Abstract: In this paper the development of neural network based fuzzy inference system for electricity consumption prediction is considered. The electricity consumption depends on number of factors, such as number of customers, seasons, type-s of customers, number of plants, etc. It is nonlinear process and can be described by chaotic time-series. The structure and algorithms of neuro-fuzzy system for predicting future values of electricity consumption is described. To determine the unknown coefficients of the system, the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The developed system is applied for predicting future values of electricity consumption of Northern Cyprus. The simulation of neuro-fuzzy system has been performed.
Abstract: In recent years, copulas have become very popular in
financial research and actuarial science as they are more flexible in
modelling the co-movements and relationships of risk factors as compared
to the conventional linear correlation coefficient by Pearson.
However, a precise estimation of the copula parameters is vital in
order to correctly capture the (possibly nonlinear) dependence structure
and joint tail events. In this study, we employ two optimization
heuristics, namely Differential Evolution and Threshold Accepting to
tackle the parameter estimation of multivariate t distribution models
in the EML approach. Since the evolutionary optimizer does not rely
on gradient search, the EML approach can be applied to estimation of
more complicated copula models such as high-dimensional copulas.
Our experimental study shows that the proposed method provides
more robust and more accurate estimates as compared to the IFM
approach.
Abstract: For a given specific problem an efficient algorithm has
been the matter of study. However, an alternative approach orthogonal
to this approach comes out, which is called a reduction. In general
for a given specific problem this reduction approach studies how to
convert an original problem into subproblems. This paper proposes
a formal modeling language to support this reduction approach. We
show three examples from the wide area of learning problems. The
benefit is a fast prototyping of algorithms for a given new problem.
Abstract: Coal will continue to be the predominant source of
global energy for coming several decades. The huge generation of fly
ash (FA) from combustion of coal in thermal power plants (TPPs) is
apprehended to pose the concerns of its disposal and utilization. FA
application based on its typical characteristics as soil ameliorant for
agriculture and forestry is the potential area, and hence the global
attempt. The inferences drawn suffer from the variations of ash
characteristics, soil types, and agro-climatic conditions; thereby
correlating the effects of ash between various plant species and soil
types is difficult. Indian FAs have low bulk density, high water
holding capacity and porosity, rich silt-sized particles, alkaline
nature, negligible solubility, and reasonable plant nutrients. Findings
of the demonstrations trials for more than two decades from lab/pot
to field scale long-term experiments are developed as FA soil
amendment technology (FASAT) by Central Institute of Mining and
Fuel Research (CIMFR), Dhanbad. Performance of different crops
and plant species in cultivable and problematic soils, are
encouraging, eco-friendly, and being adopted by the farmers. FA
application includes ash alone and in combination with
inorganic/organic amendments; combination treatments including
bio-solids perform better than FA alone. Optimum dose being up to
100 t/ha for cultivable land and up to/ or above 200 t/ha of FA for
waste/degraded land/mine refuse, depending on the characteristics of
ash and soil. The elemental toxicity in Indian FA is usually not of
much concern owing to alkaline ashes, oxide forms of elements, and
elemental concentration within the threshold limits for soil
application. Combating toxicity, if any, is possible through
combination treatments with organic materials and phytoremediation.
Government initiatives through extension programme
involving farmers and ash generating organizations need to be
accelerated
Abstract: Gasoline Octane Number is the standard measure of
the anti-knock properties of a motor in platforming processes, that is
one of the important unit operations for oil refineries and can be
determined with online measurement or use CFR (Cooperative Fuel
Research) engines. Online measurements of the Octane number can
be done using direct octane number analyzers, that it is too
expensive, so we have to find feasible analyzer, like ANFIS
estimators.
ANFIS is the systems that neural network incorporated in fuzzy
systems, using data automatically by learning algorithms of NNs.
ANFIS constructs an input-output mapping based both on human
knowledge and on generated input-output data pairs.
In this research, 31 industrial data sets are used (21 data for training
and the rest of the data used for generalization). Results show that,
according to this simulation, hybrid method training algorithm in
ANFIS has good agreements between industrial data and simulated
results.
Abstract: A clustering based technique has been developed and implemented for Short Term Load Forecasting, in this article. Formulation has been done using Mean Absolute Percentage Error (MAPE) as an objective function. Data Matrix and cluster size are optimization variables. Model designed, uses two temperature variables. This is compared with six input Radial Basis Function Neural Network (RBFNN) and Fuzzy Inference Neural Network (FINN) for the data of the same system, for same time period. The fuzzy inference system has the network structure and the training procedure of a neural network which initially creates a rule base from existing historical load data. It is observed that the proposed clustering based model is giving better forecasting accuracy as compared to the other two methods. Test results also indicate that the RBFNN can forecast future loads with accuracy comparable to that of proposed method, where as the training time required in the case of FINN is much less.
Abstract: This paper presents recent work on the improvement
of the robotics vision based control strategy for underwater pipeline
tracking system. The study focuses on developing image processing
algorithms and a fuzzy inference system for the analysis of the
terrain. The main goal is to implement the supervisory fuzzy learning
control technique to reduce the errors on navigation decision due to
the pipeline occlusion problem. The system developed is capable of
interpreting underwater images containing occluded pipeline, seabed
and other unwanted noise. The algorithm proposed in previous work
does not explore the cooperation between fuzzy controllers,
knowledge and learnt data to improve the outputs for underwater
pipeline tracking. Computer simulations and prototype simulations
demonstrate the effectiveness of this approach. The system accuracy
level has also been discussed.
Abstract: This article presents a method for elections between the members of a group that is founded by fuzzy logic. Linguistic variables are objects for decision on election cards and deduction is based on t-norms and s-norms. In this election-s method election cards are questionnaire. The questionnaires are comprised of some questions with some choices. The choices are words from natural language. Presented method is accompanied by center of gravity (COG) defuzzification added up to a computer program by MATLAB. Finally the method is illustrated by solving two examples; choose a head for a research group-s members and a representative for students.
Abstract: In this study, a fuzzy-logic based control system was
designed to ensure that time and energy is saved during the operation
of load elevators which are used during the construction of tall
buildings. In the control system that was devised, for the load
elevators to work more efficiently, the energy interval where the
motor worked was taken as the output variable whereas the amount
of load and the building height were taken as input variables. The
most appropriate working intervals depending on the characteristics
of these variables were defined by the help of an expert. Fuzzy expert
system software was formed using Delphi programming language. In
this design, mamdani max-min inference mechanism was used and
the centroid method was employed in the clarification procedure. In
conclusion, it is observed that the system that was designed is
feasible and this is supported by statistical analyses..
Abstract: Evolutionary robotics is concerned with the design of
intelligent systems with life-like properties by means of simulated
evolution. Approaches in evolutionary robotics can be categorized
according to the control structures that represent the behavior and the
parameters of the controller that undergo adaptation. The basic idea
is to automatically synthesize behaviors that enable the robot to
perform useful tasks in complex environments. The evolutionary
algorithm searches through the space of parameterized controllers
that map sensory perceptions to control actions, thus realizing a
specific robotic behavior. Further, the evolutionary algorithm
maintains and improves a population of candidate behaviors by
means of selection, recombination and mutation. A fitness function
evaluates the performance of the resulting behavior according to the
robot-s task or mission. In this paper, the focus is in the use of
genetic algorithms to solve a multi-objective optimization problem
representing robot behaviors; in particular, the A-Compander Law is
employed in selecting the weight of each objective during the
optimization process. Results using an adaptive fitness function show
that this approach can efficiently react to complex tasks under
variable environments.
Abstract: Recently, the issue of machine condition monitoring
and fault diagnosis as a part of maintenance system became global
due to the potential advantages to be gained from reduced
maintenance costs, improved productivity and increased machine
availability. The aim of this work is to investigate the effectiveness
of a new fault diagnosis method based on power spectral density
(PSD) of vibration signals in combination with decision trees and
fuzzy inference system (FIS). To this end, a series of studies was
conducted on an external gear hydraulic pump. After a test under
normal condition, a number of different machine defect conditions
were introduced for three working levels of pump speed (1000, 1500,
and 2000 rpm), corresponding to (i) Journal-bearing with inner face
wear (BIFW), (ii) Gear with tooth face wear (GTFW), and (iii)
Journal-bearing with inner face wear plus Gear with tooth face wear
(B&GW). The features of PSD values of vibration signal were
extracted using descriptive statistical parameters. J48 algorithm is
used as a feature selection procedure to select pertinent features from
data set. The output of J48 algorithm was employed to produce the
crisp if-then rule and membership function sets. The structure of FIS
classifier was then defined based on the crisp sets. In order to
evaluate the proposed PSD-J48-FIS model, the data sets obtained
from vibration signals of the pump were used. Results showed that
the total classification accuracy for 1000, 1500, and 2000 rpm
conditions were 96.42%, 100%, and 96.42% respectively. The results
indicate that the combined PSD-J48-FIS model has the potential for
fault diagnosis of hydraulic pumps.
Abstract: The article investigates how 14- to 15- year-olds build informal conceptions of inferential statistics as they engage in a modelling process and build their own computer simulations with dynamic statistical software. This study proposes four primary phases of informal inferential reasoning for the students in the statistical modeling and simulation process. Findings show shifts in the conceptual structures across the four phases and point to the potential of all of these phases for fostering the development of students- robust knowledge of the logic of inference when using computer based simulations to model and investigate statistical questions.
Abstract: In this paper, a fuzzy controller is designed for
stabilization of the Lorenz chaotic equations. A simple Mamdani
inference method is used for this purpose. This method is very simple
and applicable for complex chaotic systems and it can be
implemented easily. The stability of close loop system is investigated
by the Lyapunov stabilization criterion. A Lyapunov function is
introduced and the global stability is proven. Finally, the
effectiveness of this method is illustrated by simulation results and it
is shown that the performance of the system is improved.
Abstract: This paper discusses a method for improving accuracy
of fuzzy-rule-based classifiers using particle swarm optimization
(PSO). Two different fuzzy classifiers are considered and optimized.
The first classifier is based on Mamdani fuzzy inference system
(M_PSO fuzzy classifier). The second classifier is based on Takagi-
Sugeno fuzzy inference system (TS_PSO fuzzy classifier). The
parameters of the proposed fuzzy classifiers including premise
(antecedent) parameters, consequent parameters and structure of
fuzzy rules are optimized using PSO. Experimental results show that
higher classification accuracy can be obtained with a lower number
of fuzzy rules by using the proposed PSO fuzzy classifiers. The
performances of M_PSO and TS_PSO fuzzy classifiers are compared
to other fuzzy based classifiers
Abstract: A nonlinear optimal controller with a fuzzy gain
scheduler has been designed and applied to a Line-Of-Sight (LOS)
stabilization system. Use of Linear Quadratic Regulator (LQR)
theory is an optimal and simple manner of solving many control
engineering problems. However, this method cannot be utilized
directly for multigimbal LOS systems since they are nonlinear in
nature. To adapt LQ controllers to nonlinear systems at least a
linearization of the model plant is required. When the linearized
model is only valid within the vicinity of an operating point a gain
scheduler is required. Therefore, a Takagi-Sugeno Fuzzy Inference
System gain scheduler has been implemented, which keeps the
asymptotic stability performance provided by the optimal feedback
gain approach. The simulation results illustrate that the proposed
controller is capable of overcoming disturbances and maintaining a
satisfactory tracking performance.
Abstract: Two indica varieties, IR36 and ‘Suweon 258’ (“S”)
are middle-heading in southern Japan. 36U, also middle-heading, is
an isogenic line of IR36 carrying Ur1 (Undulate rachis-1) gene.
However, late-heading plants segregated in the F2 population from
the F1 of S × 36U, and so did in the following generations. The
concerning lateness gene is designated as Ex. From the F8 generation,
isogenic-line pair of early-heading and late-heading lines, denoted by
“E” (ex/ex) and “L” (Ex/Ex), were developed. Genetic analyses of
heading time were conducted, using F1s and F2s among L, E, S and
36U. The following inferences were drawn from the experimental
results: 1) L, and both of E and 36U harbor Ex and ex, respectively;
2) Besides Ex, S harbors an inhibitor gene to it, i.e. I-Ex which is a
novel finding of the present study. 3) Ex is a dominant allele at the
E1 locus.
Abstract: In this paper we introduce new data oriented modeling
of uniform random variable well-matched with computing systems. Due to this conformity with current computers structure, this modeling will be efficiently used in statistical inference.
Abstract: In this paper, we focus on the use of knowledge bases
in two different application areas – control of systems with unknown
or strongly nonlinear models (i.e. hardly controllable by the classical
methods), and robot motion planning in eight directions. The first
one deals with fuzzy logic and the paper presents approaches for
setting and aggregating the rules of a knowledge base. Te second one
is concentrated on a case-based reasoning strategy for finding the
path in a planar scene with obstacles.
Abstract: Sensors possess several properties of physical
measures. Whether devices that convert a sensed signal into an
electrical signal, chemical sensors and biosensors, thus all these
sensors can be considered as an interface between the physical and
electrical equipment. The problem is the analysis of the multitudes of
saved settings as input variables. However, they do not all have the
same level of influence on the outputs. In order to identify the most
sensitive parameters, those that can guide users in gathering
information on the ground and in the process of model calibration
and sensitivity analysis for the effect of each change made.
Mathematical models used for processing become very complex.
In this paper a fuzzy rule-based system is proposed as a solution
for this problem. The system collects the available signals
information from sensors. Moreover, the system allows the study of
the influence of the various factors that take part in the decision
system. Since its inception fuzzy set theory has been regarded as a
formalism suitable to deal with the imprecision intrinsic to many
problems. At the same time, fuzzy sets allow to use symbolic models.
In this study an example was applied for resolving variety of
physiological parameters that define human health state. The
application system was done for medical diagnosis help. The inputs
are the signals expressed the cardiovascular system parameters, blood
pressure, Respiratory system paramsystem was done, it will be able
to predict the state of patient according any input values.
Abstract: This paper attempts to establish the fact that Multi
State Network Classification is essential for performance
enhancement of Transport protocols over Satellite based Networks. A
model to classify Multi State network condition taking into
consideration both congestion and channel error is evolved. In order
to arrive at such a model an analysis of the impact of congestion and
channel error on RTT values has been carried out using ns2. The
analysis results are also reported in the paper. The inference drawn
from this analysis is used to develop a novel statistical RTT based
model for multi state network classification.
An Adaptive Multi State Proactive Transport Protocol consisting
of Proactive Slow Start, State based Error Recovery, Timeout Action
and Proactive Reduction is proposed which uses the multi state
network state classification model. This paper also confirms through
detail simulation and analysis that a prior knowledge about the
overall characteristics of the network helps in enhancing the
performance of the protocol over satellite channel which is
significantly affected due to channel noise and congestion.
The necessary augmentation of ns2 simulator is done for
simulating the multi state network classification logic. This
simulation has been used in detail evaluation of the protocol under
varied levels of congestion and channel noise. The performance
enhancement of this protocol with reference to established protocols
namely TCP SACK and Vegas has been discussed. The results as
discussed in this paper clearly reveal that the proposed protocol
always outperforms its peers and show a significant improvement in
very high error conditions as envisaged in the design of the protocol.