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polynomials

于 2013-04-02 发布 文件大小:1KB
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  matlab数据拟合 一种快速的插值方法(the matlab data fitting a fast interpolation method)

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    This simulink model simulates the damped driven pendulum, showing it s chaotic motion. theta = angle of pendulum omega = (d/dt)theta = angular velocity Gamma(t) = gcos(phi) = Force omega_d = (d/dt) phi Gamma(t) = (d/dt)omega + omega/Q + sin(theta) Play with the initial conditions (omega_0, theta_0, phi_0 = omega(t=0), theta(t=0), phi(t=0)) and the system parameters (g, Q, omega_d) and the solver parameters/method. Chaos can be seen for Q=2, omega_d=w/3. The program outputs to Matlab time, theta(time) & omega(time). Plot the phase space via: plot(mod(theta+pi, 2*pi)-pi, omega, . ) Plot the Poincare sections using: t_P = (0:2*pi/omega_d:max(time)) plot(mod(spline(time, theta+pi, t_P), 2*pi)-pi, spline(time, omega, t_P), . ) System is described in: "Fractal basin boundaries and intermittency in the driven damped pendulum" E. G. Gwinn and R. M. Westervelt PRA 33(6):4143 (1986) (This simulink model simulates the damped driven pendulum, showing it s chaotic motion. theta = angle of pendulum omega = (d/dt)theta = angular velocity Gamma(t) = gcos(phi) = Force omega_d = (d/dt) phi Gamma(t) = (d/dt)omega+ omega/Q+ sin(theta) Play with the initial conditions (omega_0, theta_0, phi_0 = omega(t=0), theta(t=0), phi(t=0)) and the system parameters (g, Q, omega_d) and the solver parameters/method. Chaos can be seen for Q=2, omega_d=w/3. The program outputs to Matlab time, theta(time) & omega(time). Plot the phase space via: plot(mod(theta+pi, 2*pi)-pi, omega, . ) Plot the Poincare sections using: t_P = (0:2*pi/omega_d:max(time)) plot(mod(spline(time, theta+pi, t_P), 2*pi)-pi, spline(time, omega, t_P), . ) System is described in: "Fractal basin boundaries and intermittency in the driven damped pendulum" E. G. Gwinn and R. M. Westervelt PRA 33(6):4143 (1986) )
    2010-02-17 07:28:51下载
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    2012-04-05 20:36:20下载
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  • ROC_spectrumsensing
    The receiver performance is quantified by depicting the receiver operating char- acteristics (ROC) curves. This curves serve as a tool to and study the performance of a sensing scheme. ROC graphs are employed to show trade-of vs between detection probability and false alarm rates, (i.e. PD versus PFA), thus allowing the determination of an optimal threshold. To illustrate the performance of energy detectors, we generate complementary ROC curves (the average probability of miss detection $ P_{md} 1 - P_d$ versus the false alarm probability $P_f$ for different receiver structures and in different fading environments.Fig. 1 illustrates the complementary ROC curves for a single channel reception (no diversity) over AWGN channels. This figure depicts the complementary ROC curves for energy detectors.
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