Abstract: Fuzzy Logic, an advanced method to support decision-making, is used by various scientists in many disciplines. Fuzzy programming is a product of fuzzy logic, fuzzy rules, and implication. In marine science, fuzzy programming for ships is dramatically increasing together with autonomous ship studies. In this paper, a program to support the decision-making process for ship navigation has been designed. The program is produced in fuzzy logic and rules, by taking the marine accidents and expert opinions into account. After the program was designed, the program was tested by 46 ship accidents reported by the Transportation Safety Investigation Center of Turkey. Wind speed, sea condition, visibility, day/night ratio have been used as input data. They have been converted into a risk factor within the Fuzzy Logic Designer application and fuzzy rules set by marine experts. Finally, the expert's meteorological risk factor for each accident is compared with the program's risk factor, and the error rate was calculated. The main objective of this study is to improve the navigational safety of ships, by using the advance decision support model. According to the study result, fuzzy programming is a robust model that supports safe navigation.
Abstract: Ignatian Discernment Process (IDP) is an intense decision-making tool to decide on life-issues. Decisions are influenced by various factors outside of the decision maker and inclination within. This paper develops IDP in the context of Fuzzy Multi-criteria Decision Making (FMCDM) process. Extended VIKOR method is a decision-making method which encompasses even conflict situations and accommodates weightage to various issues. Various aspects of IDP, namely three ways of decision making and tactics of inner desires, are observed, analyzed and articulated within the frame work of fuzzy rules. The decision-making situations are broadly categorized into two types. The issues outside of the decision maker influence the person. The inner feeling also plays vital role in coming to a conclusion. IDP integrates both the categories using Extended VIKOR method. Case studies are carried out and analyzed with FMCDM process. Finally, IDP is verified with an illustrative case study and results are interpreted. A confused person who could not come to a conclusion is able to take decision on a concrete way of life through IDP. The proposed IDP model recommends an integrated and committed approach to value-based decision making.
Abstract: This paper deals with the problem of automatic rule
generation for fuzzy systems design. The proposed approach is based
on hybrid artificial bee colony (ABC) optimization and weighted least
squares (LS) method and aims to find the structure and parameters of
fuzzy systems simultaneously. More precisely, two ABC based fuzzy
modeling strategies are presented and compared. The first strategy
uses global optimization to learn fuzzy models, the second one
hybridizes ABC and weighted least squares estimate method. The
performances of the proposed ABC and ABC-LS fuzzy modeling
strategies are evaluated on complex modeling problems and compared
to other advanced modeling methods.
Abstract: Medical Image fusion plays a vital role in medical
field to diagnose the brain tumors which can be classified as benign
or malignant. It is the process of integrating multiple images of the
same scene into a single fused image to reduce uncertainty and
minimizing redundancy while extracting all the useful information
from the source images. Fuzzy logic is used to fuse two brain MRI
images with different vision. The fused image will be more
informative than the source images. The texture and wavelet features
are extracted from the fused image. The multilevel Adaptive Neuro
Fuzzy Classifier classifies the brain tumors based on trained and
tested features. The proposed method achieved 80.48% sensitivity,
99.9% specificity and 99.69% accuracy. Experimental results
obtained from fusion process prove that the use of the proposed
image fusion approach shows better performance while compared
with conventional fusion methodologies.
Abstract: Human society, there are many uncertainties, such as economic growth rate forecast of the financial crisis, many scholars have, since the the Song Chissom two scholars in 1993 the concept of the so-called fuzzy time series (Fuzzy Time Series)different mode to deal with these problems, a previous study, however, usually does not consider the relevant variables selected and fuzzy process based solely on subjective opinions the fuzzy semantic discrete, so can not objectively reflect the characteristics of the data set, in addition to carrying outforecasts are often fuzzy rules as equally important, failed to consider the importance of each fuzzy rule. For these reasons, the variable selection (Factor Selection) through self-organizing map (Self-Organizing Map, SOM) and proposed high-end weighted multivariate fuzzy time series model based on fuzzy neural network (Fuzzy-BPN), and using the the sequential weighted average operator (Ordered Weighted Averaging operator, OWA) weighted prediction. Therefore, in order to verify the proposed method, the Taiwan stock exchange (Taiwan Stock Exchange Corporation) Taiwan Weighted Stock Index (Taiwan Stock Exchange Capitalization Weighted Stock Index, TAIEX) as experimental forecast target, in order to filter the appropriate variables in the experiment Finally, included in other studies in recent years mode in conjunction with this study, the results showed that the predictive ability of this study further improve.
Abstract: Image Edge Detection is one of the most important
parts of image processing. In this paper, by fuzzy technique, a new
method is used to improve digital image edge detection. In this
method, a 3x3 mask is employed to process each pixel by means of
vicinity. Each pixel is considered a fuzzy input and by examining
fuzzy rules in its vicinity, the edge pixel is specified and by utilizing
calculation algorithms in image processing, edges are displayed more
clearly. This method shows significant improvement compared to
different edge detection methods (e.g. Sobel, Canny).
Abstract: Artificial neural networks (ANN) have the ability to model input-output relationships from processing raw data. This characteristic makes them invaluable in industry domains where such knowledge is scarce at best. In the recent decades, in order to overcome the black-box characteristic of ANNs, researchers have attempted to extract the knowledge embedded within ANNs in the form of rules that can be used in inference systems. This paper presents a new technique that is able to extract a small set of rules from a two-layer ANN. The extracted rules yield high classification accuracy when implemented within a fuzzy inference system. The technique targets industry domains that possess less complex problems for which no expert knowledge exists and for which a simpler solution is preferred to a complex one. The proposed technique is more efficient, simple, and applicable than most of the previously proposed techniques.
Abstract: A direct adaptive controller for a class of unknown nonlinear discrete-time systems is presented in this article. The proposed controller is constructed by fuzzy rules emulated network (FREN). With its simple structure, the human knowledge about the plant is transferred to be if-then rules for setting the network. These adjustable parameters inside FREN are tuned by the learning mechanism with time varying step size or learning rate. The variation of learning rate is introduced by main theorem to improve the system performance and stabilization. Furthermore, the boundary of adjustable parameters is guaranteed through the on-line learning and membership functions properties. The validation of the theoretical findings is represented by some illustrated examples.
Abstract: In this paper we propose and examine an Adaptive
Neuro-Fuzzy Inference System (ANFIS) in Smoothing Transition
Autoregressive (STAR) modeling. Because STAR models follow
fuzzy logic approach, in the non-linear part fuzzy rules can be
incorporated or other training or computational methods can be
applied as the error backpropagation algorithm instead to nonlinear
squares. Furthermore, additional fuzzy membership functions can be
examined, beside the logistic and exponential, like the triangle,
Gaussian and Generalized Bell functions among others. We examine
two macroeconomic variables of US economy, the inflation rate and
the 6-monthly treasury bills interest rates.
Abstract: Several researchers have proposed methods about
combination of Genetic Algorithm (GA) and Fuzzy Logic (the use of
GA to obtain fuzzy rules and application of fuzzy logic in
optimization of GA). In this paper, we suggest a new method in
which fuzzy decision making is used to improve the performance of
genetic algorithm. In the suggested method, we determine the alleles
that enhance the fitness of chromosomes and try to insert them to the
next generation.
In this algorithm we try to present an innovative vaccination in the
process of reproduction in genetic algorithm, with considering the
trade off between exploration and exploitation.
Abstract: An adaptive fuzzy PID controller with gain scheduling is proposed in this paper. The structure of the proposed gain scheduled fuzzy PID (GS_FPID) controller consists of both fuzzy PI-like controller and fuzzy PD-like controller. Both of fuzzy PI-like and PD-like controllers are weighted through adaptive gain scheduling, which are also determined by fuzzy logic inference. A modified genetic algorithm called accumulated genetic algorithm is designed to learn the parameters of fuzzy inference system. In order to learn the number of fuzzy rules required for the TSK model, the fuzzy rules are learned in an accumulated way. In other words, the parameters learned in the previous rules are accumulated and updated along with the parameters in the current rule. It will be shown that the proposed GS_FPID controllers learned by the accumulated GA perform well for not only the regular linear systems but also the higher order and time-delayed systems.
Abstract: The dynamics of the Autonomous Underwater
Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate
accurately because of the variations of these coefficients with
different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain
the coupled nonlinear dynamic model of AUV as a black box. This black box has an input-output relationship based upon on-line
adaptive fuzzy model and adaptive neural fuzzy network (ANFN)
model techniques to overcome the uncertain external disturbance and
the difficulties of modelling the hydrodynamic forces of the AUVs instead of using the mathematical model with hydrodynamic parameters estimation. The models- parameters are adapted according
to the back propagation algorithm based upon the error between the
identified model and the actual output of the plant. The proposed
ANFN model adopts a functional link neural network (FLNN) as the
consequent part of the fuzzy rules. Thus, the consequent part of the
ANFN model is a nonlinear combination of input variables. Fuzzy
control system is applied to guide and control the AUV using both
adaptive models and mathematical model. Simulation results show
the superiority of the proposed adaptive neural fuzzy network
(ANFN) model in tracking of the behavior of the AUV accurately
even in the presence of noise and disturbance.
Abstract: In this paper, a novel adaptive fuzzy sliding mode
control method is proposed for the robust tracking control of robotic
manipulators. The proposed controller possesses the advantages of
adaptive control, fuzzy control, and sliding mode control. First, system
stability and robustness are guaranteed based on the sliding mode
control. Further, fuzzy rules are developed incorporating with
adaptation law to alleviate the input chattering effectively. Stability of
the control system is proven by using the Lyapunov method. An
application to a three-degree-of-freedom robotic manipulator is
carried out. Accurate trajectory tracking as well as robustness is
achieved. Input chattering is greatly eliminated.
Abstract: Automatic reusability appraisal could be helpful in
evaluating the quality of developed or developing reusable software
components and in identification of reusable components from
existing legacy systems; that can save cost of developing the software
from scratch. But the issue of how to identify reusable components
from existing systems has remained relatively unexplored. In this
paper, we have mentioned two-tier approach by studying the
structural attributes as well as usability or relevancy of the
component to a particular domain. Latent semantic analysis is used
for the feature vector representation of various software domains. It
exploits the fact that FeatureVector codes can be seen as documents
containing terms -the idenifiers present in the components- and so
text modeling methods that capture co-occurrence information in
low-dimensional spaces can be used. Further, we devised Neuro-
Fuzzy hybrid Inference System, which takes structural metric values
as input and calculates the reusability of the software component.
Decision tree algorithm is used to decide initial set of fuzzy rules for
the Neuro-fuzzy system. The results obtained are convincing enough
to propose the system for economical identification and retrieval of
reusable software components.
Abstract: In this paper we present, propose and examine
additional membership functions for the Smoothing Transition
Autoregressive (STAR) models. More specifically, we present the
tangent hyperbolic, Gaussian and Generalized bell functions.
Because Smoothing Transition Autoregressive (STAR) models
follow fuzzy logic approach, more fuzzy membership functions
should be tested. Furthermore, fuzzy rules can be incorporated or
other training or computational methods can be applied as the error
backpropagation or genetic algorithm instead to nonlinear squares.
We examine two macroeconomic variables of US economy, the
inflation rate and the 6-monthly treasury bills interest rates.
Abstract: Fuzzy logic can be used when knowledge is
incomplete or when ambiguity of data exists. The purpose of
this paper is to propose a proactive fuzzy set- based model for
reacting to the risk inherent in investment activities relative to
a complete view of portfolio management. Fuzzy rules are
given where, depending on the antecedents, the portfolio size
may be slightly or significantly decreased or increased. The
decision maker considers acceptable bounds on the proportion
of acceptable risk and return. The Fuzzy Controller model
allows learning to be achieved as 1) the firing strength of each
rule is measured, 2) fuzzy output allows rules to be updated,
and 3) new actions are recommended as the system continues
to loop. An extension is given to the fuzzy controller that
evaluates potential financial loss before adjusting the
portfolio. An application is presented that illustrates the
algorithm and extension developed in the paper.
Abstract: This paper addresses the development of an intelligent vision system for human-robot interaction. The two novel contributions of this paper are 1) Detection of human faces and 2) Localizing the eye. The method is based on visual attributes of human skin colors and geometrical analysis of face skeleton. This paper introduces a spatial domain filtering method named ?Fuzzily skewed filter' which incorporates Fuzzy rules for deciding the gray level of pixels in the image in their neighborhoods and takes advantages of both the median and averaging filters. The effectiveness of the method has been justified over implementing the eye tracking commands to an entertainment robot, named ''AIBO''.
Abstract: In order to alleviate the mental and physical problems
of persons with disabilities, animal-assisted therapy (AAT) is one of
the possible modalities that employs the merit of the human-animal
interaction. Nevertheless, to achieve the purpose of AAT for persons
with severe disabilities (e.g. spinal cord injury, stroke, and
amyotrophic lateral sclerosis), real-time animal language
interpretation is desirable. Since canine behaviors can be visually
notable from its tail, this paper proposes the automatic real-time
interpretation of canine tail language for human-canine interaction in
the case of persons with severe disabilities. Canine tail language is
captured via two 3-axis accelerometers. Directions and frequencies
are selected as our features of interests. The novel fuzzy rules based
on Gaussian-Trapezoidal model and center of gravity (COG)-based
defuzzification method are proposed in order to interpret the features
into four canine emotional behaviors, i.e., agitate, happy, scare and
neutral as well as its blended emotional behaviors. The emotional
behavior model is performed in the simulated dog and has also been
evaluated in the real dog with the perfect recognition rate.
Abstract: The requirement to improve software productivity has
promoted the research on software metric technology. There are
metrics for identifying the quality of reusable components but the
function that makes use of these metrics to find reusability of
software components is still not clear. These metrics if identified in
the design phase or even in the coding phase can help us to reduce the
rework by improving quality of reuse of the component and hence
improve the productivity due to probabilistic increase in the reuse
level. CK metric suit is most widely used metrics for the objectoriented
(OO) software; we critically analyzed the CK metrics, tried
to remove the inconsistencies and devised the framework of metrics
to obtain the structural analysis of OO-based software components.
Neural network can learn new relationships with new input data and
can be used to refine fuzzy rules to create fuzzy adaptive system.
Hence, Neuro-fuzzy inference engine can be used to evaluate the
reusability of OO-based component using its structural attributes as
inputs. In this paper, an algorithm has been proposed in which the
inputs can be given to Neuro-fuzzy system in form of tuned WMC,
DIT, NOC, CBO , LCOM values of the OO software component and
output can be obtained in terms of reusability. The developed
reusability model has produced high precision results as expected by
the human experts.
Abstract: An additive fuzzy system comprising m rules with
n inputs and p outputs in each rule has at least t m(2n + 2 p + 1)
parameters needing to be tuned. The system consists of a large
number of if-then fuzzy rules and takes a long time to tune its
parameters especially in the case of a large amount of training data
samples. In this paper, a new learning strategy is investigated to cope
with this obstacle. Parameters that tend toward constant values at the
learning process are initially fixed and they are not tuned till the end
of the learning time. Experiments based on applications of the
additive fuzzy system in function approximation demonstrate that the
proposed approach reduces the learning time and hence improves
convergence speed considerably.