报告题目: |
智能电网研究之路 |
报告人: |
Paul Werbos |
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美国自然科学基金会能源与自适应系统项目主管
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报告时间: |
2011-05-18 10:00 |
报告地点: |
中央主楼511室 |
主办单位: |
清华大学自动化系 |
简介: |
摘要: Xi Jinping has written (Qiushi No.7, 2010): "... the productive forces are always the most active and most revolutionary force driving social progress." Limits on traditional energy sources like fossil oil, and the people who control them, are the most important material factor limiting social progress in the world today. Radical transformation of the electric power grid could be a key help in making a larger transformation, to achieve a sustainable global energy system as soon as possible at least cost. New technologies have begun to emerge -- not only new sensors and meters, but new types of switching, algorithms, intelligent systems, market design, storage, software platforms and electronics -- which make that radical transformation possible, far beyond the large but cautious "smart grid" initiatives now underway in the US. Rapid deployment and encouragement of these new technologies is especially important to the rapid elimination of our dependence on oil, and to the lower-cost expansion of renewable energy technologies. This talk will give the highlights of a plan for a "fourth generation intelligent grid," in press at IEEE Computational Intelligence Magazine. As your time permits, I will also discuss related opportunities in the energy space, updating earlier discussions at www.werbos.com.
演讲人简历： Paul Werbos began training as a mathematician, taking many university courses culminating in the graduate course in logic from Alonzo Church at Princeton while in middle and high school. Realizing the limits of deductive logic, he began his quest to understand inductive logic and intelligence in the mind back in those days. He obtained two degrees in economics from Harvard and the London School of Economics, divided equally between using mathematical economics as a model for distributed intelligence and developing some broader understanding. For his Harvard M.S., he took courses in quantum field theory (QFT) from Julian Schwinger, but did not fully understand the subject until many years later, after he started an activity in quantum technology and modeling at NSF (see his papers at http://arxiv.org/.) For his 1974 Harvard PhD thesis (reprinted in The Roots of Backpropagation, Wiley 1994), he proposed the development of more powerful, more biologically plausible reinforcement learning systems by the then new idea of using neural networks to approximate dynamic programming (ADP), including the value function. In order to implement ADP in a local biologically plausible manner, he translated Freud's theory of "psychic energy" into an algorithm later called backpropagation, and a rigorous general theorem, the chain law for ordered derivatives, which later also became known as the reverse method or adjoint method for automatic or circuit-level differentiation. He has spent many years advancing the fields of ADP and backpropagation and brain-like prediction, aimed at developing and demonstrating the kind of designs which could actually explain the kind of general intelligence we see in the brain and in subjective human experience, and proposing biological experiments to test the theory. In looking for applications which are really important to areas like energy, sustainability and space, he has also gotten deep into domain issues and organization, as reflected at http://www.werbos.com/, serving on boards of the National Space Society, the Millennium Project, the Lifeboat Foundation, and the IEEE Energy Policy Committee, and as a Fellow in the Senate in 2009. From 1980-1989, he developed official econometric forecasting models (two based on backpropagation) and was lead analyst for the long-term future at EIA in the Department of Energy. He is a Fellow of IEEE and INNS, a winner of the IEEE Neural Networks Pioneer Award and winner of the Hebb Award for 2011 from the International Neural Network Society (INNS). The Hebb Award is INNS's highest award, to honor substantive contributions to the understanding of biological learning systems. |
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