TS Fuzzy Modeling Research Articles

Robust Multi-Objective H2/H∞ Load Frequency Control of Multi-Area Interconnected Power Systems Using TS Fuzzy Modeling by Considering Delay and Uncertainty

The main objective of this paper is to design a robust multi-objective H2/H∞ delayed feedback controller for load frequency control of a multi-area interconnected power system by taking into account all theoretical and practical constraints. To achieve more precise modelling and analysis, the limitation of valve position, governor, and transmission delay are considered to guarantee of LFC system’s stability in practical applications. The nonlinear delayed system is approximated by the Takagi–Sugeno fuzzy model. Then, a parallel distributed compensation scheme is utilized for designing the control system of the overall system. The proposed multi-objective and robust H2/H∞ controller simultaneously minimizes the H2 and H∞ control performance indexes. Finally, simulation results verify the robustness and effectiveness of the proposed scheme in dealing with the impact of load disturbances, model uncertainties, transmission time delays, and nonlinearities in the model.

A Takagi–Sugeno fuzzy model combined with a support vector regression for stock trading forecasting

The turning points prediction scheme for future time series analysis based on past and present information is widely employed in the field of financial applications. In this research, a novel approach to identify turning points of the trading signal using a fuzzy rule-based model is presented. The Takagi–Sugeno fuzzy rule-based model (the TS model) can accurately identify daily stock trading from sets of technical indicators according to the trading signals learned by a support vector regression (SVR) technique. In addition, when new trading points are created, the structure and parameters of the TS model are constantly inherited and updated. To verify the effectiveness of the proposed TS fuzzy rule-based modeling approach, we have acquired the stock trading data in the US stock market. The TS fuzzy approach with dynamic threshold control is compared with a conventional linear regression model and artificial neural networks. Our result indicates that the TS fuzzy model not only yields more profit than other approaches but also enables stable dynamic identification of the complexities of the stock forecasting system.

A general methodology for online TS fuzzy modeling by the extended Kalman filter

This paper presents an online TS fuzzy modeling general methodology based on the extended Kalman filter. The model can be obtained in a recursive way only based on input–output data. The methodology can work online with the system, properly in the presence of noise, is very efficient computationally and completely general. It is general in the sense theorically there are no restrictions neither in the number of inputs nor outputs, neither in the type nor distribution of membership functions used (which can even be mixed in the antecedents of the rules). Some examples and comparisons with other online fuzzy identification models from signals are provided to illustrate the skill of the online identification of the proposed methodology.

An Incremental Approach to TS Fuzzy Modeling

This paper proposes an incremental smooth support vector regression (ISSVR) method for TS fuzzy modeling. Under certain assumptions on membership functions, we propose an optimization problem for TS fuzzy modeling based on the structural risk minimization principle. We show that this problem is an SVR problem with non-positive definite kernels. It cannot be solved using conventional SVR. Then we establish a connection between this TS fuzzy modeling problem and smooth support vector regression (SSVR), which is a smoothing strategy for solving SVR. The problem is always solvable using SSVR because SSVR puts no restrictions on the kernel. Then we apply an incremental approach to the SSVR by selecting informative samples from the training dataset. Taking advantage of SSVR, more forms of membership functions can be used in our model compared with conventional methods. Experiments show that the proposed ISSVR-based TS fuzzy model has good generalization ability with small number of fuzzy rules.

TS-fuzzy modeling based on ε-insensitive smooth support vector regression

This paper establishes a connection between Takagi-Sugeno TS fuzzy systems and e-insensitive smooth support vector regression e-SSVR, a smooth strategy for solving e-SVR. In previous e-SVR-based fuzzy models, the form of membership functions is restricted by the Mercer condition. The e-SSVR formulation puts no restrictions on the kernel. Therefore, the proposed e-SSVR-based TS-fuzzy modeling method relaxes the restriction on membership functions. By applying the reduced kernel technique, the number of fuzzy rules is reduced without scarifying the generalization ability. The computational complexity is also reduced by the reduced kernel technique. The performance of our method is illustrated by extensive experiments and comparisons.

Multi-kernel learnt partial linear regularization network and its application to predict the liquid steel temperature in ladle furnace

In this study, a novel prediction model, hybrid of mechanism method, Takagi–Sugeno (T–S) fuzzy modeling, regularization network technique and Multi-kernel learning algorithm, is proposed for accurately forecasting the liquid steel temperature in ladle furnace (LF). By virtue of mechanism method and TS fuzzy modeling technique, a partial linear structured mechanism model is firstly obtained, which contains a parametric linear part with unknown coefficients and a non-parametric part with unknown functional expression. Thereafter, it is parameterized and implemented by a modified regularization network, called partial linear regularization network (PLRN), which introduces a parametric linear part into the traditional regularization network. Furthermore, to optimally design the kernel of PLRN and thereby further improve the prediction performance, Multi-kernel learning approach is employed to obtain the so called Multi-kernel learnt PLRN. The principal innovation behind the proposed method is the embedding of the prior knowledge into the model, instead of directly predicting the steel temperature using machine learning techniques which is commonly used in the previous steel temperature prediction models. This innovation leads to better final results in reducing the model complexity, improving the generalization performance and consequently promoting the prediction precise. The experiment results demonstrate that the novel predictor is superior in prediction performance over other black-box based predictors. Furthermore, the prediction accuracy is boosted via Multi-kernel learning.

A new approach to TS fuzzy modeling using dual kernel-based learning machines

This paper proposes a novel approach for structure identification of TS fuzzy model using dual kernel-based learning machines. Firstly, a convenient kernel fuzzy C-means clustering algorithm is developed to partition the data set into several clusters. Secondly, a new kernel function which is free of parameter selection is utilized to locate support vectors in each cluster. Finally, the model structure is further simplified by a combination strategy for support vectors. The experimental results show that the resulting model has concise structure and good generalization ability, especially its performance is insensitive to initial clustering number.

TS fuzzy realization of chaotic Lü system

The Lü attractor is a new chaotic attractor, which connects the Lorenz attractor and the Chen attractor and represents the transition from one to the other. The Letter presents a hybrid TS fuzzy modeling approach for the newly coined chaotic Lü system. Then the abundant and fundamental dynamical behaviors of the chaotic Lü system are completely and comprehensive investigated based on this novel hybrid TS fuzzy model.