DESIGN OF A SELF-ORGANIZING LEARNING ARRAY SYSTEM

1
DESIGN OF A SELF-ORGANIZING LEARNING ARRAY SYSTEM
IEEE International Symposium on Circuits and
Systems

• Dr. Janusz Starzyk Tsun-Ho Liu

May 25-28th, 2003
Ohio University School of Electrical Engineering
and Computer Science
2
Outline

• Introduction
• Self-Organizing Learning Array Structure
• Neuron Structure and Self-Organizing Principles
• Data Preprocessing
• Software Simulation Result
• Conclusion and Future Work

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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

4
Introduction (Contd)

• Research Objective
• Less interconnection
• Self-organizing
• Local Learning
• Nonspecific classification

5
Self-Organizing Learning Array Structure (Contd)

• Feed forward organization and structure

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Self-Organizing Learning Array Structure (Contd)

• 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
(Contd)

• Neuron Input - Data input

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Neuron Structure and Self-Organizing Principles
(Contd)

• Neuron Input - Threshold control input (TCI)

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Neuron Structure and Self-Organizing Principles
(Contd)

• 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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Neuron Structure and Self-Organizing Principles
(Contd)

• Neuron inside
• Transformation functions
• Linear and nonlinear
• Single input or multiple inputs
• Information index calculation

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Neuron Structure and Self-Organizing Principles
(Contd)
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Neuron Structure and Self-Organizing Principles
(Contd)
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Neuron Structure and Self-Organizing Principles
(Contd)

• Neuron output - System output

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Neuron Structure and Self-Organizing Principles
(Contd)

• Neuron output - Output Clock

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Neuron Structure and Self-Organizing Principles
(Contd)

• 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 gt 1

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Symbolic value numerical feature 1
1)
3)
2)
4)
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Symbolic value numerical feature 1

• Symbolic value numerical feature 1

Xs 1.0 3.0 3.0 3.5 3.5 8.5 8.5
9.0 9.0 9.0T
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Data Preprocessing (Contd)
1)
4)
2)
5)
3)
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Data Preprocessing (Contd)

• Symbolic value numerical feature gt 1

Xs 1.0 2.85 2.85 3.274 3.274 7.241 7.241
7.884 7.88 7.884T
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Software Simulation Result
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Software Simulation Result (Contd)
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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.