Abstract: In this paper some results on strict stability heve beeb extended for fuzzy differential equations with impulse effect using Lyapunov functions and Razumikhin technique.
Abstract: A new deployment of the multiple criteria decision
making (MCDM) techniques: the Simple Additive Weighting
(SAW), and the Technique for Order Preference by Similarity to
Ideal Solution (TOPSIS) for portfolio allocation, is demonstrated in
this paper. Rather than exclusive reference to mean and variance as in
the traditional mean-variance method, the criteria used in this
demonstration are the first four moments of the portfolio distribution.
Each asset is evaluated based on its marginal impacts to portfolio
higher moments that are characterized by trapezoidal fuzzy numbers.
Then centroid-based defuzzification is applied to convert fuzzy
numbers to the crisp numbers by which SAW and TOPSIS can be
deployed. Experimental results suggest the similar efficiency of these
MCDM approaches to selecting dominant assets for an optimal
portfolio under higher moments. The proposed approaches allow
investors flexibly adjust their risk preferences regarding higher
moments via different schemes adapting to various (from
conservative to risky) kinds of investors. The other significant
advantage is that, compared to the mean-variance analysis, the
portfolio weights obtained by SAW and TOPSIS are consistently
well-diversified.
Abstract: This article describes Uruk, the virtual museum of
Iraq that we developed for visual exploration and retrieval of image
collections. The system largely exploits the loosely-structured
hierarchy of XML documents that provides a useful representation
method to store semi-structured or unstructured data, which does not
easily fit into existing database. The system offers users the
capability to mine and manage the XML-based image collections
through a web-based Graphical User Interface (GUI). Typically, at an
interactive session with the system, the user can browse a visual
structural summary of the XML database in order to select interesting
elements. Using this intermediate result, queries combining structure
and textual references can be composed and presented to the system.
After query evaluation, the full set of answers is presented in a visual
and structured way.
Abstract: The steady-state operation of maintaining voltage
stability is done by switching various controllers scattered all over
the power network. When a contingency occurs, whether forced or
unforced, the dispatcher is to alleviate the problem in a minimum
time, cost, and effort. Persistent problem may lead to blackout. The
dispatcher is to have the appropriate switching of controllers in terms
of type, location, and size to remove the contingency and maintain
voltage stability. Wrong switching may worsen the problem and that
may lead to blackout. This work proposed and used a Fuzzy CMeans
Clustering (FCMC) to assist the dispatcher in the decision
making. The FCMC is used in the static voltage stability to map
instantaneously a contingency to a set of controllers where the types,
locations, and amount of switching are induced.
Abstract: This paper presents the design and implementation of
the WebGD, a CORBA-based document classification and retrieval
system on Internet. The WebGD makes use of such techniques as Web,
CORBA, Java, NLP, fuzzy technique, knowledge-based processing
and database technology. Unified classification and retrieval model,
classifying and retrieving with one reasoning engine and flexible
working mode configuration are some of its main features. The
architecture of WebGD, the unified classification and retrieval model,
the components of the WebGD server and the fuzzy inference engine
are discussed in this paper in detail.
Abstract: The application of a simple microcontroller to deal
with a three variable input and a single output fuzzy logic controller,
with Proportional – Integral – Derivative (PID) response control
built-in has been tested for an automatic voltage regulator. The
fuzzifiers are based on fixed range of the variables of output voltage.
The control output is used to control the wiper motor of the auto
transformer to adjust the voltage, using fuzzy logic principles, so that
the voltage is stabilized. In this report, the author will demonstrate
how fuzzy logic might provide elegant and efficient solutions in the
design of multivariable control based on experimental results rather
than on mathematical models.
Abstract: This research proposes a Preemptive Possibilistic
Linear Programming (PPLP) approach for solving multiobjective
Aggregate Production Planning (APP) problem with interval demand
and imprecise unit price and related operating costs. The proposed
approach attempts to maximize profit and minimize changes of
workforce. It transforms the total profit objective that has imprecise
information to three crisp objective functions, which are maximizing
the most possible value of profit, minimizing the risk of obtaining the
lower profit and maximizing the opportunity of obtaining the higher
profit. The change of workforce level objective is also converted.
Then, the problem is solved according to objective priorities. It is
easier than simultaneously solve the multiobjective problem as
performed in existing approach. Possible range of interval demand is
also used to increase flexibility of obtaining the better production
plan. A practical application of an electronic company is illustrated to
show the effectiveness of the proposed model.
Abstract: Small satellites have become increasingly popular recently as a means of providing educational institutes with the chance to design, construct, and test their spacecraft from beginning to the possible launch due to the low launching cost. This approach is remarkably cost saving because of the weight and size reduction of such satellites. Weight reduction could be realised by utilising electromagnetic coils solely, instead of different types of actuators. This paper describes the restrictions of using only “Electromagnetic" actuation for 3D stabilisation and how to make the magnetorquer based attitude control feasible using Fuzzy Logic Control (FLC). The design is developed to stabilize the spacecraft against gravity gradient disturbances with a three-axis stabilizing capability.
Abstract: In this paper, the discrete-time fuzzy BAM neural network with delays and impulses is studied. Sufficient conditions are obtained for the existence and global stability of a unique equilibrium of this class of fuzzy BAM neural networks with Lipschitzian activation functions without assuming their boundedness, monotonicity or differentiability and subjected to impulsive state displacements at fixed instants of time. Some numerical examples are given to demonstrate the effectiveness of the obtained results.
Abstract: The prediction of financial time series is a very
complicated process. If the efficient market hypothesis holds, then the predictability of most financial time series would be a rather
controversial issue, due to the fact that the current price contains already all available information in the market. This paper extends
the Adaptive Neuro Fuzzy Inference System for High Frequency
Trading which is an expert system that is capable of using fuzzy reasoning combined with the pattern recognition capability of neural networks to be used in financial forecasting and trading in high
frequency. However, in order to eliminate unnecessary input in the
training phase a new event based volatility model was proposed.
Taking volatility and the scaling laws of financial time series into consideration has brought about the development of the Intraday Seasonality Observation Model. This new model allows the observation of specific events and seasonalities in data and subsequently removes any unnecessary data. This new event based
volatility model provides the ANFIS system with more accurate input
and has increased the overall performance of the system.
Abstract: In this article a modification of the algorithm of the fuzzy ART network, aiming at returning it supervised is carried out. It consists of the search for the comparison, training and vigilance parameters giving the minimum quadratic distances between the output of the training base and those obtained by the network. The same process is applied for the determination of the parameters of the fuzzy ARTMAP giving the most powerful network. The modification consist in making learn the fuzzy ARTMAP a base of examples not only once as it is of use, but as many time as its architecture is in evolution or than the objective error is not reached . In this way, we don-t worry about the values to impose on the eight (08) parameters of the network. To evaluate each one of these three networks modified, a comparison of their performances is carried out. As application we carried out a classification of the image of Algiers-s bay taken by SPOT XS. We use as criterion of evaluation the training duration, the mean square error (MSE) in step control and the rate of good classification per class. The results of this study presented as curves, tables and images show that modified fuzzy ARTMAP presents the best compromise quality/computing time.
Abstract: This research presented in this paper is an on-going
project of an application of neural network and fuzzy models to
evaluate the sociological factors which affect the educational
performance of the students in Sri Lanka. One of its major goals is to
prepare the grounds to device a counseling tool which helps these
students for a better performance at their examinations, especially at
their G.C.E O/L (General Certificate of Education-Ordinary Level)
examination. Closely related sociological factors are collected as raw
data and the noise of these data are filtered through the fuzzy
interface and the supervised neural network is being utilized to
recognize the performance patterns against the chosen social factors.
Abstract: In this paper discrete choice models, Logit and Probit
are examined in order to predict the economic recession or expansion
periods in USA. Additionally we propose an adaptive neuro-fuzzy
inference system with triangular membership function. We examine
the in-sample period 1947-2005 and we test the models in the out-of
sample period 2006-2009. The forecasting results indicate that the
Adaptive Neuro-fuzzy Inference System (ANFIS) model outperforms
significant the Logit and Probit models in the out-of sample period.
This indicates that neuro-fuzzy model provides a better and more
reliable signal on whether or not a financial crisis will take place.
Abstract: This paper proposes an effective algorithm approach to hybrid control systems combining fuzzy logic and conventional control techniques of controlling the speed of induction motor assumed to operate in high-performance drives environment. The introducing of fuzzy logic in the control systems helps to achieve good dynamical response, disturbance rejection and low sensibility to parameter variations and external influences. Some fundamentals of the fuzzy logic control are preliminary illustrated. The developed control algorithm is robust, efficient and simple. It also assures precise trajectory tracking with the prescribed dynamics. Experimental results have shown excellent tracking performance of the proposed control system, and have convincingly demonstrated the validity and the usefulness of the hybrid fuzzy controller in high-performance drives with parameter and load uncertainties. Satisfactory performance was observed for most reference tracks.
Abstract: Process capability index Cpk is the most widely
used index in making managerial decisions since it provides bounds
on the process yield for normally distributed processes. However,
existent methods for assessing process performance which
constructed by statistical inference may unfortunately lead to fine
results, because uncertainties exist in most real-world applications.
Thus, this study adopts fuzzy inference to deal with testing of Cpk .
A brief score is obtained for assessing a supplier’s process instead of
a severe evaluation.
Abstract: Recent advances in wireless sensor networks have led
to many routing methods designed for energy-efficiency in wireless
sensor networks. Despite that many routing methods have been
proposed in USN, a single routing method cannot be energy-efficient
if the environment of the ubiquitous sensor network varies. We present
the controlling network access to various hosts and the services they
offer, rather than on securing them one by one with a network security
model. When ubiquitous sensor networks are deployed in hostile
environments, an adversary may compromise some sensor nodes and
use them to inject false sensing reports. False reports can lead to not
only false alarms but also the depletion of limited energy resource in
battery powered networks. The interleaved hop-by-hop authentication
scheme detects such false reports through interleaved authentication.
This paper presents a LMDD (Low energy method for data delivery)
algorithm that provides energy-efficiency by dynamically changing
protocols installed at the sensor nodes. The algorithm changes
protocols based on the output of the fuzzy logic which is the fitness
level of the protocols for the environment.
Abstract: This paper presents a new optimization technique based on quantum computing principles to solve a security constrained power system economic dispatch problem (SCED). The proposed technique is a population-based algorithm, which uses some quantum computing elements in coding and evolving groups of potential solutions to reach the optimum following a partially directed random approach. The SCED problem is formulated as a constrained optimization problem in a way that insures a secure-economic system operation. Real Coded Quantum-Inspired Evolution Algorithm (RQIEA) is then applied to solve the constrained optimization formulation. Simulation results of the proposed approach are compared with those reported in literature. The outcome is very encouraging and proves that RQIEA is very applicable for solving security constrained power system economic dispatch problem (SCED).
Abstract: Quality Function Deployment (QFD) is an expounded, multi-step planning method for delivering commodity, services, and processes to customers, both external and internal to an organization. It is a way to convert between the diverse customer languages expressing demands (Voice of the Customer), and the organization-s languages expressing results that sate those demands. The policy is to establish one or more matrices that inter-relate producer and consumer reciprocal expectations. Due to its visual presence is called the “House of Quality" (HOQ). In this paper, we assumed HOQ in multi attribute decision making (MADM) pattern and through a proposed MADM method, rank technical specifications. Thereafter compute satisfaction degree of customer requirements and for it, we apply vagueness and uncertainty conditions in decision making by fuzzy set theory. This approach would propound supervised neural network (perceptron) for MADM problem solving.
Abstract: The purpose of this paper is to present two different
approaches of financial distress pre-warning models appropriate for
risk supervisors, investors and policy makers. We examine a sample
of the financial institutions and electronic companies of Taiwan
Security Exchange (TSE) market from 2002 through 2008. We
present a binary logistic regression with paned data analysis. With
the pooled binary logistic regression we build a model including
more variables in the regression than with random effects, while the
in-sample and out-sample forecasting performance is higher in
random effects estimation than in pooled regression. On the other
hand we estimate an Adaptive Neuro-Fuzzy Inference System
(ANFIS) with Gaussian and Generalized Bell (Gbell) functions and
we find that ANFIS outperforms significant Logit regressions in both
in-sample and out-of-sample periods, indicating that ANFIS is a
more appropriate tool for financial risk managers and for the
economic policy makers in central banks and national statistical
services.
Abstract: Software effort estimation is the process of predicting
the most realistic use of effort required to develop or maintain
software based on incomplete, uncertain and/or noisy input. Effort
estimates may be used as input to project plans, iteration plans,
budgets. There are various models like Halstead, Walston-Felix,
Bailey-Basili, Doty and GA Based models which have already used
to estimate the software effort for projects. In this study Statistical
Models, Fuzzy-GA and Neuro-Fuzzy (NF) Inference Systems are
experimented to estimate the software effort for projects. The
performances of the developed models were tested on NASA
software project datasets and results are compared with the Halstead,
Walston-Felix, Bailey-Basili, Doty and Genetic Algorithm Based
models mentioned in the literature. The result shows that the NF
Model has the lowest MMRE and RMSE values. The NF Model
shows the best results as compared with the Fuzzy-GA based hybrid
Inference System and other existing Models that are being used for
the Effort Prediction with lowest MMRE and RMSE values.