Toward Deep Neural Networks
WASD Neuronet Models, Algorithms, and Applications
1st Edition
Published on 19. March 2019
368 pages
978-0-429-76098-3 (ISBN)
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
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
Reviews / Votes
The book is appealing for graduate students as well as academic and industrial researchers. Based on the comprehensive and systematic research of artificial neural network, especially conventional artificial neural network, the book solves the difficult problem of WASD (weights and structure determination). The book may generate curiosity and also happiness to its readers for learning more in the fields and the researches.- Professor Jinde Cao, Southeast University, Nanjing, China
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Product notice
Reflowable
Illustrations
87 Tables, black and white; 148 Illustrations, black and white
File size
22,10 MB
ISBN-13
978-0-429-76098-3 (9780429760983)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Yunong Zhang | Dechao Chen | Chengxu Ye
Toward Deep Neural Networks
WASD Neuronet Models, Algorithms, and Applications
Book
09/2020
1st Edition
Chapman & Hall/CRC
€61.89
Shipment within 15-20 days
Yunong Zhang | Dechao Chen | Chengxu Ye
Toward Deep Neural Networks
WASD Neuronet Models, Algorithms, and Applications
Book
03/2019
1st Edition
CRC Press
€200.56
Shipment within 10-20 days
Persons
Yunong Zhang received a BSc. degree from Huazhong University of Science and Technology, Wuhan, China, in 1996, an MSc. degree from South China University of Technology, Guangzhou, China, in 1999, and a PhD. degree from Chinese University of Hong Kong, Shatin, Hong Kong, China, in 2003. He is currently a professor at the School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China. Yunong Zhang was supported by the Program for New Century Excellent Talents in Universities in 2007, was presented the Best Paper Award of ISSCAA in 2008 and the Best Paper Award of ICAL in 2011, and was among the Highly Cited Scholars of China selected and published by Elsevier from year 2014 to year 2017. His web-page is now available at http://sdcs.sysu.edu.cn/content/2477.
Dechao Chen received a BSc. degree from Guangdong University of Technology, Guangzhou, China, in 2013. He is currently pursuing his PhD. degree in Communication and Information Systems at School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China, under the direction of Professor Yunong Zhang. His research interests include robotics, neuronets, and nonlinear dynamics systems.
Chengxu Ye received a BSc. degree from Shanxi Normal University, Xian, China, in 1991, an MSc. degree from Qinghai Normal University, Xining, China, in 2008, and a PhD. degree from Sun Yat-sen University, Guangzhou, China, in 2015. He is currently a professor at School of Computer, Qinghai Normal University, Xining, China. His main research interests include machine learning, neuronets, computation and optimization. He has published over 30 scientific papers in journals and conferences.
Dechao Chen received a BSc. degree from Guangdong University of Technology, Guangzhou, China, in 2013. He is currently pursuing his PhD. degree in Communication and Information Systems at School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China, under the direction of Professor Yunong Zhang. His research interests include robotics, neuronets, and nonlinear dynamics systems.
Chengxu Ye received a BSc. degree from Shanxi Normal University, Xian, China, in 1991, an MSc. degree from Qinghai Normal University, Xining, China, in 2008, and a PhD. degree from Sun Yat-sen University, Guangzhou, China, in 2015. He is currently a professor at School of Computer, Qinghai Normal University, Xining, China. His main research interests include machine learning, neuronets, computation and optimization. He has published over 30 scientific papers in journals and conferences.
Content
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
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
