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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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Journal DOI :
Korean Institute of Intelligent Systems
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Volume & Issues
Volume 5, Issue 4 - Dec 2005
Volume 5, Issue 3 - Sep 2005
Volume 5, Issue 2 - Jun 2005
Volume 5, Issue 1 - Mar 2005
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A Multimodal Emotion Recognition Using the Facial Image and Speech Signal
Go, Hyoun-Joo ; Kim, Yong-Tae ; Chun, Myung-Geun ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 1, 2005, Pages 1~6
DOI : 10.5391/IJFIS.2005.5.1.001
In this paper, we propose an emotion recognition method using the facial images and speech signals. Six basic emotions including happiness, sadness, anger, surprise, fear and dislike are investigated. Facia] expression recognition is performed by using the multi-resolution analysis based on the discrete wavelet. Here, we obtain the feature vectors through the ICA(Independent Component Analysis). On the other hand, the emotion recognition from the speech signal method has a structure of performing the recognition algorithm independently for each wavelet subband and the final recognition is obtained from the multi-decision making scheme. After merging the facial and speech emotion recognition results, we obtained better performance than previous ones.
A Study of Construct Fuzzy Inference Network using Neural Logic Network
Lee, Jae-Deuk ; Jeong, Hye-Jin ; Kim, Hee-Suk ; Lee, Malrey ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 1, 2005, Pages 7~12
DOI : 10.5391/IJFIS.2005.5.1.007
This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper. The expert system which introduces fuzzy logic in order to process uncertainties is called fuzzy expert system. The fuzzy expert system, however, has a potential problem which may lead to inappropriate results due to the ignorance of some information by applying fuzzy logic in reasoning process in addition to the knowledge acquisition problem. In order to overcome these problems, We construct fuzzy inference network by extending the concept of reasoning network in this paper. In the fuzzy inference network, the propositions which form fuzzy rules are represented by nodes. And these nodes have the truth values representing the belief values of each proposition. The logical operators between propositions of rules are represented by links. And the traditional propagation rule is modified.
Blind channel equalization using fourth-order cumulants and a neural network
Han, Soo-whan ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 1, 2005, Pages 13~20
DOI : 10.5391/IJFIS.2005.5.1.013
This paper addresses a new blind channel equalization method using fourth-order cumulants of channel inputs and a three-layer neural network equalizer. The proposed algorithm is robust with respect to the existence of heavy Gaussian noise in a channel and does not require the minimum-phase characteristic of the channel. The transmitted signals at the receiver are over-sampled to ensure the channel described by a full-column rank matrix. It changes a single-input/single-output (SISO) finite-impulse response (FIR) channel to a single-input/multi-output (SIMO) channel. Based on the properties of the fourth-order cumulants of the over-sampled channel inputs, the iterative algorithm is derived to estimate the deconvolution matrix which makes the overall transfer matrix transparent, i.e., it can be reduced to the identity matrix by simple recordering and scaling. By using this estimated deconvolution matrix, which is the inverse of the over-sampled unknown channel, a three-layer neural network equalizer is implemented at the receiver. In simulation studies, the stochastic version of the proposed algorithm is tested with three-ray multi-path channels for on-line operation, and its performance is compared with a method based on conventional second-order statistics. Relatively good results, withe fast convergence speed, are achieved, even when the transmitted symbols are significantly corrupted with Gaussian noise.
Derivative Evaluation and Conditional Random Selection for Accelerating Genetic Algorithms
Jung, Sung-Hoon ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 1, 2005, Pages 21~28
DOI : 10.5391/IJFIS.2005.5.1.021
This paper proposes a new method for accelerating the search speed of genetic algorithms by taking derivative evaluation and conditional random selection into account in their evolution process. Derivative evaluation makes genetic algorithms focus on the individuals whose fitness is rapidly increased. This accelerates the search speed of genetic algorithms by enhancing exploitation like steepest descent methods but also increases the possibility of a premature convergence that means most individuals after a few generations approach to local optima. On the other hand, derivative evaluation under a premature convergence helps genetic algorithms escape the local optima by enhancing exploration. If GAs fall into a premature convergence, random selection is used in order to help escaping local optimum, but its effects are not large. We experimented our method with one combinatorial problem and five complex function optimization problems. Experimental results showed that our method was superior to the simple genetic algorithm especially when the search space is large.
Fuzzy Topology On Fuzzy Sets: Fuzzy γ-Continuity and Fuzzy γ-Retracts
Hanafy, I.M. ; Mahmoud, F.S. ; Khalaf, M.M. ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 1, 2005, Pages 29~34
DOI : 10.5391/IJFIS.2005.5.1.029
The aim of this paper is to introduce fuzzy γ-continuity and fuzzy γ-retracts in a fuzzy topology on fuzzy sets and establish some of their properties. Also, the relations between these new concepts are discussed.
GENERALIZED NET MODEL OF INTRANET IN AN ABSTRACT UNIVERSITY WITH CURRENT ESTIMATIONS
Langova-Orozova, Daniela ; Sotirova, Evdokia ; Atanassov, Krassimir ; Melo-Pinto, Pedro ; Kim, Tae-kyun ; Park, Dal-Won ; Kim, Yung-Hwan ; Jang, Lee-Chae ; Kang, Dong-Jin ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 1, 2005, Pages 35~39
DOI : 10.5391/IJFIS.2005.5.1.035
We apply estimations of the intuitionistic fuzzy sets on the basis of which some amendments may be undertaken.
INTUITIONISTIC FUZZY RETRACTS
Hanafy, I.M. ; Mahmoud, F.S. ; Khalaf, M.M. ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 1, 2005, Pages 40~45
DOI : 10.5391/IJFIS.2005.5.1.040
The concept of a intuitionistic fuzzy topology (IFT) was introduced by Coker 1997. The concept of a fuzzy retract was introduced by Rodabaugh in 1981. The aim of this paper is to introduce a new concepts of fuzzy continuity and fuzzy retracts in an intuitionistic fuzzy topological spaces and establish some of their properties. Also, the relations between these new concepts are discussed.
Multi-Object Tracking using the Color-Based Particle Filter in ISpace with Distributed Sensor Network
Jin, Tae-Seok ; Hashimoto, Hideki ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 1, 2005, Pages 46~51
DOI : 10.5391/IJFIS.2005.5.1.046
Intelligent Space(ISpace) is the space where many intelligent devices, such as computers and sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human following by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd. And the article presents the integration of color distributions into particle filtering. Particle filters provide a robust tracking framework under ambiguity conditions. We propose to track the moving objects by generating hypotheses not in the image plan but on the top-view reconstruction of the scene. Comparative results on real video sequences show the advantage of our method for multi-object tracking. Simulations are carried out to evaluate the proposed performance. Also, the method is applied to the intelligent environment and its performance is verified by the experiments.
Neural Network Training Using a GMDH Type Algorithm
Pandya, Abhijit S. ; Gilbar, Thomas ; Kim, Kwang-Baek ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 1, 2005, Pages 52~58
DOI : 10.5391/IJFIS.2005.5.1.052
We have developed a Group Method of Data Handling (GMDH) type algorithm for designing multi-layered neural networks. The algorithm is general enough that it will accept any number of inputs and any sized training set. Each neuron of the resulting network is a function of two of the inputs to the layer. The equation for each of the neurons is a quadratic polynomial. Several forms of the equation are tested for each neuron to make sure that only the best equation of two inputs is kept. All possible combinations of two inputs to each layer are also tested. By carefully testing each resulting neuron, we have developed an algorithm to keep only the best neurons at each level. The algorithm's goal is to create as accurate a network as possible while minimizing the size of the network. Software was developed to train and simulate networks using our algorithm. Several applications were modeled using our software, and the result was that our algorithm succeeded in developing small, accurate, multi-layer networks.
On Fuzzy M-Sets and Fuzzy M-Continuity
Min, Won-Keun ; Park, Chun-Kee ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 1, 2005, Pages 59~63
DOI : 10.5391/IJFIS.2005.5.1.059
In this paper, we introduce the concept of fuzzy m-sets induced by a given fuzzy supratopology and investigate general properties of the new class consisted of fuzzy m-sets. We also introduce notions of fuzzy m-continuity, fuzzy m-open(closed) maps and study some properties of them.
On the goodness of some types of fuzzy paracompactness in Sostak's fuzzy topology
Kim, Yong-Chan ; Abbas, S.E. ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 1, 2005, Pages 64~68
DOI : 10.5391/IJFIS.2005.5.1.064
We introduce in Sostak's fuzzy topological spaces definitions of paracompactness, almost paracompactness, and near paracompactness all of which turn to be good extensions of their classical topological counterparts. Fuzzy semi-paracompact, para S-closed and weakly paracompact spaces are treated to a similar approach.
Optimal design of the PID Controller using a predictive control method
Kim, Sang-Joo ; Lee, Jang-Myung ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 1, 2005, Pages 69~75
DOI : 10.5391/IJFIS.2005.5.1.069
This paper is concerned with the design of a predictive PID controller, which has similar features to the model-based predictive controller. A PID type control structure is defined which includes prediction of the outputs and the recalculation of new set points using the future set point data. The optimal values of the PID gains are pre-calculated using the values of gains calculated from an unconstrained generalized predictive control algorithm. Simulation studies demonstrate the performance of the proposed controller and the results are compared with generalized predictive controller and the results are compared with generalized predictive control solutions.
Speaker Identification Based on Incremental Learning Neural Network
Heo, Kwang-Seung ; Sim, Kwee-Bo ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 1, 2005, Pages 76~82
DOI : 10.5391/IJFIS.2005.5.1.076
Speech signal has various features of speakers. This feature is extracted from speech signal processing. The speaker is identified by the speaker identification system. In this paper, we propose the speaker identification system that uses the incremental learning based on neural network. Recorded speech signal through the microphone is blocked to the frame of 1024 speech samples. Energy is divided speech signal to voiced signal and unvoiced signal. The extracted 12 orders LPC cpestrum coefficients are used with input data for neural network. The speakers are identified with the speaker identification system using the neural network. The neural network has the structure of MLP which consists of 12 input nodes, 8 hidden nodes, and 4 output nodes. The number of output node means the identified speakers. The first output node is excited to the first speaker. Incremental learning begins when the new speaker is identified. Incremental learning is the learning algorithm that already learned weights are remembered and only the new weights that are created as adding new speaker are trained. It is learning algorithm that overcomes the fault of neural network. The neural network repeats the learning when the new speaker is entered to it. The architecture of neural network is extended with the number of speakers. Therefore, this system can learn without the restricted number of speakers.
The Performance Improvement of Excitation System using Robust Control with DATABASE
Hong, Hyun-Mun ; Jeon, Byeong-Seok ; Kim, Jong-Gun ; Lee, Sang-Hyuk ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 1, 2005, Pages 83~87
DOI : 10.5391/IJFIS.2005.5.1.083
This paper deals with the design and evaluation of the robust controller for a synchronous generator excitation system to improve the steady state and transient stability. The nonlinear characteristics of the system is treated as model uncertainties, and then the robust control techniques are introduced into the power system stability design to take into account these uncertainties at the controller design stage. The performance of the designed controller is examined by extensive non-linear time domain simulation. It is shown that the performance of the robust controller is superior to that of the conventional PI controller. This paper also proposes an improved digital exciter control system for a synchronized generator using a digitally designed controller with database. Results show that the proposed control system manifests excellent control performance compared to existing control systems. It has also been confirmed that it is easy for the proposed control system to implement digital control.
User modeling based on fuzzy category and interest for web usage mining
Lee, Si-Hun ; Lee, Jee-Hyong ;
International Journal of Fuzzy Logic and Intelligent Systems, volume 5, issue 1, 2005, Pages 88~93
DOI : 10.5391/IJFIS.2005.5.1.088
Web usage mining is a research field for searching potentially useful and valuable information from web log file. Web log file is a simple list of pages that users refer. Therefore, it is not easy to analyze user's current interest field from web log file. This paper presents web usage mining method for finding users' current interest based on fuzzy categories. We consider not only how many times a user visits pages but also when he visits. We describe a user's current interest with a fuzzy interest degree to categories. Based on fuzzy categories and fuzzy interest degrees, we also propose a method to cluster users according to their interests for user modeling. For user clustering, we define a category vector space. Experiments show that our method properly reflects the time factor of users' web visiting as well as the users' visit number.