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DESIGN OF A SELF-ORGANIZING LEARNING ARRAY SYSTEM

DESIGN OF A SELF-ORGANIZING LEARNING ARRAY SYSTEM . IEEE International Symposium on Circuits and Systems . Dr. Janusz Starzyk Tsun-Ho Liu. May 25-28 th , 2003. Ohio University School of Electrical Engineering and Computer Science. Outline. Introduction

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DESIGN OF A SELF-ORGANIZING LEARNING ARRAY SYSTEM

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  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 School of Electrical Engineering and Computer Science

  3. 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 School of Electrical Engineering and Computer Science

  4. Introduction (Cont’d) • Research Objective: • Less interconnection • Self-organizing • Local Learning • Nonspecific classification School of Electrical Engineering and Computer Science

  5. Self-Organizing Learning Array Structure (Cont’d) • Feed forward organization and structure School of Electrical Engineering and Computer Science

  6. Self-Organizing Learning Array Structure (Cont’d) • Initial Wiring School of Electrical Engineering and Computer Science

  7. Neuron Structure and Self-Organizing Principles • Neuron Input - System clock School of Electrical Engineering and Computer Science

  8. Neuron Structure and Self-Organizing Principles (Cont’d) • Neuron Input - Data input School of Electrical Engineering and Computer Science

  9. Neuron Structure and Self-Organizing Principles (Cont’d) • Neuron Input - Threshold control input (TCI) School of Electrical Engineering and Computer Science

  10. Neuron Structure and Self-Organizing Principles (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% School of Electrical Engineering and Computer Science

  11. Neuron Structure and Self-Organizing Principles (Cont’d) • Neuron inside • Transformation functions • Linear and nonlinear • Single input or multiple inputs • Information index calculation School of Electrical Engineering and Computer Science

  12. Neuron Structure and Self-Organizing Principles (Cont’d) School of Electrical Engineering and Computer Science

  13. Neuron Structure and Self-Organizing Principles (Cont’d) School of Electrical Engineering and Computer Science

  14. Neuron Structure and Self-Organizing Principles (Cont’d) • Neuron output - System output School of Electrical Engineering and Computer Science

  15. Neuron Structure and Self-Organizing Principles (Cont’d) • Neuron output - Output Clock School of Electrical Engineering and Computer Science

  16. Neuron Structure and Self-Organizing Principles (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) School of Electrical Engineering and Computer Science

  17. 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 School of Electrical Engineering and Computer Science

  18. Symbolic value – numerical feature =1 1) 3) 2) 4) School of Electrical Engineering and Computer Science

  19. 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 9.0]T School of Electrical Engineering and Computer Science

  20. Data Preprocessing (Cont’d) 1) 4) 2) 5) 3) School of Electrical Engineering and Computer Science

  21. Data Preprocessing (Cont’d) • Symbolic value – numerical feature > 1 Xs = [1.0 2.85 2.85 3.274 3.274 7.241 7.241 7.884 7.88 7.884]T School of Electrical Engineering and Computer Science

  22. Software Simulation Result School of Electrical Engineering and Computer Science

  23. FSS Naïve Bayes 0.1405 NBTree 0.1410 C4.5-auto 0.1446 IDTM (Decision table) 0.1446 HOODG / SOLAR 0.1482 C4.5 rules 0.1494 OC1 0.1504 C4.5 0.1554 Voted ID3 (0.6) 0.1564 CN2 0.1600 Naïve-Bayes 0.1612 Voted ID3 (0.8) 0.1647 T2 0.1687 1R 0.1954 Nearest-neighbor (3) 0.2035 Nearest-neighbor (1) 0.2142 Pebls Crashed Software Simulation Result (Cont’d) School of Electrical Engineering and Computer Science

  24. Conclusion and Future Work • Conclusion • Local learning • Self-organizing • Data preprocessing • Future work • VHDL simulation • FPGA machine • VLSI design School of Electrical Engineering and Computer Science

  25. 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. School of Electrical Engineering and Computer Science

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