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Method of rules extraction for expert systems based on artificial neural networks

Method of rules extraction for expert systems based on artificial neural networks. Shvarts Alexander Saratov State Technical University. Oil and gas industry. Intellectual systems. Medicine. and many others…. Transport. Technical diagnostics. Basis of intellectual systems. Rules

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Method of rules extraction for expert systems based on artificial neural networks

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  1. Method of rules extraction for expert systems based on artificial neural networks Shvarts Alexander Saratov State Technical University

  2. Oil and gas industry Intellectual systems Medicine and many others… Transport Technical diagnostics

  3. Basis of intellectual systems • Rules • Decision trees • Regression analysis • Artificial neural networks • Multilayer perceptrons • Radial-basis functions networks • Kohonen self-organizing networks • Recurrent networks etc.

  4. Disadvantages of expert systems based on artificial neural networks: Difficulties in explaining the decision making process Problems in validation “Missing exceptions” mistakes Existing problems X1 ? X2 X3 y1 X4 y2 X5 y3 X6 X7

  5. Purpose of study: to introduce and develop new method, that could transform network structure into classification rules in order to • Easily validate expert system • Develop explaining module • Handle “missing exceptions” mistakes

  6. Stages of research • Analysis of existing methods of rules extraction • Introducing new method with following characteristics: • Structure – multilayer perceptron • No network pruning and re-training • Testing the method on trained network

  7. Wij,n X00 Wn,m X01 Attribute 0 H0 X02 C0 X10 H1 C1 X11 Attribute1 H2 C2 X12 X20 H3 Attribute2 X21 X22 Introduced method • Feedforward multilayer perceptron • One hidden layer • Hyperbolic tangent as the activation function of hidden layer • Sigmoid as the activation function of output layer • Input neurons are grouped into attributes

  8. X1 H0 H1 Xi H2 Cm H3 XI Index of importance Weight index C0

  9. X1 H0 Xi-1 H1 Attribute a Xi H2 Cm Xi+1 H3 XI Attributes calculations

  10. Importance threshold where and - reliance coefficient

  11. Combinations graph Attribute 1 Attribute 0 Attribute 7

  12. Rules generation IF [Attribute 1]=[Value 4] AND [Attribute A]=[Value I] AND … AND [Attribute 0]=[Value 2] THEN [Class m] IF [Attribute 1]=[Value 5] AND [Attribute A]=[Value I] AND … AND [Attribute 0]=[Value 1] THEN [Class m] IF [Attribute 1]=[Value 6] AND [Attribute A]=[Value (I-1)] AND … AND [Attribute 0]=[Value 2] THEN [Class m]

  13. Method application • Expert system for predicting arrhythmia, based on multilayer perceptron: • 15 inputs • 1 neuron in hidden layer • 2 classes • 92% fidelity

  14. Experimental data Number of rules Reliance coefficient

  15. Thank you!

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