Neural Network Dynamic Pattern Recognition Using Optimal Control

该项目的目标是：利用最优控制理论的概念设计递归神经网络的鲁棒动态模式识别算法;通过最优控制公式中揭示的替代设计选项，推广递归网络的导数传播算法（一个例子是最小时间最优控制训练）;使用状态反馈最优控制方案来导出在根本系统扰动期间使用的快速权重方案;并对由最优控制产生的某些神经网络动态系统进行定性分析和动态行为表征。控制培训计划。注意到最优控制理论和递归神经网络的导数传播算法之间的正式关系，本研究将进一步揭示最优控制提供的更多设计选项。因此，最优控制公式将用于推广经常性网络中的导数传播。本研究的第一阶段将使用一种设计选项，该设计选项可以缓冲训练动态模式识别系统中的噪声影响。第二阶段将采用最小时间最优控制，以减少在经常性系统的训练时间。第三阶段将提出用于减轻激进干扰的快速计划。第四阶段将对上述计划产生的一些系统进行定性分析。

The objectives of the project are: to utilize concepts from optimal control theory in designing robust dynamic pattern recognition algorithms for recurrent neural networks; to generalize derivative propagation algorithms for recurrent networks through alternative design options revealed in the optimal control formulation (One example is minimum-time optimal control training); to use state feedback optimal control schemes for deriving fast weight schemes for use during radical system disturbances; and to perform qualitative analysis and dynamic behavior characterization for certain of the neural network dynamic systems arising from the optimal control training schemes. Noting a formal relationship between optimal control theory and the derivative propagation algorithms of recurrent neural networks, this research will go further to reveal more design options offered by optimal control. Thus an optimal control formulation will be used to generalize derivative propagation in recurrent networks. The first phase of this research will use a design option that buffers against the effects of noise in trained dynamic pattern recognition systems. The second phase will incorporate minimum-time optimal control to reduce training time in recurrent systems. The third phase will propose fast schemes for use in mitigating radical disturbances. The fourth phase will perform a qualitative analysis for some of the systems resulting from the above schemes.