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: Application of Expert System in the area of agriculture would take the form of Integrated Crop Management decision aids and would encompass water management, fertilizer management, crop protection systems and identification of implements. In order to remain competitive, the modern farmer often relies on agricultural specialists and advisors to provide information for decision-making. An expert system normally composed of a knowledge base (information, heuristics, etc.), inference engine (analyzes knowledge base), and end user interface (accepting inputs, generating outputs). Software named 'CROP-9-DSS' incorporating all modern features like, graphics, photos, video clippings etc. has been developed. This package will aid as a decision support system for identification of pest and diseases with control measures, fertilizer recommendation system, water management system and identification of farm implements for leading crops of Kerala (India) namely Coconut, Rice, Cashew, Pepper, Banana, four vegetables like Amaranthus, Bhindi, Brinjal and Cucurbits. 'CROP-9-DSS' will act as an expert system to agricultural officers, scientists in the field of agriculture and extension workers for decision-making and help them in suggesting suitable recommendations.
Abstract: In this paper, the estimation of the stress-strength
parameter R = P(Y < X), when X and Y are independent and both
are Lomax distributions with the common scale parameters but
different shape parameters is studied. The maximum likelihood
estimator of R is derived. Assuming that the common scale parameter
is known, the bayes estimator and exact confidence interval of R are
discussed. Simulation study to investigate performance of the
different proposed methods has been carried out.
Abstract: In many applications there is a broad variety of
information relevant to a focal “object" of interest, and the fusion of such heterogeneous data types is desirable for classification and
categorization. While these various data types can sometimes be treated as orthogonal (such as the hull number, superstructure color,
and speed of an oil tanker), there are instances where the inference and the correlation between quantities can provide improved fusion
capabilities (such as the height, weight, and gender of a person). A
service-oriented architecture has been designed and prototyped to
support the fusion of information for such “object-centric" situations.
It is modular, scalable, and flexible, and designed to support new data sources, fusion algorithms, and computational resources without affecting existing services. The architecture is designed to simplify
the incorporation of legacy systems, support exact and probabilistic entity disambiguation, recognize and utilize multiple types of
uncertainties, and minimize network bandwidth requirements.
Abstract: This paper considers inference under progressive type II censoring with a compound Rayleigh failure time distribution. The maximum likelihood (ML), and Bayes methods are used for estimating the unknown parameters as well as some lifetime parameters, namely reliability and hazard functions. We obtained Bayes estimators using the conjugate priors for two shape and scale parameters. When the two parameters are unknown, the closed-form expressions of the Bayes estimators cannot be obtained. We use Lindley.s approximation to compute the Bayes estimates. Another Bayes estimator has been obtained based on continuous-discrete joint prior for the unknown parameters. An example with the real data is discussed to illustrate the proposed method. Finally, we made comparisons between these estimators and the maximum likelihood estimators using a Monte Carlo simulation study.
Abstract: The principal purpose of this article is to present a new method based on Adaptive Neural Network Fuzzy Inference System (ANFIS) to generate additional artificial earthquake accelerograms from presented data, which are compatible with specified response spectra. The proposed method uses the learning abilities of ANFIS to develop the knowledge of the inverse mapping from response spectrum to earthquake records. In addition, wavelet packet transform is used to decompose specified earthquake records and then ANFISs are trained to relate the response spectrum of records to their wavelet packet coefficients. Finally, an interpretive example is presented which uses an ensemble of recorded accelerograms to demonstrate the effectiveness of the proposed method.
Abstract: FACTS devices are used to control the power flow, to
increase the transmission capacity and to optimize the stability of the
power system. One of the most widely used FACTS devices is
Unified Power Flow Controller (UPFC). The controller used in the
control mechanism has a significantly effects on controlling of the
power flow and enhancing the system stability of UPFC. According
to this, the capability of UPFC is observed by using different control
mechanisms based on P, PI, PID and fuzzy logic controllers (FLC) in
this study. FLC was developed by taking consideration of Takagi-
Sugeno inference system in the decision process and Sugeno-s
weighted average method in the defuzzification process. Case studies
with different operating conditions are applied to prove the ability of
UPFC on controlling the power flow and the effectiveness of
controllers on the performance of UPFC. PSCAD/EMTDC program
is used to create the FLC and to simulate UPFC model.
Abstract: For collecting data from all sensor nodes, some
changes in Dynamic Source Routing (DSR) protocol is proposed. At
each hop level, route-ranking technique is used for distributing
packets to different selected routes dynamically. For calculating rank
of a route, different parameters like: delay, residual energy and
probability of packet loss are used. A hybrid topology of
DMPR(Disjoint Multi Path Routing) and MMPR(Meshed Multi Path
Routing) is formed, where braided topology is used in different
faulty zones of network. For reducing energy consumption, variant
transmission ranges is used instead of fixed transmission range. For
reducing number of packet drop, a fuzzy logic inference scheme is
used to insert different types of delays dynamically. A rule based
system infers membership function strength which is used to
calculate the final delay amount to be inserted into each of the node
at different clusters.
In braided path, a proposed 'Dual Line ACK Link'scheme is
proposed for sending ACK signal from a damaged node or link to a
parent node to ensure that any error in link or any node-failure
message may not be lost anyway. This paper tries to design the
theoretical aspects of a model which may be applied for collecting
data from any large hanging iron structure with the help of wireless
sensor network. But analyzing these data is the subject of material
science and civil structural construction technology, that part is out
of scope of this paper.
Abstract: The increasing importance of FlexRay systems in
automotive domain inspires unceasingly relative researches. One
primary issue among researches is to verify the reliability of FlexRay
systems either from protocol aspect or from system design aspect.
However, research rarely discusses the effect of network topology on
the system reliability. In this paper, we will illustrate how to model
the reliability of FlexRay systems with various network topologies by
a well-known probabilistic reasoning technology, Bayesian Network.
In this illustration, we especially investigate the effectiveness of error
containment built in star topology and fault-tolerant midpoint
synchronization algorithm adopted in FlexRay communication
protocol. Through a FlexRay steer-by-wire case study, the influence
of different topologies on the failure probability of the FlexRay steerby-
wire system is demonstrated. The notable value of this research is
to show that the Bayesian Network inference is a powerful and
feasible method for the reliability assessment of FlexRay systems.
Abstract: This paper deals with the application of artificial
neural network (ANN) and fuzzy based Adaptive Neuro Fuzzy
Inference System(ANFIS) approach to Load Frequency Control
(LFC) of multi unequal area hydro-thermal interconnected power
system. The proposed ANFIS controller combines the advantages of
fuzzy controller as well as quick response and adaptability nature of
ANN. Area-1 and area-2 consists of thermal reheat power plant
whereas area-3 and area-4 consists of hydro power plant with electric
governor. Performance evaluation is carried out by using intelligent
controller like ANFIS, ANN and Fuzzy controllers and conventional
PI and PID control approaches. To enhance the performance of
intelligent and conventional controller sliding surface is included.
The performances of the controllers are simulated using
MATLAB/SIMULINK package. A comparison of ANFIS, ANN,
Fuzzy, PI and PID based approaches shows the superiority of
proposed ANFIS over ANN & fuzzy, PI and PID controller for 1%
step load variation.
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: Conceptualization strengthens intelligent systems in generalization skill, effective knowledge representation, real-time inference, and managing uncertain and indefinite situations in addition to facilitating knowledge communication for learning agents situated in real world. Concept learning introduces a way of abstraction by which the continuous state is formed as entities called concepts which are connected to the action space and thus, they illustrate somehow the complex action space. Of computational concept learning approaches, action-based conceptualization is favored because of its simplicity and mirror neuron foundations in neuroscience. In this paper, a new biologically inspired concept learning approach based on the probabilistic framework is proposed. This approach exploits and extends the mirror neuron-s role in conceptualization for a reinforcement learning agent in nondeterministic environments. In the proposed method, instead of building a huge numerical knowledge, the concepts are learnt gradually from rewards through interaction with the environment. Moreover the probabilistic formation of the concepts is employed to deal with uncertain and dynamic nature of real problems in addition to the ability of generalization. These characteristics as a whole distinguish the proposed learning algorithm from both a pure classification algorithm and typical reinforcement learning. Simulation results show advantages of the proposed framework in terms of convergence speed as well as generalization and asymptotic behavior because of utilizing both success and failures attempts through received rewards. Experimental results, on the other hand, show the applicability and effectiveness of the proposed method in continuous and noisy environments for a real robotic task such as maze as well as the benefits of implementing an incremental learning scenario in artificial agents.
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: The main aim of this study was to examine whether
people understand indicative conditionals on the basis of syntactic
factors or on the basis of subjective conditional probability. The
second aim was to investigate whether the conditional probability of
q given p depends on the antecedent and consequent sizes or derives
from inductive processes leading to establish a link of plausible cooccurrence
between events semantically or experientially associated.
These competing hypotheses have been tested through a 3 x 2 x 2 x 2
mixed design involving the manipulation of four variables: type of
instructions (“Consider the following statement to be true", “Read the
following statement" and condition with no conditional statement);
antecedent size (high/low); consequent size (high/low); statement
probability (high/low). The first variable was between-subjects, the
others were within-subjects. The inferences investigated were Modus
Ponens and Modus Tollens. Ninety undergraduates of the Second
University of Naples, without any prior knowledge of logic or
conditional reasoning, participated in this study.
Results suggest that people understand conditionals in a syntactic
way rather than in a probabilistic way, even though the perception of
the conditional probability of q given p is at least partially involved in
the conditionals- comprehension. They also showed that, in presence
of a conditional syllogism, inferences are not affected by the
antecedent or consequent sizes. From a theoretical point of view these
findings suggest that it would be inappropriate to abandon the idea
that conditionals are naturally understood in a syntactic way for the
idea that they are understood in a probabilistic way.
Abstract: This paper presents an approach for repairing word order errors in English text by reordering words in a sentence and choosing the version that maximizes the number of trigram hits according to a language model. A possible way for reordering the words is to use all the permutations. The problem is that for a sentence with length N words the number of all permutations is N!. The novelty of this method concerns the use of an efficient confusion matrix technique for reordering the words. The confusion matrix technique has been designed in order to reduce the search space among permuted sentences. The limitation of search space is succeeded using the statistical inference of N-grams. The results of this technique are very interesting and prove that the number of permuted sentences can be reduced by 98,16%. For experimental purposes a test set of TOEFL sentences was used and the results show that more than 95% can be repaired using the proposed method.
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, we propose the variational approach to solve single image defogging problem. In the inference process of the atmospheric veil, we defined new functional for atmospheric veil that satisfy edge-preserving regularization property. By using the fundamental lemma of calculus of variations, we derive the Euler-Lagrange equation foratmospheric veil that can find the maxima of a given functional. This equation can be solved by using a gradient decent method and time parameter. Then, we can have obtained the estimated atmospheric veil, and then have conducted the image restoration by using inferred atmospheric veil. Finally we have improved the contrast of restoration image by various histogram equalization methods. The experimental results show that the proposed method achieves rather good defogging results.
Abstract: Segmentation of Magnetic Resonance Imaging (MRI) images is the most challenging problems in medical imaging. This paper compares the performances of Seed-Based Region Growing (SBRG), Adaptive Network-Based Fuzzy Inference System (ANFIS) and Fuzzy c-Means (FCM) in brain abnormalities segmentation. Controlled experimental data is used, which designed in such a way that prior knowledge of the size of the abnormalities are known. This is done by cutting various sizes of abnormalities and pasting it onto normal brain tissues. The normal tissues or the background are divided into three different categories. The segmentation is done with fifty seven data of each category. The knowledge of the size of the abnormalities by the number of pixels are then compared with segmentation results of three techniques proposed. It was proven that the ANFIS returns the best segmentation performances in light abnormalities, whereas the SBRG on the other hand performed well in dark abnormalities segmentation.
Abstract: Trust management is one of the drawbacks in Peer-to-Peer (P2P) system. Lack of centralized control makes it difficult to control the behavior of the peers. Reputation system is one approach to provide trust assessment in P2P system. In this paper, we use fuzzy logic to model trust in a P2P environment. Our trust model combines first-hand (direct experience) and second-hand (reputation)information to allow peers to represent and reason with uncertainty regarding other peers' trustworthiness. Fuzzy logic can help in handling the imprecise nature and uncertainty of trust. Linguistic labels are used to enable peers assign a trust level intuitively. Our fuzzy trust model is flexible such that inference rules are used to weight first-hand and second-hand accordingly.
Abstract: We constructed a method of noise reduction for
JPEG-compressed image based on Bayesian inference using the
maximizer of the posterior marginal (MPM) estimate. In this method,
we tried the MPM estimate using two kinds of likelihood, both of
which enhance grayscale images converted into the JPEG-compressed
image through the lossy JPEG image compression. One is the
deterministic model of the likelihood and the other is the probabilistic
one expressed by the Gaussian distribution. Then, using the Monte
Carlo simulation for grayscale images, such as the 256-grayscale
standard image “Lena" with 256 × 256 pixels, we examined the
performance of the MPM estimate based on the performance measure
using the mean square error. We clarified that the MPM estimate via
the Gaussian probabilistic model of the likelihood is effective for
reducing noises, such as the blocking artifacts and the mosquito noise,
if we set parameters appropriately. On the other hand, we found that
the MPM estimate via the deterministic model of the likelihood is not
effective for noise reduction due to the low acceptance ratio of the
Metropolis algorithm.