1. Jonathan Reagan Umass Dartmouth CSUMS Summer 11 August 3 rd 2011

2.  What is a Neural Network?  How does it work?  Why do we care?  Results  Issues encountered  Future work

3. Input Layers Hidden Layers Output Layers

4.  Not realistic to study every possible case  Smaller sample can be used to model the entire case  Assume connections hold

5.  (input)=[age, income, credit score, etc]  (output)=[dependability]  We want weights of α’s  X* α (Hidden)=Y

6.  Use the learning method to find α  I Y-Xα I=0

7. PerceptronLeast Square NAccuracys Failed TrialsNAccuracys Failed Trials MinAVGMaxN/Total MinAVGMaxN/Total 20.40480.52960.60810/1002.4081.5310.63060/100 30.39680.52470.61610/1003.4000.5248.61770/100 40.39680.52920.63060/1004.3984.5195.61940/100 50.40810.53120.62740/1005.4048.5154.63060/100 60.39840.54460.61612/1006.4081.5248.62260/100 70.41450.53120.61949/1007.3645.5308.63060/100 80.41940.5440.617720/1008.3790.5374.63060/100 90.39520.54440.62134/1009.3742.5410.63230/100 100.40480.54650.62949/10010.3726.5412.63710/100

10. 2 million Convergence Failed Trials 4 million convergence Failed Trials NT(Time)SecondsN/TotalNT(Time)SecondsN/Total 20.030/5020.030/50 30.040/5030.050/50 40.230/5040.240/50 50.930/5050.880/50 610.490/50610.40/50 750.24.2/50779.8.2/50 8143.07.8/508260.2.7/50 9259.815/509484.714/50 10363.622/5010727.522/50 11483.928/5011928.228/50 12560.134/50121096.133/50 13661.740/50131287.238/50 14732.743/50141416.242/50 15750.444/50151463.744/50 16778.146/50161508.946/50 17819.348/50171607.748/50 1883250/50181665.450/50

11.  Random Data can’t be learned  Deterministic Data can be learned  Adding Random variance decreases Accuracy  More values of N the Better  But more values of N take Longer

12.  Increase the speed of the Neural Network  Find more applicable data for testing of the Neural Network  Try multiple layer Neural Networks and Compare