NEURAL NETWORKS Classification & Identification COMP5235

The Problem Develop & Test a neural network model e.g.’s Back propagation, RDP, etc. Perform a Classification task from data Differentiate between individuals Data in form of electronic signals

Develop & Test a neural network model

e.g.’s Back propagation, RDP, etc.

Perform a Classification task from data

Differentiate between individuals

Data in form of electronic signals

Methodology Pre-Processing Data FFT Statistical analysis of data Neural Net design Training Testing

Pre-Processing Data

FFT

Statistical analysis of data

Neural Net design

Training

Testing

Fast Fourier Transformations Processing of data sets exhaustive Raw data set FFT data set Initial sampling method changed Time constraints

Processing of data sets exhaustive

Raw data set FFT data set

Initial sampling method changed

Time constraints

Training the Neural Network NN design Fully linked architecture with 1 layer 1 st attempt ~59% accuracy Further attempts Setting 15 hidden nodes, 700 epochs at 2000 passes 84%+ accuracy Repeated and found to be optimal

NN design

Fully linked architecture with 1 layer

1 st attempt

~59% accuracy

Further attempts

Setting 15 hidden nodes, 700 epochs at 2000 passes

84%+ accuracy

Repeated and found to be optimal

Testing the Neural Network Each column of FFT matrices an input Had to supplement testing data Same process repeated Unfortunately low accuracy Settings changed but with little improvement

Each column of FFT matrices an input

Had to supplement testing data

Same process repeated

Unfortunately low accuracy

Settings changed but with little improvement

Results & Conclusion Poor overall results (33%) Little info on data sets themselves Further testing required Improvements to NN E.g. Multi-layered, partially connected

Poor overall results (33%)

Little info on data sets themselves

Further testing required

Improvements to NN

E.g. Multi-layered, partially connected

Discrepancies & Improvements More testing time of NN parameters Network topology based on ‘trial-error’ Cons: Not intuitive Long learning process Pros: Tolerance to noise Post-assignment testing Other software & methods

More testing time of NN parameters

Network topology based on ‘trial-error’

Cons:

Not intuitive

Long learning process

Pros:

Tolerance to noise

Post-assignment testing

Other software & methods

Questions