• Journal of applied mathematics & informatics
• /
• v.26 no.5_6
• /
• pp.861-876
• /
• 2008

This paper concerns a new method called Fuzzy Supervised Method for error matrix, the method has developed based on Adoptive Neuro- Fuzzy Inference Systems(ANFIS). For the performance point of view initially the new method tested with trial data and then this paper applies the proposed method with real world problems. So that this paper generated 1000 random error matrices in programming language [R] and then it tests the new proposed method for the error matrices. The results of Fuzzy Supervised Method given in terms of Kappa Index and Congalton Accuracy Indexes, and performance of Fuzzy Supervised Method has evaluated by using Pearson's test.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.436-439
• /
• 1995

In this paper we present a method to construct fuzzy model with multi-dimension input membership function, which can construct fuzzy inference system on one node of the network directly. This method comes from a common framework called Universal Learning Network (ULN). The fuzzy model under the framework of ULN is called Universal Learning Network-based Fuzzy Inference System (ULNFIS), which possesses certain advantages over other networks such as neural network. We also introduce how to imitate a real system with ULN and a control scheme using ULNFIS.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.479-484
• /
• 1992

The resolved motion rate control (RMRC) is converting to Joint space trajectory from given Cartesian space trajectory. The RMRC requires the inverse of Jacobian matrix. Since the Jacobian matrix of the redundant robot is generally not square, the pseudo-inverse must be introduced. However the pseudo-inverse is not easy to be implemented on a digital computer in real time as well as mathematically complex. In this paper, a simple fuzzy resolved motion rate control (FRMRC) that can replace the RMRC using pseudo-inverse of Jacobian is proposed. The proposed FRMRC with appropriate fuzzy rules, membership functions and reasoning method can solve the mapping problem between the spaces without complexity. The mapped Joint space trajectory is sufficiently accurate so that it can be directly used to control redundant manipulators. Simulation results verify the efficiency of the proposed idea.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.8
• /
• pp.761-765
• /
• 2010

This paper presents the design of optimal fuzzy classifier for human detection by using genetic algorithms, one of the best-known meta-heuristic search methods. For this purpose, encoding scheme to search the optimal sequential intersection points between adjacent fuzzy membership functions is originally presented for the fuzzy classifier design for HOG (Histograms of Oriented Gradient) descriptors. The intersection points are sequentially encoded in the proposed encoding scheme to reduce the redundancy of search space occurred in the combinational problem. Furthermore, the fitness function is modified with the true-positive and true-negative of the confusion matrix instead of the total success rate. Experimental results show that the two proposed approaches give superior performance in HOG datasets.

• Proceedings of the KIEE Conference
• /
• 1999.07b
• /
• pp.534-536
• /
• 1999

Fuzzy Systems which are based on membership functions and rules, can control nonlinear, uncertain, complex systems well. However, Fuzzy logic controller(FLC) has problems; It is difficult to design the stable FLC and FLC depends mainly on individual experience. Although FLC can be designed using the error back-propagation algorithm, it takes long time to converge into global, optimal parameters. Well-developed linear system theory should not be replaced by FLC, but instead, it should be suitably used with FLC. A new methodology is introduced for designing THEN-PART membership functions of FLC based on its well-tuned state feedback controller. A example of inverted pendulum is given for demonstration of the robustness of proposed methodology.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.12
• /
• pp.5116-5134
• /
• 2015

In this paper, due to the network security defense is mainly static defense, a dynamic classification network security defense strategy model is proposed by analyzing the security situation of complex computer network. According to the network security impact parameters, eight security elements and classification standard are obtained. At the same time, the dynamic classification algorithm based on fuzzy theory is also presented. The experimental analysis results show that the proposed model and algorithm are feasible and effective. The model is a good way to solve a safety problem that the static defense cannot cope with tactics and lack of dynamic change.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.2
• /
• pp.265-272
• /
• 2009

Fuzzy number intuitionistic fuzzy sets (FNIFSs), each of which is characterized by a membership function and a non-membership function whose values are trigonometric fuzzy number rather than exact numbers, are a very useful means to describe the decision information in the process of decision making. Wang [10] developed some arithmetic aggregation operators, such as the fuzzy number intuitionistic fuzzy weighted averaging (FIFWA) operator, the fuzzy number intuitionistic fuzzy ordered weighted averaging (FIFOWA) operator and the fuzzy number intuitionistic fuzzy hybrid aggregation (FIFHA) operator. In this paper, based on the FIFHA operator and the FIFWA operator, we investigate the group decision making problems in which all the information provided by the decision-makers is presented as fuzzy number intuitionistic fuzzy decision matrices where each of the elements is characterized by fuzzy number intuitionistic fuzzy numbers, and the information about attribute weights is partially known. An example is used to illustrate the applicability of the proposed approach.

• Asia-Pacific Journal of Business
• /
• v.11 no.1
• /
• pp.1-16
• /
• 2020

Purpose - The purpose of this research is to investigate the determinants of bakery revisit intention. This research selects Paris Baguette as the research context because the market share of Paris Baguette was the highest in Korean bakery market. Design/methodology/approach -This research employed revisit intention as the dependent variable, while this research chooses six attributes to account for revisit intention. Six attributes are price fairness, taste, product variety, accessibility, display, and membership. This research uses survey as the main instrument. For the data collection, online survey using Google survey form was implemented. The survey participants are domestic consumers of Paris Baguette. The number of observation is 245. For the data analysis, this study used frequency analysis, correlation matrix, exploratory factor analysis, reliability analysis, and multiple regression model. There are four control variables, which contains age, gender, visiting frequency, and monthly income. Findings - The results shows that price fairness, taste, product diversity, and accessibility are significant attributes with the positive effect. Among the significant attributes, taste presented the highest magnitude to explain the revisit intention. However, membership and display appeared as non-significant attributes to account for bakery revisit intention. Research implications or Originality - This study provides the bakery managers with the information to design their service and product. This study also contributes to the literature by understanding the consumer behavior more in the domain of bakery service.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.1
• /
• pp.177-185
• /
• 2009

Cluster analysis or clustering is a kind of unsupervised learning method in which a set of data points is divided into a given number of homogeneous groups. Fuzzy clustering method, one of the most popular clustering method, allows a point to belong to all the clusters with different degrees, so produces more intuitive and natural clusters than hard clustering method does. Even more some of fuzzy clustering variants have noise-immunity. In this paper, we improved the Possibilistic Fuzzy C-Means (PFCM), which generates a membership matrix as well as a typicality matrix, using Gath-Geva (GG) method. The proposed method has a focus on the boundaries of clusters, which is different from most of the other methods having a focus on the centers of clusters. The generated membership values are suitable for the classification-type applications. As the typicality values generated from the algorithm have a similar distribution with the values of density function of Gaussian distribution, it is useful for Gaussian-type density estimation. Even more GG method can handle the clusters having different numbers of data points, which the other well-known method by Gustafson and Kessel can not. All of these points are obvious in the experimental results.

• Journal of the Korean Society of Safety
• /
• v.36 no.2
• /
• pp.49-57
• /
• 2021

In the case of military supplies, any potential failure and causes of failures must be considered. This study is aimed at examining the failure modes of a rotorcraft landing system to identify the priority items. Failure mode and effects analysis (FMEA) is applied to the rotorcraft landing system. In general, the FMEA is used to evaluate the reliability in engineering fields. Three elements, specifically, the severity, occurrence, and detectability are used to evaluate the failure modes. The risk priority number (RPN) can be obtained by multiplying the scores or the risk levels pertaining to severity, occurrence, and detectability. In this study, different weights of the three elements are considered for the RPN assessment to implement the FMEA. Furthermore, the FMEA is implemented using a fuzzy rule base, similarity aggregation model (SAM), and grey theory model (GTM) to perform a comparative analysis. The same input data are used for all models to enable a fair comparison. The FMEA is applied to military supplies by considering methodological issues. In general, the fuzzy theory is based on a hypothesis regarding the likelihood of the conversion of the crisp value to the fuzzy input. Fuzzy FMEA is the basic method to obtain the fuzzy RPN. The three elements of the FMEA are used as five linguistic terms. The membership functions as triangular fuzzy sets are the simplest models defined by the three elements. In addition, a fuzzy set is described using a membership function mapping the elements to the intervals 0 and 1. The fuzzy rule base is designed to identify the failure modes according to the expert knowledge. The IF-THEN criterion of the fuzzy rule base is formulated to convert a fuzzy input into a fuzzy output. The total number of rules is 125 in the fuzzy rule base. The SAM expresses the judgment corresponding to the individual experiences of the experts performing FMEA as weights. Implementing the SAM is of significance when operating fuzzy sets regarding the expert opinion and can confirm the concurrence of expert opinion. The GTM can perform defuzzification to obtain a crisp value from a fuzzy membership function and determine the priorities by considering the degree of relation and the form of a matrix and weights for the severity, occurrence, and detectability. The proposed models prioritize the failure modes of the rotorcraft landing system. The conventional FMEA and fuzzy rule base can set the same priorities. SAM and GTM can set different priorities with objectivity through weight setting.