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

P.I.将研究大规模随机系统的分析和设计，特别是二进制神经网络和自适应分布式检测系统。他将致力于这些系统的理论框架，并演示该理论在评估现有设计和设计合成技术中的应用。预期的应用领域包括机器智能和传感器融合。其目的是为神经网络和分布式检测系统的设计者和用户提供一套可靠的多项式时间算法，用于预先计算(至少是一个界限)预期性能。特别是，当设计者使用次优或“智能”(即启发式)设计算法时，这种评估非常重要。通常，无法获得封闭形式的最佳体系结构，或者从计算或硬件复杂性的观点来看，这种设计的实现是令人望而却步的。一个相关的目标是综合和评估在线学习算法，因为关于环境统计数据的可用性和统计数据的平稳性的常见假设往往是不现实的。

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.