Adaptive Critic Design and Implementations

自适应批评神经网络是一种很有前途的计算智能新技术，能够解决非线性动态识别、最优实时非线性控制和时间序列预测等各种应用问题。本项目将基于不同的神经网络模型在软件中实现各种自适应批评设计（ACDs），并证明其在仿真中解决各种问题的效率。IT部门将按照ANSI规范为ACDs的所有模块开发C/ c++代码，以提供通用图形界面和跨不同平台的可移植性。这将为早期工业合作提供工具。PI将实现复杂识别问题（例如，化学反应器动力学识别）、时间序列预测（例如，股票市场预测）和复杂最优控制问题（例如，单级到轨道（SSTO）飞机的飞行控制）示例的计算机模拟解决方案。这些将来自已出版的资料。随着应用需求的增加，大量的机会需要和快速原型设计能力，他们将把这些工具移植到专门的神经协处理器上。从数值或理论上证明ACD作为一个整体近似复杂非线性动态系统（即一个系统加上基于ACD的控制器）的Lyapunov函数的能力，以及对神经网络实时交互的研究，也是该项目的目标。这将对非线性动力系统的稳定性分析有重要的贡献。最后，将特别注重传播结果，包括为学术界、工业界和政府开展教育活动；可用于ftp、研讨会、增强出版以及与政府和行业进一步合作的软件。这项工作将通过提供更好的工具和减少与它们的使用相关的学习曲线，增强神经网络和非线性控制作为工程学科的成功。

9413120, PI-Wunsch: The Adaptive Critic Neural Network is a promising new technique of computational intelligence capable of solving large variety of applied problems in nonlinear dynamic identification, optimal real-time nonlinear control and time series forecasting. This project will implement various Adaptive Critic Designs (ACDs) in software based on different models of Neural Network and prove their efficiency for solving various problems in simulation. IT will develop C/C++ code for all modules of ACDs in accordance with ANSI specification to provide a common graphical interface and portability across various platforms. This will provide the tools for early industrial collaborations. The PI will implement computer-simulated solutions of examples of complex identification problems (e.g., identification of chemical reactor dynamics), time series predictions (e.g., stock market forecasts) and those of complex optimal control problems (e.g., flight control of a Single -Stage-To-Orbit (SSTO) plane). These will come from published sources. As application demands rise, and the volume of opportunities necessitates and rapid prototyping capability, They will port these tools to a specialized neural coprocessor. Proving numerically or theoretically the ability of ACD to approximate Lyapunov functions of a complex nonlinear dynamic system as a whole (i.e., a system plus ACD-based controller), along with a study of real-time interaction of neural networks, is also a goal of the project. This will significantly contribute to the knowledge of stability analysis of nonlinear dynamic systems. Finally, these will be a special concentration on dissemination of the results, including educational activities for academia, industry and govern ment; software available for ftp, workshops, enhanced publishing, and further collaborations with government and industry. This work will enhance the success of both neural networks and nonlinear controls as engineering disciplines by providing better tools and reducing the learning curve associated with their use.