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

Yunong (Sun Yat-sen University Zhang

ISBN 10: 0367656493 / ISBN 13: 9780367656492

Edité par Taylor & Francis Ltd Sep 2020, 2020

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Neuware.

N° de réf. du vendeur 9780367656492

Détails bibliographiques

• Titre: Toward Deep Neural Networks : WASD Neuronet Models, Algorithms, and Applications
• Éditeur: Taylor & Francis Ltd Sep 2020
• Date de parution: 2020
• Langue: anglais
• ISBN à 10 chiffres: 0367656493
• ISBN à 13 chiffres: 9780367656492
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 254 L x 178 H x 0 P (en millimètres) 254 millimètres de largeur par 178 millimètres de hauteur par 0 millimètres de profondeur
• Poids: 680 grammes

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Synopsis

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

À propos de l?auteur

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.