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Applications of Neural Networks in Electromagnetics de Christos Christopoulos

Applications of Neural Networks in Electromagnetics: Artech House Antennas and Propagation Library

Autor Christos Christopoulos, Michael Georgiopoulos
en Limba Engleză Hardback – 30 apr 2001
The high-speed capabilities and "learning" abilities of neural networks can be applied to quickly solving numerous complex optimization problems in electromagnetics, and this book shows you how. Even if you have no background in neural networks, this book helps you understand the basics of each main network architecture in use today, including its strengths and limitations. Moreover, it gives you the knowledge you need to identify situations when the use of neural networks is the best problem-solving option.
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Specificații

ISBN-13: 9780890068809
ISBN-10: 0890068801
Pagini: 512
Dimensiuni: 160 x 236 x 37 mm
Greutate: 0.95 kg
Editura: Artech House Publishers
Seria Artech House Antennas and Propagation Library

Cuprins

Preface. Acknowledgments. Introduction to Neural Networks – Preliminaries. Benefits of Neural Networks. Models of a Neuron. Types of Activation Function. Neural Network Architectures. Learning Procedures. Learning Tasks. Knowledge Representation. Brief History of Neural Networks. Why Neural Networks in Electromagnetics. Single-Layer and Multilayer Perceptron Networks – Introduction. The Single-Layer Perceptron. Perceptron Learning Algorithm. Adaline Network. Multilayer Perceptron. The Back-Propagation Algorithm. Issues With Back-Propagation Learning. Variations of the Back-Propagation Algorithm. The Mulitlayer Perceptron Neural Network for an Automatic Target Recognition Application. MATLAB Code. Radial Basis Function Networks-Kohonen Networks – Introduction. Preliminaries of Radial Basis Function Neural Networks. Learning Strategies With Radial Basis Function Neural Networks. A Radial Basis Function Neural Network Algorithm, A Radial Basis Function Neural Network Example. Comparison of Radial Basis Function Neural Network Learning Strategies. Issues With Radial Basis Function Neural Network Learning. The General Regression Neural Network (GRNN). MATLAB Code. Adaptive Resonance Theory Neural Networks – Introduction. The Fuzzy ARTMAP Neural Network. Templates in Fuzzy ARTMAP: A Geometrical Interpretation. Example. Convergence Speed of Fuzzy ARTMAP. Order of Search in Fuzzy ARTMAP. Applications of Fuzzy ARTMAP. MATLAB Code. Recurrent Neural Networks – Introduction. Preliminaries of Associative Memories. The Hopfield Model. Associative Memory Application of the Hopfield Neural Network. Discussion. Optimization Problems Using the Hopfield Neural Network. A Problem in Communications Using the Hopfield Neural Network. The RTRL Neural Network. RTRL NN Examples. The Recurrent Time Recurrent Learning Neural Network for Channel Equalization. The Elman Neural Network. Elman Neural Network Examples. Angle of Arrival Estimation Using Elman Networks. MATLAB Code. Applications in Antennas – Introduction. Design of Gratings and Frequency Selective Surfaces. Neural Network-Based Adaptive Array Antennas. Beam Shaping With Antenna Arrays. Aperture Antenna Shape Prediction. Reflector Surface Error Compensation. Resonance Frequency of Triangular Microstrip Antennas. Design of Multilayer Phased Array Antennas. Applications in Radar and Remote Sensing – Introduction. Radar Target Classification. Classification of Radar Clutter. Remote Sensing. Applications in Mobile Communications – Introduction. Adaptive Antenna Array Processing. Neural Network-Based Direction Finding. Direction of Arrival for Multiple Sources Using Multilayer Neural Networks. Adaptive Nulling and Steering. Neural Network-Based Interference Cancellation. Applications in Microwave Circuits and Devices – Introduction. Simulation and Optimization of Microwave Devices and Circuits. Modeling of Passive Devices for MMIC Design. Speeding Up and Configuring the Optimum Size for a Neural Network. A Modular, Knowledge-Based Development of Libraries of Neural Network Models. Applications in Computational Electromagnetics – Introduction. Finite Element Applications. A General Neural Network Representation of FEM. A Neural Network Approach of the Method of Moments. Combination of the Piecewise Harmonic Balance Technique and Neural Networks. About the Authors. Index.