Abstract: Nowadays, more engineering systems are using some
kind of Artificial Intelligence (AI) for the development of their
processes. Some well-known AI techniques include artificial neural
nets, fuzzy inference systems, and neuro-fuzzy inference systems
among others. Furthermore, many decision-making applications base
their intelligent processes on Fuzzy Logic; due to the Fuzzy
Inference Systems (FIS) capability to deal with problems that are
based on user knowledge and experience. Also, knowing that users
have a wide variety of distinctiveness, and generally, provide
uncertain data, this information can be used and properly processed
by a FIS. To properly consider uncertainty and inexact system input
values, FIS normally use Membership Functions (MF) that represent
a degree of user satisfaction on certain conditions and/or constraints.
In order to define the parameters of the MFs, the knowledge from
experts in the field is very important. This knowledge defines the MF
shape to process the user inputs and through fuzzy reasoning and
inference mechanisms, the FIS can provide an “appropriate" output.
However an important issue immediately arises: How can it be
assured that the obtained output is the optimum solution? How can it
be guaranteed that each MF has an optimum shape? A viable solution
to these questions is through the MFs parameter optimization. In this
Paper a novel parameter optimization process is presented. The
process for FIS parameter optimization consists of the five simple
steps that can be easily realized off-line. Here the proposed process
of FIS parameter optimization it is demonstrated by its
implementation on an Intelligent Interface section dealing with the
on-line customization / personalization of internet portals applied to
E-commerce.
Abstract: Software Reusability is primary attribute of software
quality. 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. In this paper, we have devised the framework of
metrics that uses McCabe-s Cyclometric Complexity Measure for
Complexity measurement, Regularity Metric, Halstead Software
Science Indicator for Volume indication, Reuse Frequency metric
and Coupling Metric values of the software component as input
attributes and calculated reusability of the software component. Here,
comparative analysis of the fuzzy, Neuro-fuzzy and Fuzzy-GA
approaches is performed to evaluate the reusability of software
components and Fuzzy-GA results outperform the other used
approaches. The developed reusability model has produced high
precision results as expected by the human experts.
Abstract: This paper presents the development of recurrent neural network based fuzzy inference system for identification and control of dynamic nonlinear plant. The structure and algorithms of fuzzy system based on recurrent neural network are described. To train unknown parameters of the system the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The neuro-fuzzy system is used for the identification and control of nonlinear dynamic plant. The simulation results of identification and control systems based on recurrent neuro-fuzzy network are compared with the simulation results of other neural systems. It is found that the recurrent neuro-fuzzy based system has better performance than the others.
Abstract: In this paper we present a Adaptive Neuro-Fuzzy
System (ANFIS) with inputs the lagged dependent variable for the
prediction of Gross domestic Product growth rate in six countries.
We compare the results with those of Autoregressive (AR) model.
We conclude that the forecasting performance of neuro-fuzzy-system
in the out-of-sample period is much more superior and can be a very
useful alternative tool used by the national statistical services and the
banking and finance industry.
Abstract: This paper deals with motion planning of multiple
mobile robots. Mobile robots working together to achieve several
objectives have many advantages over single robot system. However,
the planning and coordination between the mobile robots is
extremely difficult. In the present investigation rule-based and rulebased-
neuro-fuzzy techniques are analyzed for multiple mobile
robots navigation in an unknown or partially known environment.
The final aims of the robots are to reach some pre-defined goals.
Based upon a reference motion, direction; distances between the
robots and obstacles; and distances between the robots and targets;
different types of rules are taken heuristically and refined later to find
the steering angle. The control system combines a repelling influence
related to the distance between robots and nearby obstacles and with
an attracting influence between the robots and targets. Then a hybrid
rule-based-neuro-fuzzy technique is analysed to find the steering
angle of the robots. Simulation results show that the proposed rulebased-
neuro-fuzzy technique can improve navigation performance in
complex and unknown environments compared to this simple rulebased
technique.
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: This paper summaries basic principles and concepts of
intelligent controls, implemented in humanoid robotics as well as
recent algorithms being devised for advanced control of humanoid
robots. Secondly, this paper presents a new approach neuro-fuzzy
system. We have included some simulating results from our
computational intelligence technique that will be applied to our
humanoid robot. Subsequently, we determine a relationship between
joint trajectories and located forces on robot-s foot through a
proposed neuro-fuzzy technique.
Abstract: Heart disease (HD) is a major cause of morbidity and mortality in the modern society. Medical diagnosis is an important but complicated task that should be performed accurately and efficiently and its automation would be very useful. All doctors are unfortunately not equally skilled in every sub specialty and they are in many places a scarce resource. A system for automated medical diagnosis would enhance medical care and reduce costs. In this paper, a new approach based on coactive neuro-fuzzy inference system (CANFIS) was presented for prediction of heart disease. The proposed CANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach which is then integrated with genetic algorithm to diagnose the presence of the disease. The performances of the CANFIS model were evaluated in terms of training performances and classification accuracies and the results showed that the proposed CANFIS model has great potential in predicting the heart disease.
Abstract: Facial expression analysis plays a significant role for
human computer interaction. Automatic analysis of human facial
expression is still a challenging problem with many applications. In
this paper, we propose neuro-fuzzy based automatic facial expression
recognition system to recognize the human facial expressions like
happy, fear, sad, angry, disgust and surprise. Initially facial image is
segmented into three regions from which the uniform Local Binary
Pattern (LBP) texture features distributions are extracted and
represented as a histogram descriptor. The facial expressions are
recognized using Multiple Adaptive Neuro Fuzzy Inference System
(MANFIS). The proposed system designed and tested with JAFFE
face database. The proposed model reports 94.29% of classification
accuracy.
Abstract: In this paper we apply an Adaptive Network-Based
Fuzzy Inference System (ANFIS) with one input, the dependent
variable with one lag, for the forecasting of four macroeconomic
variables of US economy, the Gross Domestic Product, the inflation
rate, six monthly treasury bills interest rates and unemployment rate.
We compare the forecasting performance of ANFIS with those of the
widely used linear autoregressive and nonlinear smoothing transition
autoregressive (STAR) models. The results are greatly in favour of
ANFIS indicating that is an effective tool for macroeconomic
forecasting used in academic research and in research and application
by the governmental and other institutions
Abstract: There are several approaches in trying to solve the
Quantitative 1Structure-Activity Relationship (QSAR) problem.
These approaches are based either on statistical methods or on
predictive data mining. Among the statistical methods, one should
consider regression analysis, pattern recognition (such as cluster
analysis, factor analysis and principal components analysis) or partial
least squares. Predictive data mining techniques use either neural
networks, or genetic programming, or neuro-fuzzy knowledge. These
approaches have a low explanatory capability or non at all. This
paper attempts to establish a new approach in solving QSAR
problems using descriptive data mining. This way, the relationship
between the chemical properties and the activity of a substance
would be comprehensibly modeled.
Abstract: Nowadays, driving support systems, such as car
navigation systems, are getting common, and they support drivers in
several aspects. It is important for driving support systems to detect
status of driver's consciousness. Particularly, detecting driver's
drowsiness could prevent drivers from collisions caused by drowsy
driving. In this paper, we discuss the various artificial detection
methods for detecting driver's drowsiness processing technique. This
system is based on facial images analysis for warning the driver of
drowsiness or in attention to prevent traffic accidents.
Abstract: In this paper performance of Puma 560
manipulator is being compared for hybrid gradient descent
and least square method learning based ANFIS controller with
hybrid Genetic Algorithm and Generalized Pattern Search
tuned radial basis function based Neuro-Fuzzy controller.
ANFIS which is based on Takagi Sugeno type Fuzzy
controller needs prior knowledge of rule base while in radial
basis function based Neuro-Fuzzy rule base knowledge is not
required. Hybrid Genetic Algorithm with generalized Pattern
Search is used for tuning weights of radial basis function
based Neuro- fuzzy controller. All the controllers are checked
for butterfly trajectory tracking and results in the form of
Cartesian and joint space errors are being compared. ANFIS
based controller is showing better performance compared to
Radial Basis Function based Neuro-Fuzzy Controller but rule
base independency of RBF based Neuro-Fuzzy gives it an
edge over ANFIS
Abstract: The Neuro-Fuzzy hybridization scheme has become
of research interest in pattern classification over the past decade. The
present paper proposes a novel Modified Adaptive Fuzzy Inference
Engine (MAFIE) for pattern classification. A modified Apriori
algorithm technique is utilized to reduce a minimal set of decision
rules based on input output data sets. A TSK type fuzzy inference
system is constructed by the automatic generation of membership
functions and rules by the fuzzy c-means clustering and Apriori
algorithm technique, respectively. The generated adaptive fuzzy
inference engine is adjusted by the least-squares fit and a conjugate
gradient descent algorithm towards better performance with a
minimal set of rules. The proposed MAFIE is able to reduce the
number of rules which increases exponentially when more input
variables are involved. The performance of the proposed MAFIE is
compared with other existing applications of pattern classification
schemes using Fisher-s Iris and Wisconsin breast cancer data sets and
shown to be very competitive.
Abstract: Improving the reactive power and voltage profile of a
distribution substation is investigated in this paper. The purpose is to
properly determination of the shunt capacitors on/off status and
suitable tap changer (TC) position of a substation transformer. In
addition, the limitation of secondary bus voltage, the maximum
allowable number of switching operation in a day for on load tap
changer and on/off status of capacitors are taken into account. To
achieve these goals, an artificial neural network (ANN) is designed to
provide preliminary scheduling. Input of ANN is active and reactive
powers of transformer and its primary and secondary bus voltages.
The output of ANN is capacitors on/off status and TC position. The
preliminary schedule is further refined by fuzzy dynamic
programming in order to reach the final schedule. The operation of
proposed method in Q/V improving is compared with the results
obtained by operator operation in a distribution substation.
Abstract: The paper presents a new hybridization methodology involving Neural, Fuzzy and Rough Computing. A Rough Sets based approximation technique has been proposed based on a certain Neuro – Fuzzy architecture. A New Rough Neuron composition consisting of a combination of a Lower Bound neuron and a Boundary neuron has also been described. The conventional convergence of error in back propagation has been given away for a new framework based on 'Output Excitation Factor' and an inverse input transfer function. The paper also presents a brief comparison of performances, of the existing Rough Neural Networks and ANFIS architecture against the proposed methodology. It can be observed that the rough approximation based neuro-fuzzy architecture is superior to its counterparts.
Abstract: Various models have been derived by studying large number of completed software projects from various organizations and applications to explore how project sizes mapped into project effort. But, still there is a need to prediction accuracy of the models. As Neuro-fuzzy based system is able to approximate the non-linear function with more precision. So, Neuro-Fuzzy system is used as a soft computing approach to generate model by formulating the relationship based on its training. In this paper, Neuro-Fuzzy technique is used for software estimation modeling of on NASA software project data and performance of the developed models are compared with the Halstead, Walston-Felix, Bailey-Basili and Doty Models mentioned in the literature.
Abstract: Magneto-rheological (MR) fluid damper is a semiactive
control device that has recently received more attention by the
vibration control community. But inherent hysteretic and highly
nonlinear dynamics of MR fluid damper is one of the challenging
aspects to employ its unique characteristics. The combination of
artificial neural network (ANN) and fuzzy logic system (FLS) have
been used to imitate more precisely the behavior of this device.
However, the derivative-based nature of adaptive networks causes
some deficiencies. Therefore, in this paper, a novel approach that
employ genetic algorithm, as a free-derivative algorithm, to enhance
the capability of fuzzy systems, is proposed. The proposed method
used to model MR damper. The results will be compared with
adaptive neuro-fuzzy inference system (ANFIS) model, which is one
of the well-known approaches in soft computing framework, and two
best parametric models of MR damper. Data are generated based on
benchmark program by applying a number of famous earthquake
records.
Abstract: In this paper an algorithm based on the adaptive
neuro-fuzzy controller is provided to enhance the tipover stability of
mobile manipulators when they are subjected to predefined
trajectories for the end-effector and the vehicle. The controller
creates proper configurations for the manipulator to prevent the robot
from being overturned. The optimal configuration and thus the most
favorable control are obtained through soft computing approaches
including a combination of genetic algorithm, neural networks, and
fuzzy logic. The proposed algorithm, in this paper, is that a look-up
table is designed by employing the obtained values from the genetic
algorithm in order to minimize the performance index and by using
this data base, rule bases are designed for the ANFIS controller and
will be exerted on the actuators to enhance the tipover stability of the
mobile manipulator. A numerical example is presented to
demonstrate the effectiveness of the proposed algorithm.