CAREER: Scalable Learning and Adaptation with Intelligent Techniques and Neural Networks for Reconfiguration and Survivability of Complex Systems

职业：利用智能技术和神经网络进行可扩展的学习和适应，以实现复杂系统的重新配置和生存能力

• 批准号: 1231820
• 负责人: Ganesh Venayagamoorthy
• 金额: $ 2.39万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-01-13 至 2013-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1231820&HistoricalAwards=false
• 关键词: CAREER; Scalable; Learning; Adaptation; Intelligent

Recently, intelligent techniques and adaptive critic designs have received increasing attention. The dynamic stochastic optimization (DSO) of complex systems such as the electric power grid and its parts can be formulated as minimization and/or maximization of certain quantities. The electric power grid is faced with deregulation and an increased demand for high-quality and reliable electricity for our digital economy, and coupled with interdependencies with other critical infrastructures, it is becoming more and more stressed. Intelligent systems technology will play an important role in carrying out DSO to improve the network efficiency and eliminate congestion problems without seriously diminishing reliability and security. This project proposes to investigate ways in which the power grid can be dynamically optimized, as a testbed for advanced brain-like stochastic identifiers and controllers.This project will advance knowledge and understanding on how to carry out optimization of a dynamic stochastic system. A novel local and global dynamic stochastic optimization strategy for a large scale complex system will be designed. The operating safety margins that currently exist on the large complex systems such as the electric power grid will be minimized, thus, allowing maximum utilization of existing resources with increased system reliability and security with optimal settings on devices throughout the entire system. The capability of carrying out dynamic stochastic optimization is the dream of today. This proposal is a first step in unfolding this dream to reality using brain-like systems with learning and adaptation based on approximate dynamic programming, advanced neural networks and other intelligent techniques on complex systems. In addition, system survivability and availability will be increased by improving reliability and fault tolerance of digital hardware, where the critical algorithms are implemented, using evolution and intelligent techniques. Fault tolerant designs to the unpredictable means robustness, security and safety. The project will also include a major component of educational outreach and of international collaboration including intellectual exchange via faculty and student exchanges between the U.S. and Nigeria, and US and Brazil.

最近，智能技术和自适应评论家设计受到了越来越多的关注。复杂系统的动态随机优化（DSO），例如电力电网及其部分，可以以最小化和/或一定量的最大化为例。电力电网面临放松管制，对我们的数字经济的高质量和可靠电力的需求增加，再加上与其他关键基础设施的相互依存关系，它变得越来越压力。智能系统技术将在执行DSO以提高网络效率并消除拥塞问题的情况下发挥重要作用，而无需大大降低可靠性和安全性。该项目提议研究可以动态优化电网的方法，作为对高级脑型随机标识符和控制器的测试床。该项目将提高知识和理解如何对动态随机系统进行优化。将设计一种新型的本地和全球动态随机优化策略，用于大规模复杂系统。当前存在于大型复杂系统（例如电力网格）上的操作安全边际将被最小化，从而使现有资源最大程度地利用，并且在整个系统中的设备上具有最佳的设置，并提高了系统的可靠性和安全性。进行动态随机优化的能力是当今的梦想。该提案是将这个梦想展现为现实的第一步，该梦想使用脑型系统，基于近似动态编程，先进的神经网络和其他关于复杂系统的智能技术的学习和适应。此外，使用进化和智能技术实施关键算法的数字硬件的可靠性和可容忍度，将通过提高可靠性和容错性来提高系统的生存能力和可用性。无法预测的容错设计意味着鲁棒性，安全性和安全性。该项目还将包括教育外展和国际合作的主要组成部分，包括通过教职员工和美国和尼日利亚之间的学生交流以及美国和巴西之间的交流。