Noise-tolerant neural algorithm for online solving time-varying full-rank matrix Moore–Penrose inverse problems: A control-theoretic approach

2020 ◽  
Vol 413 ◽  
pp. 158-172
Author(s):  
Zhongbo Sun ◽  
Feng Li ◽  
Long Jin ◽  
Tian Shi ◽  
Keping Liu
Keyword(s):  
Inverse Problems ◽  
Full Rank ◽  
Theoretic Approach ◽  
Time Varying ◽  
Rank Matrix ◽  
Noise Tolerant

Noise-Tolerant ZNN Models for Solving Time-Varying Zero-Finding Problems: A Control-Theoretic Approach

2017 ◽  
Vol 62 (2) ◽  
pp. 992-997 ◽  
Author(s):  
Long Jin ◽  
Yunong Zhang ◽  
Shuai Li ◽  
Yinyan Zhang
Keyword(s):  
Theoretic Approach ◽  
Time Varying ◽  
Noise Tolerant

Zhang neural network solving for time-varying full-rank matrix Moore–Penrose inverse

Computing ◽  
2010 ◽  
Vol 92 (2) ◽  
pp. 97-121 ◽  
Author(s):  
Yunong Zhang ◽  
Yiwen Yang ◽  
Ning Tan ◽  
Binghuang Cai
Keyword(s):  
Neural Network ◽  
Full Rank ◽  
Time Varying ◽  
Rank Matrix ◽  
Zhang Neural Network

A Control-Theoretic Approach to Neural Pharmacology: Optimizing Drug Selection and Dosing

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Gautam Kumar ◽  
Seul Ah Kim ◽  
ShiNung Ching
Keyword(s):  
Design Method ◽  
Target Space ◽  
Theoretic Approach ◽  
Time Varying ◽  
Drug Selection ◽  
Modeling Paradigm ◽  
Dosing Strategies ◽  
Dose Dependent ◽  
Optimal Construction ◽  
Selection Of

The induction of particular brain dynamics via neural pharmacology involves the selection of particular agonists from among a class of candidate drugs and the dosing of the selected drugs according to a temporal schedule. Such a problem is made nontrivial due to the array of synergistic drugs available to practitioners whose use, in some cases, may risk the creation of dose-dependent effects that significantly deviate from the desired outcome. Here, we develop an expanded pharmacodynamic (PD) modeling paradigm and show how it can facilitate optimal construction of pharmacologic regimens, i.e., drug selection and dose schedules. The key feature of the design method is the explicit dynamical-system based modeling of how a drug binds to its molecular targets. In this framework, a particular combination of drugs creates a time-varying trajectory in a multidimensional molecular/receptor target space, subsets of which correspond to different behavioral phenotypes. By embedding this model in optimal control theory, we show how qualitatively different dosing strategies can be synthesized depending on the particular objective function considered.

A Non-linear and Noise-Tolerant ZNN Model and Its Application to Static and Time-Varying Matrix Square Root Finding

2018 ◽  
Vol 50 (2) ◽  
pp. 1687-1703
Author(s):  
Xiaoxiao Li ◽  
Jiguo Yu ◽  
Shuai Li ◽  
Zehui Shao ◽  
Lina Ni
Keyword(s):  
Time Varying ◽  
Square Root ◽  
Root Finding ◽  
Matrix Square Root ◽  
Non Linear ◽  
Noise Tolerant

Improved Gradient Neural Networks for Solving Moore–Penrose Inverse of Full-Rank Matrix

2019 ◽  
Vol 50 (2) ◽  
pp. 1993-2005 ◽  
Author(s):  
Xuanjiao Lv ◽  
Lin Xiao ◽  
Zhiguo Tan ◽  
Zhi Yang ◽  
Junying Yuan
Keyword(s):  
Neural Networks ◽  
Full Rank ◽  
Rank Matrix

Solvability to Robust Output Regulation of Singular Linear Systems

2012 ◽  
Vol 433-440 ◽  
pp. 2680-2686
Author(s):  
Yu Teng ◽  
Yu Zhi Huang ◽  
Zhi Rong Chen
Keyword(s):  
Linear Systems ◽  
Full Rank ◽  
Output Regulation ◽  
Rank Matrix ◽  
Robust Output Regulation ◽  
Singular Linear Systems ◽  
Feedback Regulator

In this paper, robust output regulation problems of singular linear systems are studied. Under appropriate assumptions, it is given that the solvability of robust output regulation problems equals to a specifically row full rank matrix. Moreover, the methods for constructing the feedback regulator are discussed.

Sign in / Sign up

Export Citation Format

Share Document