Scilab Function
Last update : 11/5/2006

ANN_JACOB - Function to calculate the jacobian performance vector of a feed forward artifical neural network

Calling Sequence

[JE,JJ,normJE,[JX]] = ANN_JACOB(IN,W,IN_W,HID_OUT,ERR_OUT)

Parameters

Description

This function is valid only for feed forward network with one hidden layer.

The activation function of the hidden layer is the hyperbolic tangent and the identity function for the output layer.

This function is used in the function 'LMBR' for the training of feed-forward neural network with Levenberg-Marquadt algorithm under bayesian regulation.

Examples

   // Calibration of a network with 6 input nodes, 4 nodes in the hidden layer and 1 output node
   IN   = rand(6,100);
   TARG = rand(1,100);
   W    = rand(4,7,2);
   [OUT,IN_W,HID_OUT] = ANN_SIM(IN,4,1,W);
   ERR_OUT            = TARG-OUT;
   [JE,JJ,NORMGX]     = ANN_JACOB(IN,W,IN_W,HID_OUT,ERR_OUT);    
  

See Also

ANN_CONV_W ,   ANN_LMBR ,   ANN_NORM ,   ANN_SIM ,  

Authors

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.