辅导案例-OENG1116
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OENG1116 – Summative Assessment 2
Individual Project Portfolio

1 Assignment context
This project for OENG1116 is based on your work developing and selecting the most appropriate model for
an engineering system, interpreting the simulation results of that model and comparing the results of different
model architectures. This assessment task builds on project by using lecture, tutorial classes, activities
undertaken during the semester till week 12 (Submission date is Friday, 5 June 2020, Time:
23.59 ). The following documents can be used for completion of this assessment
• Lecture notes
• Tutorial notes
• Matlab toolbox (as indicated below)
• Book chapters:

(i)H. Khayyam, G. Golkarnarenji, R.N. Jazar, “Limited Data Modelling Approaches for Engineering
Applications”, In Nonlinear Approaches in Engineering Applications; Jazar, R.N., Ed.; International
Publication Springer: Cham, Switzerland, (2018)

(ii) B Crawford, H Khayyam, AS Milani, RN Jazar, “Big Data Modeling Approaches for Engineering
Applications” Nonlinear Approaches in Engineering Applications, 307-365 (2019)


2 Assignment overview.

This individual assessment requires the student to present a project report, based on the activities proposed.
The overall aim of this assignment report is to demonstrate the technical and non-technical learning
outcomes of the unit by the student. This assignment has an overall weight of 35 % of the course.
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3 Learning Outcomes

A summary of the Course Learning Outcomes (CLOs) which will be assessed in this task are provided in table
1.



Table 1: Summary of CLOs assessed in Assessment 2.

This task assesses your
Course Learning
Outcomes (CLOs)
CLOs
1- Analysis
Ability to model non-deterministic (heuristic) systems and differentiate
between nonlinear and linear models.
Ability to numerically simulate linear and non-linear deterministic systems.
Ability to estimate and validate a model based upon input and output data.
Ability to create a model prediction based upon new input and validate the
output data.
Ability to understand and apply advanced theory of engineering fundamentals
and specialist bodies of knowledge in the selected discipline area to predict
the effect of engineering activities.
Ability to apply underpinning natural, physical and engineering sciences,
mathematics, statistics, computer and information sciences to engineering
applications.
2- Research
Ability to plan and execute a substantial research-based assessment tasks, with
creativity and initiative in new situations in professional practice and with a
high level of personal autonomy and accountability.
Awareness of knowledge development and research directions within the
engineering discipline.
Ability to develop creative and innovative solutions to (heuristic) engineering
challenges.
Ability to assess, acquire and apply the competencies and resources
appropriate to engineering activities.
Ability to demonstrate professional use and management of information.
Ability to clearly acknowledge your own contributions and the contributions
from others and distinguish contributions you may have made as a result of
discussions or collaboration with other people.


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4 Assignment details and requirements

Your report is related to the development of three different models for the given experimental data
shown in Tables 2 and 3. The aim of the report is to justify all the decisions that you made to develop
the different models, showing your skills to analyse non-deterministic (heuristic) systems.

Table 2: Experimental data (Training)


No.
Inputs Outputs
Input1 Input2 Input3 Output1
1 227 20 1 1.2446
2 227 20 4 1.2438
3 227 25 2 1.25
4 227 25 3 1.2417
5 227 30 3 1.2359
6 227 35 4 1.2244
7 230 20 2 1.2574
8 230 25 1 1.2417
9 230 25 3 1.2464
10 230 30 4 1.2341
11 230 35 1 1.2335
12 230 35 3 1.2317
13 233 20 3 1.257
14 233 25 1 1.2611
15 233 20 4 1.2601
16 233 25 2 1.2457
17 233 25 3 1.2465
18 233 25 4 1.2565
19 233 30 1 1.2429
20 233 30 3 1.2421
21 233 35 2 1.2363
22 236 20 1 1.2707
23 236 20 4 1.271
24 236 25 3 1.263
25 236 30 2 1.2547
26 236 35 1 1.2504
27 236 35 4 1.2474










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Table 3: Experimental data (Testing)


No.
Inputs Outputs
Input1 Input2 Input3 Output1
28 227 35 1 1.2339
29 230 20 4 1.2588
30 233 35 3 1.2372

The output required for this assessment task is based on the 4 key areas as defined in Table 4, which
provides full descriptions of the functionalities required for each model. In addition, the approximate
length of the content has been specified (though not fixed) including the weight of each area
(Considering the total 35 % of the assignment).


Table 4: Description of key tasks required for report.

Item Task Name (output) Description ULO(s)
Approx.
Length Weight

1
Modelling
using
Artificial
Neural
Network
Read the collected data from Table 2. Perform data pre- processing if
required. Develop a predictive model of input-output data sets based
on Artificial Neural Networks (ANN) in MATLAB. Split the data
into relevant ratios for training, validation and testing, providing
justification on the ratios chosen.
(i) Describe the network architecture, training procedure and every
step carried out to improve the model.
(ii) Define the fitting neural network through changing the number of
hidden neurons (for example 5-25).
(iii) If applicable, find a solution to achieve the best fit in terms of
model performance by choosing and controlling different training
algorithms (Levenberg–Marquardt, Conjugate Gradient, Quasi-
Newton Algorithms, Bayesian Regularization, Gradient Decent).
(iv) Evaluate the accuracy (error) of the developed model by using
the data provided in Table 3.
CLO1 ~2 to 3 page 10%

2
Modelling
using
Support Vector
Machine
Read the collected data from Table 2. Perform data pre-processing if
required. Develop a predictive model of input-output data sets based
on different Support Vector Machine (SVM) in MATLAB.
(i) Describe the training procedure and every step carried out to
improve the model.
(ii) If applicable, find a solution to achieve the best fit in terms of
model performance (use different SVM kernels Linear, Gaussian and
Polynomial).
(iii) Evaluate the accuracy (error) of the developed model by using the
data provided in Table 3.
CLO1 ~ 2-3 pages 10%
3
Modelling
using
Linear Non-
Linear
Regression

Read the collected data from Table 2. Perform data pre-processing if
required. Develop a predictive model of input-output data sets based
on different Non-Linear Regression (NLR) in MATLAB.
(i) Describe the training procedure and every step carried out to
improve the model.
(ii) If applicable, find a solution to achieve the best fit in terms of
model performance (use different NLR models: Polynomial,
Exponential, Power and combination).
(iii) Evaluate the accuracy (error) of the model using the test dataset
on Table 3.

CLO1 ~ 2-3 pages 10%
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4

Find
RMSE, MSE
and R

Find MSE, RMSE and R for the models (1-3).

Note: Use equations 1-3 to calculate RMSE, MSE and R:


CLO1 ~ 1 page 2.5%
5

Compare

Compare the three methods used. Discuss on the
advantages/disadvantages of the different models in this application CLO1 ~ 1 page 2.5%




Notes on structure and formatting: To make this task as simple as possible, the structure of the report
should be based exactly on the tasks defined above. That is, you should have 7 sections (5 tasks and 2
Appendixes) in your report which contain the headings defined by the 5 Tasks Name in Table 4, Appendix A:
Different ANN, SVM and NLR Methods Results and Appendix B: ANN, SVM and NLR Matlab Codes.
There is no need for additional introduction and conclusion sections, or formatting such as Table of Contents,
List of Figures, etc. However, you will still be assessed on the quality of the report and the clarity of the
communication, via the assessment of CLO1- 3 and throughout the report.

5 Marking criteria

The assessment criteria are based on how well you have completed the 4 tasks defined in Table 4.

• You will be scored for each of the key tasks defined in Table 4. The marks will then be weighted
according to the marking rubric shown in Table 5.

• To achieve the maximum score for each task, you will have clearly covered the information provided in
the description, demonstrating that you have met the relevant Course Learning outcomes defined for each
of the tasks in Table 4.
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Table 5. Project Assessment Rubric
Topic (Mark) Unsatisfactory
(0<30%)
Satisfactory
(30%<70)
Good
(70%<80%)
Outstanding
(>80%)
Task 1 Errors in reading the input/output
data. No attempt on pre- processing
data or no justification. Develop a
predictive model of input- output
data sets based on Artificial Neural
Networks (ANN) in MATLAB. The
ratios chosen for training, validation
and testing are not justified. There is
little or no justification on the
parameters chosen to build the ANN
(i)Brief descriptions of the network
architecture, training procedure and
every step carried out to improve the
model.
(ii) Lack of mathematical analysis of
the results
(iii) Code shows errors, does not
work properly or there is no code
shown for the ANN

Successfully reading the collected data from
Table 3, with proper justification. Perform
data pre- processing if required. Develop a
predictive model of input- output data sets
based on Artificial Neural Networks (ANN)
in MATLAB. Split the data into Different
ratios for training, validation, and testing,
discussing on the effects of different ratios
on the ANN performance.
(i)Describe the network architecture, training
procedure and every step carried out to
improve the model, showing a clear
understanding of the effects of changing the
different parameters considered
(ii) Define the fitting neural network through
changing the number of hidden neurons (for
example 5-25).
(iii)If applicable, find a solution to achieve
the best fit in terms of model performance
by choosing and controlling different
training algorithms (more than one)
(Levenberg–Marquardt, Conjugate Gradient,
Quasi-Newton Algorithms, Bayesian
Regularization, Gradient Decent).
Successfully reading the collected data from Table
3, with proper justification. Perform data pre-
processing if required. Develop a predictive model
of input- output data sets based on Artificial Neural
Networks (ANN) in MATLAB. Split the data into
Different ratios for training, validation, and testing,
discussing on the effects of different ratios on the
ANN performance.
(i)Describe the network architecture, training
procedure and every step carried out to improve the
model, showing a clear understanding of the effects
of changing the different parameters considered
(ii) Define the fitting neural network through
changing the number of hidden neurons (for
example 5-25).
(iii)If applicable, find a solution to achieve the best
fit in terms of model performance by choosing and
controlling different training algorithms (more than
one) (Levenberg–Marquardt, Conjugate Gradient,
Quasi-Newton Algorithms, Bayesian Regularization,
Gradient Decent).
(iv) Mathematical analysis of the different ANNs
performance provided to show less error.

Successfully reading the collected data from Table 3, with
proper justification. Perform data pre- processing if required.
Develop a predictive model of input- output data sets based on
Artificial Neural Networks (ANN) in MATLAB. Split the data
into Different ratios for training, validation, and testing,
discussing on the effects of different ratios on the ANN
performance.
(i)Describe the network architecture, training procedure and
every step carried out to improve the model, showing a clear
understanding of the effects of changing the different parameters
considered
(ii) Define the fitting neural network through changing the
number of hidden neurons (for example 5-25).
(iii)If applicable, find a solution to achieve the best fit in terms
of model performance by choosing and controlling different
training algorithms (more than one) (Levenberg–Marquardt,
Conjugate Gradient, Quasi-Newton Algorithms, Bayesian
Regularization, Gradient Decent).
(iv) Mathematical analysis of the different ANNs performance
provided to show less error.
(v) Code compiles and work properly, built with a logical
structure and including enough comments to help understanding
the ANN developed.
Task 2 Errors in reading the input/output
data. No attempt on pre- processing
data or no justification. Develop a
predictive model of input- output
data sets based on Support Vector
Machine (SVM) in MATLAB.
Input/output data is not used
correctly for a SVM
(i)Brief descriptions of the network
architecture and every step carried
out to improve the model.
(ii) Lack of mathematical analysis of
the results
(iii) Code shows errors, does not
work properly or there is no code
shown for the SVM

Successfully reading the input/output data.
Perform data pre- processing if required.
Develop a predictive model of input-output
data sets based on Support Vector Machine
(SVM) in MATLAB
(i) Detailed description of the model
architecture, and the steps carried out to
improve the model, with proper
justification of every parameter considered.
(ii)If applicable, find a solution to achieve
the best fit in terms of model performance
by choosing and controlling different
training algorithms (more than one)
((Linear, Gaussian and Polynomial).

Successfully reading the input/output data. Perform
data pre- processing if required. Develop a
predictive model of input-output data sets based on
Support Vector Machine (SVM) in MATLAB
(i) Detailed description of the model architecture,
and the steps carried out to improve the model, with
proper justification of every parameter considered.
(ii)If applicable, find a solution to achieve the best
fit in terms of model performance by choosing and
controlling different training algorithms (more than
one) ((Linear, Gaussian and Polynomial).
(iii) Evaluation of the accuracy of the different SVR
models. Mathematical analysis of the SVRs
performance provided to show less error.

Successfully reading the input/output data. Perform data pre-
processing if required. Develop a predictive model of input-
output data sets based on Support Vector Machine (SVM) in
MATLAB
(i) Detailed description of the model architecture, and the steps
carried out to improve the model, with proper justification of
every parameter considered.
(ii)If applicable, find a solution to achieve the best fit in terms of
model performance by choosing and controlling different
training algorithms (more than one) ((Linear, Gaussian and
Polynomial).
(iii) Evaluation of the accuracy of the different SVR models.
Mathematical analysis of the SVRs performance provided to
show less error.
iv) Code compiles and work properly, built with a logical
structure and including enough comments to help understanding
the SVR developed

Task3 Errors in reading the input/output
data. No attempt on pre- processing
data or no justification. Develop a
Successfully reading the input/output data.
Perform data pre- processing if required.
Develop a predictive model of input-output
Successfully reading the input/output data. Perform
data pre- processing if required. Develop a
predictive model of input-output data sets based on
Successfully reading the input/output data. Perform data pre-
processing if required. Develop a predictive model of input-
output data sets based on Non-Linear Regression (NLR) in
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predictive model of input-output data
sets based on Non-Linear Regression
(NLR) in MATLAB.
Input/output data is not used
correctly for a NLR
(i)Brief descriptions of the
implementation of the mathematical
model and every step carried out to
improve the model.
(ii) Lack of mathematical analysis of
the results
(iii) Code shows errors, does not
work properly or there is no code
shown for the NLR


data sets based on Non-Linear Regression
(NLR) in MATLAB.
(i) Detailed description of the model
architecture, and the steps carried out to
improve the model, with proper
justification of every parameter considered.
(ii) If applicable, find a solution to achieve
the best fit in terms of model performance
by choosing and controlling different NLR
approaches (more than one) (Polynomial,
Exponential, Power and combination).
Mathematical analysis of the different
NLR performances provided (to show the
error).

Non-Linear Regression (NLR) in MATLAB.
(i) Detailed description of the model architecture,
and the steps carried out to improve the model, with
proper justification of every parameter considered.
(ii) If applicable, find a solution to achieve the best
fit in terms of model performance by choosing and
controlling different NLR approaches (more than
one) (Polynomial, Exponential, Power and
combination). Mathematical analysis of the different
NLR performances provided (to show the error).
(iii) Evaluation of the accuracy of the different NLR
models. Mathematical analysis of the NLRs
performance provided

MATLAB.
(i) Detailed description of the model architecture, and the steps
carried out to improve the model, with proper justification of
every parameter considered.
(ii) If applicable, find a solution to achieve the best fit in terms of
model performance by choosing and controlling different NLR
approaches (more than one) (Polynomial, Exponential, Power and
combination). Mathematical analysis of the different NLR
performances provided (to show the error).
(iii) Evaluation of the accuracy of the different NLR models.
Mathematical analysis of the NLRs performance provided
(iv) Code compiles and work properly, built with a logical
structure and including enough comments to help understanding
the NLRs developed.
Task 4


No mathematical analysis, wrong
RMSE, MSE and R values
Only one of the parameters (RMSE, MSE or
R) is correct

Two of the parameters (RMSE, MSE or R) are
correct

Correct calculation of RMSE and MSE
and R
Task 5 No discussion of the findings or poor
discussion with lots of fundamental
mistakes.
Description of the findings with no
comparison/analysis between methods. The
description does not contain fundamental
mistakes.
Correct description of the findings and
comparison/analysis between the three different
models. Minor mistakes in the analysis.
Correct description of the findings and comparison/analysis
between the three different models. Discussion clearly shows a
strong understanding of the models built.
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6 Submission requirements

It is required to submit two files: a Report PDF file and a Matlab file on Canvas as follows:

1. Report PDF file: Prepare a report PDF file including the 5 tasks and Matlab code. PDF file are
strongly recommended. Other file formats will not be accepted. (Use the name_surname_ID and
extension for example: hamid_khayyam_s33784.PDF

2. Matlab file: Prepare a Matlab file including the 3 Matlab code with format m file. Other file formats
will not be accepted.
a. If you created a single Matlab file with different sections, name the file name_surname_ID.m
b. If you created a Matlab file for every different section name them as follow
i. name_surname_ID_ANN.m
ii. name_surname_ID_SVM.m
iii. name_surname_ID_NLR.m
Then zip all the Matlab files of them in a unique file with the named name_surname_ID.zip
3. To access Assignment, please, LOG IN into your RMIT account and then access the Canvas:
4. Upload both the PDF file and the Matlab code.

7 Penalties and bonus marks
Grace period for submission is 30 minutes. There are no bonus points associated with this assessment task

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