Toward deep neural networks : WASD neuronet models, algorithms, and applications
著者
書誌事項
Toward deep neural networks : WASD neuronet models, algorithms, and applications
（Chapman & Hall/CRC artificial intelligence and robotics series）（A Chapman & Hall book）
CRC Press, Taylor & Francis Group, c2019
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注記
Bibliography: p. 321-338
Includes index
内容説明・目次
内容説明
Toward Deep Neural Networks: WASD Neuronet Models, Algorithms, and Applications introduces the outlook and extension toward deep neural networks, with a focus on the weights-and-structure determination (WASD) algorithm. Based on the authors' 20 years of research experience on neuronets, the book explores the models, algorithms, and applications of the WASD neuronet, and allows reader to extend the techniques in the book to solve scientific and engineering problems. The book will be of interest to engineers, senior undergraduates, postgraduates, and researchers in the fields of neuronets, computer mathematics, computer science, artificial intelligence, numerical algorithms, optimization, simulation and modeling, deep learning, and data mining.
Features
Focuses on neuronet models, algorithms, and applications
Designs, constructs, develops, analyzes, simulates and compares various WASD neuronet models, such as single-input WASD neuronet models, two-input WASD neuronet models, three-input WASD neuronet models, and general multi-input WASD neuronet models for function data approximations
Includes real-world applications, such as population prediction
Provides complete mathematical foundations, such as Weierstrass approximation, Bernstein polynomial approximation, Taylor polynomial approximation, and multivariate function approximation, exploring the close integration of mathematics (i.e., function approximation theories) and computers (e.g., computer algorithms)
Utilizes the authors' 20 years of research on neuronets
目次
I Single-Input-Single-Output Neuronet
1 Single-Input Euler-PolynomialWASD Neuronet
2 Single-Input Bernoulli-PolynomialWASD Neuronet
3 Single-Input Laguerre-PolynomialWASD Neuronet
II Two-Input-Single-Output Neuronet
4 Two-Input Legendre-PolynomialWASD Neuronet
5 Two-Input Chebyshev-Polynomial-of-Class-1WASD Neuronet
6 Two-Input Chebyshev-Polynomial-of-Class-2WASD Neuronet
III Three-Input-Single-Output Neuronet
7 Three-Input Euler-PolynomialWASD Neuronet
8 Three-Input Power-ActivationWASD Neuronet
IV General Multi-Input Neuronet
9 Multi-Input Euler-PolynomialWASD Neuronet
10 Multi-Input Bernoulli-PolynomialWASD Neuronet
11 Multi-Input Hermite-PolynomialWASD Neuronet
12 Multi-Input Sine-ActivationWASD Neuronet
V Population Applications Using Chebyshev-Activation Neuronet
13 Application to Asian Population Prediction
14 Application to European Population Prediction
15 Application to Oceania Population Prediction
16 Application to Northern American Population Prediction
17 Application to Indian Subcontinent Population Prediction
18 Application toWorld Population Prediction
VI Population Applications Using Power-Activation Neuronet
19 Application to Russian Population Prediction
20 WASD Neuronet versus BP Neuronet Applied to Russia Population Prediction
21 Application to Chinese Population Prediction
22 WASD Neuronet versus BP Neuronet Applied to Chinese Population Prediction
VII Other Applications
23 Application to USPD Prediction
24 Application to Time Series Prediction
25 Application to GFR Estimation
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