Scilab Function
Last update : 13/3/2006
ANN_SIM - Function to simulate the outputs of a feed-forward artificial neural network with one hidden layer
Calling Sequence
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[OUT,[IN_W,HID_OUT]] = ANN_SIM(IN,Nhid,Nout,W)
Parameters
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IN
: Input data (matrix [PxN] where P is the number of input neurons and N the number of input patterns)
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Nhid
: Number of neurons in the hidden layer
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Nout
: Number of neurons in the ouput layer
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W
: Weight and bias values (2 dimensions Matrix [max(Nhid,M) x max(P+1,Nhid+1) x 2]).
Wini(1:Nhid,1,1) are the bias for the hidden neurons Wini(1:Nhid,2:P+1,1) are the weights for the hidden neurons (P weights for each hidden neuron) Wini(1:M,1,2) are the bias for the ouput neurons Wini(1:M,2:Nhid+1,2) are the weights for the ouput neurons (Nhid weights for each output neuron)
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OUT
: Network outputs (Matrix [NoutxN])
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IN_W
: Weighted input from the input layer (matrix [Nhid x N])
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HID_OUT
: Outputs from the hidden layer (matrix [Nhid x N])
Description
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The activation function of the hidden layer is the hyperbolic tangent and the identity function for the output layer.
Note : weighted output from the hidden layer is equal to HID_OUT because of the identity activation function
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Examples
// Ouput from a network with 6 input nodes, 4 nodes in the hidden layer and 1 output node
IN = rand(6,100);
W = rand(4,7,2);
[OUT,IN_W,HID_OUT] = ANN_SIM(IN,4,1,W)
See Also
ANN_LMBR
,
ANN_NORM
,
ANN_JACOB
,
ANN_CONV_W
,
ANN_JACOB
,
Authors
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Julien Lerat
CEMAGREF Antony, HBAN Unit, julien.lerat@cemagref.fr
Bibliography
MacKay, Neural Computation, vol. 4, no. 3, 1992, pp. 415-447.
Foresee and Hagan, Proceedings of the International Joint Conference on Neural Networks, June, 1997.
Used Function
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