New Concept English there (1)Typing speed exercise

伦敦动物园收到报告称,在伦敦南部45英里处发现了一只野生的美洲狮。起初这些报告并未受到重视,但随着越来越多相似的目击描述出现,动物园的专家们开始进行调查。美洲狮在多个地点被目击,它所到之处留下了不少猎物如鹿和兔子等。尽管进行了搜索,但考虑到美洲狮不会主动攻击人类,除非感到威胁,因此人们并不恐慌。

Today,I start learn new concept english there,Mainly for listening practice and typing speed exercise。

1 Listen to the video and answer the quetion。

2 Listen and typing it,understandind  the main ideals  。

3 speak and  record,make sure pronunciation correctly

 

 

Lesson1 A puma at large

pumas are large, cat-like animals which are found in America ,when reports came into London Zoo that a wild puma had been spotted forty-five miles south of London ,they were not taken seriously ,However, as the evidence began to accumulate ,experts from the zoo felt obliged to investigate, for the descriptions given by people who claimed to have seen the puma were extraordinarily similar.

  the hunt for the puma began in a small village where a woman picking blackberries saw “a lager cat” only five yards away from her, It immediately ran away when she saw it ,and experts confirmed that a puma will not attack a human being unless it is coned ,The search proved difficult for the puma was often observed at one place in the morning and at another place twenty miles away in the evening ,Wherever it went, it left behind it a trail of dead deer and small animals like rabbits, paw prints were seen in a member of places and puma fur was found clinging to bushes ,Several people complained

转载于:https://www.cnblogs.com/huangbaobaoi/p/7594832.html

内容概要:本文围绕六自由度机械臂的人工神经网络(ANN)设计展开,重点研究了正向与逆向运动学求解、正向动力学控制以及基于拉格朗日-欧拉法推导逆向动力学方程,并通过Matlab代码实现相关算法。文章结合理论推导与仿真实践,利用人工神经网络对复杂的非线性关系进行建模与逼近,提升机械臂运动控制的精度与效率。同时涵盖了路径规划中的RRT算法与B样条优化方法,形成从运动学到动力学再到轨迹优化的完整技术链条。; 适合人群:具备一定机器人学、自动控制理论基础,熟悉Matlab编程,从事智能控制、机器人控制、运动学六自由度机械臂ANN人工神经网络设计:正向逆向运动学求解、正向动力学控制、拉格朗日-欧拉法推导逆向动力学方程(Matlab代码实现)建模等相关方向的研究生、科研人员及工程技术人员。; 使用场景及目标:①掌握机械臂正/逆运动学的数学建模与ANN求解方法;②理解拉格朗日-欧拉法在动力学建模中的应用;③实现基于神经网络的动力学补偿与高精度轨迹跟踪控制;④结合RRT与B样条完成平滑路径规划与优化。; 阅读建议:建议读者结合Matlab代码动手实践,先从运动学建模入手,逐步深入动力学分析与神经网络训练,注重理论推导与仿真实验的结合,以充分理解机械臂控制系统的设计流程与优化策略。
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