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function pi = mc_stat_distrib(P) % MC_STAT_DISTRIB Compute stationary distribution of a Markov chain % function pi = mc_stat_distrib(P) % % Each row of P should sum to one; pi is a column vector

function pi = mc_stat_distrib(P) % MC_STAT_DISTRIB Compute stationary distribution of a Markov chain % function pi = mc_stat_distrib(P) % % Each row of P should sum to one; pi is a column vector

function pi = mc_stat_distrib(P) % MC_STAT_DISTRIB Compute stationary distribution of a Markov chain % function pi = mc_stat_distrib(P) % % Each row of P should sum to one; pi is a column vector

function pi = mc_stat_distrib(P) % MC_STAT_DISTRIB Compute stationary distribution of a Markov chain % function pi = mc_stat_distrib(P) % % Each row of P should sum to one; pi is a column vector

t=0.00001-0.00000000001:0.000000000000001:0.00001+0.00000000002; f1=800000000-100000; f2=800000000; f3=800000000+100000; theta1target=0; theta2target=0; theta3target=0; r1=0.005*randn; r2=0.00

function [s1]=S1(y,sita,m1,k0,u,q) k1=y^2; g=9.8; kc=(g/u^2)/(2*k0); if m1==1 S=(0.0081/k1^4)*exp(-0.74*kc^2/k1^2); G=(4/(3*pi))*(cos(sita/2-q/2))^4; s1=S*G; elsei

function pi = mc_stat_distrib(P) % MC_STAT_DISTRIB Compute stationary distribution of a Markov chain % function pi = mc_stat_distrib(P) % % Each row of P should sum to one; pi is a column vector

function pi = mc_stat_distrib(P) % MC_STAT_DISTRIB Compute stationary distribution of a Markov chain % function pi = mc_stat_distrib(P) % % Each row of P should sum to one; pi is a column vector

function angles = normalize(angles) %------------------------------------------------------- % University of Zaragoza % Centro Politecnico Superior % Robotics and Real Time Group % Authors: J. N

clf clc clear all a=0.1; A=0.2:0.1:1; s=size(A); N1=50; e=8.854e-12; mu=pi*4e-7; for K=1:s(2) AA=A(K); dal_n=2*pi*a/N1; N2=2*pi*AA/dal_n; div1=2*pi/N1; div2=2*pi/N2;