DESIGN OF A SELF-ORGANIZING LEARNING ARRAY SYSTEM

DESIGN OF A SELF-ORGANIZING LEARNING

ARRAY SYSTEM

Dr. Janusz Starzyk Tsun-Ho Liu

Ohio UniversityOhio University

School of Electrical School of Electrical Engineering and Engineering and

Computer ScienceComputer Science

May 25-28th, 2003

IEEE International Symposium on Circuits and Systems

May 25-28th, 2003

School of Electrical Engineering and Computer Science

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Outline Introduction Self-Organizing Learning Array

Structure Neuron Structure and Self-

Organizing Principles Data Preprocessing Software Simulation Result Conclusion and Future Work

May 25-28th, 2003


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Introduction Digital computers are good at:

Fast arithmetic calculation Precise software execution

Artificial Neural Networks are good at: Software free Robust classification and pattern

recognition Recommendation of an action Massive parallelism

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Introduction (Cont’d) Research Objective:

Less interconnection Self-organizing Local Learning Nonspecific classification

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Self-Organizing Learning Array Structure (Cont’d)

Feed forward organization and structure

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Self-Organizing Learning Array Structure (Cont’d)

Initial Wiring

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Neuron Structure and Self-Organizing Principles Neuron Input - System clock

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Neuron Structure and Self-Organizing Principles

(Cont’d) Neuron Input - Data input

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(Cont’d) Neuron Input - Threshold control input (TCI)

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(Cont’d) Neuron Input - Input information deficiency Indication of how much the input

space (corresponding to this selected TCI) has been learned

[0 , 1] 1 is set initially at the first input layer 0 indicates this neuron has solved the

problem 100%

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(Cont’d) Neuron inside Transformation functions

Linear and nonlinear Single input or multiple inputs

Information index calculation

cc

c

sisisic

sicsicsssc

scsc

log

loglogloglog1

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(Cont’d)

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(Cont’d)

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(Cont’d) Neuron output - System output

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(Cont’d) Neuron output - Output Clock

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(Cont’d) Neuron output - Output information deficiency of TCO = Input information deficiency of TCOT = Input information

deficiency * local information deficiency (pass threshold)

of TCOTI = Input information deficiency * local information deficiency (does not pass threshold)

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Data Preprocessing Missing data recovery

All features are independent Some features are dependent

Ref: [Liu] & [Starzyk & Zhu] Symbolic values assignment

Number of numerical feature = 1 Number of numerical features > 1

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Symbolic value – numerical feature =1

cccddbbaae10989843421~

ntsr

~

s

rE 1)

2)

3)

rpinv 4)

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cccddbbaae10989843421~

Xs = [1.0 3.0 3.0 3.5 3.5 8.5 8.5 9.0 9.0 9.0]T

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Data Preprocessing (Cont’d)

1224240221

10989843421~ cccddbbaae

02

r

02

r

1)

2)

rsrrr

rr

r

3)

4)

1~

1CCpinv s

5)

01

~~1

~

12

ss

ss CCQ

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Data Preprocessing (Cont’d)

Symbolic value – numerical feature > 1

1224240221

10989843421~ cccddbbaae

Xs = [1.0 2.85 2.85 3.274 3.274 7.241 7.241 7.884 7.88 7.884]T

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Software Simulation Result

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Software Simulation Result (Cont’d)

FSS Naïve Bayes 0.1405NBTree 0.1410

C4.5-auto 0.1446IDTM (Decision table) 0.1446

HOODG / SOLAR 0.1482C4.5 rules 0.1494

OC1 0.1504C4.5 0.1554

Voted ID3 (0.6) 0.1564CN2 0.1600

Naïve-Bayes 0.1612Voted ID3 (0.8) 0.1647

T2 0.16871R 0.1954

Nearest-neighbor (3) 0.2035Nearest-neighbor (1) 0.2142

Pebls Crashed

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Conclusion and Future Work

Conclusion Local learning Self-organizing Data preprocessing

Future work VHDL simulation

FPGA machine VLSI design

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Reference Information & Computer Science (ICS), University of

California at Irvine (UCI). (1995, December), Machine Learning Repository, Available FTP: Hostname: ftp.ics.uci.edu Directory: /pub/machine-learning-databases/

Liu T. H. (2002), Thesis, Future Hardware Realization of Self-Organizing Learning Array and Its Software Simulation. School of Electrical Engineering and Computer Science, Ohio University.

Starzyk A. J. and Zhu Z. (2002), Software Simulation of a Self-Organizing Learning Array. Int. Conf. on Artificial Intelligence and Soft Computing.

DESIGN OF A SELF-ORGANIZING LEARNING ARRAY SYSTEM

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