• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4987-5005
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• 2017

This paper introduces a new method to solve Similarity Recognition for Cloth Products, which is based on Fuzzy logic and Particle swarm optimization algorithm. For convenience, it is called the SRCPFP method hereafter. In this paper, the SRCPFP method combines Fuzzy Logic (FL) and Particle Swarm Optimization (PSO) algorithm to solve similarity recognition for cloth products. First, it establishes three features, length, thickness, and temperature resistance, respectively, for each cloth product. Subsequently, these three features are engaged to construct a Fuzzy Inference System (FIS) which can find out the similarity between a query cloth and each sampling cloth in the cloth database D. At the same time, the FIS integrated with the PSO algorithm can effectively search for near optimal parameters of membership functions in eight fuzzy rules of the FIS for the above similarities. Finally, experimental results represent that the SRCPFP method can realize a satisfying recognition performance and outperform other well-known methods for similarity recognition under considerations here.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.7
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• pp.1374-1383
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• 2001

In the fuzzy control method behaves more robustness than conventional control method, we propose a intelligent control method that membership functions and scaling factor of the fuzzy logic controller are optimized by genetic algorithm under off-line, and then fuzzy logic controller is constructed by the optimization parameters under on-line. In order to verify the usefulness of the proposed control method, we are applied to one link manipulator, and confirmed that the proposed control method is reduced the fuzzy rule base and is the better performance than the conventional fuzzy control method.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.238-242
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• 1991

This paper presents an approach to self-tuning PI control of dynamic plants, based on fuzzy logic application. A fuzzy logic composed of linguistic conditional statements is employed by defining the relations of input-output variables of the controller. In the synthesis of a fuzzy logic controller, one of the most difficult problem is the selection of linguistic control rules and parameters. To overcome this difficulty, self-tuning fuzzy PI controller (STFPIC) with a hierarchical structure in which the fuzzy PI controller is assigned as the lower level and the rule modification and parameter adjustment as the higher level. The rules and parameters are generated by the adjustment of membership function through performance index(PE). In this paper, the algorithm for of the controller performance is estimated by means of computer simulation.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.321-328
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• 1998

A controller and sensor fault tolerant system jot a steam generator is designed with fuzzy logic. A structure of the : proposed fault tolerant redundant system is composed of a supervisor and two fuzzy weighting modulators. A supervisor alternatively checks a controlled and a sensor induced performances to identify Which Part, a controller or a sensor, is faulty. In order to analyze controller induced performance both an error and a charge in error of the system output an chosen as fuzzy variables. The fuzzy logic jot a sensor induced performance uses two variables : a deviation between two sensor outputs and its frequency, Fuzzy weighting modulator generates an output signal compensated for faulty input signal. Simulations show that the : proposed fault tolerant control scheme jot a steam generator regulates welt water level by suppressing fault effect of either controllers or sensors. Therefore through duplicating sensors and controllers with the proposed fault tolerant scheme, both a reliability of a steam generator control and sensor system and that of a power plant increase even mote.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.7-12
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• 2003

In recent years, many attempts have been made to predict the behavior of bonds, currencies, stock or other economic markets. Most previous experiments used the neural network models for the stock market forecasting. The KOSPI 200 (Korea Composite Stock Price Index 200) is modeled by using different neural networks and fuzzy logic. In this paper, the neural network, the dynamic polynomial neural network (DPNN) and the fuzzy logic employed for the prediction of the KOSPI 200. The prediction results are compared by the root mean squared error (RMSE) and scatter plot, respectively. The results show that the performance of the fuzzy system is little bit worse than that of the DPNN but better than that of the neural network. We can develop the desired fuzzy system by optimization methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.788-792
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• 2001

Although the general sliding model control has the robust property, bounds on the disturbances and parameter variations should be known a prior to the designer of the control system. However, these bounds may not be easily obtained. Fuzzy logic provides an effective way to design a controller of the system with disturbances and parameter variations. Therefore, combination of the best feature of the fuzzy logic control and the sliding mode control is considered. In this paper, the adaptive fuzzy variable structure controller developed for variables of fuzzy logic. A variable length pendulum system is used to demonstrate the availability of the proposed algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.200-208
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• 2014

This paper presents the control of an inverted pendulum system using intelligent algorithms, such as fuzzy logic and neural networks, for advanced control education. The swing up balancing control of the inverted pendulum system was performed using fuzzy logic. Because the switching time from swing to standing motion is important for successful balancing, the fuzzy control method was employed to regulate the energy associated with the angular velocity required for the pendulum to be in an upright position. When the inverted pendulum arrived within a range of angles found experimentally, the control was switched from fuzzy to proportional-integral-derivative control to balance the inverted pendulum. When the pendulum was balancing, a joystick was used to command the desired position for the pendulum to follow. Experimental results demonstrated the performance of the two intelligent control methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.6
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• pp.727-737
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• 2014

This paper presents the design of Fuzzy PI controller that is used at BESS(Battery Energy Storage System) charging and discharging process for islanded operation in microgrid. Most of the PI controllers have fixed PI gains, but real-time updated gains are applied to PI controller using Fuzzy logic in this paper. The performances of suggested Fuzzy PI controller are simulated by PSCAD/EMTDC. As a result, output characteristics of ESS applied real-time updated gains to PI controller are faster than those of using fixed gains.

• Industrial Engineering and Management Systems
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• v.7 no.1
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• pp.99-107
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• 2008

This paper is concerned with scheduling in Flexible Manufacturing Systems (FMS) using a Fuzzy Logic (FL) approach. Four fuzzy input variables: machine allocated processing time, machine priority, machine available time and transportation priority are defined. The job priority is the output fuzzy variable, showing the priority status of a job to be selected for the next operation on a machine. The model will first select the machines and then assign operations based on a multi-criteria scheduling scheme. System/machine utilization, minimizing mean flow time and balancing machine usage will be covered. Experimental and comparative tests indicate the superiority of this fuzzy based scheduling model over the existing approaches.

• Structural Engineering and Mechanics
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• v.76 no.6
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• pp.781-797
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• 2020

In recent years, there is an increasing interest to optimize the fuzzy logic controller with different methods. This paper focuses on the optimization of a fuzzy logic controller applied to a seismically excited nonlinear building. In most cases, this problem is formulated based on the linear behavior of the structure, however in this paper, four sets of objective functions are considered with respect to the nonlinear responses of the structure as the peak interstory drift ratio, the peak level acceleration, the ductility factor and the maximum control force. The Improved Charged System Search is used to optimize the membership functions and the rule base of the fuzzy controller. The obtained results of the optimized and the non-optimized fuzzy controllers are compared to the uncontrolled responses of the structure. Also, the performance of the utilized method is compared with various classical and advanced optimization algorithms.