| 1. | Hybrid dynamical system originated from the application of discrete event systems to supervising continuous state systems is discussed
摘要混合动态系统起因于离散事件系统用于监控连续状态系统的行为。 |
| 2. | Using finite time control techniques for continuous systems , a continuous state feedback control law for trajectory tracking is developed
利用该连续系统有限时间控制技术，设计一种连续的状态反馈跟踪控制算法。 |
| 3. | However , because coordination graph needs discrete state variant , it ca n ' t be applied in continuous state space such as robocup which communication condition is limited
但是协作图要求离散状态变量，所以无法直接应用到类似robocup这种通讯条件受到限制的连续状态空间。 |
| 4. | The continuous state system is described by using abstract language method , and the discrete event plant model is attained by consistent partition of the space of continuous state system
用抽象语言方法描述连续状态系统，通过对连续状态系统空间的一致性分划，抽象出离散事件对象模型。 |
| 5. | It induces logic and delay to waveform , and describes the continuous states of nodes in netlist by waveform . it can realize simulating continuous states for integrated circuits by computing waveforms
它把逻辑和延迟有机地结合起来归纳为波形，并用波形来描述电路网表中节点的连续时间状态，通过对波形的计算实现整个电路的连续时间状态模拟。 |
| 6. | Reinforcement learning algorithms that use cerebellar model articulation controller ( cmac ) are studied to estimate the optimal value function of markov decision processes ( mdps ) with continuous states and discrete actions . the state discretization for mdps using sarsa - learning algorithms based on cmac networks and direct gradient rules is analyzed . two new coding methods for cmac neural networks are proposed so that the learning efficiency of cmac - based direct gradient learning algorithms can be improved
在求解离散行为空间markov决策过程( mdp )最优策略的增强学习算法研究方面，研究了小脑模型关节控制器( cmac )在mdp行为值函数逼近中的应用，分析了基于cmac的直接梯度算法对mdp状态空间离散化的特点，研究了两种改进的cmac编码结构，即：非邻接重叠编码和变尺度编码，以提高直接梯度学习算法的收敛速度和泛化性能。 |
| 7. | Supported by the national natural science foundation of china ( nsfc ) 2 , the research topic of this paper has been focused on reinforcement learning and its applications in mobile robot navigation . one part of the main contents in this paper is the generalization methods for reinforcement learning in solving markov decision problems with continuous states and actions . another part of the main contents is the applications of reinforcement learning methods in the optimization of the path tracking controllers and the autonomous navigation controllers for mobile robots
本文在国家自然科学基金项目“增强学习泛化方法研究及其在移动机器人导航中的应用”的资助下，以增强学习及其在移动机器人导航控制中的应用为研究内容，重点研究了增强学习在求解连续状态和行为空间markov决策问题时的泛化( generalization )方法，并针对移动机器人在未知环境中的自主导航和路径跟踪控制器的优化设计问题，研究了增强学习在上述领域中的应用。 |
| 8. | Based on the above analysis , the research topic of this paper has been focused on 5 parts as follows : 1 ) the algorithms and theory of temporal difference learning ; 2 ) gradient learning algorithms for solving markov decision problems with continuous state or action space ; 3 ) hybrid learning methods for solving markov decision problems ; 4 ) the applications of reinforcement learning in the path tracking problems of mobile robots ; 5 ) reactive navigation methods based on reinforcement learning for mobile robots in unknown environments
在此基础上，本文的研究工作主要从5个方面展开，即：时域差值学习算法和理论；求解马氏决策问题的梯度增强学习算法；求解马氏决策问题的进化-梯度混合学习算法；增强学习在移动机器人路径跟踪控制器优化中的应用；基于增强学习的移动机器人反应式导航控制。 |
| 9. | Abstract : the system , in the span - by - span construction of brid ge , willexperience three kinds of shifts from the state of simple beam to the continuous state , from overhanging beam to the continuous beam and from few - spans continuous beam to required degree span by span . meanwhile , structural calculatin g graphics and the internal force are changing with the three shifts
文摘:在桥梁工程逐孔施工过程中，体系将发生由简支梁状态到连续梁状态，由悬臂梁到连续梁，由少跨连续梁逐孔延伸到所要求的体系三种转换，同时结构计算图式和内力也发生变化。 |
