| 1. | The numerical modeling of bus body is the base for bus styling and space discretization of flow field around bus
客车车身表面数值模型建立是客车造型和客车流场数值模拟空间离散化的基础。 |
| 2. | A program for space discretization of flow field around bus is developed under the platform of windows98 . this program has achieved the main work of the fem preprocess
本研究在windows98平台上编制了客车周围流场三维湍流有限元数值模拟的空间离散化程序，初步完整地实现了有限元前处理的各项主要工作。 |
| 3. | The method of generating elements and nodes automatically by reading coordinate data on surface of bus body is introduced and realized to discrete the topology space of three - dimensional turbulent flow field around buses
客车周围流场三维湍流数值模拟的空间离散化研究实现了由车身外表面离散点坐标数据文件自动剖分生成单元、节点和边界条件的方法。 |
| 4. | The fem is a method of which transform the partial differential - coefficient equation ' s initial and boundary value issue to ordinary differential - coefficient equation ' s initial and boundary value problem ( after space disperse ) or a set of regular algebra equation
有限元法在数学上是将偏微分方程的初边值问题划归一组常微分方程的初值问题(在空间离散化之后)或一组规则代数方程。 |
| 5. | This dissertation is mainly discussed in the following three perspectives : the numerical modeling of bus body surface , the space discretization for numerical simulation of flow field around bus using finite element method ( fem ) and wind tunnel test for bus model
本课题主要在客车车身表面数值模型建立、客车周围流场有限元数值模拟的空间离散化和客车模型风洞试验三方面进行了研究。 |
| 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。 |
