Uncertain induction of knowledge based on cloud model in complex system simulation

2011 ◽  
Author(s):  
Hongli Wang
Keyword(s):  
Complex System ◽  
Cloud Model ◽  
System Simulation ◽  
Knowledge Based

scholarly journals A Probabilistic Prediction Approach for Memory Resource of Complex System Simulation in Cloud Computing Environment

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1826
Author(s):  
Shuai Wang ◽  
Yiping Yao ◽  
Feng Zhu ◽  
Wenjie Tang ◽  
Yuhao Xiao
Keyword(s):  
Complex System ◽  
Cloud Computing ◽  
System Simulation ◽  
Probabilistic Prediction ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Ensemble Approach ◽  
Memory Resource ◽  
Optimistic Synchronization ◽  
Prediction Approach

Accurate memory resource prediction can achieve optimal performance for complex system simulation (CSS) using optimistic parallel execution in the cloud computing environment. However, because of the varying memory resource demands of CSS applications caused by the simulation entity scale and frequent optimistic synchronization, the existing approaches are unable to predict the memory resource required by a CSS application accurately, which cannot take full advantage of the elasticity and symmetry of cloud computing. In this paper, a probabilistic prediction approach based on ensemble learning, which regards the entity scale and frequent optimistic synchronization as the important features, is proposed. The approach using stacking strategy consists of a two-layer architecture. The first-layer architecture includes two kinds of base models, namely, back-propagation neural network (BPNN) and random forest (RF). The root mean squared error-based pruning algorithm is designed to choose the optimal subset of the base models. The second-layer is the Gaussian process regression (GPR) model, which is applied to quantify the uncertainty information in the probabilistic prediction for memory resources. A series of experiments are presented to prove that the proposed approach can achieve higher accuracy and performance compared to RF, BPNN, GPR, Bagging ensemble approach, and Regressive Ensemble Approach for Prediction.

scholarly journals Service-Oriented Model Encapsulation and Selection Method for Complex System Simulation Based on Cloud Architecture

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 891
Author(s):  
Xiong ◽  
Zhu ◽  
Yao ◽  
Tang ◽  
Xiao
Keyword(s):  
Complex System ◽  
System Simulation ◽  
Simulation Models ◽  
Selection Method ◽  
Semantic Search ◽  
Distributed Scheduling ◽  
Parallel Operation ◽  
Optimal Service ◽  
Service Oriented ◽  
Search And Selection

With the rise in cloud computing architecture, the development of service-oriented simulation models has gradually become a prominent topic in the field of complex system simulation. In order to support the distributed sharing of the simulation models with large computational requirements and to select the optimal service model to construct complex system simulation applications, this paper proposes a service-oriented model encapsulation and selection method. This method encapsulates models into shared simulation services, supports the distributed scheduling of model services in the network, and designs a semantic search framework which can support users in searching models according to model correlation. An optimization selection algorithm based on quality of service (QoS) is proposed to support users in customizing the weights of QoS indices and obtaining the ordered candidate model set by weighted comparison. The experimental results showed that the parallel operation of service models can effectively improve the execution efficiency of complex system simulation applications, and the performance was increased by 19.76% compared with that of scatter distribution strategy. The QoS weighted model selection method based on semantic search can support the effective search and selection of simulation models in the cloud environment according to the user’s preferences.

Knowledge Based Collaborative Modeling and Simulation Platform to Design Complex Product

2011 ◽  
Vol 346 ◽  
pp. 455-459
Author(s):  
Dong Kai Shen ◽  
Zhao Bin Yan
Keyword(s):  
Complex System ◽  
Modeling And Simulation ◽  
Simulation Platform ◽  
Complex Product ◽  
Collaborative Modeling ◽  
Knowledge Based ◽  
Development Cycle

To complex system, its performance is related to many disciplines nowadays. This article proposed a new multidisciplinary collaborative modeling and simulation platform (MCMSP) in designing complex product. The platform is knowledge based so earlier design results and model can help new designers to shorten the development cycle. Then the author uses this platform to design Electro hydrostatic actuator (EHA) which is widely used in Power-By-Wire (PBW) system.

Application of MetaSynthesis in Complex System Simulation

2015 ◽  
Vol 713-715 ◽  
pp. 1631-1634
Author(s):  
Ling Jie Kong ◽  
Jin Li ◽  
Xin Jun Zhao ◽  
Fang Zhang
Keyword(s):  
Complex System ◽  
System Simulation ◽  
Key Technology ◽  
Simulation Based ◽  
Complexity Problem ◽  
Study Methodology ◽  
Equipment System

Metasynthesis is the dialectical unity of reductionism and holism, which is the study methodology of complex giant system and complexity problem. Based on joint demonstrating of equipment system as an example, this paper studies the metasynthesis in the application of complex system simulation. Based on analyzing key technology about hall for workshop of metasynthetic engineering of joint demonstrating,the HWME(Hall for Workshop of Metasynthetic Engineering) of joint demonstrating of equipment system is established.

Complex Systems Concepts in Simulations

2009 ◽  
pp. 452-461
Author(s):  
David Gibson
Keyword(s):  
Complex System ◽  
Complex Systems ◽  
Postsecondary Education ◽  
Teaching And Learning ◽  
System Simulation ◽  
Development Project ◽  
Department Of Education ◽  
Simulation Engine ◽  
Complex Phenomena ◽  
Core Characteristics

This chapter discusses how a teaching simulation can embody core characteristics of a complex system. It employs examples of specific frameworks and strategies used in simSchool, a research and development project supported by two programs of the U. S. Department of Education: Preparing Tomorrow’s Teachers to Use Technology (2004-2006), and currently, the Fund for the Improvement of Postsecondary Education (2006-2009). The chapter assumes that a complex system simulation engine and representation is needed in teaching simulations because teaching and learning are complex phenomena. The chapter’s two goals are to introduce core ideas of complex systems and to illustrate with examples from simSchool, a simulation of teaching and learning.

923 Complex System Simulation on Probability Distribution of Accidents

2013 ◽  
Vol 2013.88 (0) ◽  
pp. _9-23_
Author(s):  
Ayaka YAMAMOTO ◽  
Mamoru OZAWA ◽  
Yoji SHIBUTANI
Keyword(s):  
Complex System ◽  
Probability Distribution ◽  
System Simulation
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