• 한국지능시스템학회논문지
• /
• 제22권1호
• /
• pp.114-118
• /
• 2012

본 연구는 시스템의 구조 인식에 유효한 FSM을 이용하여 기업프로젝트 성과요인에 대한 층별 구조는 알 수 있지만 각 층에 속해 있는 성과요인에 대한 층별 간의 관계에 대해서는 알 수 없다는 관점에서 퍼지시스템을 이용하여 기업프로젝트 성과요인의 층별 관계에 대한 구조를 분석하고자 한다. 최하층과 중간층, 중간층과 최상층과의 관계를 가능적인 면과 필연적인 면이 섞여 있는 형태의 기업프로젝트 평가모델을 이용하여 구한 퍼지관계행렬로서 퍼지관계분석을 행하고 그 유용성을 보였다.

• 대한전기학회논문지:전력기술부문A
• /
• 제49권3호
• /
• pp.107-117
• /
• 2000

This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models. The primary goal of the proposed method is to improve the performance of the prediction model in terms of accuracy and reliability. For this, the proposed method explores the advantages of the structure learning of the neuro-fuzzy model. The proposed load forecasting system first builds an initial structure off-line for each hour of four day types and then stores the resultant initial structures in the initial structure bank. Whenever a prediction needs to be made, the proposed system initializes the neuro-fuzzy model with the appropriate initial structure stored and trains the initialized model. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1993 and 1994 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability compared with the prediction systems based on multilayer perceptrons, radial basis function networks, and neuro-fuzzy models without the structure learning.

• 전기학회논문지
• /
• 제58권4호
• /
• pp.832-839
• /
• 2009

In this paper, we introduce an design of fuzzy relation-based fuzzy neural networks with multi-output. Fuzzy relation-based fuzzy neural networks comprise the network structure generated by dividing the entire input space. The premise part of the fuzzy rules of the network reflects the relation of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions such as constant, linear, and modified quadratic. For the multi-output structure the neurons in the output layer were connected with connection weights. The learning of fuzzy neural networks is realized by adjusting connections of the neurons both in the consequent part of the fuzzy rules and in the output layer, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, learning rate and momentum coefficient are automatically optimized by using real-coded genetic algorithm. Two examples are included to evaluate the performance of the proposed network.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제5권4호
• /
• pp.327-332
• /
• 2005

We investigatea new fuzzy-neural networks-Hybrid Fuzzy set based polynomial Neural Networks (HFSPNN). These networks consist of genetically optimized multi-layer with two kinds of heterogeneous neurons thatare fuzzy set based polynomial neurons (FSPNs) and polynomial neurons (PNs). We have developed a comprehensive design methodology to determine the optimal structure of networks dynamically. The augmented genetically optimized HFSPNN (namely gHFSPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of gHFSPNN leads to the selection leads to the selection of preferred nodes (FSPNs or PNs) available within the HFSPNN. In the sequel, the structural optimization is realized via GAs, whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through experimentation where we use a number of modeling benchmarks synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.

• Smart Structures and Systems
• /
• 제21권4호
• /
• pp.389-395
• /
• 2018

This paper presents the application of a semi-active fuzzy based control system for seismic response reduction of a single degree-of-freedom (SDOF) framed structure using a Magnetorheological (MR) damper. Semi-active vibration control with MR dampers has been shown to be a viable approach to protect building structures from earthquake excitation. Moreover, intelligent damping systems based on soft-computing techniques such as fuzzy logic models have the inherent robustness to deal with typical uncertainties and non-linearities present in civil engineering structures. Thus, the proposed semi-active control system uses fuzzy logic based models to simulate the behavior of MR damper and also to develop the control algorithm that computes the required control signal to command the actuator. The results of the numerical simulations show the effectiveness of the suggested semi-active control system in reducing the response of the SDOF structure.

• Journal of Mechanical Science and Technology
• /
• 제16권4호
• /
• pp.454-467
• /
• 2002

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. Toidentifythelocation and depth of a crack in a structure, a method is presented in this paper which uses neuro-fuzzy-evolutionary technique, that is, Adaptive-Network-based Fuzzy Inference System (ANFIS) solved via hybrid learning algorithm (the back-propagation gradient descent and the least-squares method) and Continuous Evolutionary Algorithms (CEAs) solving sir ale objective optimization problems with a continuous function and continuous search space efficiently are unified. With this ANFIS and CEAs, it is possible to formulate the inverse problem. ANFIS is used to obtain the input(the location and depth of a crack) - output(the structural Eigenfrequencies) relation of the structural system. CEAs are used to identify the crack location and depth by minimizing the difference from the measured frequencies. We have tried this new idea on beam structures and the results are promising.

• 한국지능시스템학회논문지
• /
• 제3권3호
• /
• pp.56-67
• /
• 1993

A Class of fuzzy controller based on the variable structure system(VSS) technique in which different structures of controllers are fuzzily switched according to the switching rules in proppsed. The structure of proposed controllers was motivated by the characteristics of position type fuzzy controller and velocity type fuzzy controller ; the former generally gives good performance in transient perod and the latter are capable of reducing steady state error of response. To show the usefulness of the proposed controller, it is applied to several systems that is difficult to stabilize or difficult to get satisfactory responsed by conventional fuzzy controllers.

• Ocean Systems Engineering
• /
• 제9권2호
• /
• pp.179-190
• /
• 2019

This paper deals with the problem of the global stabilization for a class of ocean structure systems. It is well known that, in general, the global asymptotic stability of the ocean structure subsystems does not imply the global asymptotic stability of the composite closed-loop system. The classical fuzzy inference methods cannot work to their full potential in such circumstances because given knowledge does not cover the entire problem domain. However, requirements of fuzzy systems may change over time and therefore, the use of a static rule base may affect the effectiveness of fuzzy rule interpolation due to the absence of the most concurrent (dynamic) rules. Designing a dynamic rule base yet needs additional information. In this paper, we demonstrate this proposed methodology is a flexible and general approach, with no theoretical restriction over the employment of any particular interpolation in performing interpolation nor in the computational mechanisms to implement fitness evaluation and rule promotion.

• Journal of applied mathematics & informatics
• /
• 제29권3_4호
• /
• pp.1059-1066
• /
• 2011

We introduce the concept of fuzzy S-weakly r-M-continuous functions on fuzzy r-minimal spaces, and investigate some characterizations of such functions and the relations between the continuity and fuzzy r-minimal compactness on fuzzy r-minimal spaces.

• Journal of applied mathematics & informatics
• /
• 제42권4호
• /
• pp.801-818
• /
• 2024

The purpose of this paper is to introduce the concept of (∈, ∈ ∨q)-fuzzy congruence relation over ring and discuss some properties of the (∈, ∈ ∨q)-fuzzy congruence relation. We also establish a brief relation between (∈, ∈ ∨q)-fuzzy ideal and (∈, ∈ ∨q)-fuzzy congruence relation. The image and preimage of (∈, ∈ ∨q)-fuzzy congruence are also studied under the so called semibalanced map.