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Autonomous NavigationBen McElroy

21st April 2011University of Essex

Part-financed by the European Regional Development Fund

2Part-financed by the European

Regional Development Fund

RAM-based Weightless Networks• One Shot Learning

• Arbitrary Mappings From Inputs to Outputs

• Easier Direct Hardware Implementation

3Part-financed by the European

Regional Development Fund

0 1 0 0 1 01 1 1 1 1 11 1 1 1 1 11 0 0 0 0 11 1 0 0 1 00 0 1 0 0 10 0 0 1 0 1

Input Matrix Sample Layer

Part-financed by the European Regional Development Fund

Address 1 2 3 4 5

10 0 0 0 0 0

11 1 0 0 0 0

12 0 0 0 0 0

4

1 0 0 10 0

1 1 1 11 1

1 1 1 11 1

0 0 0 01 1

1 0 0 11 0

0 1 0 00 1

0 0 1 00 1

5Part-financed by the European

Regional Development Fund

Problems faced with WNNs

• Number of Layers• Number of Neurons Per Layer• Connectivity Map per Layer

Architecture

6Part-financed by the European

Regional Development Fund

Meta-Network

4 03 02 01 0-1 0-2 0-3 0-4 0

Input ‘Design’

Create population

Crossover function

Rank and prune this generation

Test each architecture

Mutation

4 03 02 01 0-1 0-2 0-3 0-4 0

0 -40 -30 -20 -10 10 20 30 4

4 03 02 01 00 10 20 30 4

4 03 02 01 0-1 0-2 0-3 0-4 0

1 01 1-1 01 -10 01 00 10 -1

5 04 11 02 -1-1 0-1 0-3 1-4 -1

7Part-financed by the European

Regional Development Fund

Paper

• Differing number of neurons per layer

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 300

5

10

15

20

25

30

35

40

45

Using 1 test-case per class

Best IndividualGeneration Mean

Generation

Fit

ness

Val

ue

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 300

5

10

15

20

25

30

35

Using 3 test-cases per class

Best Individual

Generation Mean

Generation

Fit

ness

Val

ue

2.1

2.3

2.5

2.7

2.9

3.1

3.3

3.5

3.7

3.9

4.1

4.3

4.5

4.7

4.9

5.1

5.3

5.5

5.7

5.9

6.1

6.3

6.5

6.7

6.9

7.1

7.3

7.5

7.7

7.9

01020304050607080

Without Meta-Network

Fitn

ess V

alue

Using Sonar Data from ISEN

8Part-financed by the European

Regional Development Fund

Other Variables

• Number of Inputs/Outputs– Not all scenarios require the same set of

outputs– Not all outputs will work out

9Part-financed by the European

Regional Development Fund

How it will fit together

• Modularised• Global/Local Goal

10Part-financed by the European

Regional Development Fund

Next

• Further Study into Modifying the inputs and outputs

• Split input data into multiple iterations of WNNs

• Work closely with control