Applications of Neural Networks in Electromagnetics
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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SingleLayer and Multilayer Perceptron Networks
Radial Basis Function NetworksKohonen Networks
Adaptive Resonance Theory Neural Networks
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activation function applied ARTa ARTh back-propagation back-propagation learning bottom-up input Calculate choice parameter classification cluster Computation corresponding desired output Electromagnetic elements Elman network Epoch equal equations error function example FDTD feed-forward fuzzy ARTMAP Gaussian Gaussian functions gradient descent hidden layer hidden nodes Hopfield network Hopfield NN IEEE IEEE Transactions input layer input nodes input patterns input vector input/output pairs interconnection weights iterations KMAX learning algorithm learning rate linear list presentation mapping MATLAB matrix method Microwave MLP-NN multilayer multilayer perceptron Neural Networks neuron NN architecture NN structure nonlinear optimization output layer output nodes output pattern perceptron prediction problem Radar RBF-NN rectangle recurrent NN Reprinted with permission sequence shown in Figure sigmoid function signal solution solve Source Step supervised learning target template TRAINGDM training data training phase training set uncommitted node update vigilance criterion vigilance parameter weight vector weights converging