Toward Deep Neural Networks : WASD Neuronet Models, Algorithms, and Applications (Chapman & Hall/crc Artificial Intelligence and Robotics Series)

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Toward Deep Neural Networks : WASD Neuronet Models, Algorithms, and Applications (Chapman & Hall/crc Artificial Intelligence and Robotics Series)

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  • 製本 Hardcover:ハードカバー版/ページ数 368 p.
  • 言語 ENG
  • 商品コード 9781138387034
  • DDC分類 006.32

Full Description

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

Contents

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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