Neural Networks

What is a Neural Network?

A neural network is an artificial representation of the human brain that tries to simulate its learning process. Like the human brain, a neural net also consists of neurons and connections between them. The neurons are transporting incoming information on their outgoing connections to other neurons. In neural net terms these connections are called weights. The "electrical" information is simulated with specific values stored in those weights. By simply changing these weight values the changing of the connection structure can also be simulated. The following figure shows an idealized neuron of a neural net. 

 

 

Figure N-1  

 

As you can see, an artificial neuron looks similar to a biological neural cell. And it works in the same way. Information (called the input) is sent to the neuron on its incoming weights. This input is processed by a propagation function that adds up the values of all incoming weights. The resulting value is compared with a certain threshold value by the neuron's activation function. If the input exceeds the threshold value, the neuron will be activated, otherwise it will be inhibited. If activated, the neuron sends an output on its outgoing weights to all connected neurons and so on.

In a neural net, the neurons are grouped in layers, called neuron layers. Usually each neuron of one layer is connected to all neurons of the preceding and the following layer (except the input layer and the output layer of the net). The information given to a neural net is propagated layer-by-layer from input layer to output layer through either none, one or more hidden layers. Depending on the learning algorithm, it is also possible that information is propagated backwards through the net. The Figure N-2 shows a neural net with three neuron layers.

NN have been used for many applications of recognition including Speech Recognition

Potential Benefit of NN in Speech Recognition The neural network approach does not require such some of the assumptions made using HMMs . This approach uses a distributed representation of simple nodes, whose connections are trained to recognize speech. Unlike in HMMs where knowledge or constraints are not encoded in individual units, rules or procedures, but distributed across many simple computing units. Uncertainty is modeled not as unlikelihood of a single unit, but by the pattern of activity in many units. These computing units are simple in nature, and knowledge is not programmed into any individual unit's function; rather it lies in the connections and interactions between linked processing elements.

Potential Disadvantage of NN in Speech Unlike HMMs, you must first effectively segment the speech into appropriate feature information to pass as input into the NN. This is one of them most challenging aspects. Garbage in yields Garbage out in a NN.

Learning Methods

As mentioned before, neural nets try to simulate the human brain's ability to learn. That is, the artificial neural net is also made of neurons and dendrites. Unlike the biological model, a neural net has an unchangeable structure, built of a specified number of neurons and a specified number of connections between them (called "weights"), which have certain values. What changes during the learning process are the values of those weights. Compared to the original this means: Incoming information "stimulates" (exceeds a specified threshold value of) certain neurons that pass the information to connected neurons or prevent further transportation along the weighted connections. The value of a weight will be increased if information should be transported and decreased if not.

While learning different inputs, the weight values are changed dynamically until their values are balanced, so each input will lead to the desired output. The training of a neural net results in a matrix that holds the weight values between the neurons. Once a neural net had been trained correctly, it will probably be able to find the desired output to a given input that had been learned, by using these matrix values.

Backpropagation Neural Network

The Backpropagation Net was first introduced by G.E. Hinton, E. Rumelhart and R.J. Williams in 1986 and is one of the most powerful neural net types. It has the same structure as the Multi-Layer-Perceptron and uses the backpropagation learning algorithm. Figure N-3 shows backpropagation net diagram.

Figure N-2

Figure N-3

Backpropagation Net is a Feed-Forward, supervised neural network that uses backpropagarion algorithm for learning. The backpropagation algorithm uses a computed output error to change the weight values in backward direction. To get this net error, a forwardpropagation phase must have been done before. While propagating in forward direction, the neurons are being activated using the sigmoid activation function.

         

The formula of sigmoid activation is:

f(x) = 1 / 1 + e ^ -input

         

The algorithm works as follows: 1. Perform the forwardpropagation phase for an input pattern and calculate the output error. 2. Change all weight values of each weight matrix using the formula weight(old) + learning rate * output error * output(neurons(neurons i) * output(neurons i+1) * ( 1 - output(neurons i+1) ) 3. Go to step 1 4. The algorithm ends, if all output patterns match their target patterns

 

Training Neural Network

NN is trained with {Feature Vector, Class Identification} pairs. Figure below shows the example of a neural network used in a computer vision application trying to detect the kind of fog in outdoor scenes.

NN Options and Training Options:

·      Layer 2 Size

A type of neuron Layer that lies between a neural net's input and output layers. Its neuron values are not visible outside the net. The usage of this layers extends a neural net's ability to learn logical operations.

Default: 5

·Learning Rate

A changeable value used by the learning algorithms, which effects the changing of weight values. The greater the learning rate, the

more the weight values are changed. Is usually decreased during the learning process.

Default: 0.5

· Global Error

Used by neural nets with supervised learning, by comparing the current output values with the desired output values of the net. The smaller the net's error is, the better the net had been trained. Usually the error is always a value greater than zero.

Default: 0.9

·   Maximum Iterations

Maximum Iterations determines how many times neural network repeat its learning process if the desired global error value is not achieved.

Default: 10000

click here for a NN Applet Demo

 

Java Package for NN -  JaNet

jaNet was created by two students, Wilfred Gander and Lorenzo Patocchi at Biel School of Engineering.

jaNet package is a Java neural network toolkit. With jaNet one can design, test, train and optimize an ideal neural network for private applications.  When the network is ready, one can save it in a file and then include such network in private applications using the jaNet.backprop package.

The package can be found at their home page. http://zwww.isbiel.ch/I/Projects/janet/index.html