Team:TCU Taiwan/Modeling
From 2014.igem.org
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<p class="qbody"></p><br><br><br><br><p><font face="Trebuchet MS" size="5" color="#90B849">ANFIS</font><br> | <p class="qbody"></p><br><br><br><br><p><font face="Trebuchet MS" size="5" color="#90B849">ANFIS</font><br> | ||
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- | <font size="3" face="Verdana" color="#333">The architecture and learning procedure underlying ANFIS (<strong>A</strong>daptive | + | <font size="3" face="Verdana" color="#333">The architecture and learning procedure underlying ANFIS (<strong>A</strong>daptive <strong>N</strong>etwork-based <strong>F</strong>uzzy <strong>I</strong>nference <strong>S</strong>ystem) 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 13:09, 15 October 2014
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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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