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Neural Networks
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A presentation I made for my MSc Neural Networks module.

A presentation I made for my MSc Neural Networks module.

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Neural Networks Presentation Transcript

  • 1. NEURAL NETWORKS Classification & Identification COMP5235
  • 2. 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
  • 3. Methodology
    • Pre-Processing Data
      • FFT
      • Statistical analysis of data
    • Neural Net design
    • Training
    • Testing
  • 4. Fast Fourier Transformations
    • Processing of data sets exhaustive
    • Raw data set FFT data set
    • Initial sampling method changed
      • Time constraints
  • 5. 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
  • 6. 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
  • 7. Results & Conclusion
    • Poor overall results (33%)
    • Little info on data sets themselves
    • Further testing required
    • Improvements to NN
      • E.g. Multi-layered, partially connected
  • 8. 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
  • 9. Questions