Abstract: The measurement of aerodynamic forces and moments
acting on an aircraft model is important for the development of wind
tunnel measurement technology to predict the performance of the full
scale vehicle. The potentials of an aircraft model with and without
winglet and aerodynamic characteristics with NACA wing No. 65-3-
218 have been studied using subsonic wind tunnel of 1 m × 1 m
rectangular test section and 2.5 m long of Aerodynamics Laboratory
Faculty of Engineering (University Putra Malaysia). Focusing on
analyzing the aerodynamic characteristics of the aircraft model, two
main issues are studied in this paper. First, a six component wind
tunnel external balance is used for measuring lift, drag and pitching
moment. Secondly, Tests are conducted on the aircraft model with
and without winglet of two configurations at Reynolds numbers
1.7×105, 2.1×105, and 2.5×105 for different angle of attacks. Fuzzy
logic approach is found as efficient for the representation,
manipulation and utilization of aerodynamic characteristics.
Therefore, the primary purpose of this work was to investigate the
relationship between lift and drag coefficients, with free-stream
velocities and angle of attacks, and to illustrate how fuzzy logic
might play an important role in study of lift aerodynamic
characteristics of an aircraft model with the addition of certain
winglet configurations. Results of the developed fuzzy logic were
compared with the experimental results. For lift coefficient analysis,
the mean of actual and predicted values were 0.62 and 0.60
respectively. The coreelation between actual and predicted values
(from FLS model) of lift coefficient in different angle of attack was
found as 0.99. The mean relative error of actual and predicted valus
was found as 5.18% for the velocity of 26.36 m/s which was found to
be less than the acceptable limits (10%). The goodness of fit of
prediction value was 0.95 which was close to 1.0.
Abstract: This paper deals with a portfolio selection problem
based on the possibility theory under the assumption that the returns
of assets are LR-type fuzzy numbers. A possibilistic portfolio model
with transaction costs is proposed, in which the possibilistic mean
value of the return is termed measure of investment return, and the
possibilistic variance of the return is termed measure of investment
risk. Due to considering transaction costs, the existing traditional
optimization algorithms usually fail to find the optimal solution
efficiently and heuristic algorithms can be the best method. Therefore,
a particle swarm optimization is designed to solve the corresponding
optimization problem. At last, a numerical example is given to
illustrate our proposed effective means and approaches.
Abstract: The main objective of Automatic Generation Control (AGC) is to balance the total system generation against system load losses so that the desired frequency and power interchange with neighboring systems is maintained. Any mismatch between generation and demand causes the system frequency to deviate from its nominal value. Thus high frequency deviation may lead to system collapse. This necessitates a very fast and accurate controller to maintain the nominal system frequency. This paper deals with a novel approach of artificial intelligence (AI) technique called Hybrid Neuro-Fuzzy (HNF) approach for an (AGC). The advantage of this controller is that it can handle the non-linearities at the same time it is faster than other conventional controllers. The effectiveness of the proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in a two area interconnected power system. The result shows that intelligent controller is having improved dynamic response and at the same time faster than conventional controller.
Abstract: In this study, control performance of a smart base
isolation system consisting of a friction pendulum system (FPS) and a
magnetorheological (MR) damper has been investigated. A fuzzy
logic controller (FLC) is used to modulate the MR damper so as to
minimize structural acceleration while maintaining acceptable base
displacement levels. To this end, a multi-objective optimization
scheme is used to optimize parameters of membership functions and
find appropriate fuzzy rules. To demonstrate effectiveness of the
proposed multi-objective genetic algorithm for FLC, a numerical
study of a smart base isolation system is conducted using several
historical earthquakes. It is shown that the proposed method can find
optimal fuzzy rules and that the optimized FLC outperforms not only a
passive control strategy but also a human-designed FLC and a
conventional semi-active control algorithm.
Abstract: Based on the fuzzy set theory this work develops two
adaptations of iterative methods that solve mathematical programming
problems with uncertainties in the objective function and in
the set of constraints. The first one uses the approach proposed by
Zimmermann to fuzzy linear programming problems as a basis and
the second one obtains cut levels and later maximizes the membership
function of fuzzy decision making using the bound search method.
We outline similarities between the two iterative methods studied.
Selected examples from the literature are presented to validate the
efficiency of the methods addressed.
Abstract: Petri Net (PN) has proven to be effective graphical, mathematical, simulation, and control tool for Discrete Event Systems (DES). But, with the growth in the complexity of modern industrial, and communication systems, PN found themselves inadequate to address the problems of uncertainty, and imprecision in data. This gave rise to amalgamation of Fuzzy logic with Petri nets and a new tool emerged with the name of Fuzzy Petri Nets (FPN). Although there had been a lot of research done on FPN and a number of their applications have been anticipated, but their basic types and structure are still ambiguous. Therefore, in this research, an effort is made to categorize FPN according to their structure and algorithms Further, literature review of the applications of FPN in the light of their classifications has been done.
Abstract: This paper investigates experimental studies on
vibration suppression for a cantilever beam using an
Electro-Rheological (ER) sandwich shock absorber. ER fluid (ERF) is a
class of smart materials that can undergo significant reversible changes
immediately in its rheological and mechanical properties under the
influence of an applied electric field. Firstly, an ER sandwich beam is
fabricated by inserting a starch-based ERF into a hollow composite
beam. At the same time, experimental investigations are focused on the
frequency response of the ERF sandwich beam. Second, the ERF
sandwich beam is attached to a cantilever beam to become as a shock
absorber. Finally, a fuzzy semi-active vibration control is designed to
suppress the vibration of the cantilever beam via the ERF sandwich
shock absorber. To check the consistency of the proposed fuzzy
controller, the real-time implementation validated the performance of
the controller.
Abstract: Nowadays the control of stator voltage at a constant frequency is one of the traditional and low expense methods in order to control the speed of induction motors near its nominal speed. The torque of induction motor is a nonlinear function of the firing angle, phase angle and speed. In this paper the speed control of induction motor regarding various load torque and under different conditions will be investigated based on a fuzzy controller with inverse training.
Abstract: This paper proposes a methodology for analysis of
the dynamic behavior of a robotic manipulator in continuous
time. Initially this system (nonlinear system) will be decomposed
into linear submodels and analyzed in the context of the Linear
and Parameter Varying (LPV) Systems. The obtained linear
submodels, which represent the local dynamic behavior of the
robotic manipulator in some operating points were grouped in
a Takagi-Sugeno fuzzy structure. The obtained fuzzy model was
analyzed and validated through analog simulation, as universal
approximator of the robotic manipulator.
Abstract: The anti-homomorphic image of fuzzy ideals, fuzzy
ideals of near-rings and anti ideals are discussed in this note. A
necessary and sufficient condition has been established for near-ring
anti ideal to be characteristic.
Abstract: The design of technological procedures for
manufacturing certain products demands the definition and
optimization of technological process parameters. Their
determination depends on the model of the process itself and its
complexity. Certain processes do not have an adequate mathematical
model, thus they are modeled using heuristic methods. First part of
this paper presents a state of the art of using soft computing
techniques in manufacturing processes from the perspective of
applicability in modern CAx systems. Methods of artificial
intelligence which can be used for this purpose are analyzed. The
second part of this paper shows some of the developed models of
certain processes, as well as their applicability in the actual
calculation of parameters of some technological processes within the
design system from the viewpoint of productivity.
Abstract: In this paper we propose a multi-agent architecture for web information retrieval using fuzzy logic based result fusion mechanism. The model is designed in JADE framework and takes advantage of JXTA agent communication method to allow agent communication through firewalls and network address translators. This approach enables developers to build and deploy P2P applications through a unified medium to manage agent-based document retrieval from multiple sources.
Abstract: We present a general comparison of punctual kriging based image restoration for different neighbourhood sizes. The formulation of the technique under consideration is based on punctual kriging and fuzzy concepts for image restoration in spatial domain. Three different neighbourhood windows are considered to estimate the semivariance at different lags for studying its effect in reduction of negative weights resulted in punctual kriging, consequently restoration of degraded images. Our results show that effect of neighbourhood size higher than 5x5 on reduction in negative weights is insignificant. In addition, image quality measures, such as structure similarity indices, peak signal to noise ratios and the new variogram based quality measures; show that 3x3 window size gives better performance as compared with larger window sizes.
Abstract: In determining the electromagnetic properties of
magnetic materials, hysteresis modeling is of high importance. Many
models are available to investigate those characteristics but they tend
to be complex and difficult to implement. In this paper a new
qualitative hysteresis model for ferromagnetic core is presented,
based on the function approximation capabilities of adaptive neuro
fuzzy inference system (ANFIS). The proposed ANFIS model
combined the neural network adaptive capabilities and the fuzzy
logic qualitative approach can restored the hysteresis curve with a
little RMS error. The model accuracy is good and can be easily
adapted to the requirements of the application by extending or
reducing the network training set and thus the required amount of
measurement data.
Abstract: In this study, fuzzy rule-based classifier is used for the
diagnosis of congenital heart disease. Congenital heart diseases are
defined as structural or functional heart disease. Medical data sets
were obtained from Pediatric Cardiology Department at Selcuk
University, from years 2000 to 2003. Firstly, fuzzy rules were
generated by using medical data. Then the weights of fuzzy rules
were calculated. Two different reasoning methods as “weighted vote
method" and “singles winner method" were used in this study. The
results of fuzzy classifiers were compared.
Abstract: One of the most important parts of a cement factory is
the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral
movement of air and materials, together with chemical reactions take
place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not worked out yet. Only
in exceptional case; however, a large number of the involved parameter were crossed out and an approximation model was
presented instead. This issue caused many problems for designing a
cement rotary kiln controller. In this paper, we presented nonlinear predictor and simulator models for a real cement rotary kiln by using
nonlinear identification technique on the Locally Linear Neuro-
Fuzzy (LLNF) model. For the first time, a simulator model as well as
a predictor one with a precise fifteen minute prediction horizon for a
cement rotary kiln is presented. These models are trained by
LOLIMOT algorithm which is an incremental tree-structure
algorithm. At the end, the characteristics of these models are expressed. Furthermore, we presented the pros and cons of these
models. The data collected from White Saveh Cement Company is used for modeling.
Abstract: Inventory decisional environment of short life-cycle
products is full of uncertainties arising from randomness and
fuzziness of input parameters like customer demand requiring
modeling under hybrid uncertainty. Prior inventory models
incorporating fuzzy demand have unfortunately ignored stochastic
variation of demand. This paper determines an unambiguous optimal
order quantity from a set of n fuzzy observations in a newsvendor
inventory setting in presence of fuzzy random variable demand
capturing both fuzzy perception and randomness of customer
demand. The stress of this paper is in providing solution procedure
that attains optimality in two steps with demand information
availability in linguistic phrases leading to fuzziness along with
stochastic variation. The first step of solution procedure identifies
and prefers one best fuzzy opinion out of all expert opinions and the
second step determines optimal order quantity from the selected
event that maximizes profit. The model and solution procedure is
illustrated with a numerical example.
Abstract: In this paper, a TSK-type Neuro-fuzzy Inference
System that combines the features of fuzzy sets and neural networks
has been applied for the identification of MIMO systems. The procedure of adapting parameters in TSK model employs a Shuffled
Frog Leaping Algorithm (SFLA) which is inspired from the memetic evolution of a group of frogs when seeking for food. To demonstrate
the accuracy and effectiveness of the proposed controller, two nonlinear systems have been considered as the MIMO plant, and results have been compared with other learning methods based on
Particle Swarm Optimization algorithm (PSO) and Genetic
Algorithm (GA).
Abstract: This paper presents the application of Intelligent
Techniques to the various duties of Intelligent Condition Monitoring
Systems (ICMS) for Unmanned Aerial Vehicle (UAV) Robots. These
Systems are intended to support these Intelligent Robots in the event
of a Fault occurrence. Neural Networks are used for Diagnosis, whilst
Fuzzy Logic is intended for Prognosis and Remedy. The ultimate
goals of ICMS are to save large losses in financial cost, time and
data.
Abstract: In this paper, we investigate a class of fuzzy Cohen- Grossberg neural networks with time delays and impulsive effects. By virtue of stochastic analysis, Halanay inequality for stochastic differential equations, we find sufficient conditions for the global exponential square-mean synchronization of the FCGNNs under noise perturbation. In particular, the traditional assumption on the differentiability of the time-varying delays is no longer needed. Finally, a numerical example is given to show the effectiveness of the results in this paper.