DChaos (version 0.1-7)

w0.net: Estimates the initial parameter vector of the neural net model

Description

This function estimates the initial parameter vector of the neural net model.

Usage

w0.net(
  x,
  y,
  m = 2,
  h = 2,
  rangx = 1/max(abs(x)),
  w0maxit = 100,
  seed.t = TRUE,
  seed = 56666459
)

Value

The optimal initial parameter vector of the neural net model considering the argument set selected by the user.

Arguments

x

a matrix or a data.frame denoting the explanatory variables.

y

a vector, a matrix or a data.frame denoting the response variable.

m

a non-negative integer denoting the embedding dimension (Default 2).

h

a non-negative integer denoting the number of neurones (or nodes) in the single hidden layer (Default 2).

rangx

a non-negative integer denoting the range of the explanatory variables (Default 1/max(abs(x)).

w0maxit

a non-negative integer denoting the maximum iterations to estimate the initial parameter vector of the neural net models (Default 100).

seed.t

a logical value denoting if the user wants to fix the seed TRUE or not FALSE (Default TRUE).

seed

a non-negative integer denoting the value of the seed selected if seed.t = TRUE (Default 56666459).

Author

Julio E. Sandubete, Lorenzo Escot

References

Ripley, B.D. 1996 Pattern Recognition and Neural Networks. Cambridge.

Venables, W.N., Ripley, B.D. 2002 Modern Applied Statistics with S. Fourth edition. Springer.

Hornik, K., Stinchcombe, M., White, H. 1989 Multilayer feedforward networks are universal approximators. Neural Networks 2(5):359-366.