Three qualitative simulation extensions for supporting economics models

2002 ◽  
Author(s):  
D.M. Bridgeland
Keyword(s):  
Qualitative Simulation

Qualitative simulation and related approaches for the analysis of dynamic systems

2004 ◽  
Vol 19 (2) ◽  
pp. 93-132 ◽  
Author(s):  
HIDDE DE JONG
Keyword(s):  
Qualitative Analysis ◽  
Dynamic Systems ◽  
Quantitative Information ◽  
Simulation Methods ◽  
Science And Engineering ◽  
Computational Framework ◽  
Combinatorial Explosion ◽  
Qualitative Simulation ◽  
Qualitative Phase ◽  
Made In

Methods for qualitative simulation allow predictions on the dynamics of a system to be made in the absence of quantitative information, by inferring the range of possible qualitative behaviors compatible with the structure of the system. This article reviews QSIM and other qualitative simulation methods. It discusses two problems that have seriously compromised the application of these methods to realistic problems in science and engineering: the occurrence of spurious behavior predictions and the combinatorial explosion of the number of behavior predictions. In response to these problems, related approaches for the qualitative analysis of dynamic systems have emerged: qualitative phase-space analysis and semi-quantitative simulation. The article argues for a synthesis of these approaches in order to obtain a computational framework for the qualitative analysis of dynamic systems. This should provide a solid basis for further upscaling and for the development of model-based reasoning applications of a wider scope.

Thruster fault diagnosis in autonomous underwater vehicle based on grey qualitative simulation

2015 ◽  
Vol 105 ◽  
pp. 247-255 ◽  
Author(s):  
Ming-jun Zhang ◽  
Yu-jia Wang ◽  
Jian-an Xu ◽  
Zheng-chen Liu
Keyword(s):  
Fault Diagnosis ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Qualitative Simulation

scholarly journals Qualitative Simulation of Process Plants

1987 ◽  
Vol 20 (5) ◽  
pp. 329-334
Author(s):  
O.O. Oyeleye ◽  
M.A. Kramer
Keyword(s):  
Qualitative Simulation ◽  
Process Plants

Research on Qualitative Simulation of Weapon Equipment Wasting in a War

2011 ◽  
Vol 214 ◽  
pp. 673-677
Author(s):  
Li Yun Chen ◽  
Han Feng Zhang ◽  
Yu Lu ◽  
Kai Huang
Keyword(s):  
Simulation Theory ◽  
Qualitative Information ◽  
Qualitative Simulation ◽  
Model Based

There are many factors which can influence the weapon equipment wastage in a war, and it is very difficult to forecast the wastage of weapon equipment. A wartime weapon equipment wastage synthesis model based on qualitative simulation is brought forward, and the qualitative reason of weapon equipment wasting in wartime is conducted based on Kuipers qualitative simulation theory. Then the qualitative variety chart of weapon equipment wastage is given. Simulation result accords with the fact, therefore, the method can provide the quite important qualitative information for the commander of equipment supporting.

A DIVIDE-AND-CONQUER STRATEGY FOR QUALITATIVE SIMULATION AND FUZZY IDENTIFICATION OF COMPLEX DYNAMICAL SYSTEMS

2011 ◽  
Vol 19 (03) ◽  
pp. 423-452 ◽  
Author(s):  
RAFFAELLA GUGLIELMANN ◽  
LILIANA IRONI
Keyword(s):  
Scale Up ◽  
Fuzzy Rule ◽  
Qualitative Reasoning ◽  
Divide And Conquer ◽  
Rule Base ◽  
Qualitative Simulation ◽  
Biological Domain ◽  
Large Systems ◽  
Behavioural Description

Fuzzy systems properly integrated with Qualitative Reasoning approaches yield a hybrid identification method, called FS-QM, that outperforms traditional data-driven approaches in terms of robustness, interpretability and efficiency in both rich and poor data contexts. This results from the embedment of the entire system dynamics predicted by the simulation of its qualitative model, represented by fuzzy-rules, into the fuzzy system. However, the intrinsic limitation of qualitative simulation to scale up to complex and large systems significantly reduces its efficient applicability to real-world problems. The novelty of this paper deals with a divide-and-conquer approach that aims at making qualitative simulation tractable and the derived behavioural description comprehensible and exhaustive, and consequently usable to perform system identification. The partition of the complete model into smaller ones prevents the generation of a complete temporal ordering of all unrelated events, that is one of the major causes of intractable branching in qualitative simulation. The set of generated behaviours is drastically but beneficially reduced as it still captures the entire range of possible dynamical distinctions. Thus, the properties of the correspondent fuzzy-rule base, that guarantee robustness and interpretability of the identified model, are preserved. The strategy we propose is discussed through a case study from the biological domain.

Qualitative simulation of organization quality specific immune decision-making of manufacturing enterprises based on QSIM algorithm simulation

2021 ◽  
pp. 1-18
Author(s):  
Qiang Liu ◽  
Xiaoli Qu ◽  
Danyu Zhao ◽  
Yu Guo
Keyword(s):  
Decision Making ◽  
Simulation Method ◽  
Engineering Management ◽  
Decision Variable ◽  
Science And Engineering ◽  
Qualitative Simulation ◽  
Manufacturing Enterprises ◽  
Decision Variables ◽  
Organization Quality ◽  
Simulation Results

Quality is the core of the enterprise, strengthening organization quality specific immune is the key channel. Organization quality specific immune belongs to science and engineering management field, QSIM qualitative simulation method that refer to computational simulation algorithm is widely used in the science and engineering management field, QSIM qualitative simulation method can solve science and engineering management issues effectively. In this study, qualitative simulation QSIM theory is used to analyze and reason the organization quality specific immune decision of manufacturing enterprises. Combined with the pressure-state-response framework, the management mechanism of organization quality specific immune is analyzed according to state variables, decision variables, system variables and environment variables, and further the qualitative simulation rules for organization quality specific immune decision-making are set according to the causal relationships among variables of organization quality specific immune. This study sets organization quality monitor, organization quality defense and organization quality memory as the decision variables, uses QSIM algorithm for simulating organization quality specific immune decision-making reasoning, compares with the influences of single decision variable and multi-decision variables on organization quality specific immune system through simulation results. Simulation results indicate that QSIM algorithm simulation can be used to simulate and reason organization quality specific immune decision-making in order to help manufacturing enterprises reasonably enhance organization quality specific immune performance and quality performance through three decision variables of organization quality monitor, organization quality defense and organization quality memory. The simulation results will provide new revelation for organization quality specific immune decision-making of manufacturing enterprises.

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