Team:TCU Taiwan/Modeling

MATLAB

MATLAB(MATrix LABoratory) 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™.

ANFIS

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.

To build the model,we created, trained, and tested Adaptive Network-based Fuzzy Inference System (ANFIS).We performed the following tasks:

1.Data pre-processing
2.Loading the Data
3.Generating the Initial ANFIS Structure
4.Training the ANFIS
5.Validating the Trained ANFIS

Before we start the ANFIS training, we specify an initial ANFIS model structure as below:

Fig3.1  Initial ANFIS Model Structure

4.Training the ANFIS

After loading the training data and generating the initial ANFIS structure, we started training the ANFIS. The number of training Epochs(Epochs means number of iterations) is over 2500.

During training we can see how Training error develops as below:

Fig3.2  Training Error

5.Validating the Trained ANFIS

Finally,we had to test our data against the trained ANFIS.After the ANFIS is trained, validate the model using a Testing data that differs from the one we used to train the ANFIS.

When we test the testing data against the FIS, it looks satisfactory.

Fig3.3  Testing our data against the trained ANFIS

Model 1 : Release

We chose two variables to find the best release amount of phage after helper phage infecting. The first variable is the time after we input helper phages M13KO7 into E.Coli (JM101) with phagemid pBluescript II SK(-).(We have put BBa_I13521 inside as an reporter gene.)This is the time for helper phages to infect bacterium.

After the infection, we added kanamycin into these JM101 for selection becauseJM101 can get kanamycin resistance only when they are infected by M13KO7. Then we incubated these JM101 so they can have time to release phagemid-carrying phage, and the incubating time is the second variable in our test.

As we can see in this figure, the most amount of phage being released is at the time when we add kanamycin after 30 minutes of infection and then incubate them for 14 hours. Under this condition, the best releasing amount of phage is 4×10¹⁰ pfu/ml.

Fig4.1  Model1