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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Journal of Korean Institute of Intelligent Systems
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Korean Institute of Intelligent Systems
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Volume & Issues
Volume 7, Issue 5 - Dec 1997
Volume 7, Issue 4 - Oct 1997
Volume 7, Issue 3 - Aug 1997
Volume 7, Issue 2 - Jun 1997
Volume 7, Issue 1 - Mar 1997
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An Algorithmic approach for Fuzzy Logic Application to Decision-Making Problems
Journal of Korean Institute of Intelligent Systems, volume 7, issue 2, 1997, Pages 3~15
In order to apply fuzzy logic, two major tasks need to be performed: the derivation of fuzzy rules and the determination of membership functions. These tasks are often difficult and time-consuming. This paper presents an algorithmic method for generating membership functions and fuzzy rules applicable to decision-making problems; the method includes an entropy minimization for clustering analog samples. Membership functions are derived by partitioning the variables into desired number of fuzzy terms, and fuzzy rules are obtained using minimum entropy clustering. In the mle derivation process, rule weights are also calculated. Inference and defuzzification for classification problems are also discussed.
A Genetic Algorithm with Ageing chromosomes
Journal of Korean Institute of Intelligent Systems, volume 7, issue 2, 1997, Pages 16~24
This paper proposes a modified GA whose individuals have their own ages. Thus, a chromosome will die only when the age becomes zero, as a result, the population size of this method increases according to the generations. This helps a GA to preserve the good characteristics of a few chromosomes during several generations if the ages are evaluated with fitness values. As a result, the performance of the method is better than that of existing ones. A multi-modal function optimization problem is employed to simulate the performance of this method. To show the effective:~esso f ageing paradigm, three ageing evaluation methods are introduced. A paper whose itlea is similar to that of ours have been published in a conference. We also experimented a method that showed the best performance in the paper. Original simple GA was also experimented and the performance is compared with others. However, the perforniance of the previous method shows worse than that of our methods in some aspects because the previous method didn't take the fitness value into account in the selection process.
Optimization and reasoning for Discrete Event System in a Temporal Logic Frameworks
Journal of Korean Institute of Intelligent Systems, volume 7, issue 2, 1997, Pages 25~33
A DEDS is a system whose states change in response to the occurence of events from a predefined event set. In this paper, we consider the optimal control and reasoning problem for Discrete Event Systems(DES) in the Temporal Logic Framework(TEL) which have been recnetly defined. The TLE is enhanced with objective functions(event cost indices) and a measurement space is alos deined. A sequence of event which drive the system form a give initial state to a given final state is generated by minimizing a cost functioin index. Our research goal is the reasoning of optimal trajectory and the design of the optimal controller for DESs. This procedure could be guided by the heuristic search methods. For the heuristic search, we suggested the Stochastic Ruler algorithm, instead of the A algorithm with difficulties as following ; the uniqueness of solutions, the computational complexity and how to select a heuristic function. This SR algorithm is used for solving the optimal problem. An example is shown to illustrate our results.
Robust Planar Shape Recognition Using Spectrum Analyzer and Fuzzy ARTMAP
Journal of Korean Institute of Intelligent Systems, volume 7, issue 2, 1997, Pages 34~42
This paper deals with the recognition of closed planar shape using a three dimensional spectral feature vector which is derived from the FFT(Fast Fourier Transform) spectrum of contour sequence and fuzzy ARTMAP neural network classifier. Contour sequences obtained from 2-D planar images represent the Euclidean distance between the centroid and all boundary pixels of the shape, and are related to the overall shape of the images. The Fourier transform of contour sequence and spectrum analyzer are used as a means of feature selection and data reduction. The three dimensional spectral feature vectors are extracted by spectrum analyzer from the FFT spectrum. These spectral feature vectors are invariant to shape translation, rotation and scale transformation. The fuzzy ARTMAP neural network which is combined with two fuzzy ART modules is trained and tested with these feature vectors. The experiments including 4 aircrafts and 4 industrial parts recognition process are presented to illustrate the high performance of this proposed method in the recognition problems of noisy shapes.
Design of a fuzzy model predictive controller for combustion control of refuse incineration plant
Journal of Korean Institute of Intelligent Systems, volume 7, issue 2, 1997, Pages 43~50
Refuse incineration plant operations involve many kinds of uncertain factors, such as the variable physical properties of refuse as fuel and the complexity of the burning phenomenon. This makes it very dificult to apply conventional control methods to the combustion control of the refuse. So most of the refuse incineration plant are operated by operators. In this paper, an multi-variable fuzzy model predictive controller is proposed for the combustion control of the re:fuse. Adaptive network based fuzzy inference system is used for modeling of the refuse incineration plant and multi-variable fuzzy model predictive controller is designed based on the identified fuzzy model. And computer simulation was carried out to evaluate performance of the proposed controller.
Load Frequency Control using Parameter Self-Tuning Fuzzy Controller
Journal of Korean Institute of Intelligent Systems, volume 7, issue 2, 1997, Pages 52~65
This paper presents a design technique of self tuning fuzzy controller for load frequency control of power system. The proposed parameter self tuning algorithm of fuzzy controller is based on the gradient method using four direction vectors which make error between inference values of fuzzy controller and output values of the specially selected optimal controller reduce steepestly. Using input-output data pair obtained from optimal controller, the parameters in antecedent part and in consequent part of fuzzy inference rules are learned and tuned automatically using the proposed gradient method. The related simulation results show that the proposed fuzzy controller is more powerful than the conventional ones for reductions of undershoot and steady-state load frequency deviation and for minimization of settling time.
A Study on the Fuzzy Similarity Measure
Journal of Korean Institute of Intelligent Systems, volume 7, issue 2, 1997, Pages 66~69
In this paper a fuzzy similarity measure is proposed. The proposed fuzzy similarity measure considers the relative distance between data and cluster centers in addition to the Euclidean distance to decide the degree of similarity. The boundary of a cluster center is constracted on the competitive region and expanded on the less competitive region. This result shows the possibility of using relative distance as a similarity measure.
Derivation of a Linear PID Control Law from a Fuzzy Control Theory
Journal of Korean Institute of Intelligent Systems, volume 7, issue 2, 1997, Pages 70~78
Proportional-integral-derivative(P1D) controllers have been still widely used in industrial processes due to their simplicity, effectiveness, robustness for a wide range of operating conditions, and the familiarity of control engineers. And a number of recent papers in fuzzy systems are showing that fuzzy systems are universal approximators. That is, fuzzy controllers are capable of approximating any real continuous function on a compact set of arbitrary accuracy. In this paper, we derive the linear PID control law from the fuzzy control algorithm where all fuzzy sets for representing plant state variables and a control variable use common triangular types. We first lead a linear PD control law from a fuzzy logic control with only two fuzzy sets for error and change-of-error. And then we derive the linear PID control law from a fuzzy controller. We here assumed that the intervals of error, change-of-error, and integral error could be partitioned into arbitrary numbers, respectively. As a result, a linear PID controller is only a sort of various fuzzy logic controls.
An Evaluation of Computer Integrated Manufacturing(CIM) System Using Fuzzy Set
Journal of Korean Institute of Intelligent Systems, volume 7, issue 2, 1997, Pages 79~86
As envirc~nmental changes occur radically (unpredictable development of the information communication and automatic technology) the necessity of the CIM is increasing rapidly in the manufacturing field. In this paper, we propose an evaluation method using the Fuzzy set and conventional technology (which is composed of both a proposal from t.he RFP and knowlodge expertise) to constract the successful CIM.
An Optimal Design Procedure for Brain-state-in-a-box Neural Network
Journal of Korean Institute of Intelligent Systems, volume 7, issue 2, 1997, Pages 87~95
This paper presents an optimal design procedure to realize an BSB neural networks by means of the parametrization of solution space and optimization of parameters using evaluation program. In particular, the performance index based on DOA analysis may make an associative memory implementation reach on the level of practical success.
Fuzzy Polynomial Neural Network Algorithm using GMDH Mehtod and its Application to the Wastewater Treatment Process
Journal of Korean Institute of Intelligent Systems, volume 7, issue 2, 1997, Pages 96~105
In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed FPNN(Fuzzy Polynomial Neural Network) modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) method and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH method and regression polynomial fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnace and those for wastewater treatment process are used for the purpose of evaluating the performance of the proposed FPNN modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.
Notes on Fuzzy Equivalence Relations
Journal of Korean Institute of Intelligent Systems, volume 7, issue 2, 1997, Pages 106~109
In this paper we define the t-fuzzy equivalence relation on a set and we prove some properties in connection with t-fuzzy relations.
On ths Stability Issues of Linear Takagi-Sugeno Fuzzy Models
Joh, Joongseon ;
Journal of Korean Institute of Intelligent Systems, volume 7, issue 2, 1997, Pages 110~121
Stability issues of linear Takagi-Sugeno fuzzy modles are thoroughly investigated. At first, a systematic way of searching for a common symmetric positive definite P matrix (common P matrix in short), which is related to stability, is proposed for N subsystems which are under a pairwise commutativity assumption. Robustness issue under modeling uncertainty in each subsystem is then considered by proposing a quadratic stability criterion and a method of determining uncertainty bounds. Finally, it is shown that the pairwise commutative assumption can be in fact relaxed by interpreting the uncertainties as mismatch parts of non-commutative system matrices. Several examples show the validity of the proposed methods.