Extraction of maximum wind power of variable speed wind turbines in hybrid wind-diesel-storage system (HWDSS) is considered due to economical purposes. The proposed control algorithm utilizes extended fuzzy-linear matrix equalities (FLMEs) systems design of stabilizing fuzzy controllers for nonlinear systems described by Takagi-Sugeno (TS) fuzzy models. The algorithm maximizes the power coefficient for a fixed pitch. Moreover, it reduces the voltage ripple and stabilizes the system over a wide range of wind speed variations. The control scheme is tested for different profiles of wind speed pattern and provides satisfactory results.

Elkhatib Kamal

Magdy Koutb

Abdul Azim Sobaih

Sahar Kaddah

Maximum Power Control of Hybrid Wind-Diesel-Storage System

The aim of this paper is the generalization of the notion of fuzzy vector spaces to fuzzy hypervector spaces. In this regard, by considering the notion of fuzzy hypervector spaces, we characterized a fuzzy hypervector space based on its level sub-hyperspace. The algebraic nature of fuzzy hypervector space under transformations is studied. Certain conditions are obtained under which a given fuzzy hypervector space can or cannot be realized as a union of two fuzzy hypervector spaces such that none is contained in the other. The construction of a fuzzy hypervector space generated by a given fuzzy subset of a hypervector space is given. The set of all fuzzy cosets of a fuzzy hypervector space is shown to be a hypervector space. Finally, a fuzzy quotient hypervector space is defined and an analogue of a consequence of the “fundamental theorem of homomorphisms” is obtained.

R. Ameri

O. R. Dehghan

Fuzzy Hypervector Spaces

A novel digital image stabilization technique is proposed in this paper. It is based on a fuzzy Kalman compensation of the global motion vector (GMV), which is estimated in the log-polar plane. The GMV is extracted using four local motion vectors (LMVs) computed on respective subimages in the logpolar plane. The fuzzy Kalman system consists of a fuzzy system with the Kalman filter's discrete time-invariant definition. Due to this inherited recursiveness, the output results into smoothed image sequences. The proposed stabilization system aims to compensate any oscillations of the frame absolute positions, based on the motion estimation in the log-polar domain, filtered by the fuzzy Kalman system, and thus the advantages of both the fuzzy Kalman system and the log-polar transformation are exploited. The described technique produces optimal results in terms of the output quality and the level of compensation.

Nikolaos Kyriakoulis

Antonios Gasteratos

A Recursive Fuzzy System for Efficient Digital Image Stabilization

We present several properties of fuzzy boundary and fuzzy
semiboundary which have been supported by examples. Properties of
fuzzy semi-interior, fuzzy semiclosure, fuzzy boundary, and fuzzy
semiboundary have been obtained in product-related spaces. We give
necessary conditions for fuzzy continuous (resp., fuzzy
semicontinuous, fuzzy irresolute) functions. Moreover, fuzzy
continuous (resp., fuzzy semicontinuous, fuzzy irresolute)
functions have been characterized via fuzzy-derived (resp., fuzzy-semiderived) sets.

M. Athar

B. Ahmad

Fuzzy Boundary and Fuzzy Semiboundary

This paper presents the real-time implementation of a fuzzy coordinated classical PI control scheme for controlling the pressure in a pilot pressure tank system. The fuzzy system has been designed to track the variation parameters in a feedback loop and tune the classical controller to achieve a better control action for load disturbances and set point changes. The error and process inputs are chosen as the inputs of fuzzy system to tune the conventional PI controller according to the process condition. This online conventional controller tuning technique will reduce the human involvement in controller tuning and increase the operating range of the conventional controller. The proposed control algorithm is experimentally implemented for the real-time pressure control of a pilot air tank system and validated using a high-speed 32-bit ARM7 embedded microcontroller board (ATMEL AT91M55800A). To demonstrate the performance of the fuzzy coordinated PI control scheme, results are compared with a classical PI and PI-type fuzzy control method. It is observed that the proposed controller structure is able to quickly track the parameter variation and perform better in load disturbances and also for set point changes.

N. Kanagaraj

P. Sivashanmugam

S. Paramasivam

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

We provide a way to combine matrix Lyapunov systems
with fuzzy rules to form a new fuzzy system called fuzzy dynamical matrix
Lyapunov system, which can be regarded as a new approach to intelligent
control. First, we study the controllability property of the fuzzy dynamical
matrix Lyapunov system and provide a sufficient condition for its controllability
with the use of fuzzy rule base. The significance of our result is that given
a deterministic system and a fuzzy state with rule base, we can determine the
rule base for the control. Further, we discuss the concept of observability and
give a sufficient condition for the system to be observable. The advantage of
our result is that we can determine the rule base for the initial value without
solving the system.

M. S. N. Murty

G. Suresh Kumar

On Controllability and Observability of Fuzzy Dynamical Matrix Lyapunov Systems

The main goal of this study is to investigate the relationship between psychosocial variables and diabetic children patients and to obtain a classifier function with which it was possible to classify the patients on the basis of assessed adherence level. The rough set theory is used to identify the most important attributes and to induce decision rules from 302 samples of Kuwaiti diabetic children patients aged 7–13 years old. To increase the efficiency of the classification process, rough sets with Boolean reasoning discretization algorithm is introduced to discretize the data, then the rough set reduction technique is applied to find all reducts of the data which contains the minimal subset of attributes that are associated with a class label for classification. Finally, the rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. A comparison between the obtained results using rough sets with decision tree, neural networks, and statistical discriminate analysis classifier algorithms has been made. Rough sets show a higher overall accuracy rates and generate more compact rules.

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 - Table of contents 2008

title

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

Advances in Fuzzy Systems