Highlights
Suggestions for improvement
Generation of the training set
Ensure the training set contains sufficient information about the input-output mapping to be learned. Is precision high enough?
Ensure the range of the training data suits the designed network. Is normalisation necessary and why?
Use a suitable method to generate the training set.
Was training data collected in both arenas?
Was data collected for problematic spots in the arenas?
Design of the neural network
Use suitable type of neural network for the task.
Use a neural network with appropriate architecture to learn the mapping represented by the training set.
Discuss the pros and cons of having a more or less complicated architecture.
Use suitable transfer functions.
Ensure all details of the design are explained.
Ensure important design decisions are always explained.
Ensure the output(s) of the network are re-scaled appropriately for the task. Are negative speeds avoided in your design?
Training
Associate your network with an appropriate training algorithm.
Explain clearly how a validation set is used to prevent overfitting.
Was a solution pool generated through training with various initial weights?
Discuss the change/converge of MSE through the training process.
Discuss the performance of the trained network.
Explain the problems you experienced and discuss the solution you used to each.
Explain your design decisions at the training stage thoroughly and logically.
Testing
Discuss the suitability of a neural network for this control application.
Test the performance for both wall following and allocation at various points.
Compare the performance of the trained neural network with that of the given FIS.
Coding
Ensure your Matlab code for the control task is correct and suitable.
Ensure your Matlab code for generating training data is correct.
Include comments to the code.
Presentation
Take more care over the presentation (spelling, layout, …) of your work.
Number figures and tables for later references in the text. Always use page number.
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