Neuro-Fuzzy Systems(NFS)

Presented by Sagar Ahire

Neuro-Fuzzy System=

Neural Network+

Fuzzy System

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

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

Neural Network

• Simplified Mathematical model of brain-like systems

• Functions like a massively parallel distributed computation network

• Is not programmed, but is trained

Neural Network• Input• Weights• Output

ComparisonPoint 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

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

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]

Types of NFSType 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

Models of NFS

• Model 1: Fuzzy System → Neural Network• Model 2: Neural Network → Fuzzy Systems

Models of NFS #1:Fuzzy System → Neural Network

Models of NFS #2:Neural Network → Fuzzy System

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