Deep Neural Networks

WASD Neuronet Models, Algorithms, and Applications

; Dechao Chen ; Chengxu Ye

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. Les mer
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Vår pris: 1553,-

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Leveringstid: Sendes innen 7 virkedager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

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

Fakta

Innholdsfortegnelse

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

Om forfatteren

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