recursive model meaning in English
递归模型
Examples
- Non - linear structural identification using a recursive model reference adaptive algorithm
基于模型参考自适应算法的非线性结构损伤识别 - In this thesis , a parametric identification method for non - linear hysteretic systems is presented based on recursive model reference ( rmr ) adaptive algorithm
本文采用了一种基于模型参考自适应算法的结构参数辨识方法来识别线性和非线性结构的系统参数。 - The system realize the random binary three - dimensional recursive model , and generate three - dimensional tree model to compose natural scene ; in addition , it apply the accelerate algorithms based on the integrated polygon and texture , and realize interactive walkthrough in virtual environment
该系统用c + +语言实现了随机二叉三维递归模型，生成三维树木造型组成自然景物，并应用基于几何和图像的混合漫游加速算法，实现了虚拟环境的交互漫游。 - In this thesis , our research work mainly focuses on the following four aspects : 1 , studying and perfecting the generation algorithms of plant modeling , building three - dimensional plant model on the basis of studying current algorithms of plant modeling , combining iterated function system and l - system , we brought forward the random binary three - dimensional recursive model
本文的主要工作内容集中在以下四方面： 1 、研究并改进植物建模算法，生成三维的植物模型在研究目前植物建模常用算法的基础上，结合迭代函数系统法( ifs )和l系统法提出了树木类植物的随机二叉三维递归模型。 - This research addressed an urban traffic intelligent control system , which adopts a multi - agents coordination in urban traffic control to coordinate the signal of adjacent intersections for eliminating the congestion of traffic network . an agent represents a signal intersection control , and multi - agents realize coordination of multiple intersections to eliminate congestion . based on recursive modeling method and bayesian learning that enables an agent to select his rational action by examining with other agents by modeling their decision making in conjunction with dynamic belief update . based on this method , a simplified multi - agent traffic control system is established and the results demonstrate its effectiveness . it is very important for its
本文中提出一种城市交通智能控制系统，针对城市交通网络中相邻交叉口的交通流可能相互冲突，即局部交通流的优化可能引起其他区域交通状况的恶化的问题，采用多智能体协调控制方法来协调相邻交叉口处的控制信号消除网络中的交通拥塞.提出以一个智能体的方式实现一个信号灯交叉口控制，对多个信号灯交叉口形成的交通网络采用多智能体协调控制的方式实现网络流量优化来消除拥塞.文中提出由递归建模和改进的贝叶斯学习相结合的多智能体系统来使智能体可以确定其他智能体的准确模型并实时更新信息，并基于上述方法在简单的交通网络模型上建立了多智能体交通控制系统，仿真结果表明了方法的有效性，对实现智能交通系统有重要意义