Linear Model Report Assessment

Linear models are the most basic models that may be identified with the help of MATLAB or any other type of programming tool. When a linear model is sufficient to capture the dynamics of a system completely, linear model identification is typically used in the research process. A linear model is identified by starting with time-dornain or frequency-domain data and a model structure such as a state-space or transfer function model and then doing regression analysis on the data. The software iteratively adjusts the free model parameters in order to minimize the discrepancy between the measured output and the simulated model response to the input data. You are given sample data of an engineer working on optimizing the power consumption of an electronic device. The engineer is comparing the input voltage supplied to the device in comparison to the output of the device.

You are required to model this process with the given input-output data as attached along with this file (ie., imoutData.mar).

This project aims to help students gain hands-on experience with parametric modelling for working with actual (real-life) data. Mainly, we use a combination of skills and knowledge from our advanced digital signal processing course and MATLAB programming to model real-life signals. It is worth mentioning that when dealing with real-world data, there will never be one unique solution that we look for but is rather how intelligent the solution is.The project will be carried out individually and a report is expected to be generated towards the end of the project execution. The report must include but not limited to:

• workflow of the project,
• design and decision,
• problem formulation,
• methodology used to achieve the results, and
• conclusions on findings

The project report is to be submitted to Brighten and every student is expected to present his work to the subject lecturer before or on 26 of January 2021.

The characteristics of a good project are summarized in the following four points:

1. The report must show clear visualization of the data, c.g. input-output plot frequency and other related plots.
2. Perform preprocessing on the data and carry out data cleaning and similar preprocessing
   • problem formulation,
   • methodology used to achieve the results, and
   • conclusions on findings

The project report is to be submitted to Brighten and every student is expected to present his work to the subject lecturer before or on 26 of January 2021.

The characteristics of a good project are summarized in the following four points:

1. The report must show clear visualization of the data, eg, input-output plot, frequency and other related plots.
2. Perform preprocessing on the data and carry out data cleaning and similar preprocessing techniques that improve the data quality for better modeling process.
3. Try multiple models and record the accuracy and error as well as save the graphs of cach model for intensive comparisons of different models.
4. Carry out a simulation to find the best model order or combination of parameters to enhance the model. Most importantly, verify the result why is this result satisfactory?
5. Include a concise summary of your contribution in your report's abstract and 'conclusions sections. When it comes to standard approaches, what kinds of contributions can be made, and how do the models support such claims?

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