Toward deep neural networks WASD neuronet models, algorithms, and applications / [electronic resource] :

• 作者： Zhang, Yunong, author.
• 其他作者：
  • Chen, Dechao, author.
  • Ye, Chengxu, author.
• 其他題名：
  • Chapman & Hall/CRC artificial intelligence and robotics series.
• 出版： Boca Raton, Florida : CRC Press
• 叢書名： Chapman & Hall/CRC artificial intelligence and robotics series
• 主題： Neural networks (Computer science) , Electronic books.
• ISBN： 9780429426445 (electronic bk.) 、 0429426445 (electronic bk.) 、 9780429760983 (electronic bk.) 、 0429760981 (electronic bk.) 、 9780429760990 (electronic bk.) 、 042976099X (electronic bk.) 、 9780429760976 (electronic bk.) 、 0429760973 (electronic bk.)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Includes bibliographical references and index.
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• 摘要註: 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
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