• Proceedings of the KIEE Conference
• /
• 1999.11c
• /
• pp.824-826
• /
• 1999

In this paper, we design an Fuzzy-Neural Networks(FNN) by means of divisions of fuzzy input space with multi-input variables. Fuzzy input space of Yamakawa's FNN is divided by each separated input variable, but that of the proposed FNN is divided by mutually combined input variables. The membership functions of the proposed FNN use both triangular and gaussian membership types. The parameters such as apexes of membership functions, learning rates, momentum coefficients, weighting value, and slope are adjusted using genetic algorithms. Also, an aggregate objective function(performance index) with weighting value is utilized to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the data of sewage treatment process.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.421-426
• /
• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1716-1722
• /
• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes bacteria foraging algorithm based optimal learning fuzzy-neural network (BA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by bacteria foraging algorithm. The learning algorithm of the BA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, bacteria foraging algorithm is used for tuning of membership functions of the proposed model.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.7 no.2
• /
• pp.3-15
• /
• 1997

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.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.12 no.1
• /
• pp.201-208
• /
• 2017

This paper propose triangular fuzzy membership function to make model that based on awareness of human in the love model that with external force, which have the basic love model of Romeo and Juliet. This paper represents the phenomena of behaviors by time series and phase portraits after using this fuzzy triangular membership function as an external force and also confirms existence of nonlinear characteristics.

• Asia pacific journal of information systems
• /
• v.12 no.3
• /
• pp.21-43
• /
• 2002

This paper examines internet shopping mall members' relationship quality and its antecedents variables. For this purpose, five types of membership customers' perceived variables, including convenience, product assortment, product information, shopping mall design, and service quality are proposed to affect customer satisfaction and consequently relationship quality. This study, which used data from customers of membership internet shopping malls, showed satisfactory data-fit to the proposed model and except product information hypothesis, supported all of research hypotheses. Also four types of membership customers' perceived variables(convenience, product assortment, shopping mall design, and service quality) take significant effect on customer satisfaction, and the satisfaction in turn have influence on relationship quality.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.25 no.4
• /
• pp.748-755
• /
• 2001

This paper concerns rotor vibration reduction using magnetic damper system. The fuzzy control logic is utilized to fulfill desired motion. The fuzzy system structure and membership function were first determined by simulation results. The researched control logic contains two fuzzy controller : reference position variation according to the rotor whirling status and error compensation algorithm to minimize the rotor vibration due to unbalance and unstable fluid film force. The Sugeno type output membership function was utilized by several trials and optimized membership function constants were selected from experiments. The experimental results show that the proposed method effectively control and reduce the rotor vibration with fluid film bearings.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10a
• /
• pp.874-879
• /
• 1992

It is shown that there exists a nonlinear mappping which transforms features and their changes to the desired camera motion without measurement of the relative distance between the camera and the part, and the nonlinear mapping can eliminate several difficulties encountered when using the inverse of the feature Jacobian as in the usual feature-based visual feedback controls. And instead of analytically deriving the closed form of such a nonlinear mapping, a fuzzy membership function (FMF) based neural network is then proposed to approximate the nonlinear mapping, where the structure of proposed networks is similar to that of radial basis function neural network which is known to be very useful in function approximations. The proposed FMF network is trained to be capable of tracking moving parts in the whole work space along the line of sight. For the effective implementation of proposed IMF networks, an image feature selection processing is investigated, and required fuzzy membership functions are designed. Finally, several numerical examples are illustrated to show the validities of our proposed visual servoing method.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.9 no.3
• /
• pp.97-103
• /
• 1995

In this paper, for fuzzy control of linear servo system using the moving coil type linear DC motor, we propose a new fuzzy control method using parameter tuning for membership functions. A proposed fuzzy control method tunes parameters of membership function to have an appropriate control input signal for system when error exceeds predefined value and makes an inference using conventional fuzzy control rules when error reduces to a predefined value. To verify usefulness of a proposed fuzzy control method, making simulation and experiment, we compare with characteristics for conventional fuzzy control method.

• Journal of Information Processing Systems
• /
• v.14 no.5
• /
• pp.1215-1224
• /
• 2018

Interval-valued neutrosophic hesitant fuzzy set (IVNHFS) is an extension of neutrosophic set (NS) and hesitant fuzzy set (HFS), each element of which has truth membership hesitant function, indeterminacy membership hesitant function and falsity membership hesitant function and the values of these functions lie in several possible closed intervals in the real unit interval [0,1]. In contrast with NS and HFS, IVNHFS can be more flexibly used to deal with uncertain, incomplete, indeterminate, inconsistent and hesitant information. In this study, I propose the novel correlation coefficient of IVNHFSs and my paper discusses its properties. Then, based on the novel correlation coefficient, I develop an approach to deal with multi-attribute decision-making problems within the framework of IVNHFS. In the end, a practical example is used to show that the approach is reasonable and effective in dealing with decision-making problems.