learning robot 中文意思是什麼
learning robot
解釋
學習機撲-
During the procedure of system design and implementation, the author has made some innovative efforts such as : ( d establishing the user interest orientated model, the model receiving user interests continuously and conjecturing user interests by interaction with the user, accumulating user preferences in information demand, thereby achieving self - adaptive retrieval, ? roviding a feedback method which is based on the human - machine interaction, summarizing the user operations on the interface of result presentation, and designing an algorithm for capturing user operation behaviors, by which the changes in user interests and preferences can be learned potentially, ? ffering a method for user interest mining which can extract subjects of information confirmed by user, thereby conjecturing or predicting different kinds of expressions of the same interest or extracting the new interests or unexpressed interests, ? roposing a solution of personalized internet information retrieval based on the user interests in accordance with the above - mentioned work, the solution having very strong feasibility and practicality with taking user interest model as center, employing machine learning ( active learning and passive learning ) and data mining as tools, and being assisted with network robot,
Piirs系統分析與設計過程中所做的創新性的嘗試主要有以下幾個方面：實現了基於用戶興趣的用戶模型，該模型通過與用戶的交互（主動交互和被動交互） ，不斷地接收用戶的興趣和推測用戶的興趣，積累用戶信息需求的偏好，實現自適應的檢索；提供了一種基於人機交互的反饋方法，對用戶在結果呈現界面上的操作進行了歸納總結，設計了用戶操作捕獲演算法， 「隱性地」學習用戶興趣和偏好的變化；提供了一種用戶需求挖掘的方法，對用戶已確定的信息做進一步的主題挖掘，由此推測或預測用戶同一興趣的不同表述方式或者挖掘出用戶新的或未表達出來的興趣；在上述工作基礎上提出了一套完整的基於用戶興趣的個性化網路信息檢索的解決方案，該方案以用戶興趣模型為中心，以機器學習（主動學習和被動學習）和數據挖掘為手段，輔以網路機器人，具有很強的可行性和實用性。 -
The specific application and experimental results in soccer robot system prove the feasibility and validity of the learning technique in complex adversarial systems. robots " intelligence level can be unproved through the use of this intelligent learning method
機器人足球比賽的環境是一個動態、復雜、對抗的環境，並且每個機器人只能得到場上的不完全信息，如何使機器人能通過不斷的學習提高自己的性能是開發足球機器人系統的關鍵內容。 -
The soccer robot system is a dynamic environment with multiple obstacles. it is a problem of high complexity to perform path planning in such environments. the traditional methods are not efficient in such complex environments. in this paper, a self - learning method of robot navigation is proposed based on the reinforcement learning method and artificial potential field method
本論文將增強式學習演算法和人工勢場法相結合，提出狀態評價函數和勢場的對應關系，以及控制策略和勢場力方向的對應關系，通過機器人的自適應學習，來形成優化的人工勢場，使機器人能夠以最短路徑繞過障礙，到達目標。 -
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
本文在國家自然科學基金項目「增強學習泛化方法研究及其在移動機器人導航中的應用」的資助下，以增強學習及其在移動機器人導航控制中的應用為研究內容，重點研究了增強學習在求解連續狀態和行為空間markov決策問題時的泛化（ generalization ）方法，並針對移動機器人在未知環境中的自主導航和路徑跟蹤控制器的優化設計問題，研究了增強學習在上述領域中的應用。 -
Then an impedance control strategy with robust performance is presented aiming at uncertainties of robot, fnn is used to learning the uncertainties in order to eliminate disturbance, which have good robust and high value in practice. finally, an adaptive method is presented
為此，本文又在機器人阻抗控制的基礎上，針對機器人和環境的不確定性，提出一種具有魯棒性的阻抗控制結構，使用模糊神經網路作為補償控制器消除力控制中的所有不確定性，具有較強的魯棒性和較好的實用價值。 -
Grandar also provides lightscape for government. elementary, middle and high school educational robot dept we provide the students of the world with powerful learning tools
中小學教育機器人事業部向全球中小學提供革命性的教育機器人產品和系統解決方案，立志推動全球技術教育的發展。 -
As a part of the project of national “ 863 ” project “ the key technology of medical tele - robot and system development ” and national natural science foundation of china “ modeling and control scheme research of robot assisted orthopedic system ”, this paper intends to analyze the biomechanical characteristics of human leg in orthopedic surgery through developing a numeric human leg model, which provides a key means for surgical training and surgical rehearse. and this model can be used to improve operator ’ s learning curve and success ratio of the surgery
課題結合國家863計劃項目「遠程醫療機器人關鍵技術與系統研發」和國家自然科學基金項目「機器人輔助骨外科系統建模與控制方法研究」 ，通過建立人體腿部的數字化模型來分析研究矯形外科手術中人體腿部的生物力學特性，從而為外科手術培訓和手術預演的研究提供重要手段，以改進操作者的學習曲線，提高手術的成功率。 -
The characteristic of method is, in every process of iterative learning, after obtaining better approaching precision of network training for model identification iteratively, the feed - forward action of iterative learning control law for the next trail is constructed by output signals of the neural network, and then integrated with feedback control to track the desired trajectory of robot in real time
該方法的特點是，在每一次迭代學習過程中，使神經網路訓練到對模型的辨識達到比較好的逼近精度后，利用神經元網路的輸出構造下一次迭代學習過程中控制律的前饋部分，再將它與實時反饋控制結合，形成本文提出的魯棒迭代學習控制演算法，並對機器人系統進行控制。 -
The application of iterative learning control under the condition of non - zero initial error. novel d - type and pd - type iterative learning control algorithms. proofs of convergence and their application in robot are presented
主要介紹了在非零初始誤差條件下d型改進型迭代學習控制演算法和pd型改進型迭代學習控制演算法、收斂性分析及其在機器人中的應用。 -
This paper presents the application of iterative learning control in robot. iterative learning control algorithms have been designed under the condition of zero initial error and non - zero initial error. the convergence of the algorithms has been proved using the lyapunov stability theory
本文主要研究了迭代學習控制在機器人中的應用，並分別在零初始誤差條件下和非零初始誤差條件下設計了迭代學習控制演算法，並採用lyapunov穩定性理論證明了演算法的收斂性，最後將迭代學習控制演算法應用於機器人中進行了模擬研究。 -
In order to obtain an optimal estimate of the tilt angle and angle velocity, an indirect kalman filter configuration combining a rate gyroscope sensor and an accelerometer is implemented. because of the extreme nonlinearity of the two - wheeled self - balancing robot, application feasibility of nonlinear system control strategy based on linear approximation method, exact linearization and intelligent control have been investigated. according to robustness, balancing performance and environment adaptability, robust tracking control, linear quadratic regulator and fuzzy model reference learning control are implemented to the velocity controller
針對具有強非線性的前進子系統，論證了基於近似線性化、精確線性化及智能控制的非線性系統控制策略的應用可行性，據此按照不同性能要求設計了三種前進速度控制器:漸近跟蹤魯棒調節器簡單精確，具有良好的干擾抑制能力;二次型最優跟蹤控制器，在耗能最小的條件下，大大提高系統的平衡能力;而基於動態聚焦學習的模糊模型參考學習控制則兼具平衡性能好、環境適應性強、精度高及魯棒性好的優點。 -
Secondly, to deal with adverse effects caused by unknown robot finger masses and unknown object mass, in the thesis presented are two intelligent control schemes, the control scheme based on reinforcement learning and adaptive fuzzy sliding mode control scheme
其次，針對模型參數不確定，本文給出了兩種有效的智能控制方法，以消除模型參數不確定給系統帶來的負面影響。 （ 1 ）基於強化學習的多指手控制方法，該方法將反饋控制與強化學習相結合。 -
Learning fuzzy rules by evolution for mobile robot adaptive navigation
進化學習模糊規則實現移動機器人的自適應導航 -
Robot performance skill learning and merging for tasks in contact states
機器人接觸狀態下作業的技能獲取及融合 -
Control of a micro - mobile robot for position and orientation problem based on reinforcement learning
基於增強式學習的微小型移動機器人位姿控制 -
Novel d - type and pd - type iterative learning control algorithms proofs of convergence and their application in robot are presented
非零初始誤差條件下迭代學習控制在機器人中的應用。 -
In the first chapter of this paper, a comprehensive survey on the research of reinforcement learning algorithms, theory and applications is provided. the recent developments and future directions for mobile robot navigation are also discussed
本文的第一章對增強學習理論、演算法和應用研究的發展情況進行了全面深入的綜述評論，同時分析了移動機器人導航控制的研究現狀和發展趨勢。 -
A new control strategy is presented by combining fuzzy - neural control with feedback control under considering the uncertainties of robotic system firstly based on robot force / position hybrid control. fuzzy - neural network is used to learning the boundary of envelope function of uncertainties, and the feedback controller is used to enhance the complete performance of fuzzy - neural control strategy
在機器人力/位置混合控制的基礎上，首先設計了一種模糊神經網路控制器與反饋控制器相結合的控制方案，採用模糊神經網路在線學習所有不確定性的包絡函數的上界，引入反饋控制器，以增強模糊神經網路控制策略的完備性。
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