FUZZY NEURAL NETWORKS TECHNIQUES AND THEIR APPLICATIONS PRESENTED BY KARN PATANUKHOM 4206017

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

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)

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

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”

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

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

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

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

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

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

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

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

Example Samsung’s fan heater system

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

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