Non - linear structural identification using a recursive model reference adaptive algorithm 基于模型參考自適應算法的非線性結構損傷識別
In this thesis , a parametric identification method for non - linear hysteretic systems is presented based on recursive model reference ( rmr ) adaptive algorithm 本文采用了一種基于模型參考自適應算法的結構參數辨識方法來識別線性和非線性結構的系統參數。
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 該系統用c + +語言實現了隨機二叉三維遞歸模型,生成三維樹木造型組成自然景物,并應用基于幾何和圖像的混合漫游加速算法,實現了虛擬環境的交互漫游。
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 本文的主要工作內容集中在以下四方面: 1 、研究并改進植物建模算法,生成三維的植物模型在研究目前植物建模常用算法的基礎上,結合迭代函數系統法( ifs )和l系統法提出了樹木類植物的隨機二叉三維遞歸模型。
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 本文中提出一種城市交通智能控制系統,針對城市交通網絡中相鄰交叉口的交通流可能相互沖突,即局部交通流的優化可能引起其他區域交通狀況的惡化的問題,采用多智能體協調控制方法來協調相鄰交叉口處的控制信號消除網絡中的交通擁塞.提出以一個智能體的方式實現一個信號燈交叉口控制,對多個信號燈交叉口形成的交通網絡采用多智能體協調控制的方式實現網絡流量優化來消除擁塞.文中提出由遞歸建模和改進的貝葉斯學習相結合的多智能體系統來使智能體可以確定其他智能體的準確模型并實時更新信息,并基于上述方法在簡單的交通網絡模型上建立了多智能體交通控制系統,仿真結果表明了方法的有效性,對實現智能交通系統有重要意義
To apply neural networks to the simulation of ship maneuvering motion , an nnrm ( neural network recursive model ) is designed and used to simulate a serial full - scale tests conducted in yangtze river and the comparison between simulated results and the measured ones is satisfactory . ship trajectory tracking is a well - known maneuvering problem with an increasing practical and theoretical interest . but the real - world tracking applications encounter a number of difficulties caused by the presence of different kinds of uncertainty due to the unknown or not precisely known system model and environmental effects 本文利用智能控制技術的優越性,嘗試將智能化控制技術用于船舶操縱運動模擬,初步探索了將現代控制理論和智能技術融入船舶操縱預報、模擬的研究方法,提出了用于船舶操縱運動模擬的線性神經網絡( lnn ) 、神經網絡遞推模型( nnrm )和nnrm 、交錯航跡距離( cte )和視距( los )混合控制器模型三種控制模型:并將控制模型的理論研究應用到實船試驗數據分析、計算,將模擬結果與實際的試驗結果作了比較。