Team:TCU Taiwan/Modeling

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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="58" 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"><font size="3" face="Verdana" color="#333"><img src="https://static.igem.org/mediawiki/2012/3/3f/MathWorks_logo.png" width="300" height="58" style=" float: left; margin-right: 5px;">MATLAB® is a high-level language and interactive environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java™.</font></p>
<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>
<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 15:55, 14 October 2014



 
Modeling
  
 
1.Introduction
TEST.
 
 
2.Software


MATLAB® is a high-level language and interactive environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java™.

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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