Presidential Young Investigator Award: Application of Neural Networks to Controller Design Using Feedback Linearization

私家侦探将研究分析和设计大型- 规模随机系统，特别是二进制神经 网络和自适应分布式检测系统。 他将致力于为这些理论框架 系统，以及示范 评估现有设计的理论， 设计合成技术。 预期 应用程序是在机器智能和 传感器融合 其目的是为设计师和用户提供 神经网络和分布式检测系统 使用一组可靠的多项式时间算法， 预先计算（至少是其上的边界） 预期业绩。 特别是这种 当设计师使用 次优或“智能”（即启发式）设计 算法 通常，一个近似的最佳架构 是不可用的，或者这种设计的实现是 从计算或硬件禁止- 复杂性观点 一个相关的目标是综合和评估- 线学习算法，因为常见的 关于环境统计数据可得性的假设 在统计数据的平稳性上， 现实

The P.I. will study the analysis and design of large- scale stochastic systems, specifically binary neural networks and adaptive distributed detection systems. He will work on a theoretical framework for these systems, and on demonstration of the application of the theory in assessing existing designs and in devising synthesis techniques. The expected applications are in Machine Intelligence and in Sensor Fusion. The objective is to provide designers and users of neural networks and distributed-detection systems with a set of reliable polynomial-time algorithms, for calculating in advance (at least a bound on) the expected performance. In particular, such assessments are important when the designer using suboptimal or "intelligent" (i.e. heuristic) design algorithms. Often, a close-form optimal architecture is not available, or the implementation such design is prohibitive from the computational or the hardware- complexity viewpoints. A related objective is to synthesize and assess on- line learning algorithms, since the common assumptions on availability of environmental statistics and on stationary of the statistics are often not realistic.