• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.491-499
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• 2001

In this paper, the scheme of an efficient fuzzy rule generation and fuzzy system construction using GA(genetic algorithm) and FCM(fuzzy c-means) clustering algorithm is proposed for TSK(Takagi-Sugeno-Kang) type fuzzy system. In the structure identification, input data is transformed by PCA(Principal Component Analysis) to reduce the correlation among input data components. And then, a set fuzzy rules are generated for a given criterion by FCM clustering algorithm . In the parameter identification premise parameters are optimally searched by GA. On the other hand, the consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. From this one can systematically obtain the valid number of fuzzy rules which shows satisfying performance for the given problem. Finally, we applied the proposed method to the Box-Jenkins data and rice taste data modeling problems and obtained a better performance than previous works.

• Journal of Power Electronics
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• v.13 no.4
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• pp.516-527
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• 2013

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.2
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• pp.72-81
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• 2000

In this paper, we design an adaptive neuro-fuzzy inference system(ANFIS) precompensator for load frequency control of 2-area power systems. While proportional integral derivative (PID) controllers are used in power systems, they may have some problems because of high nonlinearities of the power systems. So, a neuro-fuzzy-based precompensation scheme is incorporated with a convectional PID controller to obtain robustness to the nonlinearities. The proposed precompensation technique can be easily implemented by adding a precompensator to an existing PID controller. The applied neruo-fuzzy inference system precompensator uses a hybrid learning algorithm. This algorithm is to use both a gradient descent method to optimize the premise parameters and a least squares method to solve for the consequent parameters. Simulation results show that the proposed control technique is superior to a conventional Ziegler-Nichols PID controller in dynamic responses about load disturbances.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.4
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• pp.165-173
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• 2002

The goal of this paper is optimal switching angle of switched reluctance motor drive system fur maximum energy ratio. A new magnetizing method with a low-frequency increasing the energy conversion ratio that is related to the efficiency of motor is proposed. As the results, it improved the efficiency about 2[%]. And a torque ripple is also sufficiently reduced compared with that of the conventional approach. In order tn start softly regardless of large ripple torque, the profile of phase current is predicted by the ANFIS, and current control mode was adapted when it is operated under the starting speed. Variable implementations en the fields will guarantee the more practical drive system.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.675-678
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• 1997

In this paper, we present a fast and robust algorithm for the design of fuzzy controller and identifying fuzzy model from numerical data by combining the cluster estimation method with a linear least squares estimation procedure. The proposed method is compared with Adaptive Neuro-Fuzzy Inference System(ANFIS) as the standard example of neuro-fuzzy model. Finally we will show its usefulness and effectiveness for the design of fuzzy controller of a cart-pole system and fuzzy modeling for the coagulant dosing of a water purification system.

• Proceedings of the KIPE Conference
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• 2001.07a
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• pp.18-21
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• 2001

This paper describes the method of vibration supprssion on a control algorithm using Neuro-Fuzzy Theory in Linear Pulse Motor (LPM). The total thrust force Is distorted by magnetic and coil flux, and we classify the harmonic parts of it. A modulated current from harmonic components of static thrust characteristics of LPM compensates with reference current to total thrust force. Low vibration is obtatained by the method of current compensation using ANFIS.

• International Journal of Reliability and Applications
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• v.16 no.1
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• pp.35-53
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• 2015

This investigation deals with reliability and sensitivity analysis of a repairable embedded system with standby wherein repairman takes multiple vacations. The hardware system consists of 'M' operating and 'S' standby components. The repairman can leave for multiple vacations of random length during its idle time. Whenever any operating unit fails, it is immediately replaced by a standby unit if available. Moreover, governing equations of an embedded system are constructed using appropriate birth-death rates. The vacation and repair time of repairman are exponentially distributed. The matrix method is used to find the steady-state probabilities of the number of failed components in the embedded system as well as other performance measures. Reliability indexes are presented. Further, numerical experiments are carried out for various system characteristics to examine the effects of different parameter. Using a special class of neuro-fuzzy systems i.e. Adaptive Network-based Fuzzy Interference Systems (ANFIS), we also approximate various performance measures. Finally, the conclusions and future research directions are provided.

• Journal of Electrical Engineering and Technology
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• v.5 no.2
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• pp.209-219
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• 2010

In this paper, a novel dynamic available transfer capability (DATC) has been computed for real time applications using three different intelligent techniques viz. i) back propagation algorithm (BPA), ii) radial basis function (RBF), and iii) adaptive neuro fuzzy inference system (ANFIS) for the first time. The conventional method of DATC is tedious and time consuming. DATC is concerned with calculating the maximum increase in point to point transfer such that the transient response remains stable and viable. The ATC information is to be continuously updated in real time and made available to market participants through an internet based Open Access Same time Information System (OASIS). The independent system operator (ISO) evaluates the transaction in real time on the basis of DATC information. The dynamic contingency screening method [1] has been utilized and critical contingencies are selected for the computation of DATC using the energy function based potential energy boundary surface (PEBS) method. The PEBS based DATC has been utilized to generate patterns for the intelligent techniques. The three different intelligent methods are tested on New England 68-bus 16 machine and 39-bus 10 machine systems and results are compared with the conventional PEBS method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.201-208
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• 2005

Neuro-Fuzzy modeling approach is proposed to predict the dynamic behavior of a single-degree-of-freedom structure that is equipped with hybrid base isolation system. Hybrid base isolation system consists of friction pendulum systems (FPS) and a magnetorheological (MR) damper. Fuzzy model of the M damper is trained by ANFIS using various displacement, velocity, and voltage combinations that are obtained from a series of performance tests. Modelling of the FPS is carried out with a nonlinear analytical equation that is derived in this study and neuro-fuzzy training. Fuzzy logic controller is employed to control the command voltage that is sent to MR damper. The dynamic responses or experimental structure subjected to various earthquake excitations are compared with numerically simulated results using neuro-fuzzy modeling method. Numerical simulation using neuro-fuzzy models of the MR damper and FPS predict response of the hybrid base isolation system very well.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.31-36
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• 2001

We design the intelligent diagnosis system for deciding on operation state of TMS Analyzer in this paper. We propose the method to model the neno-fuzzy model for diagnosing the operation state of analyzer by using input and output signals of TMS to measure NOx and SOx. By using experiment data, neuro-fuzzy model is investigated. Validity of the proposed system is asserted by numerical simulation.