Week 5

Team Meeting and Research discussions :-

What is neural network?

The neural network is constituted by neural cell, as for these cells they have contact with each other. Therefore we can build a neural network can simulate the biological neural network to make feedback when input a signal. The processing of artificial neural network is based on training to sample information, the training make it has human characteristic, such as memory, identification ability, so it can deal with input information. The field of neural networks can be thought of as being related to artificial intelligence, machine learning, parallel processing, statistics, and other fields. The attraction of neural networks is that they are best suited to solving the problems that are the most difficult to solve by traditional computational methods.

The back propagation neural network model:

The above figure shows the network functions:

Each neuron receives a signal from the neurons in the previous layer, and each of those signals is multiplied by a separate weight value. The weighted inputs are summed, and passed through a limiting function which scales the output to a fixed range of values. The output of the limiter is then broadcast to all of the neurons in the next layer. So, to use the network to solve a problem, we apply the input values to the inputs of the first layer, allow the signals to propagate through the network, and read the output values.

In the project design we design three layers which include input, hidden and output layer. In the practical, after feature extraction there is 35 (5*7) data to describe the image so the in the input layer there are 35 neurons. As for the hidden layer, the number of neurons can be calculated as following equation:

.   is the number of neurons in the hidden layer; n is the number of input neurons of the input layer which is defined by 5*7 which is equals to 35; m is the number of the neurons in the output layer which is equals to 10; a is a constant number between 1 and 10. Some calculation from this function, 10 is chosen to be the number of neurons in the hidden layer). In the hidden layer, we apply ‘logsig’ function in node transformation and use the ‘trainingdx’ in BP network training processing. As for the third layer which represent output layer, the project will output ten numbers therefore the neurons is set as 10. The performance of target is set 0.1, set interval times at 20 and the maximum training numbers is set at 1000. The network design is shown below:

 Meetting for poster:-

flow chart