- We will send in 10–14 business days.
/images/books/14538585/9781032806242.jpg)
- Author: Yunong Zhang
- Publisher: CRC Press
- ISBN-10: 1032806249
- ISBN-13: 9781032806242
- Format: 17.8 x 25.4 x 2.1 cm, kieti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Zhang Time Discretization (Ztd) Formulas and Applications (e-book) (used book) | bookbook.eu
Reviews
Description
The book aims to solve the discrete implementation problems of continuous-time neural network models while improving the performance of neural networks by using various Zhang Time Discretization (ZTD) formulas.
The authors summarize and present the systematic derivations and complete researches of ZTD formulas from special 3S-ZTD formulas to general NS-ZTD formulas. These finally lead to their proposed discrete-time Zhang neural network (DTZNN) algorithms, which are more efficient, accurate, and elegant. The book will open the door to scientific and engineering applications of ZTD formulas and neural networks, and will be a major inspiration for studies in neural network modeling, numerical algorithm design, prediction, and robot manipulator control.
The book will benefit engineers, senior undergraduates, graduate students, and researchers in the fields of neural networks, computer mathematics, computer science, artificial intelligence, numerical algorithms, optimization, robotics, and simulation modeling.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 21d.02:15:47
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Yunong Zhang
- Publisher: CRC Press
- ISBN-10: 1032806249
- ISBN-13: 9781032806242
- Format: 17.8 x 25.4 x 2.1 cm, kieti viršeliai
- Language: English English
The book aims to solve the discrete implementation problems of continuous-time neural network models while improving the performance of neural networks by using various Zhang Time Discretization (ZTD) formulas.
The authors summarize and present the systematic derivations and complete researches of ZTD formulas from special 3S-ZTD formulas to general NS-ZTD formulas. These finally lead to their proposed discrete-time Zhang neural network (DTZNN) algorithms, which are more efficient, accurate, and elegant. The book will open the door to scientific and engineering applications of ZTD formulas and neural networks, and will be a major inspiration for studies in neural network modeling, numerical algorithm design, prediction, and robot manipulator control.
The book will benefit engineers, senior undergraduates, graduate students, and researchers in the fields of neural networks, computer mathematics, computer science, artificial intelligence, numerical algorithms, optimization, robotics, and simulation modeling.
Reviews