Team:NCTU Formosa/modeling

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===ANFIS Introduction===
===ANFIS Introduction===
<p>Adaptive-Network-Based Fuzzy Inference System, in short ANFIS, is a power tool for constructing a set of fuzzy if-then rules to generate stipulated output and input pairs. Unlike system modeling using mathematical rules that lacks the ability to deal with ill-defined and uncertain system, ANFIS can transform human knowledge into rule base, and therefore, ANFIS can effectively tune membership functions, minimizing the output error.</p>
<p>Adaptive-Network-Based Fuzzy Inference System, in short ANFIS, is a power tool for constructing a set of fuzzy if-then rules to generate stipulated output and input pairs. Unlike system modeling using mathematical rules that lacks the ability to deal with ill-defined and uncertain system, ANFIS can transform human knowledge into rule base, and therefore, ANFIS can effectively tune membership functions, minimizing the output error.</p>
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Revision as of 03:42, 26 October 2013

Modeling

Modeling was our first step forward. When validated with our experimental data, modeling is also a verification of the accuracy of our experiments.

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Contents

MATLAB Introduction

MATLAB (matrix laboratory) is a numerical computing environment and fourth-generation programming language. It is developed by MathWorks, a company in United States. MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, and Fortran. Although MATLAB is intended primarily for numerical computing, an optional toolbox uses the MuPAD symbolic engine, allowing access to symbolic computing capabilities. An additional package, Simulink, adds graphical multi-domain simulation and Model-Based Design for dynamic and embedded systems.


Fig.  Images of MATLAB

ANFIS Introduction

Adaptive-Network-Based Fuzzy Inference System, in short ANFIS, is a power tool for constructing a set of fuzzy if-then rules to generate stipulated output and input pairs. Unlike system modeling using mathematical rules that lacks the ability to deal with ill-defined and uncertain system, ANFIS can transform human knowledge into rule base, and therefore, ANFIS can effectively tune membership functions, minimizing the output error.

the above

Light-regulated System

Red Promoter

Temperature-regulated system

Figure 1. Input 1= Time (hr), Input 2= Temperature (oC), Output = Normalized expression (AU).


From Figure 1, the maximum output is obtained at 37oC. Under the same time frame, the output (the normalized expression of the reporter gene) is maximized at 37oC while minimized at 25oC. There is a dramatic decrease in the output near 30o and the outputs around 37oC are much higher. This modeling demonstrates that using 37oC RBS is a plausible approach for achieving gene expression through temperature.

Small RNA-regulated System

rRBS efficiency