• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.552-563
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• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2254-2259
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• 2005

In this paper, we propose a wavelet based fuzzy neural network(WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges advantages of neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of mobile robot using the gradient descent(GD) method. In addition, an approach that uses adaptive learning rates for the training of WFNN controller is driven via a Lyapunov stability analysis to guarantee the fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control performance of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.377-382
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• 2021

A fuzzy relation between X and Y as fuzzy subset of X × Y was proposed by Zadeh. Subsequently, several researchers have applied the notion of fuzzy subsets to various branches of mathematics and computer sciences. Murali an Nemitz have studied fuzzy relations connected with fuzzy equivalence relations and fuzzy functions. Ounalli and Jaoua defined a fuzzy difunctional relation on a set. difunctional relations are versatile mathematical tool, which can be used in software design and in database theory. Their work have revealed the usefulness of difunctional relations in program specification and in defining program correctness. The main goal of this paper is to define a fuzzy deterministic relation on a set, characterize the fuzzy deterministic relation as its level subsets and investigate some properties in connection with fuzzy deterministic relation. In particular we prove that a fuzzy relation R is fuzzy deterministic iff R is a fuzzy function.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2633-2635
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• 2003

In this study, we propose a fuzzy polynomial neural networks (FPNN) and a genetically optimized fuzzy polynomial neural networks(GoFPNN) for identification of non-linear system. GoFPNN architecture is designed by a FPNN based on fuzzy set and its structure and parameters are optimized by genetic algorithms. A fuzzy neural networks(FNN) based on fuzzy set divide into two structures that is simplified inference structure and linear inference structure. The proposed FPNN is resulted from integration and extension of simplified and linear inference structure of FNN. The consequence structure of the FPNN consist of polynomials represented by networks using connection weights for rules. The networks comprehend simplified(Type 0), linear (Type 1), and quadratic(Type 3) inferences. The proposed FPNN can select polynomial type of consequence part for each rule. Therefore, proposed scheme can offer flexible structure design capability for a system characteristics. Moreover, GAs is applied to networks structure and parameters tuning of proposed FPNN, and its efficient application method is discussed, these subjects are result in GoFPNN that is optimal FPNN. To evaluate proposed model performance, a numerical experiment is carried out.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2681-2683
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• 2005

In this paper, we present the structure of fuzzy neural network(FNN) based on wavelet function, and apply this network structure to the identification of nonlinear systems. For adjusting the shape of membership function and the connection weights, the parameter learning method based on the gradient descent scheme is adopted. And an approach that uses adaptive learning rates is driven via a Lyapunov stability analysis to guarantee the fast convergence. Finally, to verify the efficiency of our network structure. we compare the Identification performance of proposed wavelet based fuzzy neural network(WFNN) with those of the FNN, the wavelet fuzzy model(WFM) and the wavelet neural network(WNN) through the computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1371-1376
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• 1990

The paper presents a method of constructing simple fuzzy control rules for the determination of stabilizing signals of automatic voltage regulator and governor, which are controllers of electric power systems. Fuzzy control rules are simplified by considering a coordinate transformation with the rotation angle .theta. on the phase plane, and by expanding the range of membership functions. Also, two rotation angles .theta. $_{1}$ and .theta. $_{2}$ are selected for the linearizable region and the nonlinear one of the system, respectively. Here, .theta. $_{1}$ is chosen by the pole assignment method, and .theta. $_{2}$ by a performance index. Fuzzy inference is applied to the connection of two rotation angles .theta. $_{1}$ and .theta. $_{1}$ by regarding the distance from the desired equilibrium point as a variable of condition parts. The control effect is demonstrated by an application of the proposed method to one-machine infinite-bus power system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.207-212
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• 2004

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy neural network, and back propagation algorithm to train the fuzzy neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• ETRI Journal
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• v.44 no.4
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• pp.560-572
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• 2022

H²RDC (heuristic handover based on RCC-DTSK-C), a heuristic algorithm based on a highly interpretable deep Takagi-Sugeno-Kang fuzzy classifier, is proposed for suppressing the mobile heterogeneous networks problem of frequent handover and handover ping-pong in the multibase-station scenario. This classifier uses a stack structure between subsystems to form a deep classifier before generating a base station (BS) priority sequence during the handover process, and adaptive handover hysteresis is calculated. Simulation results show that H²RDC allows user equipment to switch to the best antenna at the optimal time. In high-BS density load and mobility scenarios, the proposed algorithm's handover success rate is similar to those of classic algorithms such as best connection (BC), self tuning handover algorithm (STHA), and heuristic for handover based on AHP-TOPSIS-FUZZY (H²ATF). Moreover, the handover rate is 83% lower under H²RDC than under BC, whereas the handover ping-pong rate is 76% lower.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1901-1910
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• 2023

The safety-class (1E) digital control system (DCS) of nuclear power plant characterized structural multiple redundancies, therefore, it is important to quantitatively evaluate the reliability of DCS in different degree of backup loss. In this paper, a reliability evaluation model based on T-S fuzzy fault tree (FT) is proposed for 1E DCS of nuclear power plant, in which the connection relationship between components is described by T-S fuzzy gates. Specifically, an output rejection control system is chosen as an example, based on the T-S fuzzy FT model, the key indicators such as probabilistic importance are calculated, and for a further discussion, the T-S fuzzy FT model is transformed into Bayesian Network(BN) equivalently, and the fault diagnosis based on probabilistic analysis is accomplished. Combined with the analysis of actual objects, the effectiveness of proposed method is proved.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1160-1169
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• 1996

In this study, an experiment of information retrieval using fuzzy connection matrix of keywords was conducted. A query for retrieval was constructed from each keyword and Boolean operator such as AND, OR, NOT. In a workstation environment, the performance of the fuzzy retrieval system was proved to be considerably effective than that of the system using the crisp set theory. And both recall ratio and precision ratio showed that the proposed technique would be a possible alternative in future information retrieval. Some special features of this experimental system were ; ranking the results in the order of connectivity, making the retrieval results correspond flexibly by changing the threshold value, trying to accord the retrieval process with the retrieval semantics by treating the averse-connectivity (fuzzy value) as a semantic approximation between kewords.