Toward Deep Neural Networks: WASD Neuronet Models, Algorithms, and Applications

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

Table of 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