this paper present new method onlin estim stator rotor resist induct motor indirect vectorcontrol drive artifici neural network the back propag algorithm use train neural network the error between rotor flux linkag base neural network model voltag model back propag adjust weight neural network model rotor resist estim for stator resist estim error between measur stator current estim stator current use neural network back propag adjust weight neural network the perform stator rotor resist estim torqu flux respons drive togeth estim investig help simul variat stator rotor resist from nomin valu both type resist estim experiment use propos neural network vectorcontrol induct motor drive data track perform estim present with approach rotor resist estim found insensit stator resist variat both simul experi

Baburaj Karanayil

Muhammed Fazlur Rahman

Colin Grantham

Identification of Induction Motor Parameters in Industrial Drives with Artificial Neural Networks

extraction maximum wind power variabl speed wind turbin hybrid winddieselstorag system hwdss consid due econom purpos the propos control algorithm util extend fuzzylinear matrix equal flmes system design stabil fuzzi control nonlinear system describ takagisugeno fuzzi model the algorithm maxim power coeffici fix pitch moreov reduc voltag rippl stabil system over wide rang wind speed variat the control scheme test differ profil wind speed pattern provid satisfactori result

Elkhatib Kamal

Magdy Koutb

Abdul Azim Sobaih

Sahar Kaddah

Maximum Power Control of Hybrid Wind-Diesel-Storage System

the aim paper general notion fuzzi vector space fuzzi hypervector space regard consid notion fuzzi hypervector space character fuzzi hypervector space base it level subhyperspac the algebra natur fuzzi hypervector space under transform studi certain condit obtain under which given fuzzi hypervector space can cannot realiz union two fuzzi hypervector space none contain other the construct fuzzi hypervector space generat given fuzzi subset hypervector space given the set all fuzzi coset fuzzi hypervector space shown hypervector space final fuzzi quotient hypervector space defin analogu consequ fundament theorem homomorph obtain

R. Ameri

O. R. Dehghan

Fuzzy Hypervector Spaces

novel digit imag stabil techniqu propos paper base fuzzi kalman compens global motion vector gmv which estim logpolar plane the gmv extract use four local motion vector lmvs comput respect subimag logpolar plane the fuzzi kalman system consist fuzzi system kalman filter discret timeinvari definit due inherit recurs output result smooth imag sequenc the propos stabil system aim compens ani oscil frame absolut posit base motion estim logpolar domain filter fuzzi kalman system thus advantag both fuzzi kalman system logpolar transform exploit the describ techniqu produc optim result term output qualiti level compens

Nikolaos Kyriakoulis

Antonios Gasteratos

A Recursive Fuzzy System for Efficient Digital Image Stabilization

present sever properti fuzzi boundari fuzzi semiboundari which have been support exampl properti fuzzi semiinterior fuzzi semiclosur fuzzi boundari fuzzi semiboundari have been obtain productrel space give necessari condit fuzzi continu resp fuzzi semicontinu fuzzi irresolut function moreov fuzzi continu resp fuzzi semicontinu fuzzi irresolut function have been character via fuzzyderiv resp fuzzysemideriv set

M. Athar

B. Ahmad

Fuzzy Boundary and Fuzzy Semiboundary

this paper present realtim implement fuzzi coordin classic control scheme control pressur pilot pressur tank system the fuzzi system has been design track variat paramet feedback loop tune classic control achiev better control action load disturb set point chang the error process input chosen input fuzzi system tune convent control accord process condit this onlin convent control tune techniqu reduc human involv control tune increas oper rang convent control the propos control algorithm experiment implement realtim pressur control pilot air tank system valid use highspe 32bit arm7 embed microcontrol board atmel at91m55800a demonstr perform fuzzi coordin control scheme result compar classic pitype fuzzi control method observ propos control structur abl quick track paramet variat perform better load disturb also set point chang

N. Kanagaraj

P. Sivashanmugam

S. Paramasivam

Fuzzy Coordinated PI Controller: Application to the Real-Time Pressure Control Process

provid way combin matrix lyapunov system fuzzi rule form new fuzzi system call fuzzi dynam matrix lyapunov system which can regard new approach intellig control first studi control properti fuzzi dynam matrix lyapunov system provid suffici condit it control use fuzzi rule base the signific our result given determinist system fuzzi state rule base can determin rule base control further discuss concept observ give suffici condit system observ the advantag our result can determin rule base initi valu without solv system

M. S. N. Murty

G. Suresh Kumar

On Controllability and Observability of Fuzzy Dynamical Matrix Lyapunov Systems

the main goal studi investig relationship between psychosoci variabl diabet children patient obtain classifi function which possibl classifi patient basi assess adher level the rough set theori use identifi most import attribut induc decis rule from 302 sampl kuwaiti diabet children patient age 713 year old increas effici classif process rough set boolean reason discret algorithm introduc discret data rough set reduct techniqu appli find all reduct data which contain minim subset attribut associ class label classif final rough set depend rule generat direct from all generat reduct rough confus matrix use evalu perform predict reduct class comparison between obtain result use rough set decis tree neural network statist discrimin analysi classifi algorithm has been made rough set show higher overal accuraci rate generat more compact rule

Aboul ella Hassanien

Mohamed E. Abdelhafez

Hala S. Own

Rough Sets Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients

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Advances in Fuzzy Systems provides an international forum for original research articles in the theory and applications of fuzzy subsets and systems.

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Chief Editor, Professor Melin, is a professor at the Tijuana Institute of Technology. Her research interests include modular neural networks, type-2 fuzzy logic, pattern recognition, fuzzy control, neuro-fuzzy and genetic-fuzzy hybrid approaches.

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Advances in Fuzzy Systems