%%可视化 plot(t,x_1,t,x_2,t,x_3,'linewidth',1.5); legend('$x_1$','$x_2$','$x_3$','interpreter','latex'); gridon; 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 2
plot(x1(:,2),x2(:,2),'r',x1(:,2),(abs(-x1(:,2))).^(q/p).*sign(-x1(:,2)),'k','linewidth',2); end xlabel('x1');ylabel('x2'); 4 结论 可以看到快速滑模控制和非奇异终端滑模控制得跟踪效果和控制力基本相同,但是从相轨迹上来看,都能快速趋于0,且好像非...
e, Bar plot showing the APA signals corresponding to (d). Data are presented as mean values (n = 2 biological replicates for both conditions). Source Data Extended Data Fig. 12 Polymer simulation unveils extrusive- and cohesive-cohesin interaction pattern. a, Schematic illustration showing ...
Langmuir–Hill plot Loop-extrusion experiments were performed at different concentrations of WT Smc5/6. These measurements, each of 10 s duration, were recorded after an incubation period of 15 min. At this time point an equilibrium state is reached and the fraction of looped DNA remains ...
(Y) P = 10000 # number of particles a = particlefilter(Y, w, psi, phi, M, N, P, T, sigmax, sigmay) t = np.arange(0, T, 1) plt.plot(t, X[0, :], 'r', label = 'X') plt.plot(t, a[0, :], 'b', label = 'Xf') plt.legend() plt.xlabel('time') plt.ylabel...
plot(1:tmx, y, tpe='l',xx = 'n') 1. 对数收益率 序列蒙特卡罗Sequential Monte Carlo 运行 n= 5000 # 粒子的数量 var= c('x') # 要监测的变量 out = smc(moe, vra, n) 模型诊断 diagnosis(out) 1. 绘图平滑 ESS plt(ess, tpe='l') ...
Plot the progression of the SMC algorithm.Thijs Janzen
plot(1:tmax, dt.y) Biips序列蒙特卡罗SMC 运行SMC n_part = 5000; % 粒子数 {'x'}; % 要监控的变量 smc = samples(npart); 算法的诊断。 diag (smc); 绘图平滑 ESS sem(ess) plot(1:tmax, 30*(tmax,1), '--k') 绘制加权粒子
ifeLan'matlab','7.2') rnd('state',0) else rng('default') end 加载模型和数据 模型参数 tmax=100; sig=.4; 解析编译BUGS模型,以及样本数据 model(file, data,'sample', true); data =model; 绘制数据 figure('nae','Lrtrs') plot(1:tmax, dt.y) 。
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