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四元数体上微分方程的理论及其应用(英文版)

四元数体上微分方程的理论及其应用(英文版)

出版社:科学出版社出版时间:2021-07-01
开本: 16开 页数: 266
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四元数体上微分方程的理论及其应用(英文版) 版权信息

  • ISBN:9787030690562
  • 条形码:9787030690562 ; 978-7-03-069056-2
  • 装帧:一般胶版纸
  • 册数:暂无
  • 重量:暂无
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四元数体上微分方程的理论及其应用(英文版) 内容简介

四元数体上微分方程理论已经在微分方程定性与稳定性研究中发挥着重要的作用,并以其丰富的理论思想和复杂的数学技巧应用到数学的各个研究领域之中,本书总结国内外知名学者的研究成果下,作者根据几年来在这方面的研究总结,把一些近期新的研究进展和新成果介绍给广大读者,希望读者能进一步了解它。目前靠前上没有一本关于四元数体上微分方程的著作。本书内容翔实,适合高等院校数学方向的教师、研究生或相关研究领域的科研人员阅读参考。

四元数体上微分方程的理论及其应用(英文版) 目录

Contents
Preface
Athors’ biography
Chapter 1 Background of Quaternion and Quaternion-valued Differential Equations 1
1.1 Background for quaternions 1
1.2 Background for QDEs 5
1.2.1 Quaternion Frenet frames in differential geometry 5
1.2.2 QDEs appears in kinematic modelling and attitude dynamics 6
1.2.3 QDE appears in fluid mechanics 8
1.2.4 QDE appears in quantum mechanics 8
1.3 History and motivation of our research 9
Chapter 2 Preliminary Concepts and Notations 12
2.1 Quaternion algebra 12
2.2 Biquaternion algebra 14
2.3 Definitions of determinants 15
2.4 Groups, rings, modules 17
2.5 Existence and uniqueness of solution to QDEs 20
Chapter 3 Basic Theory of Linear Homogeneous Quaternion-valued Differential Equations 22
3.1 Structure of general solutions for 2D QDEs 22
3.2 Structure of general solutions for any finite dimensional QDEs based on permutation 30
3.3 Fundamental matrix and solution to QDEs 40
3.4 Algorithm for computing fundamental matrix 47
3.4.1 Method 1: using expansion of exp{At} 48
3.4.2 Method 2: eigenvalue and eigenvector theory 51
Chapter 4 Algorithm for Linear Homogeneous QDEs when Linear Homogeneous System Has Multiple Eigenvalues 55
4.1 Motivations 55
4.2 Solving linear homogenous QDEs when linear homogeneous system has multiple eigenvalues 56
4.2.1 Multiple eigenvalues with enough eigenvectors 56
4.2.2 Multiple eigenvalues with fewer eigenvectors 58
Chapter 5 Floquet Theory of Quaternion-valued Differential Equations 66
5.1 Preliminary results 67
5.2 Stability of linear homogeneous QDEs with constant coefficients 70
5.3 Floquet theory for QDEs 75
5.4 Quaternion-valued Hill’s equations 84
Chapter 6 Solve Linear Nonhomogeneous Quaternion-valued Differential Equations 88
6.1 Notations 88
6.2 Main results 90
6.3 Some examples 92
Chapter 7 Linear Quaternion Dynamic Equations on Time Scale 97
7.1 Notations and preliminary results 98
7.1.1 Notations and lemmas 98
7.1.2 Calculus on time scales 100
7.2 First order linear QDETS 103
7.3 Linear systems of QDETS 109
7.4 Linear QDETS with constant coefficients 119
Chapter 8 Laplace Transform: a New Approach in Solving Linear Quaternion Differential Equations 129
8.1 Introduction 129
8.2 Biquaternion algebra 130
8.2.1 Biquaternion exponential function 131
8.2.2 Fundamental theorem of quaternion algebra and factorization theorem revisited 134
8.3 Definition and properties of the Laplace transform in biquaternion domain 134
8.4 Using QLT to solve QDEs 144
Chapter 9 Solving Quaternion Differential Equations with Two-sided Coefficients 150
9.1 Introduction 150
9.2 Notations and preliminary results 151
9.3 Solving QDEs with unilateral coefficients 152
9.4 Solving QDEs with two-sided coefficients 160
9.4.1 Homogeneous linear QDEs with two-sided coefficients 160
9.4.2 Nonhomogeneous linear QDEs with two-sided coefficients 168
Chapter 10 Controllability and Observability of Linear Quaternionvalued Systems 172
10.1 Motivations 172
10.2 Notations and preliminary results 174
10.3 Main results on the controllability and observability of linear QVS 178
10.3.1 Controllability 178
10.3.2 Observability 183
10.3.3 Duality 187
Chapter 11 Stability Analysis of Quaternion-valued Neural Networks 190
11.1 Notations and preliminary results 190
11.2 Main results 197
11.3 Examples 205
Chapter 12 Convex Function Optimization Problems with Quaternion Variables 209
12.1 Notations and preliminary results 210
12.1.1 Quaternion algebra analysis 210
12.1.2 Generalized gradient 213
12.2 Main results on the convex function optimization problems with quaternion variables 217
12.3 Examples and simulations 230
12.4 Proof of the Proposition 12.1.4 232
Chapter 13 Penalty Method for Constrained Distributed Quaternionvariable Optimization 236
13.1 Introduction 236
13.2 Preliminaries 238
13.3 Main results 245
13.4 An example 250
Bibliography 253
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四元数体上微分方程的理论及其应用(英文版) 节选

Chapter 1 Background of Quaternion and Quaternion-valued Differential Equations First of all, why should we study the quaternion-valued differential equations (QDEs)? In the following, we will introduce the background and motivations of the quaternion-valued differential equations? 1.1 Background for quaternions Quaternions are 4-vectors whose multiplication rules are governed by a simple noncommutative division algebra. We denote the quaternion q = (q0, q1, q2, q3)T ∈ R4 where q0, q1, q2, q3 are real numbers and i, j, k satisfy the multiplication table formed Throughout this book, we use the notations i, j, k to denote the imaginary units. The concept was originally invented by Hamilton in 1843 that extends the complex numbers to four-dimensional space. Quaternions have shown advantages over real-valued vectors in physics and engineering applications for their powerful modeling of rotation and orientation. Orientation can be defined as a set of parameters that relates the angular position of a frame to another reference frame. There are numerous methods for describing this relationship. Some are easier to visualize than the others. Each has some kind of limitations. Among them, Euler angles and quaternions are commonly used. We give an example from [21, 22] for illustration. Attitude and Heading Sensors (AHS) from CH robotics can provide orientation information using both Euler angles and quaternions. Compared to quaternions, Euler angles are simple and intuitive (see Figures 1.1.1—1.1.4). The complete rotation matrix for moving from the inertial frame to the body frame is given by the multiplication of three matrix taking the form where RBV2 (.),RV2 V1 (θ),RV1 I (ψ) are the rotation matrix moving from vehicle-2 frame to the body frame, vehicle-1 frame to vehicle-2 frame, inertial frame to vehicle-1 frame, respectively. Figure 1.1.1 Inertial frame Figure 1.1.2 Vehicle-1 frame On the other hand, Euler angles are limited by a phenomenon called “Gimbal Lock”. The cause of such phenomenon is that when the pitch angle is 90 degrees (see Figure 1.1.5). An orientation sensor that uses Euler angles will always fail to produce reliable estimates when the pitch angle approaches 90 degrees. This is a serious shortcoming of Euler angles and can only be solved by switching to a different representation method. Quaternions provide an alternative measurement technique that does not suffer from Gimbal Lock. Therefore, all CH Robotics attitude sensors use quaternions so that the output is always valid even when Euler Angles are not. Figure 1.1.3 Vehicle-2 frame Figure 1.1.4 Body frame The attitude quaternion estimated by CH Robotics orientation sensors encodes rotation from the “inertial frame” to the sensor “body frame”. The inertial frame is an Earth-fixed coordinate frame defined so that the x-axis points north, the y-axis points east, and the z-axis points down as shown in Figure 1.1.1. The sensor bodyframe is a coordinate frame that remains aligned with the sensor at all times. Unlike Euler angle estimation, only the body frame and the inertial frame are needed when quaternions are used for estimation. Let the vector q = (q0, q1, q2, q3)T be defined as the unit-vector quaternion encoding rotation from the inertial frame to the body frame of the sensor, where T is the vector transpose operator. The elements q1, q2, and q3 are the “vector part” of the quaternion, and can be thought of as a vector about which rotation should be performed. The element q0 is the “scalar part” that specifies the amount of rotation that should be performed about the vector part. Specifically, if θ is the angle of rotation and the vector e = (ex, ey, ez) is a unit vector representing the axis of rotation, then the quaternion elements are defined as Figure 1.1.5 Gimbal Lock In fact, Euler’s theorem states that given two coordinate systems, there is one invariant axis (namely, Euler axis), along which measurements are the same in both coordinate systems. Euler’s theorem also shows that it is possible to move from one coordinate system to the other through one rotation θ about that invariant axis. Quaternionic representation of the attitude is based on Euler’s theorem. Given a unit vector e = (ex, ey, ez) along the Euler axis, the quaternion is defined to be Besides the attitude orientation, quaternions have been widely applied to study life science, physics and engineering. For instances, an interesting application is the description of protein structure (see Figure 1.1.6) [23], neural networks [24], spatial rigid body transformation [25], Frenet frames in differential geometry, fluid mechanics, quantum mechanics, and so on. Figure 1.1.6 The transformation that sends one peptide plane to the next is a screw motion 1.2 Background for QDEs 1.2.1 Quaternion Frenet frames in differential geometry The differential geometr

四元数体上微分方程的理论及其应用(英文版) 作者简介

Professor Yong-Hui Xia(夏永辉), Distinguished Professor in Zhejiang Normal University, received his Ph.D. degree in applied mathematics in 2009 from Shanghai Normal University, Shanghai, China. From Mar. 2004 to Sept. 2009, he was with Fuzhou University. From Sept. 2009 to May 2016, he was with Zhejiang Normal University. He was a Min-Jiang-Distinguished Professor in Huaqiao University from Apr. 2016 to May 2018. In Jun. 2018, he rejoined Zhejiang Normal University as a Distinguished Professor, Jinhua, China. From Oct. 2007 to Jan. 2008, he was a Visiting Scholar in the School of Information Systems, York University, Toroto, Canada. From-Jul. 2012 to Aug. 2013, he was a Research Fellow at CAMTP, Maribor Univesity, Slovenia. From 16 Jan. 2015 to 15 Feb. 2015, he was as a visiting Professor in Macau University. From 1 Jul. 2015 to 31 Dec. 2016, he was visiting Macau University as research fellow.

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