The main aim of this book is to present a unified, systematic description of
basic and advanced problems, methods and algorithms of the modern con-
trol theory considered as a foundation for the design of computer control
and management systems. The scope of the book differs considerably from
the topics of classical traditional control theory mainly oriented to the
needs of automatic control of technical devices and technological proc-
esses. Taking into account a variety of new applications, the book presents
a compact and uniform description containing traditional analysis and op-
timization problems for control systems as well as control problems with
non-probabilistic MODELS of uncertainty, problems of learning, intelligent,
knowledge-based and operation systems – important for applications in the
control of manufacturing processes, in the project management and in the
control of computer systems.
Signals convey information. Systems transform signals. This book introduces the mathe-
matical MODELS used to design and understand both. It is intended for students interested
in developing a deep understanding of how to digitally create and manipulate signals to
measure and control the physical world and to enhance human experience and communi-
cation.
Pattern recognition has its origins in engineering, whereas machine learning grew
out of computer science. However, these activities can be viewed as two facets of
the same field, and together they have undergone substantial development over the
past ten years. In particular, Bayesian methods have grown from a specialist niche to
become mainstream, while graphical MODELS have emerged as a general framework
for describing and applying probabilistic MODELS. Also, the practical applicability of
Bayesian methods has been greatly enhanced through the development of a range of
approximate inference algorithms such as variational Bayes and expectation propa-
gation. Similarly, new MODELS based on kernels have had significant impact on both
algorithms and applications.
Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers
usually do not explain their predictions which is a barrier to the adoption of machine learning.
This book is about making machine learning MODELS and their decisions interpretable.
After exploring the concepts of interpretability, you will learn about simple, interpretable MODELS
such as decision trees, decision rules and linear regression. Later chapters focus on general model-
agnosticmethodsforinterpretingblackboxMODELSlikefeatureimportanceandaccumulatedlocal
effects and explaining individual predictions with Shapley values and LIME.
This Getting Started Guide is written for Maxwell beginners and experienced users who would like to quickly re familiarize themselves with the capabilities of MaxwelL.This guide leads you step-by-step through solving and analyzing the results of a rotational actuator magnetostatic problem with motion By following the steps in this guide, you will learn how to perform the following tasks Modify a MODELS design parameters y Assign variables to a model's design parameters.Specify solution settings for a design Validate a designs setupRun a maxwell simulation v Plot the magnetic flux density vecto v Include motion in the simulation本《入門指南》是為希望快速重新熟悉MaxwelL功能的Maxwell初學者和有經驗的用戶編寫的。本指南將引導您逐步解決和分析旋轉致動器靜運動問題的結果。按照本指南中的步驟，您將學習如何執行以下任務。修改模型設計參數y將變量分配給模型的設計參數。指定設計的解決方案設置驗證設計設置運行maxwell模擬v繪制磁通密度vecto v在模擬中包含運動
以STC12C5A60S2單片機為控制核心,采用2.4G(JF24D)無線遙控模塊進行無線發射與接收,設計了一種雙電機遙控船模控制系統.該系統通過切換檔桿實現前進后退,方向盤左右轉動、暫停按鈕等控制直流電機的正轉、反轉、暫停,使得電機驅動的遙控船模實現前進后退、左右轉向、暫停等功能,有效解決了驅動功率小和船模之間相互干擾等問題,可廣泛應用于遙控船模領域.Using STC12C5A60S2 single-chip microcomputer as the controller and 2.4 G(JF24D)wireless remote control module for wireless transmission and reception, a dual-motor remote control ship model control system is designed. The system realizes forward and backward by switching the gear lever. The steering wheel rotates left and right and the pause button controls the forward, reverse and pause of the dc motor. The remote controller of ship model driven by the motor realizes forward and backward, left and right steering, pause and other functions. The ship model control system can effectively solve the problems of small driving power and mutual interference between ship MODELS, and can be widely used in the field of remote controller of ship model.
統計學習基礎:數據挖掘、推理與預測介紹了這些領域的一些重要概念。盡管應用的是統計學方法，但強調的是概念，而不是數學。許多例子附以彩圖。《統計學習基礎:數據挖掘、推理與預測》內容廣泛，從有指導的學習（預測）到無指導的學習，應有盡有。包括神經網絡、支持向量機、分類樹和提升等主題，是同類書籍中介紹得最全面的。計算和信息技術的飛速發展帶來了醫學、生物學、財經和營銷等諸多領域的海量數據。理解這些數據是一種挑戰，這導致了統計學領域新工具的發展，并延伸到諸如數據挖掘、機器學習和生物信息學等新領域。許多工具都具有共同的基礎，但常常用不同的術語來表達。【內容推薦】《統計學習基礎:數據挖掘、推理與預測》試圖將學習領域中許多重要的新思想匯集在一起，并且在統計學的框架下解釋它們。隨著計算機和信息時代的到來，統計問題的規模和復雜性都有了急劇增加。數據存儲、組織和檢索領域的挑戰導致一個新領域“數據挖掘”的產生。數據挖掘是一個多學科交叉領域，涉及數據庫技術、機器學習、統計學、神經網絡、模式識別、知識庫、信息提取、高性能計算等諸多領域，并在工業、商務、財經、通信、醫療衛生、生物工程、科學等眾多行業得到了廣泛的應用。【作者簡介】Trevor Hastie,Robert Tibshirani和Jerome Friedman都是斯坦福大學統計學教授，并在這個領域做出了杰出的貢獻。Hastie和Tibshirani提出了廣義和加法模型，并出版專著“Generalized Additive MODELS”。Hastie的主要研究領域為：非參數回歸和分類、統計計算以及生物信息學、醫學和工業的特殊數據挖掘問題。他提出主曲線和主曲面的概念，并用S-PLUS編寫了大量統計建模軟件。Tibshirani的主要研究領域為：應用統計學、生物統計學和機器學習。他提出了套索的概念，還是“An Introduction to the Bootstrap”一書的作者之一。Friedman是CART、MARS和投影尋蹤等數據挖掘工具的發明人之一。他不僅是位統計學家，而且是物理學家和計算機科學家，先后在物理學、計算機科學和統計學的一流雜志上表發論文80余篇。
Mathematical modeling has become an important part of the research and devclopment work in engineering and scicnce. Retaining a competitive edge requiresa fast path between ideas and prototypes, and in this regard mathematical modeling and simulation provide a valuable shortcut for understanding both qualitative and quantitative aspects of scientific and engineering design. To assist you in gaining this edge, COMSOL Multiphysics offers state-of-the art performance, being built from the ground up with a Java3D interface and C/C++ solvers.The Acoustics Module is an optional package that extends the COMSOL Multiphysicsmodcling cnvironment with customized user interfaces and functionality optimizcd for the analysis of acoustics. Like all modules in the COMSOL family, it provides a brary of prewritten ready-to-run MODELS that make it quicker and casier to analyze disciplinc-specific problcms.
