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Fungsi Aktivasi
• Fungsi aktivasi dengan notasi: µ(.)
mendefinisikan nilai output dari suatu neuron
dalam level aktivasi tertentu berdasarkan nilai
output pengkombinasi linier ui.
• Beberapa fungsi aktivasi yg biasa digunakan:
– Hardlimiter
– Threshold
– Sigmoid
– Tangen Hiperbolik
Fungsi Aktivasi
1. Hardlimiter
2. Piecewise Linear

2. 66//1010//20132013
Fungsi Aktivasi
3. Threshold
xx
f(.)f(.)
++11
tt
f(x) = 0 jika x ≤ t
f(x) = 1 jika x > t
Fungsi Aktivasi
4. Sigmoid
5. Tangen Hiperbolik

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Arsitektur JST
Single layerSingle layer Multiple layerMultiple layer
fully connectedfully connected
Recurrent networkRecurrent network
without hidden unitswithout hidden units
inputsinputs
outputsoutputs
{
}
Recurrent networkRecurrent network
with hidden unitswith hidden units
Unit delayUnit delay
operatoroperator
Standard Activation Functions
• The hard-limiting threshold function
– Corresponds to the biological paradigm
• either fires or not
• Sigmoid functions ('S'-shaped curves)
– The logistic function
– The hyperbolic tangent (symmetrical)
– Both functions have a simple differential
– Only the shape is important
)exp(1
1
)(
av
vf
−+
=

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• Representation of Boolean function AND
• Linear threshold is used
Perceptron Training
t = 0.0t = 0.0
YY
XX
WW11 = 1.5= 1.5
WW33 = 1= 1
--11
WW22 = 1= 1
11 ifif ΣΣ wwiixxii >t>t
OutputOutput== {{00 otherwiseotherwise
Perceptron Training
• Epoch
– Presentation of the entire training set to the neural network.
– In the case of the AND function an epoch consists of four sets
of inputs being presented to the network (i.e. [0,0], [0,1], [1,0],
[1,1])
• Error
– a simple definition of error
– The error value is the amount by which the value output by
the network differs from the target value.
– For example, if we required the network to output 0 and it
output a 1, then Error = -1
Sum of squaredSum of squared
errors :errors :

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Perceptron Training
• Target Value (T)
– Value required to be produced
– If we present the network with [1,1] for the AND function,
T will be 1
• Output (O)
– The output value from the neuron
• Ij - Inputs being presented to the neuron
• Wj - Weight from input neuron (Ij) to the output neuron
• LR( ) - The learning rate
This dictates how quickly the network converges
It is set by a matter of experimentation
η
Perceptron Training
• Algorithm
While epoch produces a non null errorWhile epoch produces a non null error
End WhileEnd While