Relevant bibliographies by topics / Sugeno-Takage-Kang model (TSK model)

Academic literature on the topic 'Sugeno-Takage-Kang model (TSK model)'

Author: Grafiati
Published: 10 December 2022
Last updated: 19 February 2023

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Journal articles on the topic "Sugeno-Takage-Kang model (TSK model)"

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Shapoval, Nataliia. "TSK Fuzzy Neural Network Use for COVID-19 Classification." Electronics and Control Systems 1, no. 71 (June 27, 2022): 50–54. http://dx.doi.org/10.18372/1990-5548.71.16825.

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It is considered t the Takagi-Sugeno-Kang fuzzy neural network and its modern variations. The use of regularization, random exclusion of rules from the rule base allows solving the problem of excessive similarity of rules in the rule base. The use of batch normalization to increase the generalizing properties of the network allows to increase the accuracy of the model, while maintaining the possibility of interpreting the results, which is characteristic of fuzzy neural networks. It is proposed to use an ensemble of fuzzy neural networks to increase the generalizing capabilities of the network. Studies of the Takagi-Sugeno-Kang fuzzy neural network for the task of diagnosing the coronavirus disease show that the proposed model works well and allows to improve the result.
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GUO, Xifeng, Dazhi WANG, and Wei LIU. "A Takagi-Sugeno-Kang (TSK) Power Model Using Compressed-sensing Sampling." Chinese Journal of Chemical Engineering 20, no. 6 (December 2012): 1161–66. http://dx.doi.org/10.1016/s1004-9541(12)60602-8.

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NGUYEN, ERIC M., and NADIPURAM R. PRASAD. "MODEL IDENTIFICATION OF A SERVO-TRACKING SYSTEM USING FUZZY CLUSTERING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 04 (August 1999): 337–46. http://dx.doi.org/10.1142/s0218488599000295.

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This paper investigates the use of Fuzzy Clustering as a means for model identification of a complex and highly non-linear servo-tracking system when only observational data is available. The use of Fuzzy Clustering facilities automatic generation of rules and its antecedent parameters. The consequent of the model is then formulated in the form of Takagi, Sugeno and Kang (TSK), and its parameters determined by the Least Squares Method (LSM).
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Hua, Xu, Xue Hengxin, and Chen Zhiguo. "Application of hydrologic forecast model." Water Science and Technology 66, no. 2 (July 1, 2012): 239–46. http://dx.doi.org/10.2166/wst.2012.161.

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In order to overcome the shortcoming of the solution may be trapped into the local minimization in the traditional TSK (Takagi-Sugeno-Kang) fuzzy inference training, this paper attempts to consider the TSK fuzzy system modeling approach based on the visual system principle and the Weber law. This approach not only utilizes the strong capability of identifying objects of human eyes, but also considers the distribution structure of the training data set in parameter regulation. In order to overcome the shortcoming of it adopting the gradient learning algorithm with slow convergence rate, a novel visual TSK fuzzy system model based on evolutional learning is proposed by introducing the particle swarm optimization algorithm. The main advantage of this method lies in its very good optimization, very strong noise immunity and very good interpretability. The new method is applied to long-term hydrological forecasting examples. The simulation results show that the method is feasibile and effective, the new method not only inherits the advantages of traditional visual TSK fuzzy models but also has the better global convergence and accuracy than the traditional model.
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Yordanova, Snejana. "An Approach to Observability and Controllability Analysis of Nonlinear Plants on the Basis of TSK Models." Information Technologies and Control 13, no. 1-2 (June 1, 2015): 35–45. http://dx.doi.org/10.1515/itc-2016-0009.

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Abstract Most industrial plants are nonlinear, multivariable, inertial and with model uncertainty. They are difficult to model using classical approaches and thus their observability and controllability necessary for the design of the controller are hard to analyze. The aim of the present research is to derive conditions for the analysis of the observability and the controllability of nonlinear plants, represented by state space Takagi-Sugeno-Kang (TSK) models. The main results are a simple and general approach to observability and controllability study of nonlinear plants, which is based on equivalent linear systems and illustrated on a two-variable nonlinear plant - a laboratory two-tank system. The TSK plant model needed can be derived from an existing nonlinear plant model or applying a suggested procedure for development of modified transfer-functions-based TSK models from expert and experimentation data.
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Wang, Shun-Yuan, Chuan-Min Lin, and Chen-Hao Li. "Design of Adaptive TSK Fuzzy Self-Organizing Recurrent Cerebellar Model Articulation Controller for Chaotic Systems Control." Applied Sciences 11, no. 4 (February 9, 2021): 1567. http://dx.doi.org/10.3390/app11041567.

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The synchronization and control of chaos have been under extensive study by researchers in recent years. In this study, an adaptive Takagi–Sugeno–Kang (TSK) fuzzy self-organizing recurrent cerebellar model articulation controller (ATFSORC) is proposed, which is composed of a set of TSK fuzzy rules, a cerebellar model articulation controller (CMAC), a recurrent CMAC (RCMAC), a self-organizing CMAC (SOCMAC), and a compensation controller. Specifically, SOCMAC, RCMAC, and adaptive laws are adopted so that the association memory layers of ATFSORC can be modulated in accordance with the layer decision-making mechanism in order to reduce the structure complexity and improve the control performance of ATFSORC. Moreover, the Takagi–Sugeno–Kang fuzzy rules are introduced to increase the learning speed of ATFSORC, and the improved compensating controller is designed to dispel the errors between an ideal controller and the TFSORC. Moreover, the proposed ATFSORC is applied to chaotic systems in order to validate its performance and feasibility. Several simulation schemes are demonstrated to show the effectiveness of the proposed method. Simulation results show that the proposed ATFSORC can obtain a favorable control performance when the chaotic systems are operated at different parameters. Specifically, ATFSORC can achieve faster convergence of the tracking error than fuzzy CMAC (FCMAC) and CMAC.
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Yen, John, and Wayne Gillespie. "Fuzzy Modeling with Local and Global Objectives." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 5 (October 20, 1999): 373–85. http://dx.doi.org/10.20965/jaciii.1999.p0373.

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Most of the techniques for constructing fuzzy models from data focus only on minimizing the error between the model’s output and the training data; however, these approaches may result in a fuzzy model where individual rules are misleading. The goal of our research is to develop a scheme for identifying Takagi-Sugeno-Kang (TSK) models whose individual rules approximate the training data covered by a single rule, local fitness, while the entire model approximates the whole training set, global fitness. We propose an approach that is a modification of a current method for estimating the consequence portion of a TSK model with predefined membership functions. Then we propose a method for developing membership functions which partition the input space into regions that are more easily modeled in the TSK framework to provide consistent local behavior for all the rules of the model. This approach ensures that a TSK model constructed not only approximates the input-output mapping relationship in the data, but also captures insights about the local behavior of the model.
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Lin, Cheng-Jian, Chi-Yung Lee, and Cheng-Hung Chen. "A Novel Neuro-Fuzzy Inference System with Multi-Level Membership Function for Classification Applications." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 4 (April 20, 2007): 365–72. http://dx.doi.org/10.20965/jaciii.2007.p0365.

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In this paper, a novel neuro-fuzzy inference system with multi-level membership function (NFIS_MMF) for classification applications is proposed. The NFIS_MMF model is a five-layer structure, which combines the traditional Takagi-Sugeno-Kang (TSK). Layer 2 of the NFIS_MMF model contains multi-level membership functions, which are multilevel activation functions. A self-constructing learning algorithm, which consists of the self-clustering algorithm (SCA), fuzzy entropy, and the backpropagation algorithm, is also proposed to construct the NFIS_MMF model and perform parameter learning. Simulations were conducted to show the performance and applicability of the proposed model.
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Du, Aiyan, Xiaofen Shi, Xiaoyi Guo, Qixiao Pei, Yijie Ding, Wei Zhou, Qun Lu, and Hua Shi. "Assessing the Adequacy of Hemodialysis Patients via the Graph-Based Takagi-Sugeno-Kang Fuzzy System." Computational and Mathematical Methods in Medicine 2021 (July 27, 2021): 1–11. http://dx.doi.org/10.1155/2021/9036322.

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Maintenance hemodialysis is the main method for the treatment of end-stage renal disease in China. The K t / V value is the gold standard of hemodialysis adequacy. However, K t / V requires repeated blood drawing and evaluation; it is hard to monitor dialysis adequacy frequently. In order to meet the need for repeated clinical assessments of dialysis adequacy, we want to find a noninvasive way to assess dialysis adequacy. Therefore, we collect some clinically relevant data and develop a machine learning- (ML-) based model to predict dialysis adequacy for clinical hemodialysis patients. We collect 250 patients, including gender, age, ultrafiltration (UF), predialysis body weight (preBW), postdialysis body weights (postBW), blood pressure (BP), heart rate (HR), and blood flow (BF). An efficient graph-based Takagi-Sugeno-Kang Fuzzy System (G-TSK-FS) model is proposed to predict the dialysis adequacy of hemodialysis patients. The root mean square error (RMSE) of our model is 0.1578. The proposed model can be used as a feasible method to predict dialysis adequacy, providing a new way for clinical practice. Our G-TSK-FS model could be used as a feasible method to predict dialysis adequacy, providing a new way for clinical practice.
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Gu, Xiaoqing, Kaijian Xia, Yizhang Jiang, and Alireza Jolfaei. "Multi-task Fuzzy Clustering–Based Multi-task TSK Fuzzy System for Text Sentiment Classification." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 2 (March 31, 2022): 1–24. http://dx.doi.org/10.1145/3476103.

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Text sentiment classification is an important technology for natural language processing. A fuzzy system is a strong tool for processing imprecise or ambiguous data, and it can be used for text sentiment analysis. This article proposes a new formulation of a multi-task Takagi-Sugeno-Kang fuzzy system (TSK FS) modeling, which can be used for text sentiment image classification. Using a novel multi-task fuzzy c-means clustering algorithm, the common (public) information among all tasks and the individual (private) information for each task are extracted. The information about clustering, for example, cluster centers, can be used to learn the antecedent parameters of multi-task TSK fuzzy systems. With the common and individual antecedent parameters obtained, a corresponding multi-task learning mechanism for learning consequent parameters is devised. Accordingly, a multi-task fuzzy clustering–based multi-task TSK fuzzy system (MTFCM-MT-TSK-FS) is proposed. When the proposed model is built, the information conveyed by the fuzzy rules formed is two-fold, including (1) common fuzzy rules representing the inter-task correlation information and (2) individual fuzzy rules depicting the independent information of each task. The experimental results on several text sentiment datasets demonstrate the validity of the proposed model.
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Dissertations / Theses on the topic "Sugeno-Takage-Kang model (TSK model)"

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(20390), Baolin Wu. "Fuzzy modelling and identification with genetic algorithms based learning." Thesis, 1996. https://figshare.com/articles/thesis/Fuzzy_modelling_and_identification_with_genetic_algorithms_based_learning/21345057.

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Modelling is an essential step towards a solution to complex system problems. Traditional mathematical methods are inadequate in describing the complex systems when the complexity increases. Fuzzy logic has provided an alternative way in dealing with complexity in real world.

This thesis looks at a practical approach for complex system modelling using fuzzy logic. This approach is usually called fuzzy modelling. The main aim of this thesis is to explore the capabilities of fuzzy logic in complex system modelling using available data. The fuzzy model concerned is the Sugeno-Takage-Kang model (TSK model). A genetic algorithm based learning algorithm (GABL) is proposed for fuzzy modelling. It basically contains four blocks, namely the partition, GA, tuning and termination blocks. The functioning of each block is described and the proposed algorithm is tested using a number of examples from different applications such as function approximation and processing control.

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Conference papers on the topic "Sugeno-Takage-Kang model (TSK model)"

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Hong, Sung-Kyung, and Reza Langari. "Robust Fuzzy Control of a Magnetic Bearing System Subject to Harmonic Disturbances." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-0560.

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Abstract This paper proposes a robust fuzzy logic based control scheme for a rotating active magnetic bearing (AMB) system. We represent the nonlinear magnetic bearing by means of a Takagi-Sugeno-Kang (TSK) fuzzy model. Subsequently a systematic synthesis procedure is used to derive a nonlinear fuzzy logic control strategy that incorporates an integral control for improved tracking. Experimentation and simulation results demonstrate that the proposed fuzzy controller yields robustness against harmonic disturbances and parameter uncertainty.
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