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This book is intended for researchers, teachers, and students willing to ex- plore conceptual bridges between the fields of Automatic Control and Power Electronics. The need to bring the two disciplines closer has been felt, for many years, both by Power Electronics specialists and by Automatic Control theorists, as a means of fruitful interaction between the two scientific com- munities.
標(biāo)簽： Techniques Control Design
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
There exist two essentially different approaches to the study of dynamical systems, based on the following distinction: time-continuous nonlinear differential equations ? time-discrete maps One approach starts from time-continuous differential equations and leads to time-discrete maps, which are obtained from them by a suitable discretization of time. This path is pursued, e.g., in the book by Strogatz [Str94]. 1 The other approach starts from the study of time-discrete maps and then gradually builds up to time-continuous differential equations, see, e.g., [Ott93, All97, Dev89, Has03, Rob95]. After a short motivation in terms of nonlinear differential equations, for the rest of this course we shall follow the latter route to dynamical systems theory. This allows a generally more simple way of introducing the important concepts, which can usually be carried over to a more complex and physically realistic context.
標(biāo)簽： Systems_Rainer Introduction Dynamical Klages to
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
This book is an outgrowth of a course developed at Stanford University over the past five years. It is suitable as a self-contained textbook for second-level undergraduates or for first-level graduate students in almost every field that employs quantitative methods. As prerequisites, it is assumed that the student may have had a first course in differential equations and a first course in linear algebra or matrix analysis. These two subjects, however, are reviewed in Chapters 2 and 3, insofar as they are required for later developments.
標(biāo)簽： Introduction_to_Dynamic_Systems
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
A kinematically redundant manipulator is a serial robotic arm that has more independently driven joints than are necessary to define the desired pose (position and orientation) of its end-effector. With this definition, any planar manipulator (a manipulator whose end-effector motion is restrained in a plane) with more than three joints is a redundant manipulator. Also, a manipulator whose end-effector can accept aspatialposeisaredundant manipulator ifithas morethan sixindependently driven joints. For example, the manipulator shown in Fig. 1.1 has two 7-DOF arms mounted on a torso with three degrees of freedom (DOFs). This provides 10 DOFs for each arm. Since the end-effector of each arm can have a spatial motion with six DOFs, the arms are redundant.
標(biāo)簽： Autonomous Modeling Planning Robots Path
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
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.
標(biāo)簽： Bishop-Pattern-Recognition-and-Ma chine-Learning
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
Current field forecast verification measures are inadequate, primarily because they compress the comparison between two complex spatial field processes into one number. Discrete wavelet transforms (DWTs) applied to analysis and contemporaneous forecast fields prove to be an insightful approach to verification problems. DWTs allow both filtering and compact physically interpretable partitioning of fields. These techniques are used to reduce or eliminate noise in the verification process and develop multivariate measures of field forecasting performance that are shown to improve upon existing verification procedures.
標(biāo)簽： field forecast verification
上傳時(shí)間： 2020-07-22
上傳用戶：
An Arduino core for the ATmega328, ATmega168, ATmega88, ATmega48 and ATmega8, all running a [custom version of Optiboot for increased functionality](#write-to-own-flash). This core requires at least Arduino IDE v1.6.2, where v1.8.5+ is recommended. <br/> **This core gives you two extra IO pins if you're using the internal oscillator!** PB6 and PB7 is mapped to [Arduino pin 20 and 21](#pinout).<br/> If you're into "generic" AVR programming, I'm happy to tell you that all relevant keywords are being highlighted by the IDE through a separate keywords file. Make sure to test the [example files](https://github.com/MCUdude/MiniCore/tree/master/avr/libraries/AVR_examples/examples) (File > Examples > AVR C code examples). Try writing a register name, <i>DDRB</i> for instance, and see for yourself!
標(biāo)簽： MiniCore
上傳時(shí)間： 2021-02-22
上傳用戶：
%this is an example demonstrating the Radial Basis Function %if you select a RBF that supports it (Gausian, or 1st or 3rd order %polyharmonic spline), this also calculates a line integral between two %points.
標(biāo)簽： RBF 神經(jīng)網(wǎng)絡(luò)
上傳時(shí)間： 2021-07-02
上傳用戶：19800358905
PLL(Phase Locked Loop)： 為鎖相回路或鎖相環(huán)，用來(lái)統(tǒng)一整合時(shí)鐘信號(hào)，使高頻器件正常工作，如內(nèi)存的存取資料等。PLL用于振蕩器中的反饋技術(shù)。 許多電子設(shè)備要正常工作，通常需要外部的輸入信號(hào)與內(nèi)部的振蕩信號(hào)同步。一般的晶振由于工藝與成本原因，做不到很高的頻率，而在需要高頻應(yīng)用時(shí)，由相應(yīng)的器件VCO，實(shí)現(xiàn)轉(zhuǎn)成高頻，但并不穩(wěn)定，故利用鎖相環(huán)路就可以實(shí)現(xiàn)穩(wěn)定且高頻的時(shí)鐘信號(hào)。
上傳時(shí)間： 2021-07-23
上傳用戶：紫陽(yáng)帝尊
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