AMST 3253W Fall 2024 Paper 2: Partial Rough DraftSQL

Java Python AMST 3253W

Fall 2024

Paper 2: Partial Rough Draft

Description:

Academic writing involves asking questions (which you did in your prompt) and then attempting to answer those questions by working through multiple drafts. As you move from a rough draft to a more polished, final version, your thinking should evolve.

In light of this, you are being asked to turn in a rough draft of Paper 2. Turning in a rough draft not only will help you get started on the assignment, but also will   provide you an opportunity to get feedback on your draft from your peers and (if you choose) from your TA and/or your instructor.

Basic Requirements:

•   The partial rough draft is due, on Canvas, by Sunday, Nov. 10.

•   The partial rough draft must be at least 1,000 words in length. (The final draft is longer—the final draft must be 1,200 to  AMST 3253W Fall 2024 Paper 2: Partial Rough DraftSQL 1,500 words, not including the Works Cited Page).

•   The partial rough draft must be a written-out draft. Notes, an outline, or unintegrated quotations you plan to use do not count as a written-out draft.

Tips for Getting Started on Your Draft:

•   In your early drafts, write for your own understanding. Don’t feel like you have to have an argument right off the bat in your first draft and don’t worry about your audience until you start revising. Instead, describe what you noticed about your object, note what seemed strange, ask questions as they come up, and observe any patterns that emerge.

o It may seem like doing this sort of exploratory writing takes precious time away from writing the paper, but it will ultimately make the paper go faster—you are generating ideas and getting your thoughts straight, not wasting time!

•    Dwell on what puzzles you.

•   Consider which course reading(s) help you analyze your object         

【无人车路径跟踪】基于神经网络的数据驱动迭代学习控制(ILC)算法,用于具有未知模型和重复任务的非线性单输入单输出(SISO)离散时间系统的无人车的路径跟踪(Matlab代码实现)内容概要:本文介绍了一种基于神经网络的数据驱动迭代学习控制(ILC)算法,用于解决具有未知模型和重复任务的非线性单输入单输出(SISO)离散时间系统的无人车路径跟踪问题,并提供了完整的Matlab代码实现。该方法无需精确系统模型,通过数据驱动方式结合神经网络逼近系统动态,利用迭代学习机制不断提升控制性能,从而实现高精度的路径跟踪控制。文档还列举了大量相关科研方向和技术应用案例,涵盖智能优化算法、机器学习、路径规划、电力系统等多个领域,展示了该技术在科研仿真中的广泛应用前景。; 适合人群:具备一定自动控制理论基础和Matlab编程能力的研究生、科研人员及从事无人车控制、智能算法开发的工程技术人员。; 使用场景及目标:①应用于无人车在重复任务下的高精度路径跟踪控制;②为缺乏精确数学模型的非线性系统提供有效的控制策略设计思路;③作为科研复现与算法验证的学习资源,推动数据驱动控制方法的研究与应用。; 阅读建议:建议读者结合Matlab代码深入理解算法实现细节,重点关注神经网络与ILC的结合机制,并尝试在不同仿真环境中进行参数调优与性能对比,以掌握数据驱动控制的核心思想与工程应用技巧。
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