The basic topic of this book is SOlving problems from system and control theory using convex optimization. We show that a wide variety of problems arising in system and control theory can be reduced to a handful of standard convex and quasiconvex optimization problems that involve matrix inequalities. For a few special cases there are “analytic solutions” to these problems, but our main point is that they can be solved numerically in all cases. These standard problems can be solved in polynomial- time (by, e.g., the ellipsoid algorithm of Shor, Nemirovskii, and Yudin), and so are tractable, at least in a theoretical sense. Recently developed interior-point methods for these standard problems have been found to be extremely efficient in practice. Therefore, we consider the original problems from system and control theory as solved.
標(biāo)簽： Linear_Matrix_Inequalities_in_Sys tem
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
Recent years have seen a rapid development of neural network control tech- niques and their successful applications. Numerous simulation studies and actual industrial implementations show that artificial neural network is a good candidate for function approximation and control system design in SOlving the control problems of complex nonlinear systems in the presence of different kinds of uncertainties. Many control approaches/methods, reporting inventions and control applications within the fields of adaptive control, neural control and fuzzy systems, have been published in various books, journals and conference proceedings.
標(biāo)簽： Stable_adaptive_neural_network_co ntrol
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
n recent years, there have been many books published on power system optimization. Most of these books do not cover applications of artifi cial intelligence based methods. Moreover, with the recent increase of artifi cial intelligence applications in various fi elds, it is becoming a new trend in SOlving optimization problems in engineering in general due to its advantages of being simple and effi cient in tackling complex problems. For this reason, the application of artifi cial intelligence in power systems has attracted the interest of many researchers around the world during the last two decades. This book is a result of our effort to provide information on the latest applications of artifi cial intelligence to optimization problems in power systems before and after deregulation.
標(biāo)簽： Intelligence Artificial System Power in
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
General paradigm in SOlving a computer vision problem is to represent a raw image using a more informative vector called feature vector and train a classifier on top of feature vectors collected from training set. From classification perspective, there are several off-the-shelf methods such as gradient boosting, random forest and support vector machines that are able to accurately model nonlinear decision boundaries. Hence, SOlving a computer vision problem mainly depends on the feature extraction algorithm
標(biāo)簽： Convolutional Networks Neural Guide to
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
The present era of research and development is all about interdisciplinary studies attempting to better comprehend and model our understanding of this vast universe. The fields of biology and computer science are no exception. This book discusses some of the innumerable ways in which computational methods can be used to facilitate research in biology and medicine—from storing enormous amounts of biological data to SOlving complex biological problems and enhancing the treatment of various diseases.
標(biāo)簽： Learning Machine IoT and
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
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(xué)者和有經(jīng)驗(yàn)的用戶編寫的。本指南將引導(dǎo)您逐步解決和分析旋轉(zhuǎn)致動(dòng)器靜運(yùn)動(dòng)問題的結(jié)果。按照本指南中的步驟，您將學(xué)習(xí)如何執(zhí)行以下任務(wù)。修改模型設(shè)計(jì)參數(shù)y將變量分配給模型的設(shè)計(jì)參數(shù)。指定設(shè)計(jì)的解決方案設(shè)置驗(yàn)證設(shè)計(jì)設(shè)置運(yùn)行maxwell模擬v繪制磁通密度vecto v在模擬中包含運(yùn)動(dòng)
上傳時(shí)間： 2022-03-10
上傳用戶：
像計(jì)算機(jī)科學(xué)家一樣思考Python-第2版，本書的目標(biāo)是教你像計(jì)算機(jī)科學(xué)家一樣思考。這一思考方式集成了數(shù)學(xué)、工程以及自然科學(xué)的一些最好的特點(diǎn)。像數(shù)學(xué)家一樣，計(jì)算機(jī)科學(xué)家使用形式語(yǔ)言表示思想（具體來說是計(jì)算）。像工程師一樣，計(jì)算機(jī)科學(xué)家設(shè)計(jì)東西，將零件組成系統(tǒng)，在各種選擇之間尋求平衡。像科學(xué)家一樣，計(jì)算機(jī)科學(xué)家觀察復(fù)雜系統(tǒng)的行為，形成假設(shè)并且對(duì)預(yù)測(cè)進(jìn)行檢驗(yàn)。對(duì)于計(jì)算機(jī)科學(xué)家，最重要的技能是問題求解 的能力。問題求解 (problem SOlving) 意味著對(duì)問題進(jìn)行形式化，尋求創(chuàng)新型的解決方案，并且清晰、準(zhǔn)確地表達(dá)解決方案的能力。事實(shí)證明，學(xué)習(xí)編程的過程是鍛煉問題解決能力的一個(gè)絕佳機(jī)會(huì)。這就是為什么本章被稱為 ‘‘程序之道’’。一方面，你將學(xué)習(xí)如何編程，這本身就是一個(gè)有用的技能。另一方面，你將把編程作為實(shí)現(xiàn)自己目的的手段。隨著學(xué)習(xí)的深入，你會(huì)更清楚自己的目的。
標(biāo)簽： 計(jì)算機(jī)科學(xué)家 python
上傳時(shí)間： 2022-07-26
上傳用戶：
蟲蟲下載站版權(quán)所有 京ICP備2021023401號(hào)-1
