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How To Plot Transfer Functions In Matlab?

  How can I plot this state space like the graph I attached by using tf() and step() command? Thank you!   I2/E0=1/(s^3+s^2+3*s+1)         NOTE:- Matlabsolutions.com  provide latest  MatLab Homework Help, MatLab Assignment Help  ,  Finance Assignment Help  for students, engineers and researchers in Multiple Branches like ECE, EEE, CSE, Mechanical, Civil with 100% output.Matlab Code for B.E, B.Tech,M.E,M.Tech, Ph.D. Scholars with 100% privacy guaranteed. Get MATLAB projects with source code for your learning and research. Try these codes below please;   clc; clear; close all; numerator = 1; denominator = [1,1,3,1]; sys = tf(numerator,denominator); yyaxis left SEE COMPLETE ANSWER CLICK THE LINK https://www.matlabsolutions.com/resources/how-to-plot-transfer-functions-in-matlab-.php
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Problem with System Identification Toolbox and 'sim' command

 Hello everyone

 
I want to model a time -domain dynamic system (A: as input signal and B: as output signal) using System Identification Toolbox. I have used Nonlinear models in this toolbox and the obtained model has a good accuracy with about 95% fitness. I transferred the model to the workspace, just simulated it again with input data A and using sim command ( sim(model,A) ) but the output result was completely wrong.
 
Anyone can help me?

NOTE:-


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Some questions and comments:
 
1. What kind of nonlinear model did you create? If it was a nonlinear ARX model (idnlarx), what estimation focus did you use? If you did not specify focus for nonlinear ARX model estimation, it defaults to "prediction" which estimates the parameters to minimize the 1-step head prediction error. Then, the 95% fit refers to the comparison of prediction results to the data. SIM cannot compute n-step ahead predicted response. For that, you will need to use the PREDICT command. Note that a good prediction model need not be a good simulation model. If you really want to create a model for best possible simulation results, use Focus = 'simulation' during estimation. In the GUI, this option would be available under "Algorithm Options" dialog.
 
If you are estimating a Hammerstein Wiener (idnlhw) or a nonlinear grey box (idnlgrey) model, then there is no difference between simulation and prediction models and you don't have to worry about setting the focus.
 
2. What initial conditions did you use for simulation? The results shown in the GUI are based on use of best (estimated) initial states that would maximize the fit tot data. The command that GUI uses is COMPARE which gives you several choices for handling initial conditions. When using SIM, the initial conditions that maximize the fit to data can be obtained using the FINDSTATES command (true for idnlhw and idnlgrey models).

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