USING ANN CONTROLLER IN MATLAB






ANN CONTROLLER

Figures are uploaded based on requisition to sabareeshwarank@gmail.com or howtousematlab@gmail.com
 
               The usage of ANN controller in the matlab is described below with step by step diagrams.
Basically the controller operates based on the error signal. Your system is first developed on the PI controller base and then used ANN by the training of the neurons…..
Fig 2: shows the nftool i.e Neural network fitting tool in MATLAB 2013a version….
The controller alters the output quicker for the input data….
First we run the system with the PI controller and we take datas from it i.e Variation in the output as per the input..
How to get get datas from PI controller…
Fig 1 explains how the datas gathered from the PI controller…. The datas present infront of the controller and after the controller is taken to the workspace (to MATLAB command window).. We save that datas obtained for training the neurons….
Fig 1
The below diagram shows the training process…. The nftool asks for the input and output to be trained… We will feed the data’s obtained from the workspace.. As per our iteration time the number of values are separated into 1000 periods…. i.e 1, 2, 3…., 1000..
ANN 1.bmp
Fig 2
After giving the input and output the training process is to be started… For that we use Levenberg-Marqardt back propogation training. On clicking train the following fig appears… From this we can see that the input has “3” denoting three signals are given to the controller…. Hiden layer consists of weights, bias and neurons… Normally neurons are kept at 20 for efficient result. Again the output layer converts to “3” as output from the controller.. 
ANN 2.bmp
The Epoch denotes the number of iterations done…. i.e 1000 combination of input and output is created. If the training stops in between 1000 iterations it should be trained again and again…
After doing training for several times the epoch value reaches the 1000 showing the “Maximum epoch is reached”. From here we can see the performance of the trained neural network..
ANN 3.bmp
The below fig shows the performance of the trained neurons i.e how they adopted for the system…. The trained and tested datas are to be on the best performance area… i.e reducing the error value…
ANN 4.bmp
The below fig shows how the training is done at different stages…. On maximum iteration of 1000 the performance is increased….
ANN 5.bmp
This is the histogram…. The minimum error value denotes the maximum performance… The neurons trained to be at the zero error.
ERROR =SET VALUE – ACTUAL VALUE
ANN 6.bmp
The regression data’s is shown for the training, validation and test. The neurons are trained for the data’s and those data’s are validated and checked for the data’s to fit with the actual data’s…
ANN 7.bmp
This is the architecture of thr formed network.. We can alter the hidden layers.. the three inputs are given to the controller and the three outputs are obtained (for three phase)… If the system is not suiting our system i.e not performing well we will alter the neurons no. do yhe training process again.
ANN 8.bmp
After the training process we can generate the network diagram and check the input , hidden and output layers.
ANN 9.bmp
On this click on the simulation diagram to get the ANN CONTROLLER for our specified system. This controller is replaced in the place of old PI controller…
ANN 10.bmp
These are the developed neural networks for our simulation… We can use this in place of the specific PI controller where we taken the data’s to workspace.
ANN 11.bmp
This is the Solar and wind output DC voltage…. Red one denotes the solar and pink one denotes the wind.
If we train the ANN it will automatically starts to produce the output as per our training….
INPUT
OUTPUT
1
3
2
4
3
5
If the first three datas are taken as input and output datas to train during the training the training the neural network automatically adjusts for the untrained data as follows
8
10
20
22


Copy writted material of SABAREESHWARAN


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