Includes Index, bibliography and glossary

I Single-Input-Single-Output Neuronet

1 Single-Input Euler-Polynomial WASD Neuronet

2 Single-Input Bernoulli-Polynomial WASD Neuronet

3 Single-Input Laguerre-Polynomial WASD Neuronet

II Two-Input-Single-Output Neuronet

4 Two-Input Legendre-Polynomial WASD Neuronet

5 Two-Input Chebyshev-Polynomial-of-Class-1WASD Neuronet

6 Two-Input Chebyshev-Polynomial-of-Class-2 WASD Neuronet

III Three-Input-Single-Output Neuronet

7 Three-Input Euler-Polynomial WASD Neuronet

8 Three-Input Power-Activation WASD Neuronet

IV General Multi-Input Neuronet

9 Multi-Input Euler-Polynomial WASD Neuronet

10 Multi-Input Bernoulli-Polynomial WASD Neuronet

11 Multi-Input Hermite-Polynomial WASD 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 to World 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

This book introduces 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.