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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Natsheh, Essam. "Dissimilarity Clustering Algorithm for Designing the PID-like Fuzzy Controllers." Journal of information and organizational sciences 45, no. 1 (June 15, 2021): 267–86. http://dx.doi.org/10.31341/jios.45.1.12.
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Fuzzy logic controller is one of the most prominent research fields to improve efficiency for process industries, which usually stick to the conventional proportional-integral-derivative (PID) control. The paper proposes an improved version of the three-term PID-like fuzzy logic controller by removing the necessity of having user-defined parameters in place for the algorithm to work. The resulting non-parametric three-term dissimilarity-based clustering fuzzy logic controller algorithm was shown to be very efficient and fast. The performance study was conducted by simulation on armature-controlled and field-controller DC motors, for linguistic type and Takagi-Sugeno-Kang (TSK) models. Comparison of the created algorithm with fuzzy c-means algorithm resulted in improved accuracy, increased speed and enhanced robustness, with an especially high increase for the TSK type model.
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Douiri, Moulay Rachid, Ouissam Belghazi, and Mohamed Cherkaoui. "Recurrent Self-Tuning Neuro-Fuzzy for Speed Induction Motor Drive." Journal of Circuits, Systems and Computers 24, no. 09 (August 27, 2015): 1550131. http://dx.doi.org/10.1142/s0218126615501315.
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This paper proposes a hybrid recurrent neuro-fuzzy (RNF) architecture for rotor speed regulation of indirect field oriented controlled (IFOC) induction motor (IM) drive. This approach incorporates Takagi–Sugeno–Kang (TSK) model-based fuzzy logic (FL) laws with a four-layer artificial neural networks (ANNs) scheme. Moreover, for the proposed RNF an improved self-tuning method is developed based on the IM theory and its high performance requirements. The principal task of the tuning method is to adjust the parameters of the FL in order to minimize the square of the error between actual and reference output. The convergence/divergence of the weights is discussed and investigated by simulation.
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Li, Kang, and Yizhang Jiang. "Prediction Method of Biological Fermentation Data Based on Deep Neural Network." Journal of Physics: Conference Series 2278, no. 1 (May 1, 2022): 012029. http://dx.doi.org/10.1088/1742-6596/2278/1/012029.
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Abstract This paper proposes a (Takagi-Sugeno-Kang) TSK fuzzy regression model that based on self-supervised learning and deep autoencoder to predict and monitor the real-time concentration of each ingredient in the fermentation process. The entire model consists of the following steps: obtaining and preprocessing sample spectral data to obtain a training set; using the training set to train a self-supervised feature extraction network model to optimize the parameters of the feature extraction network model; training the autoencoder network model to establish a dimensionality reduction model by using the feature-extracted data; performing TSK fuzzy regression on the data selected by the dimensionality reduction model to establish a concentration prediction model; inputting the spectral data of the solution to be tested to predict the concentration of the solution. Combined with the deep autoencoder feature extraction method of self-supervised learning, our model can not only construct a more complex nonlinear map than the traditional principal component analysis (PCA), but also ensure that the extracted features have semantic information that is beneficial to the subsequent regression prediction method. Combined with TSK regression prediction, our model can avoid the problem of excessive spectral data dimension and redundant information, and can give accurate and interpretable results.
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Sari, Nadia Roosmalita, Wayan Firdaus Mahmudy, and Aji Prasetya Wibawa. "Mengukur Performa Model TSK Fuzzy Logic Menggunakan Faktor Eksternal untuk Peramalan Laju Inflasi." MATICS 9, no. 1 (March 21, 2017): 27. http://dx.doi.org/10.18860/mat.v9i1.3932.
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Pertumbuhan ekonomi merupakan salah satu tolak ukur menilai perkembangan ekonomi negara. Inflasi merupakan kecenderungan naiknya harga barang secara umum dan terjadi terus-menerus. Sehingga inflasi dapat dijadikan sebagai tolak ukur untuk menilai perkembangan suatu negara. Inflasi merupakan salah satu permasalahan yang sering menjadi topik pembahasan di kalangan pakar ekonomi. Inflasi dapat dipengaruhi oleh berbagai faktor, misalnya pola konsumtif masyarakat yang tinggi. Perekonomian Indonesia akan menurun jika inflasi tidak dikendalikan dengan baik. Untuk mengendalikan laju inflasi dibutuhkan sebuah peramalan terhadap laju inflasi di Indonesia. Hasil peramalan digunakan sebagai informasi bagi pemerintah untuk menyiapkan kebijakan agar laju inflasi tetap dalam keadaan stabil. Penelitian ini mengusulkan Takaghi Sugeno Kang (TSK) fuzzy logic untuk peramalan laju inflasi. Penelitian ini bertujuan untuk mengukur performa sistem dengan menggunakan faktor-faktor yang mempengaruhi laju inflasi. Data yang digunakan pada penelitian ini adalah data historis dan faktor eksternal sebagai parameter. Untuk mengevaluasi hasil peramalan digunakan teknik analisis <em>Root Mean Square Error</em> (RMSE). Hasil penelitian menunjukkan bahwa penggunaan parameter time series dan faktor eksternal CPI memiliki performa sistem yang lebih baik dibandingkan faktor-faktor lain dengan RMSE sebesar 1.328.
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Ramos, Geraldo A. R., Bruno Elias, and Kyari Yates. "Screening Reservoir Candidates for Enhanced Oil Recovery (EOR) in Angolan Offshore Projects." Angolan Mineral, Oil & Gas Journal 1, no. 1 (May 6, 2020): 6–10. http://dx.doi.org/10.47444/amogj.v1i1.3.
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The neuro-fuzzy (NF) approach presented in this work is based on five (5) layered feedforward backpropagation algorithm applied for technical screening of enhanced oil recovery (EOR) methods. Associated reservoir rock-fluid oilfield data from successful EOR projects were used as input and predicted output in the training and validation processes, respectively. The developed model was then tested by using data set from Block B of an Angolan oilfield. The results of the sensitivity analysis between the Mamdani and the Takagi-Sugeno-Kang (TSK) approach incorporated in the algorithm has shown the robustness of the TSK ANFIS (Adaptive Neuro-Fuzzy Inference System) approach in comparison to the other approach for the prediction of a suitable EOR technique. The simulation test results showed that the model presented in this study can be used for technical selection of suitable EOR techniques. Within the area investigated (Block B, Angola) polymer, hydrocarbon gas, and combustion were identified as the suitable techniques for EOR.
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Mahmoud, Ahmed Abdulhamid, Salaheldin Elkatatny, Abdulwahab Z. Ali, Mohamed Abouelresh, and Abdulazeez Abdulraheem. "Evaluation of the Total Organic Carbon (TOC) Using Different Artificial Intelligence Techniques." Sustainability 11, no. 20 (October 13, 2019): 5643. http://dx.doi.org/10.3390/su11205643.
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Total organic carbon (TOC) is an essential parameter used in unconventional shale resources evaluation. Current methods that are used for TOC estimation are based, either on conducting time-consuming laboratory experiments, or on using empirical correlations developed for specific formations. In this study, four artificial intelligence (AI) models were developed to estimate the TOC using conventional well logs of deep resistivity, gamma-ray, sonic transit time, and bulk density. These models were developed based on the Takagi-Sugeno-Kang fuzzy interference system (TSK-FIS), Mamdani fuzzy interference system (M-FIS), functional neural network (FNN), and support vector machine (SVM). Over 800 data points of the conventional well logs and core data collected from Barnett shale were used to train and test the AI models. The optimized AI models were validated using unseen data from Devonian shale. The developed AI models showed accurate predictability of TOC in both Barnett and Devonian shale. FNN model overperformed others in estimating TOC for the validation data with average absolute percentage error (AAPE) and correlation coefficient (R) of 12.02%, and 0.879, respectively, followed by M-FIS and SVM, while TSK-FIS model showed the lowest predictability of TOC, with AAPE of 15.62% and R of 0.832. All AI models overperformed Wang models, which have recently developed to evaluate the TOC for Devonian formation.
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Rajab, Sharifa. "Rule Base Simplification and Constrained Learning for Interpretability in TSK Neuro-Fuzzy Modelling." International Journal of Fuzzy System Applications 9, no. 2 (April 2020): 31–58. http://dx.doi.org/10.4018/ijfsa.2020040102.
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Neuro-fuzzy systems based on a fuzzy model proposed by Takagi, Sugeno and Kang known as the TSK fuzzy model provide a powerful method for modelling uncertain and highly complex non-linear systems. The initial fuzzy rule base in TSK neuro-fuzzy systems is usually obtained using data driven approaches. This process induces redundancy into the system by adding redundant fuzzy rules and fuzzy sets. This increases complexity which adversely affects generalization capability and transparency of the fuzzy model being designed. In this article, the authors explore the potential of TSK fuzzy modelling in developing comparatively interpretable neuro-fuzzy systems with better generalization capability in terms of higher approximation accuracy. The approach is based on three phases, the first phase deals with automatic data driven rule base induction followed by rule base simplification phase. Rule base simplification uses similarity analysis to remove similar fuzzy sets and resulting redundant fuzzy rules from the rule base, thereby simplifying the neuro-fuzzy model. During the third phase, the parameters of membership functions are fine-tuned using a constrained hybrid learning technique. The learning process is constrained which prevents unchecked updates to the parameters so that a highly complex rule base does not emerge at the end of model optimization phase. An empirical investigation of this methodology is done by application of this approach to two well-known non-linear benchmark forecasting problems and a real-world stock price forecasting problem. The results indicate that rule base simplification using a similarity analysis effectively removes redundancy from the system which improves interpretability. The removal of redundancy also increased the generalization capability of the system measured in terms of increased forecasting accuracy. For all the three forecasting problems the proposed neuro-fuzzy system demonstrated better accuracy-interpretability tradeoff as compared to two well-known TSK neuro-fuzzy models for function approximation.
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Sulthana, Ayesha, K. C. Latha, Mohammad Imran, Ramya Rathan, R. Sridhar, and S. Balasubramanian. "Non-linear modeling using fuzzy principal component regression for Vidyaranyapuram sewage treatment plant, Mysore – India." Water Science and Technology 70, no. 6 (July 29, 2014): 1040–47. http://dx.doi.org/10.2166/wst.2014.333.
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Fuzzy principal component regression (FPCR) is proposed to model the non-linear process of sewage treatment plant (STP) data matrix. The dimension reduction of voluminous data was done by principal component analysis (PCA). The PCA score values were partitioned by fuzzy-c-means (FCM) clustering, and a Takagi–Sugeno–Kang (TSK) fuzzy model was built based on the FCM functions. The FPCR approach was used to predict the reduction in chemical oxygen demand (COD) and biological oxygen demand (BOD) of treated wastewater of Vidyaranyapuram STP with respect to the relations modeled between fuzzy partitioned PCA scores and target output. The designed FPCR model showed the ability to capture the behavior of non-linear processes of STP. The predicted values of reduction in COD and BOD were analyzed by performing the linear regression analysis. The predicted values for COD and BOD reduction showed positive correlation with the observed data.
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Nanda, Santosh Kumar, D. P. Tripathy, and Sarat Kumar Patra. "A Sugeno Fuzzy Model for Noise Induced Hearing Loss in the Mining Industry." Noise & Vibration Worldwide 39, no. 10 (November 2008): 25–36. http://dx.doi.org/10.1260/095745608786927368.
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This paper describes a fuzzy system approach to modeling of noise-induced hearing loss, one of the most dangerous effects of noise in the mining industry. Hearing loss has been considered as a function of noise level, frequency, and exposure time. The model is simulated using MATLAB for Takagi-Sugeno-Kang (TSK) inference mechanism. The model results are compared with the survey findings of U.S. Environmental Protection Agency (USEPA) and National Institute for Occupational Safety and Health (NIOSH), Pittsburgh and were found to be in good agreement. The model clearly brings out the salient features of the surveys concerning the variation of hearing loss with frequency for various duration of exposure times, viz., the hearing loss is not appreciable below 2kHz. The model results closely match with the NIOSH results in 2–6 kHz at 90 dB (A) and with the EPA results in 2–8 kHz at 85 dBA. It was observed that for 0–6 years of exposure, the hearing loss as per NIOSH was between 0 – 20 dB, whereas it was between 0–25 dB (not significant) as per American Academy of Ophthalmology and Otolaryngology (AAOO). The model clearly shows that the duration of exposure can be used to infer the hearing loss for mining and industrial workers of different age groups.
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Karami-Mollaee, Ali, and Oscar Barambones. "Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization." Axioms 12, no. 1 (December 26, 2022): 25. http://dx.doi.org/10.3390/axioms12010025.
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To achieve the maximum power from wind in variable-speed regions of wind turbines (WTs), a suitable control signal should be applied to the pitch angle of the blades. However, the available uncertainty in the modeling of WTs complicates calculations of these signals. To cope with this problem, an optimal controller is suitable, such as particle swarm optimization (PSO). To improve the performance of the controller, fractional order PSO (FPSO) is proposed and implemented. In order to construct this approach for a two-mass WT, we propose a new state feedback, which was first applied to the turbine. The idea behind this state feedback was based on the Taylor series. Then, a linear model with uncertainty was obtained with a new input control signal. Thereafter, the conventional PSO (CPSO) and FPSO were used as optimal controllers for the resulting linear model. Finally, a comparison was performed between CPSO and FPSO and the fuzzy Takagi–Sugeno–Kang (TSK) inference system. The provided comparison demonstrates the advantages of the Taylor series with combination to these controllers. Notably, without the state feedback, CPSO, FPSO, and TSK fuzzy systems cannot stabilize WTs in tracking the desired trajectory.
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Jiang, Yizhang, Xiaoqing Gu, Lei Hua, Kang Li, Yuwen Tao, and Bo Li. "Forecasting Trend of Coronavirus Disease 2019 using Multi-Task Weighted TSK Fuzzy System." ACM Transactions on Internet Technology 22, no. 3 (August 31, 2022): 1–24. http://dx.doi.org/10.1145/3475870.
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Artificial intelligence– (AI) based fog/edge computing has become a promising paradigm for infectious disease. Various AI algorithms are embedded in cooperative fog/edge devices to construct medical Internet of Things environments, infectious disease forecast systems, smart health, and so on. However, these systems are usually done in isolation, which is called single-task learning. They do not consider the correlation and relationship between multiple/different tasks, so some common information in the model parameters or data characteristics is lost. In this study, each data center in fog/edge computing is considered as a task in the multi-task learning framework. In such a learning framework, a multi-task weighted Takagi-Sugeno-Kang (TSK) fuzzy system, called MW-TSKFS, is developed to forecast the trend of Coronavirus disease 2019 (COVID-19). MW-TSKFS provides a multi-task learning strategy for both antecedent and consequent parameters of fuzzy rules. First, a multi-task weighted fuzzy c-means clustering algorithm is developed for antecedent parameter learning, which extracts the public information among all tasks and the private information of each task. By sharing the public cluster centroid and public membership matrix, the differences of commonality and individuality can be further exploited. For consequent parameter learning of MW-TSKFS, a multi-task collaborative learning mechanism is developed based on ε-insensitive criterion and L2 norm penalty term, which can enhance the generalization and forecasting ability of the proposed fuzzy system. The experimental results on the real COVID-19 time series show that the forecasting tend model based on multi-task the weighted TSK fuzzy system has a high application value.
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Prasad, Mukesh, Yu-Ting Liu, Dong-Lin Li, Chin-Teng Lin, Rajiv Ratn Shah, and Om Prakash Kaiwartya. "A New Mechanism for Data Visualization with Tsk-Type Preprocessed Collaborative Fuzzy Rule Based System." Journal of Artificial Intelligence and Soft Computing Research 7, no. 1 (January 1, 2017): 33–46. http://dx.doi.org/10.1515/jaiscr-2017-0003.
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Abstract A novel data knowledge representation with the combination of structure learning ability of preprocessed collaborative fuzzy clustering and fuzzy expert knowledge of Takagi- Sugeno-Kang type model is presented in this paper. The proposed method divides a huge dataset into two or more subsets of dataset. The subsets of dataset interact with each other through a collaborative mechanism in order to find some similar properties within each-other. The proposed method is useful in dealing with big data issues since it divides a huge dataset into subsets of dataset and finds common features among the subsets. The salient feature of the proposed method is that it uses a small subset of dataset and some common features instead of using the entire dataset and all the features. Before interactions among subsets of the dataset, the proposed method applies a mapping technique for granules of data and centroid of clusters. The proposed method uses information of only half or less/more than the half of the data patterns for the training process, and it provides an accurate and robust model, whereas the other existing methods use the entire information of the data patterns. Simulation results show the proposed method performs better than existing methods on some benchmark problems.
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Ferreira, João P., Manuel Crisóstomo, and A. Paulo Coimbra. "Sagittal stability PD controllers for a biped robot using a neurofuzzy network and an SVR." Robotica 29, no. 5 (October 12, 2010): 717–31. http://dx.doi.org/10.1017/s0263574710000627.
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SUMMARYThe real-time balance PD control of an eight-link biped robot using a zero-moment point (ZMP) dynamic model is implemented using two alternative intelligent computing control techniques that were compared: one based on support vector regression (SVR) and another based on a first order Takagi–Sugeno–Kang (TSK) -type neural-fuzzy (NF). Both methods use the ZMP error, and its variation as inputs and the output is the correction of the robot's torso necessary for its sagittal balance. The SVR and the NF were trained based on simulation data, and their performance was verified with a real biped robot. Two performance indexes are proposed to evaluate and compare the online performance of the two control methods.The ZMP is calculated by reading four force sensors placed under each robot's foot. The gait implemented in this biped is based on ankle and hip human trajectories that were acquired and adapted to the robot's size. Some experiments are presented and the results show that the implemented gait combined either with the SVR controller or with the TSK NF network controller can be used to control this biped robot. The SVR and the NF controllers exhibit similar stability, but the SVR controller runs at 0.2 ms, about 50 times faster than the NF controller and much faster than a controller based on full ZMP dynamic model equations.
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Machrus Ali, Ruslan Hidayat, and Iwan Cahyono. "Penggunaan ANFIS pada Pengaturan Debit Air Berdasarkan Volume Air Dalam Tangki." ALINIER: Journal of Artificial Intelligence & Applications 1, no. 1 (March 12, 2020): 24–32. http://dx.doi.org/10.36040/alinier.v1i1.2519.
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Adaptive Neuro-Fuzzy Inference System (ANFIS) adalah penggabungan mekanisme Fuzzy Inference System (FIS) dan Neural Network (NN) yang digambarkan dalam arsitektur jaringan syaraf. Sistem inference fuzzy yang digunakan adalah sistem inference fuzzy model Tagaki-Sugeno-Kang (TSK) orde satu dengan pertimbangan kesederhanaan dan kemudahan komputasi. Pada penelitian ini sebagai pembanding didesain tanpa control, desain dengan PID standart, desain dengan Fuzzy Login Controller (FLC), dan ANFIS controller. Dalam desain penelitian ini yang dikontrol adalah ball valve electric pada tangki agar debit air yang keluar dari tangki sesuai dengan yang dibutuhkan dalam proses produksi dengan menggunakan empat control. Dari simulasi diapatkan bahwa Dsain Water Level yang paling baik pada percobaan ini adalah menggunakan metode ANFIS dengan nilai overshot dan undershot terkecil pada water level dan output flow. Sehingga desain ini bias dipakai acuan untuk menghasilkan control aliran air sesuai dengan harapan yang diinginkan. Hasil simulasi ini akan dibandingkan lagi dengan metode kecerdasan buatan yang lain, sehingga adan didapatkan hasil yang paling sesuai.
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Yang, Da Lin, Wei Dong Yang, and Zhu Zhang. "Online Adaptive Fuzzy Neural Identification of a Piezoelectric Tube Actuator System." Applied Mechanics and Materials 275-277 (January 2013): 915–24. http://dx.doi.org/10.4028/www.scientific.net/amm.275-277.915.
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A coupled actuator-flap-circuit system model and its online identification are presented. The coupled system consists of a piezoelectric tube actuator, a trailing-edge flap, and a series R-L-C circuit. The properties of the coupled system are examined using a Mach-scaled rotor simulation on hovering state. According to the high nonlinear hysteretic characteristics of the coupled system, the generalized dynamic fuzzy neural networks (GD-FNN) implementing Takagi-Sugeno-Kang (TSK) fuzzy systems based on extended ellipsoidal radial basis function (EBF) neural network is used to identify the coupled system. The structures and parameters are adaptive adjusted during the learning process, and don’t need too much expert experiences. Simulation studies show that the piezoelectric tube actuator has high authority with a broad frequency bandwidth, satisfies the requirements for helicopter vibration reduction; GD-FNN has a high learning speed, the final networks have a parsimonious network structure and generalize well, possessing broad application prospects in the helicopter vibration reduction.
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Hannon, C., V. V. Toropov, and O. M. Querin. "A neuro-fuzzy approach to the weight estimation of aircraft structural components." Aeronautical Journal 115, no. 1174 (December 2011): 739–48. http://dx.doi.org/10.1017/s0001924000006485.
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AbstractThis paper explores the issues related to the application of fuzzy logic techniques to aid the process of weight estimation for aircraft structures at preliminary design stages. The focus lays on the design of a neuro-fuzzy system for the weight analysis, through the use of the Neuro-Fuzzy Function Approximator (NEFPROX) algorithm. The paper introduces a three-level process designed around Mamdani fuzzy systems derived through NEFPROX and analyses its application to the sizing and weight estimation of spoiler attachment ribs. Problems such as structure parameterisation, variable selection and model optimisation are explored as part of the process validation phase on the selected structural case study. The model performance is evaluated with respect to modelling accuracy, generalisation capabilities and the interdependencies between the variables the model is able to derive from the analysis of the given structural examples. The results are then compared to the performance obtained from the application of Takagi-Sugeno-Kang (TSK) fuzzy models derived using Adaptive Network-based Fuzzy Inference System (ANFIS) on the same sample of spoiler attachment ribs. Results highlight the benefits of adopting NEFPROX for the derivation of fuzzy systems to be applied in structural weight estimation problems, from the point of view of both accuracy of the approximation provided and the quality and interpretability of the final rulebase derived by the system.
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Xu, Zhaofei, Weidong Lu, Zhenyu Hu, Ta Zhou, Yi Zhou, Wei Yan, and Feifei Jiang. "Decision-Refillable-Based Two-Material-View Fuzzy Classification for Personal Thermal Comfort." Applied Sciences 12, no. 22 (November 17, 2022): 11700. http://dx.doi.org/10.3390/app122211700.
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The personal thermal comfort model is used to design and control the thermal environment and predict the thermal comfort responses of individuals rather than reflect the average response of the population. Previous individual thermal comfort models were mainly focused on a single material environment. However, the channels for individual thermal comfort were various in real life. Therefore, a new personal thermal comfort evaluation method is constructed by means of a reliable decision-based fuzzy classification model from two views. In this study, a two-view thermal comfort fuzzy classification model was constructed using the interpretable zero-order Takagi–Sugeno–Kang (TSK) fuzzy classifier as the basic training subblock, and it is the first time an optimized machine learning algorithm to study the interpretable thermal comfort model is used. The relevant information (including basic information, sampling conditions, physiological parameters, physical environment, environmental perception, and self-assessment parameters) was obtained from 157 subjects in experimental chambers with two different materials. This proposed method has the following features: (1) The training samples in the input layer contain the feature data under experimental conditions with two different materials. The training models constructed from the training samples under these two conditions complement and restrict each other and improve the accuracy of the whole model training. (2) In the rule layer of the training unit, interpretable fuzzy rules are designed to solve the existing layers with the design of short rules. The output of the intermediate layer of the fuzzy classifier and the fuzzy rules are difficult to explain, which is problematic. (3) Better decision-making knowledge information is obtained in both the rule layer of the single-view training model and in the two-view fusion model. In addition, the feature mapping space is generated according to the degree of contribution of the decision-making information from the two single training views, which not only preserves the feature information of the source training samples to a large extent but also improves the training accuracy of the model and enhances the generalization performance of the training model. Experimental results indicated that TMV-TSK-FC has better classification performance and generalization performance than several related state-of-the-art non-fuzzy classifiers applied in this study. Significantly, compared with the single view fuzzy classifier, the training accuracies and testing accuracies of TMV-TSK-FC are improved by 3–11% and 2–9%, respectively. In addition, the experimental results also showed good semantic interpretability of TMV-TSK-FC.
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Prayitno, Agung, Veronica Indrawati, and Ivan Immanuel Trusulaw. "Fuzzy Gain Scheduling PID Control for Position of the AR.Drone." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (August 1, 2018): 1939. http://dx.doi.org/10.11591/ijece.v8i4.pp1939-1946.
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This paper describes the design and implementation of fuzzy gain scheduling PID control for position of the AR.Drone. This control scheme uses 3 PID controllers as the main controller of the AR.Drone, in this case to control pitch, roll and throttle. The process of tuning parameters for each PID is done automatically by scheduling determined by Takagi-Sugeno-Kang (TSK) fuzzy logic model. This paper uses five function sets of PID parameters that will be evaluated by fuzzy logic in order to tune PID controllers. Error position (x,y,z), as inputs of controller, enters the PID Signal block yielding the ouputs in term of error, integral error and differential error. These signal become the inputs of the fuzzy scheduler to yield outputs pitch, roll and throttle to the AR.drone. The control scheme is implemented on the AR.Drone to make it fly to forming a square in the room. The experimental results show that the control scheme can follow the desired points, and process scheduling PID parameters can be shown.
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Rim, Yong Hoon, Young Jin Kim, Sang Sik Lee, and Joung H. Mun. "A New Severity Index for Nondestructive Dynamic Analyses in Equinus Gait." Key Engineering Materials 321-323 (October 2006): 1086–89. http://dx.doi.org/10.4028/www.scientific.net/kem.321-323.1086.
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Equinus gait, defined as walking on one forefoot or both forefeet, has long been considered an undesirable characteristic in patients with a variety of neuromuscular disorders. In the equinus gait, the heel contact pattern is changed according to the severity, because an excessive ankle plantar flexion instigates rearfoot lifting in patients. However, no biomechanical severity index exists to evaluate the rehabilitation procedure of equinus gait. Therefore, we developed an SIEG (Severity Index of Equinus Gait) for nondestructive evaluation of the equinus gait and to validate the index with regard to 11 kinematic and kinetic factors of gait analysis. In this study, the 3-D heel contact pattern was considered for the development of a severity index. In order to verify the result, we compared the developed severity index values with ankle joint kinematic and kinetic factors in 3 test groups. As a result, the average SIEG values ranged between 10.45 (Normal group) and 26.61 (Severe group) and the highest correlation with regard to the 3 groups was shown in the developed severity index. Additionally, we also presented a fuzzy model using Takagi-Sugeno-Kang(TSK) logic with regard to the 12 factors in order to more accurately classify equinus gait.
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BENREJEB, Mohamed, Dhaou SOUDANI, Anis SAKLY, and Pierre BORNE. "New Discrete Tanaka Sugeno Kang Fuzzy Systems Characterization and Stability Domain." International Journal of Computers Communications & Control 1, no. 4 (October 1, 2006): 9. http://dx.doi.org/10.15837/ijccc.2006.4.2302.
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In this paper, an analytical approach to characterize discrete Tanaka Sugeno Kang (TSK) fuzzy systems is presented. This characterization concerns the choice of the adequate conjunctive operator between input variables of discrete TSK fuzzy models, t-norm, and its impact on stability domain estimation. This new approach is based on stability conditions issued from vector norms corresponding to a vector-Lyapunov function. In particular, second order discrete TSK models are considered and this work concludes that Zadeh’s t-norm, logic product min, gives the largest estimation of stability domain.
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Tran, Hoai Linh, Van Nam Pham, and Hoang Nam Vuong. "Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy." International Journal of Applied Mathematics and Computer Science 24, no. 3 (September 1, 2014): 647–55. http://dx.doi.org/10.2478/amcs-2014-0047.
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Abstract The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron), modified TSK (Takagi-Sugeno-Kang) and the SVM (Support Vector Machine), will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to-peak periods of the ECG signals will be used as features for the classifiers. Numerical experiments will be performed for the recognition of different types of arrhythmia in the ECG signals taken from the MIT-BIH (Massachusetts Institute of Technology and Boston’s Beth Israel Hospital) Arrhythmia Database. The results will be compared with individual base classifiers’ performances and with other integration methods to show the high quality of the proposed solution
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Chen, Chi-Chung, and Yi-Ting Liu. "Enhanced Ant Colony Optimization with Dynamic Mutation and Ad Hoc Initialization for Improving the Design of TSK-Type Fuzzy System." Computational Intelligence and Neuroscience 2018 (2018): 1–15. http://dx.doi.org/10.1155/2018/9485478.
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This paper proposes an enhanced ant colony optimization with dynamic mutation and ad hoc initialization, ACODM-I, for improving the accuracy of Takagi-Sugeno-Kang- (TSK-) type fuzzy systems design. Instead of the generic initialization usually used in most population-based algorithms, ACODM-I proposes an ad hoc application-specific initialization for generating the initial ant solutions to improve the accuracy of fuzzy system design. The generated initial ant solutions are iteratively improved by a new approach incorporating the dynamic mutation into the existing continuous ACO (ACOR). The introduced dynamic mutation balances the exploration ability and convergence rate by providing more diverse search directions in the early stage of optimization process. Application examples of two zero-order TSK-type fuzzy systems for dynamic plant tracking control and one first-order TSK-type fuzzy system for the prediction of the chaotic time series have been simulated to validate the proposed algorithm. Performance comparisons with ACOR and different advanced algorithms or neural-fuzzy models verify the superiority of the proposed algorithm. The effects on the design accuracy and convergence rate yielded by the proposed initialization and introduced dynamic mutation have also been discussed and verified in the simulations.
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Іванюк, О. І. "Navigation of autonomous systems based on situation control with dynamic replanning." Системи обробки інформації, no. 3(162), (September 30, 2020): 44–51. http://dx.doi.org/10.30748/soi.2020.162.05.
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The solution to the problem of autonomous mobile systems navigation is a complex task, traditionally presented in the form of solving the sequence of subtasks: perception of information about the environment, localization and mapping, path planning, and motion control. A large number of scientific works are devoted to the solution of the listed subtasks. However, existing research does not pay enough attention to the integration of individual elements of the navigation cycle solutions into a single homogeneous system. This leads to an additional accumulation of errors in the process of a complex solution to the navigation problem. In previous works, a model was proposed that provides homogeneous integration, using for this a multi-level structure of representing an autonomous system's knowledge in the form of sets of fuzzy rules and facts. The five-level model represents the autonomous system's knowledge of goals, paths, an environment map, strategies, and specific controls necessary to achieve the goal. To ensure adequate processing of fuzzy rules, a modified Takagi-Sugeno-Kang fuzzy inference model is proposed. In this work, the previously proposed model is expanded. The model was tested in conditions of noisy sensor data. A method is proposed for the formation of level 2 rules, which describe an autonomous system's cartographic knowledge about the environment, using the well-known methods of global path planning. Extension of the model provides dynamic paths replanning of the autonomous system, using the processing of present knowledge about existing paths. Such replanning is effective in terms of computational time and independent of the completeness of the knowledge base of complete paths.
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Yehoshkin, Danylo, and Natalia Guk. "Automatic construction of a fuzzy system with a matrix representation of rules and a correct knowledge base." Eastern-European Journal of Enterprise Technologies 6, no. 4 (120) (December 30, 2022): 14–22. http://dx.doi.org/10.15587/1729-4061.2022.268908.
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The object of this study is the process of automatic formation of fuzzy production rules on the basis of a training sample for solving the classification problem. The problem of automatically creating and then checking the correctness of a fuzzy inference model for a classification task is solved. The result is an automatically constructed correct database of rules for solving the classification problem. Analysis of the correctness of the knowledge base is carried out using the criteria of completeness, minimality, coherence, and consistency. To prove the completeness of the rule base, Hoare logic and the resolution method are used. The quality of the classification is assessed using such metrics as accuracy, precision, recall, f1-score. The dependence of the classification result on the size of the training sample is considered. The expert system has the following features: the ability to learn from data; high level of accuracy; the correct knowledge base. The knowledge base is formed using the objects of the training sample on the basis of linguistic variables and term sets. A production model of knowledge representation is applied, combining the Mamdani and Takagi-Sugeno-Kang models. It is assumed that the left parts of the production rules describe combinations of the features of objects, and the right parts correspond to classes. The matrix representation of the antecedents of the rules is used. Consequents are represented as a column vector. For the automatic construction of the matrix of antecedents, it is proposed to use the Cartesian product. The formation of the consequent vector is carried out automatically using domain data and a training procedure. The resulting expert system can be used to solve the problems of classification, clustering, data mining, and big data analysis
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Viaña, Javier, Stephan Ralescu, Anca Ralescu, Kelly Cohen, and Vladik Kreinovich. "Explainable fuzzy cluster-based regression algorithm with gradient descent learning." Complex Engineering Systems 2, no. 2 (2022): 8. http://dx.doi.org/10.20517/ces.2022.14.
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We propose an algorithm for n-dimensional regression problems with continuous variables. Its main property is explainability, which we identify as the ability to understand the algorithm’s decisions from a human perspective. This has been achieved thanks to the simplicity of the architecture, the lack of hidden layers (as opposed to deep neural networks used for this same task) and the linguistic nature of its fuzzy inference system. First, the algorithm divides the joint input-output space into clusters that are subsequently approximated using linear functions. Then, we fit a Cauchy membership function to each cluster, therefore identifying them as fuzzy sets. The prediction of each linear regression is merged using a Takagi-Sugeno-Kang approach to generate the prediction of the model. Finally, the parameters of the algorithm (those from the linear functions and Cauchy membership functions) are fine-tuned using Gradient Descent optimization. In order to validate this algorithm, we considered three different scenarios: The first two are simple one-input and two-input problems with artificial data, which allow visual inspection of the results. In the third scenario we use real data for the prediction of the power generated by a Combined Cycle Power Plant. The results obtained in this last problem (3.513 RMSE and 2.649 MAE) outperform the state of the art (3.787 RMSE and 2.818 MAE).
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Song, Xinjian, Feng Gu, Xiude Wang, Songhua Ma, and Li Wang. "Interpretable Recognition for Dementia Using Brain Images." Frontiers in Neuroscience 15 (September 24, 2021). http://dx.doi.org/10.3389/fnins.2021.748689.
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Machine learning-based models are widely used for neuroimage-based dementia recognition and achieve great success. However, most models omit the interpretability that is a very important factor regarding the confidence of a model. Takagi–Sugeno–Kang (TSK) fuzzy classifiers as the high interpretability and promising classification performance have widely used in many scenarios. TSK fuzzy classifier can generate interpretable fuzzy rules showing the reasoning process. However, when facing high-dimensional data, the antecedent become complex which may reduce the interpretability. In this study, to keep the antecedent of fuzzy rule concise, we introduce the subspace clustering technique and use it for antecedent learning. Experimental results show that the used model can generate promising recognition performance as well as concise fuzzy rules.
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Zhang, Yuanpeng, Yizhang Jiang, and Alireza Jolfaei. "Mutual Supervised Fusion & Transfer Learning with Interpretable Linguistic Meaning for Social Data Analytics." ACM Transactions on Asian and Low-Resource Language Information Processing, October 20, 2022. http://dx.doi.org/10.1145/3568675.
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Social data analytics is often taken as the most commonly used method for community discovery, product recommendations, knowledge graph, etc. In this study, social data are firstly represented in different feature spaces by using various feature extraction algorithms. Then we build a transfer learning model to leverage knowledge from multiple feature spaces. During modeling, since the assumption that the training and the testing data have the same distribution is always true, we give a theorem and its proof which asserts the necessary and sufficient condition for achieving a minimum testing error. We also theoretically demonstrate that maximizing the classification error consistency across different feature spaces can improve the classification performance. Additionally, the cluster assumption derived from semi-supervised learning is introduced to enhance knowledge transfer. Finally, a Tagaki-Sugeno-Kang (TSK) fuzzy system-based learning algorithm is proposed, which can generate interpretable fuzzy rules. Experimental results not only demonstrate the promising social data classification performance of our proposed approach but also show its interpretability which is missing in many other models.
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Zeyneb, Tamrabet, Marouf Nadir, and Remini Boualem. "Modeling of suspended sediment concentrations by artificial neural network and adaptive neuro fuzzy interference system method -study of five largest basins in Eastern Algeria-." Water Practice and Technology, May 11, 2022. http://dx.doi.org/10.2166/wpt.2022.050.
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Abstract Suspended Sediment Concentrations (SSC) Prediction in arid and semi-arid areas has aroused increasing interest in recent years because of its primary role in water resources planning and management. Today, given its simplicity and reliability, SSC modeling by artificial neural networks (ANN) and adaptive neuro-fuzzy Interference (ANFIS) are the most developed and widely used methods. The main aim of this study is suspended sediment concentrations modeling using ANN) and ANFIS methods at the five largest basins in eastern Algeria: the Constantinois Coastal, Highlands, Kébir-Rhumel, Seybouse, and Soummam basin, which are characterized by high water erosion and a lack of SSC measurements. An application was given for historical time series: liquid flows Ql and solid flows Qs as inputs, and daily SSC as outputs, for the 14 hydrometric stations controlling the entire area. The best models were achieved using a Multi-Layer Perceptrons (MLP) Feed Forward Networks (FFN) trained with a Levenberg-Marquardt (LM) algorithm for ANN modeling and a First-order Takagi-Sugeno-Kang (TSK) Feed-Forward Network (FFN) with a hybrid learning method for Anfis modeling. The reliability of the created models was evaluated using five validation cretaria: determination coefficient R2, Nash-Sutcliffe coefficient NSE, mean square error MSE, root-mean-square error RMSE, and the mean absolute error MAE. The ANN and ANFIS models showed high accuracy, confirmed by excellent R2 values ranging from 0.77 to 0.98. The NSE ranged from 0.67 to 0.97. The error values were very good, the MAE varies from 0.004 g/L to 0.028 g/L for both models. The comparison of the ANN and ANFIS models revealed that ANN models slightly outperformed the ANFISs; both of them had high accuracy in SSC prediction.