Research Article

Parallel kd-Tree Based Approach for Computing the Prediction Horizon Using Wolf’s Method

Algorithm 2

Parallel kd-tree based approach for computing using Wolf’s method.
Program  pkdfet1(, , , )
Input:
    : data record of scalar quantities;
    : embedding delay;
    : minimal embedding dimension;
    : fixed evolution time;
Output:
    : maximal Lyapunov exponent estimation;
    : prediction horizon;
(1) in parallel do:
  /  A process is treated as  MASTER  and other processes are treated as slaves.
The number of processes is  .                          /
(2) begin
   /  Initialization.                                 /
(3) Set as the useful size of ;
(4) Set as the number of replacement steps;
(5) Compute and as local bounds, where ;
(6) Build using (2);
(7) Call newkdtree();
(8) Compute as the standard deviation of ;
(9) Set as the noise scale;
(10)Set as an estimation of the useful length scale;
(11) Set ;
   /  Computing maximal Lyapunox exponent.                      /
(12)Call frnn();
(13)Compute the local nearest neighbor from ;
(14)synchronization Gather all the local nearest neighbor ;
(15)if   = MASTER then Compute the global nearest neighbor from the candidates;
(16)synchronization Broadcast the global nearest neighbor ;
(17)for    to    do
(18)if   = MASTER then
(19)  Set as the initial separation;
(20)  Set as the final separation;
(21)  Set ;
(22)repeat
(23)  Call frnn();
(24)  Compute the local replacement point from ;
(25)  synchronization Gather all the local replacement point ;
(26)  if   = MASTER then Compute the global replacement point from the candidates;
(27)  synchronization Broadcast the global replacement point ;
(28)until    was a minimum;
(29)if  a replacement point    was found  then    else  ;
(30) Set ;
(31)if   = MASTER then Print , ;
(32) end