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Artifical Neural Network: Theory and Its Applications

Price: \$15.78 \$11.09 (Save \$4.69)

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This book comprehensively and deeply introduces the artificial neural network theory and its application. The book consists of three sections: the foundation of neural network, artificial neural network theory, the design and practical application of neural network. First section mainly includes the theoretical basis of biological neural network, the review of artificial artificial neural network and the mathematical basis of artificial neural network. Second section includes some artificial neural network theory and algorithm, such as Perceptron, BP neural network, RBF neural network, Adaline neural network, Hopfield neural network, deep convolutional learning neural network, generative adversarial network, AdaBoost neural network, Elman neural network and SOFM neural network. Third section is the design and practical application of artificial neural network including the artificial neural network modeling based on Simulink, and artificial neural network design based on GUI using MATLAB and Python.
This book can be used as a textbook for undergraduate and graduate students who are engaged in the theory, design and application of artificial neural network, It can also be used as a self-study and reference book for professional engineers.
Section 1 Foundation of neural network
Chapter 1 Theoretical basis of biological neural network
1．1 Structure and function of biological neurons
1．2 Electrical activity of the nervous system
1．3 Information storage of human brain
1．4 Human brain and computer
Exercises
References
Chapter 2 Review of artificial neural network
2．1 Development history of artificial neural network
2．2 Characteristics of artificial neural network
2．3 Applications of artificial neural network
Exercises
References
Chapter 3 Mathematical basis of artificial neural network
3．1 Neuron model
3．1．1 Symbol description
3．1．2 Single input neuron
3．1．3 Transfer function
3．1．4 Multiple input neurons
3．2 Derivatives
3．3 Differential
3．4 Integrals
3．6 Determinant
3．7 Matrices
3．7．1 Concept
3．7．2 Operation of matrices
3．7．3 Operational properties of matrices
3．8 Vector
3．9 Eigenvalues and eigenvectors
3．10 Random events and probabilities
3．11 Norm
Exercises
References
……

Section 2 Theory of artificial neural network
Section 3 Design and practical application of artificial neural network

Appendix

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