Neuro-fuzzy systems

3,541 views

Published on

My slides for a presentation on Neuro-Fuzzy Systems in college...

Published in: Education
0 Comments
6 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
3,541
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
392
Comments
0
Likes
6
Embeds 0
No embeds

No notes for slide
  • - Automatically extract fuzzy rules from numerical data
  • Neuro-fuzzy systems

    1. 1. Neuro-Fuzzy Systems (NFS) Presented by Sagar Ahire
    2. 2. Neuro-Fuzzy System = Neural Network + Fuzzy System
    3. 3. Fuzzy Logic• A form of logic that deals with approximate reasoning• Created to model human reasoning processes• Uses variables with truth values between 0 and 1
    4. 4. Characteristics of Fuzzy Logic• Everything is a matter of degree• Knowledge is interpreted as a collection of fuzzy constraints on a collection of variables• Inference is viewed as the process of propagation of these constraints• Any logic system can be fuzzified
    5. 5. Neural Network• Simplified Mathematical model of brain-like systems• Functions like a massively parallel distributed computation network• Is not programmed, but is trained
    6. 6. Neural Network• Input• Weights• Output
    7. 7. ComparisonPoint Fuzzy Systems Neural NetworkKnowledge Source Human Experts Sample SetsLearning Mechanism Induction Adjusting WeightsReasoning Mechanism Heuristic Search Parallel ComputationLearning Speed High LowReasoning Speed Low HighFault Tolerance Low Very HighImplementation Explicit ImplicitFlexibility Low High
    8. 8. Neuro-Fuzzy Systems (NFS)• Were created to solve the trade-off between: – The mapping precision & automation of Neural Networks – The interpretability of Fuzzy Systems• Combines both such that either: – Fuzzy system gives input to Neural Network – Neural Network gives input to Fuzzy Systems
    9. 9. Steps in Development of NFS• Development of Fuzzy Neural Models [Neurons]• Development of synaptic connection models which incorporate fuzziness into Neural Network [Weights]• Development of Learning Algorithms [Method of adjusting weights]
    10. 10. Types of NFSType Weights Inputs Outputs ApplicationsType 0 Crisp Crisp Crisp N/AType 1 Crisp Fuzzy Crisp ClassificationType 2 Crisp Fuzzy Fuzzy Fuzzy IF-THENType 3 Fuzzy Fuzzy Fuzzy Fuzzy IF-THENType 4 Fuzzy Crisp Fuzzy Fuzzy IF-THENType 5 Crisp Crisp Fuzzy UnrealisticType 6 Fuzzy Crisp Crisp UnrealisticType 7 Fuzzy Fuzzy Crisp Unrealistic
    11. 11. Models of NFS• Model 1: Fuzzy System → Neural Network• Model 2: Neural Network → Fuzzy Systems
    12. 12. Models of NFS #1:Fuzzy System → Neural Network
    13. 13. Models of NFS #2:Neural Network → Fuzzy System
    14. 14. Applications of NFS• Measuring opacity/transparency of water in washing machine – Hitachi, Japan• Improving the rating of convertible bonds – Nikko Securities, Japan• Adjusting exposure in photocopy machines – Sanyo, Japan• Electric fan that rotates towards the user – Sanyo, Japan

    ×