Dynamic learning super network modeling of a complex product system based on multi-organization cooperation

2018 ◽  
Vol 32 (31) ◽  
pp. 1850375
Author(s):  
Shuang Kan ◽  
Wei Lv ◽  
Fu Guo
Keyword(s):  
Complex System ◽  
Driving Forces ◽  
Network Modeling ◽  
Interaction Mechanism ◽  
Learning Performance ◽  
Systems Science ◽  
Node Degree ◽  
Dynamic Learning ◽  
The Operating Mechanism ◽  
Complex Production System

Super network modeling has become an effective approach for analyzing complex systems. In this study, a super network is proposed in terms of systems science. A vital aspect of the modeling is the unique dynamic mechanism of the complex system, particularly the preferential mechanism. The preferential mechanism is the driving force of system evolution. However, in current studies, preferential probability has been mostly related to node degree, and there has been little consideration of multi-attribute decision-making based on complex system characteristics (multi-level, multi-traffic, etc.). Furthermore, association analysis of driving forces and the topology structure of the complex system should be highlighted to explore the operating mechanism. In this study, we consider a complex production system (CoPS) as the research object, propose a unique learning motivation mechanism and interaction mechanism of the CoPS, develop a preferential algorithm based on the interval-valued intuitionistic uncertain linguistic (IVIUL) operator, and construct a multi-organization knowledge learning super network model. A simulation experiment was conducted to explore the effect of the preferential parameter on learning performance. The results show that the feature of the project team has an important influence on the learning improvement velocity of the super network.

scholarly journals Group Measures and Modeling for Social Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Vincent Levorato
Keyword(s):  
Complex System ◽  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Modeling ◽  
Complex Interactions ◽  
Social Network Modeling

Social network modeling is generally based on graph theory, which allows for study of dynamics and emerging phenomena. However, in terms of neighborhood, the graphs are not necessarily adapted to represent complex interactions, and the neighborhood of a group of vertices can be inferred from the neighborhoods of each vertex composing that group. In our study, we consider that a group has to be considered as a complex system where emerging phenomena can appear. In this paper, a formalism is proposed to resolve this problematic by modeling groups in social networks using pretopology as a generalization of the graph theory. After giving some definitions and examples of modeling, we show how some measures used in social network analysis (degree, betweenness, and closeness) can be also generalized to consider a group as a whole entity.

scholarly journals Complex Systems Science and Community-Based Research

2020 ◽  
Vol 3 ◽  
pp. 1-27
Author(s):  
Travis Robert Moore ◽  
Helena VonVille ◽  
Winnie Poel ◽  
Glory Dee A. Romo ◽  
Ian Lim ◽  
...  
Keyword(s):  
Complex System ◽  
Complex Systems ◽  
Scoping Review ◽  
Research Literature ◽  
Systems Science ◽  
Community Based ◽  
System Science ◽  
Community Based Research ◽  
Research And Practice ◽  
Complex Systems Science

There is an abundance of community-based research literature that incorporates complex system science concepts and techniques. However, currently there is a gap in how these concepts and techniques are being used, and, more broadly, how these two fields complement one another. The debate on how complex systems science meaningfully bolsters the deployment of community-based research has not yet reached consensus, therefore, we present a protocol for a new scoping review that will identify characteristics at the intersection of community-based research and complex systems science. This knowledge will enhance the understanding of how complex systems science, a quickly evolving field, is being utilized in community-based research and practice.

scholarly journals Evaluation of Degree of Coordination between Chinese Investment in Basic Research and Economic Growth

2016 ◽  
Vol 11 (6) ◽  
pp. 159 ◽  
Author(s):  
Wu Dandan ◽  
Wang Zichen ◽  
Zheng Shengming
Keyword(s):  
Complex System ◽  
Economic Growth ◽  
Basic Research ◽  
Systems Science ◽  
Chinese Investment ◽  
Steady Growth ◽  
Order Degree ◽  
Complex Systems Science ◽  
Proper Operation

<p>Based on the theory of complex systems science and synergetics, a model for the coordination degree of complex system “investment in basic research and economic growth” was built in this paper for the quantitative evaluation of the degree of coordination between investment in basic research and economic growth in China in recent 2 decades from a macro perspective. Results show that the subsystem of investment in basic research and the subsystem of economic growth and the whole complete system are showing a good momentum of steady growth. Although the subsystem of investment in basic research is slightly inferior to the subsystem of economic growth in order degree, it more matches the coordination of China's economic growth on the whole. In order to further promote and strengthen the basic research efforts in China, a number of policy recommendations were raised in this paper with a view to advancing the proper operation of the investment in basic research system in China in the new normal of economy and its long-term coordinate development with the complex system of economic growth. </p>

Modeling and Simulation of Petri Net in Iron-Making System

2013 ◽  
Vol 645 ◽  
pp. 299-302
Author(s):  
You Jun Yue ◽  
Shui Yu ◽  
Hui Zhao ◽  
Hong Jun Wang
Keyword(s):  
Complex System ◽  
Modeling And Simulation ◽  
Production Process ◽  
Dynamic Behavior ◽  
Petri Net ◽  
Production System ◽  
Logistics System ◽  
Long Time ◽  
Ideal Tool ◽  
Complex Production System

The logistics system of iron-making is a very complex production system, and it is characterized by continuity and discreteness in combination. It is difficult to establish the model of the complex system for a long time. The model of Petri net can not only characterize the structure of complex system, but also can describe the dynamic behavior of it, these features make the Petri net a wonderful tool to build the model of production process. Based on that, this paper sets up the model of the logistics system of iron-making by the Petri net. And then simulate this model by Stateflow which is an ideal tool of simulation. All these works is the foundation for the further analyzing of the logistics system.

scholarly journals Feedback-related EEG dynamics separately reflect decision parameters, biases, and future choices

2021 ◽  
Author(s):  
Hans Kirschner ◽  
Adrian G Fischer ◽  
Markus Ullsperger
Keyword(s):  
Computational Modelling ◽  
Learning Performance ◽  
Optimal Decision ◽  
Dynamic Learning ◽  
Unexpected Events ◽  
Feedback Processing ◽  
State Action ◽  
Normative Factors ◽  
Good Learning ◽  
Optimal Decision Making

Optimal decision making in complex environments requires dynamic learning from unexpected events. To speed up learning, we should heavily weight information that indicates state-action-outcome contingency changes and ignore uninformative fluctuations in the environment. Often, however, unrelated information is hard to ignore and can potentially bias our learning. Here we used computational modelling and EEG to investigate learning behaviour in a modified probabilistic choice task that introduced two types of unexpected events that were irrelevant for optimal task performance, but nevertheless could potentially bias learning: pay-out magnitudes were varied randomly and, occasionally, feedback presentation was enhanced by visual surprise. We found that participants' overall good learning performance was biased by distinct effects of these non-normative factors. On the neural level, these parameters are represented in a dynamic and spatiotemporally dissociable sequence of EEG activity. Later in feedback processing the different streams converged on a central to centroparietal positivity reflecting a final pathway of adaptation that governs future behaviour.

scholarly journals Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine

2019 ◽  
Vol 9 (5) ◽  
pp. 895 ◽  
Author(s):  
Ahmed AL-Khaleefa ◽  
Mohd Ahmad ◽  
Azmi Isa ◽  
Mona Esa ◽  
Ahmed AL-Saffar ◽  
...  
Keyword(s):  
Online Learning ◽  
Extreme Learning Machine ◽  
Learning Performance ◽  
Dynamic Learning ◽  
Term Memory ◽  
Machine Learning Model ◽  
Time Series Dataset ◽  
Learning Machine ◽  
Dynamic Generator ◽  
Cyclic Dynamic

Online learning is the capability of a machine-learning model to update knowledge without retraining the system when new, labeled data becomes available. Good online learning performance can be achieved through the ability to handle changing features and preserve existing knowledge for future use. This can occur in different real world applications such as Wi-Fi localization and intrusion detection. In this study, we generated a cyclic dynamic generator (CDG), which we used to convert an existing dataset into a time series dataset with cyclic and changing features. Furthermore, we developed the infinite-term memory online sequential extreme learning machine (ITM-OSELM) on the basis of the feature-adaptive online sequential extreme learning machine (FA-OSELM) transfer learning, which incorporates an external memory to preserve old knowledge. This model was compared to the FA-OSELM and online sequential extreme learning machine (OSELM) on the basis of data generated from the CDG using three datasets: UJIndoorLoc, TampereU, and KDD 99. Results corroborate that the ITM-OSELM is superior to the FA-OSELM and OSELM using a statistical t-test. In addition, the accuracy of ITM-OSELM was 91.69% while the accuracy of FA-OSELM and OSELM was 24.39% and 19.56%, respectively.

Urban Health as a Complex System

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
G Damiani ◽  
M Sapienza
Keyword(s):  
Public Health ◽  
Complex System ◽  
Literature Review ◽  
Complex Systems ◽  
Environmental Health ◽  
Urban Health ◽  
Driving Forces ◽  
Grey Literature ◽  
World Health ◽  
Extensive Literature

Abstract The 17 Sustainable Development Goals (SDGs) are a complex system comprising 169 targets and about 230 indicators. Urban Health could be considered a complex system since it deals with 15 out of 17 SDGs, excluding the two related to life below water and life on land. According to the World Health Organization, to achieve the SDGs, countries have committed to organize Urban Health initiatives to improve the social, economic, and physical environments promoting health and sustainability globally. Cities will become more inclusive, safer and more sustainable, which are important driving forces to implement the development equation. To set up a framework to point out that Urban Health is a complex system oriented to the achievement of the SDGs. It has been conducted an extensive literature review on databases (PubMed, Embase, Web of Science and Scopus) using keywords in 3 strings combined with Boolean operators: SDGs and Urban Health, Urban Health as Complex System and Urban Health, Environmental Health and Public Health as Complex Systems. In addition, a grey literature review has been carried out. Out of 1005 publications, 21 were eventually included: 14 publications relate to the interdependent relationships between SDGs and Urban Health, with regard to the association between Urban Health and complex system, 3 publication studied the effects and implications of such correlation, 4 focused on Environmental Health and Public Health in relation with the complex system. The selected publications suggested methodologies aimed at setting up an urban health framework to achieve SDGs. There is an initial orientation focused on the study of Urban Health problems aimed at achieving SDGs from the perspective of complex systems. We highlight the need to conduct further studies on a more detailed framework in order to address this type of approach.

Modeling of Distillation Tower Temperature Based on D-FNN

2011 ◽  
Vol 383-390 ◽  
pp. 1463-1469
Author(s):  
Shu Zhi Gao ◽  
Jing Yang ◽  
Jun Fan
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Network Modeling ◽  
Temperature Control System ◽  
Neural Network Modeling ◽  
Time Varying ◽  
Distillation Process ◽  
Dynamic Learning ◽  
Fuzzy Neural

Distillation temperature control system is characteristics of nonlinear time-varying and we use dynamic fuzzy neural network to model the temperature of distillation. Firstly, we introduce the structure and algorithm of dynamic fuzzy neural network; Second, after data preprocessing of distillation process, we use dynamic Fuzzy neural network modeling the temperature of distillation. Dynamic fuzzy neural network adopt dynamic learning algorithm, and characteristic of approximation. The simulation results show the effect and accuracy of Dynamic fuzzy neural network model ing method.

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