• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.102.2-102
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• 2002

In this paper, the improved Multi-FNN (Fuzzy-Neural Networks) model is identified and optimized using HCM (Hard C-Means) clustering method and optimization algorithms. The proposed Multi-FNN is based on FNN and use simplified and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and genetic algorithms (GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parame...

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.30-35
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• 1992

Conflicting or inconsistent rules sometimes help us to represent the control actions of an expert more freely. Also, uncertainties about the control actions of the expert may render rules with conclusions whore membership functions have different width in their shapes. Conventional inference methods for FLC may not effectively handle such inconsistencies and/or rules containing such conclusions. In this paper, an effective inference method dealing with such If-Then rules is proposed.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.436-439
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• 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
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• 2000.10a
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• pp.367-367
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• 2000

In this paper, an Adaptive neuro-fuzzy Inference system(ANFIS) using fuzzy min-max network(FMMN) is proposed. Fuzzy min-max network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregation of fuzzy set hyperboxes. Here, the proposed method transforms the hyperboxes into gaussian membership functions, where the transformed membership functions are inserted for generating fuzzy rules of ANFIS. Finally, we applied the proposed method to the classification problem of iris data and obtained a better performance than previous works.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.12 no.19
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• pp.57-65
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• 1989

This paper describes an artificial intelligence approach to machine diagnosis. Firstly, It considers how the knowledge could be organized and represented. Secondly, it considers which inference strategy could be chosen for contingent situations for the purpose of rationality, efficiency and user-friendliness. Finally, the prototype based on the suggested model is introduced briefly.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.344-351
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• 2007

In this paper, we propose an approach to design multi-fuzzy controllers for the superheat and the low pressure that have an influence on energy efficiency and stabilization of air conditioning system with multi-evaporators. Air conditioning system with multi-evaporators is composed of compressor, condenser, several evaporators and several expansion valves. It is quite difficult to control the air conditioning system because the change of the refrigerant condition give an impact on the overall air conditioning system. In order to solve the drawback, we design multi-fuzzy controllers which control simultaneously both three expansion valve and one compressor for the superheat and the low pressure of air conditioning system. The proposed multi fuzzy controllers are given as a kinds of controller types such as a simplified fuzzy inference type. Here the scaling factors of each fuzzy controller are efficiently adjusted by Hierarchical Fair Competition-based Genetic Algorithms. The values of performance index of the simulation results of the A company type compare with simulation results of simplified inference type.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.419-425
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• 2001

A method to detect lots of porno documents on the internet is presented in this parer. The proposed method applies fuzzy inference mechanism to the conventional information retrieval techniques. First, several example sites on porno arc provided by users and then candidate words representing for porno documents are extracted from theme documents. In this process, lexical analysis and stemming are performed. Then, several values such as tole term frequency(TF), the document frequency(DF), and the Heuristic Information(HI) Is computed for each candidate word. Finally, fuzzy inference is performed with the above three values to weight candidate words. The weights of candidate words arc used to determine whether a liven site is sexual or not. From experiments on small test collection, the proposed method was shown useful to detect the sexual sites automatically.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.184-197
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• 2001

In this paper, we provide the formal specification of a fuzzy object inference language and propose ICOT(Integrated C-Object Tool) as its implementation for knowledge-based programming with the disjunctive fuzzy information. The novelty of our model is that it seamlessly combines object inference and fuzzy reasoning into a unified framework without compromising a compatibility with extant databases, especially object-relational ones. In this model most of the object-oriented paradigm is successfully expressed in terms of relational constructs, tailoring fuzzy reasoning style to be well suited to the framework of the databases. It turns out to be useful in preserving its conceptual simplicity as well, since simple-to-use is one of important criteria in designing the databases. Additionally this model considerably enhanced the semantic expressiveness of data allowing disjunctive fuzzy information.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.999-1004
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• 2005

An algorithm for a hybrid controller consists of a sliding mode control part and a fuzzy logic part which ar purposely for nonlinear systems. The sliding mode part of the solution is based on "eigenvalue/vector"-type controller is used as the backstepping approach for tracking errors. The fuzzy logic part is a Mamdani fuzzy model. This is designed by applying sliding mode control (SMC) method to the dynamic model. The main objective is to keep the update dynamics in a stable region by used SMC. After that the plant behavior is presented to train procedure of adaptive neuro-fuzzy inference systems (ANFIS). ANFIS architecture is determined and the relevant formulation for the approach is given. Using the error (e) and rate of error (de), occur due to the difference between the desired output value (yd) and the actual output value (y) of the system. A dynamic adaptation law is proposed and proved the particularly chosen form of the adaptation strategy. Subsequently VSC creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode (stable trainer). This study considers the ANFIS structure with first order Sugeno model containing nine rules. Bell shaped membership functions with product inference rule are used at the fuzzification level. Finally the Mamdani fuzzy logic which is depends on adaptive neuro-fuzzy inference systems structure designed. At the transferable stage from ANFIS to Mamdani fuzzy model is adjusted for the membership function of the input value (e, de) and the actual output value (y) of the system could be changed to trapezoidal and triangular functions through tuning the parameters of the membership functions and rules base. These help adjust the contributions of both fuzzy control and variable structure control to the entire control value. The application example, control of a mass-damper system is considered. The simulation has been done using MATLAB. Three cases of the controller will be considered: for backstepping sliding-mode controller, for hybrid controller, and for adaptive backstepping sliding-mode controller. A numerical example is simulated to verify the performances of the proposed control strategy, and the simulation results show that the controller designed is more effective than the adaptive backstepping sliding mode controller.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.4
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• pp.279-286
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• 1996

In this paper, variable structure control(VSC) based on reaching law method with fuzzy inference for nonlinear systems is proposed. The reaching law means the reaching condition which forces an initial state of system to reach switching surface in finite time, and specifies the dynamics of a desired switching function. Since the conventional reaching law has fixed coefficients, the chattering can be existed largely in sliding mode. In the design of a proposed fuzzy reaching law, we fuzzify RP(representative point)'s orthogonal distance to switching surface and RP's distance the origin of the 2-dimensional space whose coordinates are the error and the error rate. The coefficients of the reaching law are varied appropriately by the fuzzy inference. Hence the state of system in reaching mode reaches fastly switching surface by the large values of reaching coefficients and the chattering is reduced in sliding mode by the small values of those. And the effectiveness of the proposed fuzzy reaching law method is showen by the simulation results of the control of a two link robot manipulator.