Neural Networks for Estimating and Compensating the Nonlinear Characteristics of Nonstationary Complex Systems

用于估计和补偿非平稳复杂系统非线性特性的神经网络

• 批准号: 0601521
• 负责人: Ronald Harley
• 金额: $ 24万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-05-01 至 2010-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0601521&HistoricalAwards=false
• 关键词: Neural; Networks; Estimating; Compensating; Nonlinear

NEURAL NETWORKS FOR ESTIMATING AND COMPENSATING THE NONLINEAR CHARACTERISTICS OF NONSTATIONARY COMPLEX SYSTEMSABSTRACTScope and Intellectual Merit. The objective of this research is to find a method of accurately quantifying the distorted currents and voltages created by certain devices in power networks. Distortion causes electromagnetic inference with communication and the fast growing digital world, light flicker, overheating of electric machines and transformers and increased losses in transmission lines. For years utilities and customers have argued about who causes the distortion. Existing measurement techniques can lead to errors of up to 40%. The approach is to use Echo State Networks and Simultaneous Recurrent Neural Networks with super fast learning algorithms (biological inspired algorithms such as particle swarm optimization), and other computational intelligence algorithms, to accurately measure the distortion by monitoring only voltage and current without the need for added transducers. Such fast and powerful neural networks could also be used for closed loop control of the offending nonlinear devices to mitigate the distortion.Broader Benefits. The economic impact of applying brain-like techniques to monitor and control physical processes is significant. Reduced power losses mean savings and more useful power over the same lines. More secure and reliable power systems of high quality are of national interest. Moreover, reduced electromagnetic interference promotes a cleaner more reliable telecommunications and digital environment. Fast intelligent nonlinear controllers will also benefit other real-world high-speed closed loop controlled nonlinear non-stationary processes. There exists a talent shortage in the US in the application of intelligent systems, and the project will train a new generation of professionals, and educators, underrepresented minorities and undergraduates in the multiple fields of the project

神经网络，用于估计和补偿非组织复杂系统的非线性特征和智力功能。这项研究的目的是找到一种准确量化电力网络中某些设备创建的变形电流和电压的方法。失真会导致电磁与通信以及快速增长的数字世界，轻闪烁，电机和变压器的过热以及传输线的损失增加。 多年来，公用事业和客户一直在争论谁导致失真。现有的测量技术可能导致高达40％的错误。该方法是使用Echo状态网络和具有超快速学习算法（生物学启发算法（例如粒子群优化））和其他计算智能算法的同时复发性神经网络，以通过不需要添加传感器来监测电压和电流，从而准确地测量失真，从而准确地测量失真。如此快速，强大的神经网络也可以用于对有问题的非线性设备的闭环控制，以减轻失真。应用类似脑的技术监测和控制物理过程的经济影响很大。 减少功率损失意味着节省和在相同线上更有用的功率。高质量的更安全和可靠的电力系统具有国家感兴趣。此外，减少的电磁干扰可促进更清洁的电信和数字环境。快速智能的非线性控制器还将使其他现实世界中的其他高速闭环控制非线性非平稳过程受益。在美国的应用中，美国存在人才短缺，该项目将培训新一代的专业人员，以及该项目多个领域的教育工作者，代表性不足的少数民族和本科生