Takagi-Sugeno-Kang Fuzzy Model Research Articles

A TSK type fuzzy rule based system for stock price prediction

In this paper, a Takagi-Sugeno-Kang (TSK) type Fuzzy Rule Based System is developed for stock price prediction. The TSK fuzzy model applies the technical index as the input variables and the consequent part is a linear combination of the input variables. The fuzzy rule based model is tested on the Taiwan Electronic Shares from the Taiwan Stock Exchange (TSE). Through the intensive experimental tests, the model has successfully forecasted the price variation for stocks from different sectors with accuracy close to 97.6% in TSE index and 98.08% in MediaTek. The results are very encouraging and can be implemented in a real-time trading system for stock price prediction during the trading period.

데이터 전송 지연을 고려한 인터넷 기반 이동 로봇의 원격 운용

오늘날 인터넷을 기반으로 하는 원격 제어 기술이 급속히 발달하고 있다. 그러나 이러한 원거리 네트워크 기반 제어는 데이터를 전송함에 있어서 지연이 불가피하며, 또한 이 지연이 일정하지 않은 문제점을 지니고 있다. 이러한 네트워크 지연은 시스템의 안정성이나 정확도에 영향을 미친다. 본 논문에서는 네트워크상의 데이터 전송 지연을 고려한 이동 로봇의 원격 운용을 위해 TSK (Takagi-Sugeno-Kang) 퍼지 시스템을 이용하여 전송 지연의 확률 분포 함수와 네트워크 모델을 구하고 이를 전송 지연 예측 알고리즘에 적용하였다. 그리고 컴퓨터 시뮬레이션으로부터 제안된 알고리즘의 실효성을 검증하고, 기존의 예측 알고리즘과의 비교분석을 통하여 그 성능을 평가하였다. The Internet has been widely applied to the remote control system. The network-based control system, however, has a random time delay and an inherent weak point of the network, when the data ate transmitted. The network delay may result in performance degradation or even system instability in teleoperation. In this paper a prediction model of network delay using TSK (Takagi-Sugeno-Kang) fuzzy model is presented. An adaptive scheme is developed to update the prediction model according to the current network status. The prediction model is applied to the control of an Internet-based mobile robot to show its usefulness. In the computer simulation the TSK Prediction model of network delay is proven superior to the conventional algorithms.

Modelling and multivariable control in anaesthesia using neural-fuzzy paradigms: Part I. Classification of depth of anaesthesia and development of a patient model

The first part of this research relates to two strands: classification of depth of anaesthesia (DOA) and the modelling of patient's vital signs. First, a fuzzy relational classifier was developed to classify a set of wavelet-extracted features from the auditory evoked potential (AEP) into different levels of DOA. Second, a hybrid patient model using Takagi-Sugeno Kang fuzzy models was developed. This model relates the heart rate, the systolic arterial pressure and the AEP features with the effect concentrations of the anaesthetic drug propofol and the analgesic drug remifentanil. The surgical stimulus effect was incorporated into the patient model using Mamdani fuzzy models. The result of this study is a comprehensive patient model which predicts the effects of the above two drugs on DOA while monitoring several vital patient's signs. This model will form the basis for the development of a multivariable closed-loop control algorithm which administers "optimally" the above two drugs simultaneously in the operating theatre during surgery.

Application of fuzzy partial least squares (FPLS) modeling nonlinear biological processes

We applied a nonlinear fuzzy partial least squares (FPLS) algorithm for modeling a biological wastewater treatment plant. FPLS embeds the Takagi-Sugeno-Kang (TSK) fuzzy model into the regression framework of the partial least squares (PLS) method, in which FPLS utilizes a TSK fuzzy model for nonlinear characteristics of the PLS inner regression. Using this approach, the interpretability of the TSK fuzzy model overcomes some of the handicaps of previous nonlinear PLS (NLPLS) algorithms. As a result, the FPLS model gives a more favorable modeling environment in which the knowledge of experts can be easily applied. Results from applications show that FPLS has the ability to model the nonlinear process and multiple operating conditions and is able to identify various operating regions in a simulation benchmark of biological process as well as in a full-scale wastewater treatment process. The result shows that it has the ability to model the nonlinear process and handle multiple operating conditions and is able to predict the key components of nonlinear biological processes.

On the State-Space Representation of Local Affine Models in Takagi–Sugeno–Kang Fuzzy Systems (Zur Zustandsdarstellung von Takagi–Sugeno–Kang-Fuzzy-Systemen mit Lokal Affinen Modellen)

Abstract Considering the constant terms in the local models of dynamic Takagi–Sugeno–Kang fuzzy models can provide for improved prediction quality. However, the constant terms have typically not been treated analytically in analysis and controller design. In this paper, minimal state-space representations are derived from local affine input/output models for usage in TSK fuzzy models. Transformation algorithms for structure, parameters and initial values are proposed.

Identification of Tagaki–Sugeno–Kang fuzzy model for power transformers' predictive overload system

Improvement of the utilisation factors of mineral-oil-filled power transformers is of critical importance in the competitive market of electricity. Utilities need to change dynamically the loadability rating of transformers without penalising their serviceability. As a key issue of loadability, all aspects of the thermal performance, and in particular those related to the determination of tolerable windings hot-spot temperature (HST), overload practice and its impact on remanent life expectation should be investigated. This paper deals with a methodology for the identification of a Takagi-Sugeno-Kang (TSK) fuzzy model able to reproduce the thermal behaviour of large mineral-oil-filled power transformers for implementing a protective overload system. The TSK fuzzy model, working on the load current waveform and on the top oil temperature (TOT), gives an accurate global prediction of the HST pattern. To validate the usefulness of the approach suggested herein, some data cases, derived from various laboratory applications, are presented to measure the accuracy and robustness of the proposed fuzzy model.

Nonlinear modeling and adaptive monitoring with fuzzy and multivariate statistical methods in biological wastewater treatment plants

A new approach to nonlinear modeling and adaptive monitoring using fuzzy principal component regression (FPCR) is proposed and then applied to a real wastewater treatment plant (WWTP) data set. First, principal component analysis (PCA) is used to reduce the dimensionality of data and to remove collinearity. Second, the adaptive credibilistic fuzzy-c-means method is used to appropriately monitor diverse operating conditions based on the PCA score values. Then a new adaptive discrimination monitoring method is proposed to distinguish between a large process change and a simple fault. Third, a FPCR method is proposed, where the Takagi–Sugeno–Kang (TSK) fuzzy model is employed to model the relation between the PCA score values and the target output to avoid the over-fitting problem with original variables. Here, the rule bases, the centers and the widths of TSK fuzzy model are found by heuristic methods. The proposed FPCR method is applied to predict the output variable, the reduction of chemical oxygen demand in the full-scale WWTP. The result shows that it has the ability to model the nonlinear process and multiple operating conditions and is able to identify various operating regions and discriminate between a sustained fault and a simple fault (or abnormalities) occurring within the process data.

퍼지 모델을 이용한 비선형 시스템의 적응 PID 제어기

This paper presents an adaptive PID control scheme for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model is used to estimate the error of control input, and the parameters of PID controller are adapted using the error. The parameters of TSK fuzzy model also adapted to plant. The proposed algorithm allows designing adaptive PID controller which Is adapted to the uncertainty of nonlinear plant and the change of parameters. The usefulness of the proposed algorithm is also certificated by the several simulations.

Design of adaptive Takagi–Sugeno–Kang fuzzy models

Abstract The paper describes a method of fuzzy model generation using numerical data as a starting point. The algorithm generates a Takagi–Sugeno–Kang fuzzy model, characterised with transparency, high accuracy and small number of rules. The training algorithm consists of three steps: partitioning of the input–output space using a fuzzy clustering method; determination of parameters of the consequent part of a rule from over-determined batch least-squares (LS) formulation of the problem, using singular value decomposition algorithm; and adaptation of these parameters using recursive least-squares method. Three illustrative well-known benchmark modelling problems serve the purpose of demonstrating the performance of the generated models. The achievable performance is compared with similar existing models, available in literature.

Nonlinear PLS modeling with fuzzy inference system

We propose a new nonlinear partial least squares (NLPLS) algorithm that embeds the Takagi–Sugeno–Kang (TSK) fuzzy model into the regression framework of the partial least squares (PLS) method. We call the new algorithm fuzzy partial least squares (FPLS). Several NLPLS algorithms have been proposed. However, they can lead to overfitting and contain ambiguities in the meaning of regression parameters. The proposed FPLS algorithm applies the TSK fuzzy model to the PLS inner regression. Using this approach, the interpretability of the TSK fuzzy model overcomes some of the handicaps of previous NLPLS algorithms. The proposed method uses the PLS method to solve the problems of high dimensionality and collinearity and the TSK fuzzy model is used to capture the nonlinearity and to increase the use of experts' knowledge. As a result, the FPLS model gives a more favorable modeling environment in which the knowledge of experts can be easily applied. In addition, we propose a new input and output weight update algorithm to enhance the regression performance of FPLS. The power of the proposed method is illustrated by application to a simple mathematical simulation data set and a real near infrared spectral data set.

선박조타의 TSK 퍼지 비선형제어시스템 설계

This paper suggests a method to design TSK(Takagi-Sugeno-Kang) fuzzy nonlinear control system for automatic steering system which contains the nonlinear component of ship`s maneuvering equation. A TSk fuzzy model can be identified using input-output data and represent a nonlinear system very well. A TSK fuzzy controller can be designed systematically from a TSK fuzzy model because the consequent part of TSK fuzzy rule is a linear input-output equation having a constant term. Therefore, this paper suggests the method identifying the TSK fuzzy model and designing the TSK fuzzy controller based on the TSK fuzzy model for ship steering.

Robust TSK fuzzy modeling for function approximation with outliers

The Takagi-Sugeno-Kang (TSK) type of fuzzy models has attracted a great attention of the fuzzy modeling community due to their good performance in various applications. Most approaches for modeling TSK fuzzy rules define their fuzzy subspaces based on the idea of training data being close enough instead of having similar functions. Besides, training data sets algorithms often contain outliers, which seriously affect least-square error minimization clustering and learning algorithms. A robust TSK fuzzy modeling approach is presented. In the approach, a clustering algorithm termed as robust fuzzy regression agglomeration (RFRA) is proposed to define fuzzy subspaces in a fuzzy regression manner with robust capability against outliers. To obtain a more precision model, a robust fine-tuning algorithm is then employed. Various examples are used to verify the effectiveness of the proposed approach. From the simulation results, the proposed robust TSK fuzzy modeling indeed showed superior performance over other approaches.