scholarly journals From Penrose Equations to Zhang Neural Network, Getz–Marsden Dynamic System, and DDD (Direct Derivative Dynamics) Using Substitution Technique

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Dongqing Wu ◽  
Yunong Zhang
Keyword(s):  
Neural Network ◽  
Computer Simulation ◽  
Problem Solving ◽  
Dynamic System ◽  
Generalized Inverse ◽  
Computer Experiment ◽  
Time Discretization ◽  
Theoretical Derivation ◽  
Depth Study ◽  
Zhang Neural Network

The time-variant matrix inversion (TVMI) problem solving is the hotspot of current research because of its frequent appearance and application in scientific research and industrial production. The generalized inverse problem of singular square matrix and nonsquare matrix can be related to Penrose equations (PEs). The PEs implicitly define the generalized inverse of a known matrix, which is of fundamental theoretical significance. Therefore, the in-depth study of PEs might enlighten problem solving of TVMI in a foreseeable way. For the first time, we construct three different matrix error-monitoring functions based on PEs and propose the corresponding models for TVMI problem solving by using the substitution technique and ZNN design formula. In order to facilitate computer simulation, the obtained continuous-time models are discretized by using ZTD (Zhang time discretization) formulas. Furthermore, the feasibility and effectiveness of the novel Zhang neural network (ZNN) multiple-multiplication model for matrix inverse (ZMMMI) and the PEs-based Getz–Marsden dynamic system (PGMDS) model in solving the problem of TVMI are investigated and shown via theoretical derivation and computer simulation. Computer experiment results also illustrate that the direct derivative dynamics model for TVMI is less effective and feasible.

Real-domain QR decomposition models employing zeroing neural network and time-discretization formulas for time-varying matrices

2021 ◽  
Vol 448 ◽  
pp. 217-227
Author(s):  
Zhenyu Li ◽  
Yunong Zhang ◽  
Liangjie Ming ◽  
Jinjin Guo ◽  
Vasilios N. Katsikis
Keyword(s):  
Neural Network ◽  
Time Discretization ◽  
Qr Decomposition ◽  
Time Varying ◽  
Real Domain ◽  
Decomposition Models

Integrated Learning: Explicit Strategies and Their Role in Problem-Solving Instruction for Students with Learning Disabilities

1993 ◽  
Vol 59 (5) ◽  
pp. 444-455 ◽  
Author(s):  
Maurice Hollingsworth ◽  
John Woodward
Keyword(s):  
Computer Simulation ◽  
Learning Disabilities ◽  
Problem Solving ◽  
Secondary Students ◽  
Comparison Group ◽  
Strategy Group ◽  
Integrated Learning ◽  
Students With Learning Disabilities ◽  
Simulation Games ◽  
Experimental Group

This study investigated the effectiveness of an explicit strategy as a means of linking facts, concepts, and problem solving in an unfamiliar domain of learning. Participants were 37 secondary students with learning disabilities. All students were taught health facts and concepts, which they then applied to problem-solving exercises presented through computer-simulation games. Students in the experimental group were taught an explicit strategy for solving the problems; the comparison group was given supportive feedback and encouraged to induce their own strategies. The explicit strategy group performed significantly better on two transfer measures, including videotaped problem-solving exercises.

Health ways: a computer simulation for problem solving in personal health management

1986 ◽  
Vol 9 (2) ◽  
pp. 60-63 ◽  
Author(s):  
John P. Woodward ◽  
Douglas Carnine ◽  
Lorraine G. Davis
Keyword(s):  
Computer Simulation ◽  
Problem Solving ◽  
Health Management ◽  
Personal Health

The Effects of Level of Knowledge upon Human Problem Solving in a Process Control Task

1983 ◽  
Vol 27 (8) ◽  
pp. 690-694
Author(s):  
Nancy M. Morris ◽  
William B. Rouse
Keyword(s):  
Problem Solving ◽  
Dynamic System ◽  
Control Task ◽  
Apparent Effect ◽  
Stable Manner ◽  
Different Types ◽  
Level Of Knowledge ◽  
Written Test ◽  
Dynamic Relationships ◽  
Human Problem

The question of what the operator of a dynamic system needs to know was investigated in an experiment using PLANT, a generic simulation of a process. Knowledge of PLANT was manipulated via different types of instructions, so that four different groups were created: 1) Minimal instructions only; 2) Minimal instructions + guidelines for operation (Procedures); 3) Minimal instructions + dynamic relationships (Principles); 4) Minimal instructions + Procedures + Principles. Subjects then controlled PLANT in a variety of familiar and unfamiliar situations. Despite the fact that these manipulations resulted in differences in subjects' knowledge as assessed via a written test at the end of the experiment, instructions had no effect upon achievement of the primary goal of production; however, those groups receiving Procedures controlled the system in a more stable manner. Principles had no apparent effect upon subjects' performance. There was no difference between groups in diagnosis of unfamiliar events.

Decision support or autonomous artificial intelligence? The case of wrong blood in tube errors

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Christopher-John L. Farrell
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Computer Simulation ◽  
Clinical Chemistry ◽  
Human Interaction ◽  
Final Decision ◽  
P Values ◽  
Laboratory Staff

Abstract Objectives Artificial intelligence (AI) models are increasingly being developed for clinical chemistry applications, however, it is not understood whether human interaction with the models, which may occur once they are implemented, improves or worsens their performance. This study examined the effect of human supervision on an artificial neural network trained to identify wrong blood in tube (WBIT) errors. Methods De-identified patient data for current and previous (within seven days) electrolytes, urea and creatinine (EUC) results were used in the computer simulation of WBIT errors at a rate of 50%. Laboratory staff volunteers reviewed the AI model’s predictions, and the EUC results on which they were based, before making a final decision regarding the presence or absence of a WBIT error. The performance of this approach was compared to the performance of the AI model operating without human supervision. Results Laboratory staff supervised the classification of 510 sets of EUC results. This workflow identified WBIT errors with an accuracy of 81.2%, sensitivity of 73.7% and specificity of 88.6%. However, the AI model classifying these samples autonomously was superior on all metrics (p-values<0.05), including accuracy (92.5%), sensitivity (90.6%) and specificity (94.5%). Conclusions Human interaction with AI models can significantly alter their performance. For computationally complex tasks such as WBIT error identification, best performance may be achieved by autonomously functioning AI models.

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