mlp                  package:neural                  R Documentation

_M_L_P _n_e_u_r_a_l _n_e_t_w_o_r_k

_D_e_s_c_r_i_p_t_i_o_n:

     The recalling method of the MLP network which was trained by the
     mlptrain function.

_U_s_a_g_e:

      mlp(inp,weigth,dist,neurons,actfns=c(),layer=NaN)

_A_r_g_u_m_e_n_t_s:

     inp: a matrix that contains one input data in each row.

  weigth: the weigths of the network.

    dist: the distortions of the network.

 neurons: a numeric vector with length equals to the number of layers
          in the network, and the ith layer will contains neurons[i]
          neuron.

  actfns: a list, which contains the numeric code of the activation
          functions, or the activation function itself. The length of
          the vector must be the same as the length of the neurons
          vector, and each element of the vector must be between 1 and
          4 or a function. The possible numeric codes are the
          following: 1: Logistic function 2: Hyperbolic tangent
          function 3: Gauss function 4: Identical function.

   layer: the number of the layer as far as the function is
          calculating. If the value is NaN, the function will calculate
          till the output layer. It can be useful if you need the
          output of one of the hidden layers.

_D_e_t_a_i_l_s:

     the "weigth, dist, neurons, actfns" arguments can be determined by
     the mlptrain algorithm.

_V_a_l_u_e:

     a matrix that contains the response data of the network, each row
     contains one response.

_S_e_e _A_l_s_o:

     `mlptrain' for training an MLP network, `rbf' and `rbftrain' for
     approximation.

