• Proceedings of the KIEE Conference
• /
• 2002.07d
• /
• pp.2382-2384
• /
• 2002

The accuracy in maneuvering target tracking using multiple models is caused by the suitability of each target motion model to be used. The interacting multiple model (IMM) algorithm and the adaptive IMM (AIMM) algorithm require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers in order to construct multiple models. In this paper, to solve these problems intelligently, a genetic algorithm (GA) based-IMM method using fuzzy logic is proposed. In the proposed method, the acceleration input is regarded as an additive noise and a sub-model is represented as a set of fuzzy rules to model the time-varying variances of the process noises of a new piecewise constant white acceleration model. The proposed method is compared with the AIMM algorithm in simulations.

• Journal of Electrical Engineering and Technology
• /
• v.7 no.4
• /
• pp.501-512
• /
• 2012

Social welfare maximization in a double-sided auction market is performed by implementing an aggregation-based particle swarm optimization (CAPSO) algorithm for optimal placement and sizing of one Static Synchronous Series Compensator (SSSC) device. Dallied simulation results (without/with line flow constraints and without/with SSSC) are generated to demonstrate the impact of SSSC on the congestion levels of the modified IEEE 14-bus test system. The proposed CAPSO algorithm employs conventional quadratic smooth and augmented quadratic nonsmooth generator cost curves with sine components to improve the accurate of the model by incorporating the valve loading effects. CAPSO also employs quadratic smooth consumer benefit functions. The proposed approach relies on particle swarm optimization to capture the near-optimal GenCos and DisCos, as well as the location and rating of SSSC while the Newton based load flow solution minimizes the mismatch equations. Simulation results of the proposed CAPSO algorithm are compared to solutions obtained by sequential quadratic programming (SQP) and a recently implemented Fuzzy based genetic algorithm (Fuzzy-GA). The main contributions are inclusion of customer benefit in the congestion management objective function, consideration of nonsmooth generator characteristics and the utilization of a coordinated aggregation-based PSO for locating/sizing of SSSC.

• Journal of Internet Computing and Services
• /
• v.2 no.2
• /
• pp.105-116
• /
• 2001

According to increase of the factory-automation's(FA) in the field of production, the autonomous guided vehicle's(AGV) role is also increased, The study about an active and effective controller which can flexibly prepare for the changeable circumstance is in progressed. For this study. the research about ac1ion base system to evolve by itself is also being actively considered In this paper. we composed an ac1ive and effective AGV fuzzy controller to be able to do self-organization, For composing it. we tuned suboptimally membership function using genetic algorithm(GA) and improved the control efficiency by the self-correction and generating the control rules. self-organizing controlled(SOC) fuzzy controller proposed in this paper is capable of Self-organizing by using the characteristics of fuzzy controller and genetic algorithm. It intuitionally controls AGV and easily adapts to the circumstance.

• Journal of Korea Multimedia Society
• /
• v.11 no.12
• /
• pp.1625-1634
• /
• 2008

In this study, a modified Fuzzy C-Means algorithm with Gaussian weights (MFCM_GW) is presented for the problem of nonlinear blind channel equalization. The proposed algorithm searches for the optimal channel output states of a nonlinear channel based on received symbols. In contrast to conventional Euclidean distance in Fuzzy C-Means (FCM), the use of the Bayesian likelihood fitness function and the Gaussian weighted partition matrix is exploited in this method. In the search procedure, all possible sets of desired channel states are constructed by considering the combinations of estimated channel output states. The set of desired states characterized by the maxima] value of the Bayesian fitness is selected and updated by using the Gaussian weights. After this procedure, the Bayesian equalizer with the final desired states is implemented to reconstruct transmitted symbols. The performance of the proposed method is compared with those of a simplex genetic algorithm (GA), a hybrid genetic algorithm (GA merged with simulated annealing (SA):GASA), and a previously developed version of MFCM. In particular, a relative]y high accuracy and a fast search speed have been observed.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.11 no.5
• /
• pp.1019-1028
• /
• 2007

In this paper, adaptive evolutionary algorithm(AEA) is proposed, which uses both genetic algorithm(GA) with good global search capability and evolution strategy(ES) with good local search capability in an adaptive manner, when population evolves to the next generation. In the reproduction procedure, proportion of the population for GA and ES is adaptively determined according to their fitness. The AEA is used to design membership functions and scaling factors of the fuzzy logic controller(FLC). To evaluate the performance of the proposed FLC design method, we make an experiment on the FLC for the speed control of an actual DC series motor system with nonlinear characteristics. Experimental results show that the proposed controller has better performance than PD controller.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.2 no.2
• /
• pp.159-165
• /
• 2002

This paper proposes a GA and Gradient Descent Method-based method for choosing an appropriate set of fuzzy rules for classification problems. The aim of the proposed method is to fond a minimum set of fuzzy rules that can correctly classify all training patterns. The number of inference rules and the shapes of the membership functions in the antecedent part of the fuzzy rules are determined by the genetic algorithms. The real numbers in the consequent parts of the fuzzy rules are obtained through the use of the descent method. A fitness function is used to maximize the number of correctly classified patterns, and to minimize the number of fuzzy rules. A solution obtained by the genetic algorithm is a set of fuzzy rules, and its fitness is determined by the two objectives, in a combinatorial optimization problem. In order to demonstrate the effectiveness of the proposed method, computer simulation results are shown.

• Smart Structures and Systems
• /
• v.12 no.6
• /
• pp.595-617
• /
• 2013

Semi-active control equipments are used to effectually enhance the seismic behavior of structures. Magneto-rheological (MR) dampers are semi-active devices that can be utilized to control the response of structures during seismic loads and have received voracious attention for response suppression. They supply the adaptability of active devices and stability and reliability of passive devices. This paper presents an optimal fuzzy logic control scheme for vibration mitigation of buildings using magneto-rheological dampers subjected to near-fault ground motions. Near-fault features including a directivity pulse in the fault-normal direction and a fling step in the fault-parallel direction are considered in the requisite ground motion records. The membership functions and fuzzy rules of fuzzy controller were optimized by genetic algorithm (GA). Numerical study is performed to analyze the influences of near-fault ground motions on a building that is equipped with MR dampers. Considering the uncontrolled system response as the base line, the proposed method is scrutinized by analogy with that of a conventional maximum dissipation energy (MED) controller to accentuate the effectiveness of the fuzzy logic algorithm. Results reveal that the fuzzy logic controllers can efficiently improve the structural responses and MR dampers are quite promising for reducing seismic responses during near-fault earthquakes.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.49 no.11
• /
• pp.599-609
• /
• 2000

The new design methodology of a hybrid fuzzy controller by means of the genetic algorithms is presented. First, a hybrid fuzzy controller(HFC) related to the optimal estimation of control parameters is proposed. The control input for the system in the HFC combined PID controller with fuzzy controller is a convex combination of the FLC's output and PID's output by a fuzzy variable, namely, membership function of weighting coefficient. Second, an auto-tuning algorithms utilizing the simplified reasoning method and genetic algorithms is presented to automatically improve the performance of hybrid fuzzy controller. Especially, in order to auto-tune scaling factors and PID parameters of HFC using GA, three kinds of estimation modes such as basic, contraction, and expansion mode are effectively utilized. The proposed HFC is evaluated and discussed to show applicability and superiority with the and of three representative processes.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.1374-1376
• /
• 1996

In this paper We proposed a new method to generate fuzzy logic controllers through genetic algorithm(GA). In designing of fuzzy logic controllers encounters difficulties in the selection of optimized member-ship functions, gains and rule base, which is conventionally achieved by a tedious trial-and-error process. This paper develops genetic algorithms for automatic design of high performance fuzzy logic controllers which can overcome nonlinearities in many engineering control applications. The rule-base is coded in base-7 strings by generated from random function. Which can be presented in discrete fuzzy linguistic value, and using membership function with Gaussian curve. To verify the validity of this fuzzy logic controller it is compared with conventional fuzzy logic controller(FLC) and PID controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.237-241
• /
• 2001

본 논문에서는 TSK (Takagi-Sugeno-Kang) 형태의 퍼지모델을 유도하는데 있어서, 동적시스템의 비선형 미분방정식을 선형화시 off-equilibrium에서 발생할 수 있는 상수항을 배제하고, TSK 퍼지 모델의 전건부 소속함수들을 GA(Genetic Algorithm)을 이용하여 최적화한후 이를 퍼지를 이용하여 합성함으로써, 실제 동적시스템을 묘사하는 비선형 미분방정식에 최적 근사화된 TSK 퍼지 모델링기법을 제시한다.