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Potential Benefit of NN in Speech Recognition
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Potential Disadvantage of NN in Speech
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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
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
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 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
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