DESIGN OF A SELF- ORGANIZING LEARNING ARRAY SYSTEM Dr. Janusz Starzyk Tsun-Ho Liu Ohio University School of Electrical Engineering and Computer Science.

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Presentation transcript:

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

May th, 2003 School of Electrical Engineering and Computer Science2 Outline  Introduction  Self-Organizing Learning Array Structure  Neuron Structure and Self-Organizing Principles  Data Preprocessing  Software Simulation Result  Conclusion and Future Work

May th, 2003 School of Electrical Engineering and Computer Science3 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

May th, 2003 School of Electrical Engineering and Computer Science4 Introduction (Cont’d)  Research Objective:  Less interconnection  Self-organizing  Local Learning  Nonspecific classification

May th, 2003 School of Electrical Engineering and Computer Science5 Self-Organizing Learning Array Structure (Cont’d)  Feed forward organization and structure

May th, 2003 School of Electrical Engineering and Computer Science6 Self-Organizing Learning Array Structure (Cont’d)  Initial Wiring

May th, 2003 School of Electrical Engineering and Computer Science7 Neuron Structure and Self- Organizing Principles  Neuron Input - System clock

May th, 2003 School of Electrical Engineering and Computer Science8 Neuron Structure and Self- Organizing Principles (Cont’d)  Neuron Input - Data input

May th, 2003 School of Electrical Engineering and Computer Science9 Neuron Structure and Self- Organizing Principles (Cont’d)  Neuron Input - Threshold control input (TCI)

May th, 2003 School of Electrical Engineering and Computer Science10 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%

May th, 2003 School of Electrical Engineering and Computer Science11 Neuron Structure and Self- Organizing Principles (Cont’d)  Neuron inside  Transformation functions  Linear and nonlinear  Single input or multiple inputs  Information index calculation

May th, 2003 School of Electrical Engineering and Computer Science12 Neuron Structure and Self- Organizing Principles (Cont’d)

May th, 2003 School of Electrical Engineering and Computer Science13 Neuron Structure and Self- Organizing Principles (Cont’d)

May th, 2003 School of Electrical Engineering and Computer Science14 Neuron Structure and Self- Organizing Principles (Cont’d)  Neuron output - System output

May th, 2003 School of Electrical Engineering and Computer Science15 Neuron Structure and Self- Organizing Principles (Cont’d)  Neuron output - Output Clock

May th, 2003 School of Electrical Engineering and Computer Science16 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)

May th, 2003 School of Electrical Engineering and Computer Science17 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

May th, 2003 School of Electrical Engineering and Computer Science18 Symbolic value – numerical feature =1 1) 2) 3) 4)

May th, 2003 School of Electrical Engineering and Computer Science19 Symbolic value – numerical feature =1  Symbolic value – numerical feature =1 X s = [ ] T

May th, 2003 School of Electrical Engineering and Computer Science20 Data Preprocessing (Cont’d) 1) 2) 3) 4) 5)

May th, 2003 School of Electrical Engineering and Computer Science21 Data Preprocessing (Cont’d)  Symbolic value – numerical feature > 1 X s = [ ] T

May th, 2003 School of Electrical Engineering and Computer Science22 Software Simulation Result

May th, 2003 School of Electrical Engineering and Computer Science23 Software Simulation Result (Cont’d) FSS Naïve Bayes NBTree C4.5-auto IDTM (Decision table) HOODG / SOLAR C4.5 rules OC C Voted ID3 (0.6) CN Naïve-Bayes Voted ID3 (0.8) T R Nearest-neighbor (3) Nearest-neighbor (1) PeblsCrashed

May th, 2003 School of Electrical Engineering and Computer Science24 Conclusion and Future Work  Conclusion  Local learning  Self-organizing  Data preprocessing  Future work  VHDL simulation  FPGA machine  VLSI design

May th, 2003 School of Electrical Engineering and Computer Science25 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.