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Neural Networks

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A presentation I made for my MSc Neural Networks module.

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Neural Networks

  1. 1. NEURAL NETWORKS Classification & Identification COMP5235
  2. 2. The Problem <ul><li>Develop & Test a neural network model </li></ul><ul><ul><li>e.g.’s Back propagation, RDP, etc. </li></ul></ul><ul><li>Perform a Classification task from data </li></ul><ul><ul><li>Differentiate between individuals </li></ul></ul><ul><ul><li>Data in form of electronic signals </li></ul></ul>
  3. 3. Methodology <ul><li>Pre-Processing Data </li></ul><ul><ul><li>FFT </li></ul></ul><ul><ul><li>Statistical analysis of data </li></ul></ul><ul><li>Neural Net design </li></ul><ul><li>Training </li></ul><ul><li>Testing </li></ul>
  4. 4. Fast Fourier Transformations <ul><li>Processing of data sets exhaustive </li></ul><ul><li>Raw data set FFT data set </li></ul><ul><li>Initial sampling method changed </li></ul><ul><ul><li>Time constraints </li></ul></ul>
  5. 5. Training the Neural Network <ul><li>NN design </li></ul><ul><ul><li>Fully linked architecture with 1 layer </li></ul></ul><ul><li>1 st attempt </li></ul><ul><ul><li>~59% accuracy </li></ul></ul><ul><li>Further attempts </li></ul><ul><ul><li>Setting 15 hidden nodes, 700 epochs at 2000 passes </li></ul></ul><ul><ul><li>84%+ accuracy </li></ul></ul><ul><ul><li>Repeated and found to be optimal </li></ul></ul>
  6. 6. Testing the Neural Network <ul><li>Each column of FFT matrices an input </li></ul><ul><li>Had to supplement testing data </li></ul><ul><li>Same process repeated </li></ul><ul><ul><li>Unfortunately low accuracy </li></ul></ul><ul><li>Settings changed but with little improvement </li></ul>
  7. 7. Results & Conclusion <ul><li>Poor overall results (33%) </li></ul><ul><li>Little info on data sets themselves </li></ul><ul><li>Further testing required </li></ul><ul><li>Improvements to NN </li></ul><ul><ul><li>E.g. Multi-layered, partially connected </li></ul></ul>
  8. 8. Discrepancies & Improvements <ul><li>More testing time of NN parameters </li></ul><ul><ul><li>Network topology based on ‘trial-error’ </li></ul></ul><ul><li>Cons: </li></ul><ul><ul><li>Not intuitive </li></ul></ul><ul><ul><li>Long learning process </li></ul></ul><ul><li>Pros: </li></ul><ul><ul><li>Tolerance to noise </li></ul></ul><ul><li>Post-assignment testing </li></ul><ul><ul><li>Other software & methods </li></ul></ul>
  9. 9. Questions

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