3. Introduction
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Currently, great need emerges for better
techniques, tools and practices.
Modeling could be applied to various area →
minimize cost & Optimization
Self Organizing Map → ANN(connectionist
paradigms) → support and changes in
approaches & modeling technique
Disparate data analysis in 2 scales, regional and
global.
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4. Self Organizing Map
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Proposed by Tuevo Kohonen (1972)
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Unsupervised Neural Network
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Data driven learning process
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Reduce dimensions,display similarities
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5. SOM (Cont..)
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Mapping Nodes to group of class
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Selection of Best Matching Unit
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Cooperative Learning
Algorithm :
2. choose random vector
3. examined & select BMU
4. Calculate Neighbourhood
5. Update appropriate weights
6. Repeat step 2 for N times
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Y, Red, Elevation,..
1. Initialize weight of nodes
X, Blue, Density,..
6. SOM → Modeling
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Clustering Capability
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Modeling & Prediction
Prediction
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Ecological
Modeling
Regional Data
Analysis
7. Application → Prediction
“ Predicting Stock Prices Using a Hybrid Kohonen Self Organizing
Map (SOM) “ , Mark & Olatoyosi, 2007
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Main aim → Stock Prices Prediction
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Applied on LucentI Inc, using five years data → 1251 points
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Hybridization of SOM with Multilayer Perceptron
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8. HSOM → Prediction (cont)
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Flow of Process
Net Configuration
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9. HSOM → Prediction (cont)
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Hybrid HSOM outperform SOM & BPN
BPN comes inaccurate when price > $60 →
Significant Loss in investment
HSOM has lowest error
(0~12) → Increase in return
of Investment (ROI)
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10. Conclusion
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ANN can be used to enhance and alter the
modeling technique
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SOM is an Unsupervised Neural Network
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Clustering classes with mapping nodes
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Various application of SOM on Modeling &
Simulation → prediction
By collaborating SOM with other method →
greater results.
Saturday, November 23, 2013