Name

ANN_LMBR — Function to train a feed-forward artificial neural network with one hidden layer.

Calling Sequence

[W,OUT,RMSE,[SSE,GAMK,SSX]] = ANN_LMBR(IN,TARG,Nhid,Wini,[EPOCH,EpochShow,GraphFin,...])

Parameters

IN

Input data (matrix [PxN] where P is the number of input neurons and N the number of input patterns)

TARG

Target data (matrix [MxN] where M is the number of ouput neurons and N the number of input patterns)

Nhid

Number of neurons in the hidden layer

Wini

Initial 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)

EPOCH

Number of epochs (should >2). Default = 30

EpochShow

Periodicity of results display during network calibration. Default = 10

GraphFin

Graphical display of calibration progresses (%T or %F). Default = %T

W

Final weight and biais values (same matrix structure than Wini).

OUT

Final network outputs (Matrix [MxN] where M is the number of ouput neurons and N the number of input patterns)

RMSE

Root Mean square error of final output compared with target

SSE

Serie of SSE value (1 value for each epoch)

GAMK

Serie of GAMK value (1 value for each epoch)

SSX

Serie of SSX value (1 value for each epoch)

Description

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

    The objective function to be minimized is the Sum of Squared Errors (SSE).

    The training algorithm is Levenberg-Marquadt algorithm with 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);
   Wini = rand(4,7,2);
   [W,OUT,RMSE] = ANN_LMBR(IN,TARG,4,Wini);
  

See Also

ANN_CONV_W , ANN_JACOB , 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.

Used Functions

ANN_CONV_W, ANN_JACOB