Team:TCU Taiwan/Modeling

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<div class="wrapper" style="background-color: white;"><font size="3" face="Verdana" color="#333">TEST.</font></div>
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<p class="qbody"><font size="3" face="Verdana" color="#333"><img src="https://static.igem.org/mediawiki/2012/3/3f/MathWorks_logo.png" width="300" height="19" style=" float: left; margin-right: 5px;">Emergency Medical Technician (EMT) are terms used in some countries to denote a <a href="http://en.wikipedia.org/wiki/Health_professional" target="_blank">health care provider</a> of <a href="http://en.wikipedia.org/wiki/Emergency_medical_services" target="_blank">emergency medical services</a>.EMTs are clinicians, trained to respond quickly to emergency situations regarding medical issues, traumatic injuries and accident scenes.</font></p>
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<p class="qbody"></p><font size="3" face="Verdana" color="#333">The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed.</font></div>
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Revision as of 15:53, 14 October 2014



 
Modeling
  
 
1.Introduction
TEST.
 
 
2.Software


Emergency Medical Technician (EMT) are terms used in some countries to denote a health care provider of emergency medical services.EMTs are clinicians, trained to respond quickly to emergency situations regarding medical issues, traumatic injuries and accident scenes.

The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed.
 

Modeling

If you choose to create a model during your project, please write about it here. Modeling is not an essential part of iGEM, but we encourage any and all teams to model some aspect of their project. See previous "Best Model" awards for more information.
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Team Members Project Parts Human Pratics Modeling Safety Notebook Attributions

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