Degenerate Problems (Matrix Case)

Author(s):  
L. A. Sakhnovich
Author(s):  
Jared Gross ◽  
Kijung Park ◽  
Gül E. Okudan Kremer

With the rise in popularity of additive manufacturing (AM), relevant design methodologies have become necessary for designers to reap the full benefits from this technology. TRIZ is a problem-solving tool developed to assist with innovative and creative solutions. This paper aims to create a new TRIZ matrix specifically developed for designers using additive manufacturing. The TRIZ matrix offers designers general innovative design solutions to improve specific features of a design while not sacrificing the effectiveness of other features. The proposed matrix can help effective design decision making for additive manufacturing in an early design process as well as a redesign process. Also, a design for additive manufacturing (DfAM) worksheet is provided to enable users to easily find specific design solutions for certain additive manufacturing techniques based on the general solutions derived by the TRIZ matrix. To illustrate the potential of this AM specific TRIZ matrix, case studies are presented.


2015 ◽  
Vol 76 (6) ◽  
pp. 1094-1100
Author(s):  
E. I. Veremey ◽  
V. V. Eremeev ◽  
N. A. Zhabko ◽  
S. V. Pogozhev
Keyword(s):  

Author(s):  
Rintani Dyan Pangastuti ◽  
Bryan Utama Angka ◽  
Mohit Amardas Lakhwani ◽  
Jitro Behuku ◽  
James Leonardo Putra
Keyword(s):  

2019 ◽  
Vol 33 (28) ◽  
pp. 1950343 ◽  
Author(s):  
Zhilian Yan ◽  
Youmei Zhou ◽  
Xia Huang ◽  
Jianping Zhou

This paper addresses the issue of finite-time boundedness for time-delay neural networks with external disturbances via weight learning. With the help of a group of inequalities and combining with the Lyapunov theory, weight learning rules are devised to ensure the neural networks to be finite-time bounded for the fixed connection weight matrix case and the fixed delayed connection weight matrix case, respectively. Sufficient conditions on the existence of the desired learning rules are presented in the form of linear matrix inequalities, which are easily verified by MATLAB software. It is shown that the proposed learning rules also guarantee the finite-time stability of the time-delay neural networks. Finally, a numerical example is employed to show the applicability of the devised weight learning rules.


2013 ◽  
Vol 2013 (1) ◽  
Author(s):  
Aida Sahmurova ◽  
Veli B Shakhmurov

Sign in / Sign up

Export Citation Format

Share Document