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Machine Learning Applications in Electromagnetics and Antenna Array Processing Unabridged edition [Kõva köide]

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  • Formaat: Hardback, 436 pages
  • Ilmumisaeg: 30-Apr-2021
  • Kirjastus: Artech House Publishers
  • ISBN-10: 1630817759
  • ISBN-13: 9781630817756
Teised raamatud teemal:
Machine Learning Applications in Electromagnetics and Antenna Array Processing Unabridged editionSuurem pilt
  • Formaat: Hardback, 436 pages
  • Ilmumisaeg: 30-Apr-2021
  • Kirjastus: Artech House Publishers
  • ISBN-10: 1630817759
  • ISBN-13: 9781630817756
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781630817756.html
Märksõnad:
This practical resource provides an overview of machine learning (ML) approaches as applied to electromagnetics and antenna array processing. Detailed coverage of the main trends in ML, including uniform and random array processing (beamforming and detection of angle of arrival), antenna optimization, wave propagation, remote sensing, radar, and other aspects of electromagnetic design are explored. An introduction to machine learning principles and the most common machine learning architectures and algorithms used today in electromagnetics and other applications is presented, including basic neural networks, gaussian processes, support vector machines, kernel methods, deep learning, convolutional neural networks, and generative adversarial networks. Applications in electromagnetics and antenna array processing that are solved using machine learning are discussed, including antennas, remote sensing, and target classification.
Preface xi
Acknowledgments xiii
1 Linear Support Vector Machines
1(34)
1.1 Introduction
1(1)
1.2 Learning Machines
2(10)
1.2.1 The Structure of a Learning Machine
3(1)
1.2.2 Learning Criteria
4(2)
1.2.3 Algorithms
6(2)
1.2.4 Example
8(1)
1.2.5 Dual Representations and Dual Solutions
8(4)
1.3 Empirical Risk and Structural Risk
12(4)
1.4 Support Vector Machines for Classification
16(11)
1.4.1 The SVC Criterion
16(4)
1.4.2 Support Vector Machine Optimization
20(7)
1.5 Support Vector Machines for Regression
27(8)
1.5.1 The v Support Vector Regression
29(3)
References
32(3)
2 Linear Gaussian Processes
35(26)
2.1 Introduction
35(1)
2.2 The Bayes' Rule
36(4)
2.2.1 Computing the Probability of an Event Conditional to Another
37(1)
2.2.2 Definition of Conditional Probabilities
38(1)
2.2.3 The Bayes' Rule and the Marginalization Operation
38(1)
2.2.4 Independency and Conditional Independency
39(1)
2.3 Bayesian Inference in a Linear Estimator
40(1)
2.4 Linear Regression with Gaussian Processes
41(3)
2.4.1 Parameter Posterior
42(2)
2.5 Predictive Posterior Derivation
44(2)
2.6 Dual Representation of the Predictive Posterior
46(7)
2.6.1 Derivation of the Dual Solution
46(3)
2.6.2 Interpretation of the Variance Term
49(4)
2.7 Inference over the Likelihood Parameter
53(3)
2.8 Multitask Gaussian Processes
56(5)
References
58(3)
3 Kernels for Signal and Array Processing
61(60)
3.1 Introduction
61(1)
3.2 Kernel Fundamentals and Theory
62(29)
3.2.1 Motivation for RKHS
63(5)
3.2.2 The Kernel Trick
68(3)
3.2.3 Some Dot Product Properties
71(5)
3.2.4 Their Use for Kernel Construction
76(4)
3.2.5 Kernel Eigenanalysis
80(9)
3.2.6 Complex RKHS and Complex Kernels
89(2)
3.3 Kernel Machine Learning
91(17)
3.3.1 Kernel Machines and Regularization
92(4)
3.3.2 The Importance of the Bias Kernel
96(4)
3.3.3 Kernel Support Vector Machines
100(6)
3.3.4 Kernel Gaussian Processes
106(2)
3.4 Kernel Framework for Estimating Signal Models
108(13)
3.4.1 Primal Signal Models
110(3)
3.4.2 RKHS Signal Models
113(3)
3.4.3 Dual Signal Models
116(2)
References
118(3)
4 The Basic Concepts of Deep Learning
121(46)
4.1 Introduction
121(2)
4.2 Feedforward Neural Networks
123(18)
4.2.1 Structure of a Feedforward Neural Network
123(3)
4.2.2 Training Criteria and Activation Functions
126(5)
4.2.3 ReLU for Hidden Units
131(1)
4.2.4 Training with the BP Algorithm
132(9)
4.3 Manifold Learning and Embedding Spaces
141(26)
4.3.1 Manifolds, Embeddings, and Algorithms
143(4)
4.3.2 Autoencoders
147(11)
4.3.3 Deep Belief Networks
158(5)
References
163(4)
5 Deep Learning Structures
167(28)
5.1 Introduction
167(1)
5.2 Stacked Autoencoders
168(7)
5.3 Convolutional Neural Networks
175(8)
5.4 Recurrent Neural Networks
183(5)
5.4.1 Basic Recurrent Neural Network
183(1)
5.4.2 Training a Recurrent Neural Network
184(2)
5.4.3 Long Short-Term Memory Network
186(2)
5.5 Variational Autoencoders
188(7)
References
193(2)
6 Direction of Arrival Estimation
195(44)
6.1 Introduction
195(2)
6.2 Fundamentals of DOA Estimation
197(5)
6.3 Conventional DOA Estimation
202(4)
6.3.1 Subspace Methods
202(2)
6.3.2 Rotational Invariance Technique
204(2)
6.4 Statistical Learning Methods
206(11)
6.4.1 Steering Field Sampling
206(7)
6.4.2 Support Vector Machine MuSiC
213(4)
6.5 Neural Networks for Direction of Arrival
217(22)
6.5.1 Feature Extraction
217(2)
6.5.2 Backpropagation Neural Network
219(3)
6.5.3 Forward-Propagation Neural Network
222(4)
6.5.4 Autoencoder Framework for DOA Estimation with Array Imperfections
226(4)
6.5.5 Deep Learning for DOA Estimation with Random Arrays
230(5)
References
235(4)
7 Beamforming
239(32)
7.1 Introduction
239(1)
7.2 Fundamentals of Beamforming
240(6)
7.2.1 Analog Beamforming
240(1)
7.2.2 Digital Beamforming/Precoding
241(2)
7.2.3 Hybrid Beamforming
243(3)
7.3 Conventional Beamforming
246(4)
7.3.1 Beamforming with Spatial Reference
246(3)
7.3.2 Beamforming with Temporal Reference
249(1)
7.4 Support Vector Machine Beamformer
250(4)
7.5 Beamforming with Kernels
254(6)
7.5.1 Kernel Array Processors with Temporal Reference
254(2)
7.5.2 Kernel Array Processor with Spatial Reference
256(4)
7.6 RBF NN Beamformer
260(2)
7.7 Hybrid Beamforming with Q-Learning
262(9)
References
267(4)
8 Computational Electromagnetics
271(26)
8.1 Introduction
271(1)
8.2 Finite-Difference Time Domain
272(8)
8.2.1 Deep Learning Approach
273(7)
8.3 Finite-Difference Frequency Domain
280(6)
8.3.1 Deep Learning Approach
283(3)
8.4 Finite Element Method
286(4)
8.4.1 Deep Learning Approach
287(3)
8.5 Inverse Scattering
290(7)
8.5.1 Nonlinear Electromagnetic Inverse Scattering Using DeepNIS
292(3)
References
295(2)
9 Reconfigurable Antennas and Cognitive Radio
297(22)
9.1 Introduction
297(1)
9.2 Basic Cognitive Radio Architecture
298(1)
9.3 Reconfiguration Mechanisms in Reconfigurable Antennas
299(1)
9.4 Examples
299(16)
9.4.1 Reconfigurable Fractal Antennas
300(4)
9.4.2 Pattern Reconfigurable Microstrip Antenna
304(3)
9.4.3 Star Reconfigurable Antenna
307(2)
9.4.4 Reconfigurable Wideband Antenna
309(3)
9.4.5 Frequency Reconfigurable Antenna
312(3)
9.5 Machine Learning Implementation on Hardware
315(1)
9.6 Conclusion
316(3)
References
316(3)
About the Authors 319(2)
Index 321