The History of Python - Introduction

by gvanrossum@gmail.com (Guido van Rossum)

Python is 19 years old now. I started the design and implementation of the language on a cold Christmas break in Amsterdam, in late December 1989. It started out as a typical hobby project. Little did I know where it would all lead.

With Python's coming of age, I am going to look back on the history of the language, from the conception as a personal tool, through the the early years of community building, (If Guido was hit by a bus?), all the way through the release of Python 3000, almost 19 years later. It's been quite an adventure, for myself as well as for the users of the language.

This won't be an ordinary blog post -- it'll be an open-ended series. I may invite guest writers. I'll be touching upon many aspects of the language's history and evolution, both technical and social.

I'll start with the gradual publication of material I wrote a few years ago, when I was invited to contribute an article on Python to HOPL-III, the third installment of ACM's prestigious History of Programming Languages conference, held roughly every ten years. Unfortunately, the demands of the rather academically inclined reviewers were too much for my poor hacker's brain. Once I realized that with every round of review the amount of writing left to do seemed to increase rather than decrease, I withdrew my draft. Bless those who persevered, but I don't believe that the resulting collection of papers gives a representative overview of the developments in programming languages of the past decade.

The next destination of the draft was a book on Python to be published by Addison-Wesley. Again, the mountain of raw material that I had collected was too large and at the same time too incomplete to serve as a major section of the book, despite the editing help I received from David Beazley, a much better writer than I am.

As they tell prospective Ph.D. students, the best way to eat an elephant is one meal at a time. So today I am publishing the first bit of the elephant, perhaps still somewhat uncooked, but at least it's out there. Hopefully others who were there at the time can help clear up the inevitable omissions and mistakes. I have many more chapters, each still requiring some editing, and I expect this to be a long-running series. Therefore I am starting a separate blog title for this, unimaginatively called The History of Python. Follow the link and enjoy!

 

from http://neopythonic.blogspot.com/2009/01/history-of-python-introduction.html

基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究(Matlab代码实现)内容概要:本文围绕“基于数据驱动的Koopman算子的递归神经网络模型线性化”展开,旨在研究纳米定位系统的预测控制问题,并提供完整的Matlab代码实现。文章结合数据驱动方法与Koopman算子理论,利用递归神经网络(RNN)对非线性系统进行建模与线性化处理,从而提升纳米级定位系统的精度与动态响应性能。该方法通过提取系统隐含动态特征,构建近似线性模型,便于后续模型预测控制(MPC)的设计与优化,适用于高精度自动化控制场景。文中还展示了相关实验验证与仿真结果,证明了该方法的有效性和先进性。; 适合人群:具备一定控制理论基础和Matlab编程能力,从事精密控制、智能制造、自动化或相关领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①应用于纳米级精密定位系统(如原子力显微镜、半导体制造设备)中的高性能控制设计;②为非线性系统建模与线性化提供一种结合深度学习与现代控制理论的新思路;③帮助读者掌握Koopman算子、RNN建模与模型预测控制的综合应用。; 阅读建议:建议读者结合提供的Matlab代码逐段理解算法实现流程,重点关注数据预处理、RNN结构设计、Koopman观测矩阵构建及MPC控制器集成等关键环节,并可通过更换实际系统数据进行迁移验证,深化对方法泛化能力的理解。
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