CSUMS Summer 11

muscleblouseAI and Robotics

Oct 19, 2013 (4 years and 25 days ago)

80 views

Jonathan Reagan

Umass

Dartmouth

CSUMS Summer 11

August 3
rd

2011


What is a Neural Network?


How does it work?


Why do we care?


Results


Issues encountered


Future work

Input

Layers

Hidden

Layers

Output

Layers


Not realistic to study every possible case


Smaller sample can be used to model the
entire case


Assume connections hold



(input)=[age, income, credit score, etc]



(output)=[dependability]




We want weights of
α
’s


X*
α
(Hidden)=Y






Use the learning method to find
α




I Y
-
X
α

I=0


Perceptron

Least Square

N

Accuracys

Failed
Trials

N

Accuracys

Failed
Trials



Min

AVG

Max

N/Total



Min

AVG

Max

N/Total

2

0.4048

0.5296

0.6081

0/100

2

.4081

.5310

.6306

0/100

3

0.3968

0.5247

0.6161

0/100

3

.4000

.5248

.6177

0/100

4

0.3968

0.5292

0.6306

0/100

4

.3984

.5195

.6194

0/100

5

0.4081

0.5312

0.6274

0/100

5

.4048

.5154

.6306

0/100

6

0.3984

0.5446

0.6161

2/100

6

.4081

.5248

.6226

0/100

7

0.4145

0.5312

0.6194

9/100

7

.3645

.5308

.6306

0/100

8

0.4194

0.544

0.6177

20/100

8

.3790

.5374

.6306

0/100

9

0.3952

0.5444

0.621

34/100

9

.3742

.5410

.6323

0/100

10

0.4048

0.5465

0.629

49/100

10

.3726

.5412

.6371

0/100

y = 0.0031e
1.3761x

R² = 0.9437

0
500
1000
1500
2000
2500
3000
3500
0
2
4
6
8
10
12
Time vs. N

100 Trials
Expon. (100 Trials)
y = 197.05x
-

1176.9

R² = 0.9865

0
100
200
300
400
500
600
700
800
900
0
2
4
6
8
10
12
Time vs. N

100 Trials
Linear (100 Trials)
2 million
Convergence



Failed Trials

4 million convergence



Failed Trials

N

T(Time)Seconds

N/Total

N

T(Time)Seconds

N/Total

2

0.03

0/50

2

0.03

0/50

3

0.04

0/50

3

0.05

0/50

4

0.23

0/50

4

0.24

0/50

5

0.93

0/50

5

0.88

0/50

6

10.49

0/50

6

10.4

0/50

7

50.24

.2/50

7

79.8

.2/50

8

143.07

.8/50

8

260.2

.7/50

9

259.8

15/50

9

484.7

14/50

10

363.6

22/50

10

727.5

22/50

11

483.9

28/50

11

928.2

28/50

12

560.1

34/50

12

1096.1

33/50

13

661.7

40/50

13

1287.2

38/50

14

732.7

43/50

14

1416.2

42/50

15

750.4

44/50

15

1463.7

44/50

16

778.1

46/50

16

1508.9

46/50

17

819.3

48/50

17

1607.7

48/50

18

832

50/50

18

1665.4

50/50


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




Increase the speed of the Neural Network


Find more applicable data for testing of
the Neural Network


Try multiple layer Neural Networks and
Compare