1. FUZZY NEURAL NETWORKS TECHNIQUES AND THEIR APPLICATIONS
PRESENTED BY
KARN PATANUKHOM

2. CLASSIFICATION OF FUZZY NEURAL NETWORKS
Fuzzy rule-based systems with learning ability
Fuzzy rule-based systems represented by network architectures
Neural networks for fuzzy reasoning
Fuzzified neural networks
Other approaches
e.g. neural fuzzy point process, fuzzy perceptron, fuzzy adaptive resonance system, max-min neuron network, fuzzy max-min neuron network, OR/AND neuron network and Yamakawa’s fuzzy neuron

3. FUZZY RULE-BASE SYSTEMS WITH LEARNING ABILITY
Instead of depending on humans to put specific fuzzy if-then rules to deal with every situation. Fuzzy if-then rules are adjusted by iterative learning algorithms similar to neural network learning.
e.g. Adaptive fuzzy systems
Input
Fuzzification
Defuzzification
Fuzzy-Inference
Output
Fuzzy if-then rule
Human decision
Neural network
Data In Rules Out (DIRO)

4. FUZZY RULE-BASE SYSTEMS REPRESENTED BY NETWORK ARCHITECTURES
Use network architectures represent fuzzy rule-based system
Membership function of each fuzzy set correspond to the activation function of each unit in the neural network
Neuron
MEMBERSHIP FUNCTION
ACTIVATION FUNCTION
Fuzzy set

5. neuron network learning “If x is small the y is large”
NEURAL NETWORKS FOR FUZZY REASONING
Neural networks are used for fuzzy reasoning
e.g. antecedent fuzzy sets and consequent fuzzy sets are represented by membership values at some reference point and those membership values are used as inputs and targets for training neural network
Neuron network for fuzzy reasoning
antecedent fuzzy sets
consequent fuzzy sets
Input and target for
neuron network learning
from fuzzy if then rule
“If x is small the y is large”

6. FUZZIFIED NEURAL NETWORKS
Neural networks can be fuzzified by using fuzzy number as inputs, targets and connection weights
Output from fuzzified neural networks are defined by fuzzy arithmetic
Fuzzification
Numeric Input
Fuzzified
neural network
Defuzzification
Numeric Output
Neural network
Linguistic value

7. FUZZIFIED NEURAL NETWORKS
FOR FUZZY RESONING
FUZZIFIED NEURAL NETWORKS
FOR FUZZY RESONING
Fuzzy Arithmetric
Fuzzy input
Fuzzy weight
Fuzzy target

8. FUZZY CLASSIFICATION
Assume that classification boundaries between different classes are not crisp but fuzzy
Possible classes of an input pattern can be suggested by the trained neuron network
FUZZY BOUNDARY

9. FUZZY MODELING
Instead of use only one neuron network to model. Using two neuron network can represent the given data much better. One neuron is used for representing the lower limit of function and the other is used for the upper limit

10. Adaptive Fuzzy Controller
Example
Adaptive Fuzzy Controller
Servo Motor
Throttle Plate
Tacho Meter
D/dt
Desired Speed
Measured
Speed
Error
CTO
Control Input
ATV
Adaptive Fuzzy Throttle Control for an All Terrain Vehicle

11. Example Non-adaptive fuzzy rule-based Triangular membership function
Center-average defuzzifier
Adaptive Fuzzy Throttle Control for an All Terrain Vehicle

12. Example Experimental results Smooth throttle movement
Robustness with respect to varying terrain
Increase commanded speed
Improvement of steady state error
Adaptive Fuzzy Throttle Control for an All Terrain Vehicle

13. Example Electric fan of Sanyo system Distance Angle
Used for detecting where their users (remote controller) are located and keep user stay in the center of rotation of fan
Then, use neuron network to estimate the angle by estimated distance and same output of three sensor
There have three sensor L,R,C
First, use fuzzy system to estimate the distance by output of three sensor
The sensor output depend on angle and distance between sensor and remote controller
Electric fan of Sanyo system

14. Example
Samsung’s fan heater system

15. A FUZZY NEURAL NETWORK MODEL FOR FORECASTING STOCK PRICE
Example
NN-driven Fuzzy Reasoning
Forecasted stock price at time i
Determine fitness of each rule
Represent to P rule
Previous data series of stock price
A FUZZY NEURAL NETWORK MODEL
FOR FORECASTING STOCK PRICE

16. A FUZZY NEURAL NETWORK MODEL FOR FORECASTING STOCK PRICE
Example
Experimental Result
THE END
Advantage of Fuzzy neuron network
Fast convergence
High precision
Strong function approximation ability
A FUZZY NEURAL NETWORK MODEL
FOR FORECASTING STOCK PRICE