My blog is start

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My blog is start
# my name is: Tianyang Xia # my srn is: 24052103 graph = { 'A':['B','C'], 'B':['A','D','E'], 'C':['A','E','F'], 'D':['B'], 'E':['B','C'], 'F':['C'], } """ give the data of the graph it will be used in the below """ my_start = 'C' # the end of my srn is 3, so I will start from 'C' def dfs(graph,my_start,visit = None): #define the function called "dfs", I will used the function to realize Depth-First Search if visit is None: #I need to make sure that visit is empty, so that the elements which has been visited will not influence the context in the new visit group visit = set() #set up a new visit group visit.add(my_start) #add the element of "start" in the group of visit firstly because I need to start the whole search from the start element print(my_start, end = '-') # make sure that the result will be in the same line, so that the result will looks clean and tidy. for neighbor in graph[my_start]: if neighbor not in visit: dfs(graph, neighbor, visit) """ the text above is the core of the whole DFS, I will explain. The first line means that neighbor group will find the element directly connected to element which was stored in the visit group. Then the second line means it will check if the same element will appear in the group of visit twice. If the element appears in the group for the secong time, the neighbor group will store the value of this element and print out after """ print("The result of Depth-Firstly Search start from my start is:") dfs(graph, my_start) print('-'.join(result)) 检查问题
11-03
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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