Post on 14-Jun-2015
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Fungsi Aktivasi
• Fungsi aktivasi dengan notasi: µ(.) mendefinisikan nilai output dari suatu neurondalam level aktivasi tertentu berdasarkan nilaioutput pengkombinasi linier ui.
• Beberapa fungsi aktivasi yg biasa digunakan:– Hardlimiter– Threshold– Sigmoid– Tangen Hiperbolik
Fungsi Aktivasi
1. Hardlimiter
2. Piecewise Linear
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Fungsi Aktivasi
3. Threshold
xx
f(.)f(.)
++11
tt
f(x) = 0 jika x ≤ tf(x) = 1 jika x > t
Fungsi Aktivasi
4. Sigmoid
5. Tangen Hiperbolik
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Arsitektur JST
Single layerSingle layer Multiple layerMultiple layerfully connectedfully connected
Recurrent networkRecurrent networkwithout hidden unitswithout hidden units
inputsinputs
outputsoutputs
{
}
Recurrent networkRecurrent networkwith hidden unitswith hidden units
Unit delayUnit delayoperatoroperator
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(11
)(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
1 1 if if ΣΣ wwiixxi i >t>tOutputOutput== {{0 0 otherwiseotherwise
Perceptron Training
• Epoch – Presentation of the entire training set to the neur al network.– In the case of the AND function an epoch consists o f 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 ou tput 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 squared Sum 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 fu nction,
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 rateThis 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