Neuro-fuzzy systems combine neural networks and fuzzy logic to overcome the limitations of each. They were created to achieve the mapping precision of neural networks and the interpretability of fuzzy systems. There are different types of neuro-fuzzy systems depending on whether the inputs, outputs, and weights are crisp or fuzzy. Two common models are fuzzy systems providing input to neural networks, and neural networks providing input to fuzzy systems. Neuro-fuzzy systems have applications in domains like measuring water opacity, improving financial ratings, and automatically adjusting devices.

1. Neuro-Fuzzy Systems
(NFS)
Presented by Sagar Ahire

2. Neuro-Fuzzy System
=
Neural Network
+
Fuzzy System

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. 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. Neural Network
• Simplified Mathematical model of brain-like
systems
• Functions like a massively parallel distributed
computation network
• Is not programmed, but is trained

6. Neural Network
• Input
• Weights
• Output

7. Comparison
Point Fuzzy Systems Neural Network
Knowledge Source Human Experts Sample Sets
Learning Mechanism Induction Adjusting Weights
Reasoning Mechanism Heuristic Search Parallel Computation
Learning Speed High Low
Reasoning Speed Low High
Fault Tolerance Low Very High
Implementation Explicit Implicit
Flexibility Low High

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. 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. Types of NFS
Type Weights Inputs Outputs Applications
Type 0 Crisp Crisp Crisp N/A
Type 1 Crisp Fuzzy Crisp Classification
Type 2 Crisp Fuzzy Fuzzy Fuzzy IF-THEN
Type 3 Fuzzy Fuzzy Fuzzy Fuzzy IF-THEN
Type 4 Fuzzy Crisp Fuzzy Fuzzy IF-THEN
Type 5 Crisp Crisp Fuzzy Unrealistic
Type 6 Fuzzy Crisp Crisp Unrealistic
Type 7 Fuzzy Fuzzy Crisp Unrealistic

11. Models of NFS
• Model 1: Fuzzy System → Neural Network
• Model 2: Neural Network → Fuzzy Systems

12. Models of NFS #1:
Fuzzy System → Neural Network

13. Models of NFS #2:
Neural Network → Fuzzy System

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