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
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