Toward Deep Neural Networks

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.

• 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

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