• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.2
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• pp.231-238
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• 1993

A rules of fuzzy control is to represent an expert‘s and engineer‘s ambiguous control knowledge of system with some lingustic rules. This rule is very difficult to represent perfectly because expert‘s knowledge is not precise and the rule is not perfect. We propose the fuzzy reasoning and learning to upgrade precision of imperfect rules successively after system running. In the proposed method, the precision of the backward part of a fuzzy rule is improved by back propagation learning method. Also, the method reasons the compatibility degree of the forward part of fuzzy rule by associative memory method. This method this is successfully applied to design auto-parking fuzzy controller in which expert‘s technology and knowledge are required in the limited area.

• Journal of Korea Spatial Information System Society
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• v.4 no.1 s.7
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• pp.5-14
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• 2002

The generalization of geospatial data is an important way in deriving a new database from an original one. The generalization of a geospatial object changes not only its geometric and aspatial attributes but also results in propagation to other objects along their relationship. We call it generalization propagation of geospatial databases. Without proper handling of the propagation, it brings about an inconsistent database or loss of semantics. Nevertheless, previous studies in the generalization have focused on the derivation of an object by isolating it from others. And they have proposed a set of generalization operators, which were intended to change the geometric and aspatial attributes of an object. In this paper we extend the definition of generalization operators to cover the propagation from an object to others. In order to capture the propagation, we discover a set of rules or constraints that must be taken into account during generalization procedure. Each generalization operator with constraints is expressed in relational algebra and it can be converted to SQL statements with ease. A prototype system was developed to verify the correctness of extended operators.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.7-12
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• 2005

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.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.525-532
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• 2003

This paper describes intrusion detection rule management using mobile agents. Intrusion detection can be divided into anomaly detection and misuse detection. Misuse detection is best suited for reliably detecting known use patterns. Misuse detection systems can detect many or all known attack patterns, but they are of little use for as yet unknown attack methods. Therefore, the introduction of mobile agents to provide computational security by constantly moving around the Internet and propagating rules is presented as a solution to misuse detection. This work presents a new approach for detecting intrusions, in which mobile agent mechanisms are used for security rules propagation. To evaluate the proposed approach, we compared the workload data between a rules propagation method using a mobile agent and a conventional method. Also, we simulated a rules management using NS-2 (Network Simulator) with respect to time.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.253-257
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• 2000

the objective of this study is to design and evaluate a methodology for diagnosing the appendicitis in a fuzzy neural network that integrates the partition of input space by fuzzy entropy and the generation of fuzzy control rules and learning algorithm. In particular the diagnosis of appendicitis depends on the rule of thumb of the experts such that it associates with the region, the characteristics, the degree of the ache and the potential symptoms. In this scheme the basic idea is to realize the fuzzy rle base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by back propagation learning rule. To eliminate the number of the parameters of the rules, the output of the consequences of the control rules is expressed by the network's connection weights. As a result we obtain a method for reducing the system's complexities. Through computer simulations the effectiveness of the proposed strategy is verified for the diagnosis of appendicitis.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.5-8
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• 2000

In this paper, we propose new algorithms for the partition of input space and the generation of fuzzy control rules. The one consists of Shannon and extended fuzzy entropy function, the other consists of adaptive fuzzy neural system with back propagation teaming rule. The focus of this scheme is to realize the optimal fuzzy rule base with the minimal number of the parameters of the rules, reducing the complexity of the system. The proposed algorithm is tested with the time series prediction problem using Mackey-Glass chaotic time series.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1181-1186
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• 1993

In the paper, a new design method of rule-based fuzzy modeling is proposed for model identification of nonlinear systems. The structure indentification is carried out, utilizing fuzzy c-means clustering. Fuzzy-neural networks composed back-propagation algorithm and linear fuzzy inference method, are used to identify parameters of the premise and consequence parts. To obtain optimal linguistic fuzzy implication rules, the learning rates and momentum coefficients are tuned automatically using a modified complex method.

• Journal of the Korea Computer Industry Society
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• v.5 no.8
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• pp.781-790
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• 2004

This paper describes intrusion detection rule mangement using mobile agents. Intrusion detection can be divided into anomaly detection and misuse detection. Misuse detection is best suited for reliably detecting known use patterns. Misuse detection systems can detect many or all known attack patterns, but they are of little use for as yet unknown attack methods. Therefore, the introduction of mobile agents to provide computational security by constantly moving around the Internet and propagating rules is presented as a solution to misuse detection. This work presents a new approach for detecting intrusions, in which mobile agent mechanisms are used for security rules propagation. To evaluate the proposed appraoch, we compared the workload data between a rules propagation method using a mobile agent and a conventional method. Also, we simulated a rules management using NS-2(Network Simulator) with respect to time.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.378-384
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• 2013

In this paper, we propose the fuzzy neural networks based on fuzzy c-means clustering algorithm. Typically, the generation of fuzzy rules have the problem that the number of fuzzy rules exponentially increases when the dimension increases. To solve this problem, the fuzzy rules of the proposed networks are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the learning of fuzzy neural networks is realized by adjusting connections of the neurons, and it follows a back-propagation algorithm. The proposed networks are evaluated through the application to nonlinear process.

• Journal of KSNVE
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• v.10 no.4
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• pp.610-614
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• 2000

Cellular Automata(CA)s are used as a simple mathematical model to investigate self-organization in statistical mechanics, which are originally introduced by von Neumann and S. Ulam at the end of the 1940s. CAs provide a framework for a large class of discrete models with homogeneous interactions, which are characterized by the following fundamental properties: 1) CAs are dynamical systems in which space and time are discrete. 2) The systems consist of a regular grid of cells. 3) Each cell is characterized by a state taken from a finite set of states and updated synchronously in discrete time steps according to a local, identical interaction rule. 4) The state of a cell is determined by the previous states of a surrounding neighborhood of cells. A cellular automaton has been attracted wide interest in modeling physical phenomena, which are described generally, partial differential equations such as diffusion and wave propagation. This paper describes one and two-dimensional analysis of wave propagation phenomena modeled by CA, where the local interaction rules were derived referring to the Lattice Gas Model reported by Chen et al., and also including finite difference scheme. Modeling processes by using CA are discussed and the simulation results of wave propagation with one wave source are compared with that by finite difference method.