辅导OENG1116讲解Matlab程序
日期：2020-07-29 08:36
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.
Page 2
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.
Page 3
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
Page 4
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%
Page 5
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.
