CSE301 – Bio-Computation

CSE301 – Bio-Computation Assessment 3
Contribution to overall module assessment 10%
Submission deadline 18:00, Friday, Dec 20 2019
1. Assessment Task
In this assessment, you are to implement MLP with back-propagation training algorithm, and RBF
network with the RBF centers initialized from k-means clustering, using MATLAB for vehicle logo
classification. Please use sigmoid activation functions where necessary.
In the dataset (logo.mat), 117 samples of 5 different types of vehicle logo images as shown in Fig. 1
are provided, with corresponding class labels (1-5) and 80 features in each sample using appropriate
feature extraction algorithm.
Fig.1 Examples of vehicle logo images
For both MLP and RBF design, divide the dataset into a training set (80%) and a testing set (20%) and
show the convergence performance using MSE for each epoch in the training process.
Based on your algorithms designed, discuss the following in the report:
(1) for MLP: the effect of different number of hidden units;
(2) for MLP: the effect of different learning rate and momentum;
(3) for RBF: the effect of different number of RBF centres;
(4) compare the best MLP model and the best RBF model using confusion matrix.
Note:
(1) it is acceptable to follow references (or using code fragments) from textbooks or internet
resources, but you must cite them clearly in your report;
(2) it is acceptable to apply Matlab toolboxes and Matlab functions.
2. Report
Each student must write an individual report in English. The report must be a single file in .pdf format
including all the plots, figures, tables and appendixes (failure to comply with this requirement will be
marked as Fail according 5. Marking Criteria).
The format of the report is: single-column A4 size, Times New Roman 12pt, single line spacing, page
numbered, 0.75-inch margin on top/bottom/left/right, and with maximum 10 pages including cover
page, reference (and appendixes if any).
The structure of the report is:
(1) introduction: task description and background;
(2) methodology: introduction of the methods and models;
(3) experimental results and analysis: experiment procedures, results discussion and analysis,
performance comparison etc.;
(4) conclusion;
(5) references.
3. Submission
You are required to:
(1) compress your written report and source code into one single .ZIP file (other format such as .rar
or .7z will be marked as Fail according to 5. Marking Criteria);
(2) name the zip file as: StudentID_GivenName_Surname (e.g. 1601234_Rui_Yang);
(3) upload to the submission folder in ICE by 18:00, Friday, Dec 20 2019.
Late submission will receive penalty in the marking in accordance with the University Code of Practice
on Assessment. For each working day after the deadline, 5 marks (out of 100) will be deducted for up
CSE301留学生作业代写、MATLAB程序语言作业调试
to 5 working days. However, the mark will not be reduced below the pass mark for the assessment.
Work assessed below the pass mark will not be penalised for late submission of up to 5 days. Work
received more than 5 working days after the deadline will receive a mark of 0.
4. Plagiarism
This assessment is an individual work. Plagiarism (e.g. copying materials from other sources without
proper acknowledgement) is a serious academic offence. Plagiarism will not be tolerated and will be
dealt with in accordance with the University Code of Practice on Assessment.
5. Marking Criteria
Category Requirement
First Class
(≥70%)
Overall outstanding work. All of the requirements have been implemented in the
program and report. Highly qualified report that closes to professional level. The
report is well-structured and organized, with all of required information included,
with very few English problems.
Second Upper
(60 to 69%)
Most of the requirements have been implemented in the program and report.
Good report which is clearly structured with most of the required information but
with few English problems.
Second Lower
(50 to 59%)
Substantial working program implementing a good range of the requirements.
Acceptable written report for Year 4 level, which contains sufficient information
but some English problems.
Third
(40 to 49%)
Executable program that generates recognizable results, which however are
incomplete. The written report is readable with insufficient information covered.
Problems may appear in the structure and organization, with many English
problems.
Fail
(0 to 39%)
Wrong format in submission. Program is not working; or most of the required
results are not produced; or without acknowledging properly sources used if any.
Poor report which covers very limited number of items required.
No submission A mark of 0 will be awarded.
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