Some New Words found in papers

本文探讨了计算机科学中空白状态的概念,特别是在自主代理的背景下,并解释了它如何应用于合成语言解析器的设计中。

In computer science, tabula rasa refers to the development of autonomous agents with a mechanism to reason and plan toward their goal, but no “built-in” knowledge-base of their environment. Thus they truly are a blank slate.

In reality autonomous agents possess an initial data-set or knowledge-base, but this cannot be immutable or it would hamper autonomy and heuristic ability.[citation needed] Even if the data-set is empty, it usually may be argued that there is a built-in bias in the reasoning and planning mechanisms.[citation needed] Either intentionally or unintentionally placed there by the human designer, it thus negates the true spirit of tabula rasa.[13]

A synthetic (programming) language parser (LR(1), LALR(1) or SLR(1), for example) could be considered a special case of a tabula rasa, as it is designed to accept any of a possibly infinite set of source language programs, within a single programming language, and to output either a good parse of the program, or a good machine language translation of the program, either of which represents a success, or, alternately, a failure, and nothing else. The “initial data-set” is a set of tables which are generally produced mechanically by a parser table generator, usually from a BNF representation of the source language, and represents a “table representation” of that single programming language.


Vice versa is a Latin phrase that means “the other way around”.

内容概要:本文档围绕六自由度机械臂的ANN人工神经网络设计展开,涵盖正向与逆向运动学求解、正向动力学控制,并采用拉格朗日-欧拉法推导逆向动力学方程,所有内容均通过Matlab代码实现。同时结合RRT路径规划与B样条优化技术,提升机械臂运动轨迹的合理性与平滑性。文中还涉及多种先进算法与仿真技术的应用,如状态估计中的UKF、AUKF、EKF等滤波方法,以及PINN、INN、CNN-LSTM等神经网络模型在工程问题中的建模与求解,展示了Matlab在机器人控制、智能算法与系统仿真中的强大能力。; 适合人群:具备一定Ma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)tlab编程基础,从事机器人控制、自动化、智能制造、人工智能等相关领域的科研人员及研究生;熟悉运动学、动力学建模或对神经网络在控制系统中应用感兴趣的工程技术人员。; 使用场景及目标:①实现六自由度机械臂的精确运动学与动力学建模;②利用人工神经网络解决传统解析方法难以处理的非线性控制问题;③结合路径规划与轨迹优化提升机械臂作业效率;④掌握基于Matlab的状态估计、数据融合与智能算法仿真方法; 阅读建议:建议结合提供的Matlab代码进行实践操作,重点理解运动学建模与神经网络控制的设计流程,关注算法实现细节与仿真结果分析,同时参考文中提及的多种优化与估计方法拓展研究思路。
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