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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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International Journal of Fuzzy Logic and Intelligent Systems
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Korean Institute of Intelligent Systems
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Volume & Issues
Volume 13, Issue 4 - Dec 2013
Volume 13, Issue 3 - Sep 2013
Volume 13, Issue 2 - Jun 2013
Volume 13, Issue 1 - Mar 2013
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Formation Control for Underactuated Autonomous Underwater Vehicles Using the Approach Angle
Kim, Kyoung Joo ; Park, Jin Bae ; Choi, Yoon Ho ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 3, 2013, Pages 154~163
DOI : 10.5391/IJFIS.2013.13.3.154
In this paper, we propose a formation control algorithm for underactuated autonomous underwater vehicles (AUVs) with parametric uncertainties using the approach angle. The approach angle is used to solve the underactuated problem for AUVs, and the leader-follower strategy is used for the formation control. The proposed controller considers the nonzero off-diagonal terms of the mass matrix of the AUV model and the associated parametric uncertainties. Using the state transformation, the mass matrix, which has nonzero off-diagonal terms, is transformed into a diagonal matrix to simplify designing the control. To deal with the parametric uncertainties of the AUV model, a self-recurrent wavelet neural network is used. The proposed formation controller is designed based on the dynamic surface control technique. Some simulation results are presented to demonstrate the performance of the proposed control method.
Improved Bimodal Speech Recognition Study Based on Product Hidden Markov Model
Xi, Su Mei ; Cho, Young Im ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 3, 2013, Pages 164~170
DOI : 10.5391/IJFIS.2013.13.3.164
Recent years have been higher demands for automatic speech recognition (ASR) systems that are able to operate robustly in an acoustically noisy environment. This paper proposes an improved product hidden markov model (HMM) used for bimodal speech recognition. A two-dimensional training model is built based on dependently trained audio-HMM and visual-HMM, reflecting the asynchronous characteristics of the audio and video streams. A weight coefficient is introduced to adjust the weight of the video and audio streams automatically according to differences in the noise environment. Experimental results show that compared with other bimodal speech recognition approaches, this approach obtains better speech recognition performance.
Fuzzy-Enforced Complementarity Constraints in Nonlinear Interior Point Method-Based Optimization
Song, Hwachang ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 3, 2013, Pages 171~177
DOI : 10.5391/IJFIS.2013.13.3.171
This paper presents a fuzzy set method to enforce complementarity constraints (CCs) in a nonlinear interior point method (NIPM)-based optimization. NIPM is a Newton-type approach to nonlinear programming problems, but it adopts log-barrier functions to deal with the obstacle of managing inequality constraints. The fuzzy-enforcement method has been implemented for CCs, which can be incorporated in optimization problems for real-world applications. In this paper, numerical simulations that apply this method to power system optimal power flow problems are included.
A Systematic Approach to Improve Fuzzy C-Mean Method based on Genetic Algorithm
Ye, Xiao-Yun ; Han, Myung-Mook ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 3, 2013, Pages 178~185
DOI : 10.5391/IJFIS.2013.13.3.178
As computer technology continues to develop, computer networks are now widely used. As a result, there are many new intrusion types appearing and information security is becoming increasingly important. Although there are many kinds of intrusion detection systems deployed to protect our modern networks, we are constantly hearing reports of hackers causing major disruptions. Since existing technologies all have some disadvantages, we utilize algorithms, such as the fuzzy C-means (FCM) and the support vector machine (SVM) algorithms to improve these technologies. Using these two algorithms alone has some disadvantages leading to a low classification accuracy rate. In the case of FCM, self-adaptability is weak, and the algorithm is sensitive to the initial value, vulnerable to the impact of noise and isolated points, and can easily converge to local extrema among other defects. These weaknesses may yield an unsatisfactory detection result with a low detection rate. We use a genetic algorithm (GA) to help resolve these problems. Our experimental results show that the combined GA and FCM algorithm`s accuracy rate is approximately 30% higher than that of the standard FCM thereby demonstrating that our approach is substantially more effective.
Semi-Supervised Recursive Learning of Discriminative Mixture Models for Time-Series Classification
Kim, Minyoung ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 3, 2013, Pages 186~199
DOI : 10.5391/IJFIS.2013.13.3.186
We pose pattern classification as a density estimation problem where we consider mixtures of generative models under partially labeled data setups. Unlike traditional approaches that estimate density everywhere in data space, we focus on the density along the decision boundary that can yield more discriminative models with superior classification performance. We extend our earlier work on the recursive estimation method for discriminative mixture models to semi-supervised learning setups where some of the data points lack class labels. Our model exploits the mixture structure in the functional gradient framework: it searches for the base mixture component model in a greedy fashion, maximizing the conditional class likelihoods for the labeled data and at the same time minimizing the uncertainty of class label prediction for unlabeled data points. The objective can be effectively imposed as individual mixture component learning on weighted data, hence our mixture learning typically becomes highly efficient for popular base generative models like Gaussians or hidden Markov models. Moreover, apart from the expectation-maximization algorithm, the proposed recursive estimation has several advantages including the lack of need for a pre-determined mixture order and robustness to the choice of initial parameters. We demonstrate the benefits of the proposed approach on a comprehensive set of evaluations consisting of diverse time-series classification problems in semi-supervised scenarios.
A Subclass of Petri Net with Reachability Equivalent to State Equation Satisfiability: Live Single Branch Petri Net
Gao, Qian ; Cho, Young Im ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 3, 2013, Pages 200~207
DOI : 10.5391/IJFIS.2013.13.3.200
Petri Nets are a system description and analysis tool. Reachability is one of the most basic properties in Petri Net research. In a sense, reachability research is the foundation study for other dynamic properties of Petri Nets through which many problems involving Petri Nets can be described. Nowadays, there are two mature analysis methods-the matrix equation and the reachability tree. However, both methods are localized, i.e., it is difficult to find a general algorithm that can determine reachability for an arbitrary Petri Net, especially an unbounded Petri Net. This paper proposes and proves three propositions in order to present a subclass of a Petri Net, the live single-branch Petri Net, whose reachability is equivalent to the satisfiability of the state equation.
Fuzzy relation equations in pseudo BL-algebras
Kim, Yong Chan ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 3, 2013, Pages 208~214
DOI : 10.5391/IJFIS.2013.13.3.208
Bandler and Kohout investigated the solvability of fuzzy relation equations with inf-implication compositions in complete lattices. Perfilieva and Noskova investigated the solvability of fuzzy relation equations with inf-implication compositions in BL-algebras. In this paper, we investigate various solutions of fuzzy relation equations with inf-implication compositions in pseudo BL-algebras.
Weak laws of large numbers for weighted sums of Banach space valued fuzzy random variables
Kim, Yun Kyong ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 3, 2013, Pages 215~223
DOI : 10.5391/IJFIS.2013.13.3.215
In this paper, we present some results on weak laws of large numbers for weighted sums of fuzzy random variables taking values in the space of normal and upper-semicontinuous fuzzy sets with compact support in a separable real Banach space. First, we give weak laws of large numbers for weighted sums of strong-compactly uniformly integrable fuzzy random variables. Then, we consider the case that the weighted averages of expectations of fuzzy random variables converge. Finally, weak laws of large numbers for weighted sums of strongly tight or identically distributed fuzzy random variables are obtained as corollaries.
Intuitionistic Fuzzy Theta-Compact Spaces
Eom, Yeon Seok ; Lee, Seok Jong ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 3, 2013, Pages 224~230
DOI : 10.5391/IJFIS.2013.13.3.224
In this paper, we introduce certain types of continuous functions and intuitionistic fuzzy
-compactness in intuitionistic fuzzy topological spaces. We show that intuitionistic fuzzy
-compactness is strictly weaker than intuitionistic fuzzy compactness. Furthermore, we show that if a topological space is intuitionistic fuzzy retopologized, then intuitionistic fuzzy compactness in the new intuitionistic fuzzy topology is equivalent to intuitionistic fuzzy
-compactness in the original intuitionistic fuzzy topology. This characterization shows that intuitionistic fuzzy
-compactness can be related to an appropriated notion of intuitionistic fuzzy convergence.
Interval-Valued Fuzzy Congruences on a Semigroup
Lee, Jeong Gon ; Hur, Kul ; Lim, Pyung Ki ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 13, issue 3, 2013, Pages 231~244
DOI : 10.5391/IJFIS.2013.13.3.231
We introduce the concept of interval-valued fuzzy congruences on a semigroup S and we obtain some important results: First, for any interval-valued fuzzy congruence
on a group G, the interval-valued congruence class Re is an interval-valued fuzzy normal subgroup of G. Second, for any interval-valued fuzzy congruence R on a groupoid S, we show that a binary operation * an S