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# Jst part2

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### Jst part2

1. 1. 66//1010//20132013 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. 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
3. 3. 6/10/20136/10/2013 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 −+ =
4. 4. 6/10/20136/10/2013 • 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 :
5. 5. 66//1010//20132013 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