applications. Reliability of neural nets is affected by (1) input novelty, (2) data consistency, and (3) time-varying system dynamics. Confidence measures can gauge network reliability by indicating when a neural network's output should be trusted and when periodic retraining should occur in slow time-varying dynamic systems. Confidence-generation algorithms complement virtually all neural nets and can help their integration into production environments. Phase II will address confidence algorithms for all reliability factors and a method for scaling and combining confidence measures to generate a single probabilistic value. Algorithms will be tested on data from an electrolytic chemical process, metal smelting process, a polymer formulation process control, and an artificial control optimization problem for supply-chain inventory management. A confidence-estimator module potentially can be used in virtually every real-world neural-network solution to improve both the acceptance and performance accuracy. Commercial applications are expected in process control, finance, retail, insurance, and imaging. The confidence-estimator module will be useful to 200-300 rapidly expanding companies currently selling neural-network-based products. ***

小行星9625725 这个小企业创新研究（SBIR）第二阶段项目将开发一个原型置信度估计器模块，可以与现实世界的神经网络应用程序集成。 神经网络的可靠性受以下因素影响：（1）输入的新奇，（2）数据的一致性，（3）时变系统动力学。 置信度可以通过指示神经网络的输出何时应该被信任以及何时应该在慢时变动态系统中进行周期性再训练来衡量网络的可靠性。 置信度生成算法补充了几乎所有的神经网络，并可以帮助它们集成到生产环境中。 第二阶段将解决所有可靠性因素的置信度算法以及缩放和组合置信度措施以生成单个概率值的方法。 算法将在电解化学过程、金属冶炼过程、聚合物配方过程控制和供应链库存管理的人工控制优化问题的数据上进行测试。 置信度估计器模块可以用于几乎所有现实世界的神经网络解决方案，以提高接受度和性能准确性。 商业应用预计在过程控制，金融，零售，保险和成像。 信心评估模块将对目前销售基于神经网络产品的200-300家快速扩张的公司有用。 ***