Kang Fuzzy System Research Articles

A fully interpretable stacking fuzzy classifier with stochastic configuration-based learning for high-dimensional data

This study proposes a stacking fuzzy classifier with stochastic configuration-based learning that can achieve higher training and testing performances and sound interpretability of fuzzy rules. By using the understandable first-order Takagi–Sugeno–Kang fuzzy system, we initially stack each successive subclassifier on both the remaining misclassified training data and the corresponding outputs of the previous subclassifier. Subsequently, a Stacking Fuzzy Classifier with Fully Interpretable and Short fuzzy Rules (FISR-SFC) further improves its prediction by linearly aggregating the outputs of all the subclassifiers. FISR-SFC trains each subclassifier using the proposed stochastic configuration-based learning procedure to utilize its training excellence on gradually smaller misclassified training data and simultaneously maintain the full interpretability of each subclassifier. Experimental results on twelve benchmarking datasets reveal that FISR-SFC is at least comparable to and even better than the comparative classifiers in terms of average testing accuracy/G-mean and/or short rules with full interpretability.

TAKAGI–SUGENO–KANG FUZZY SYSTEM MODELING BASED ON LOW-RANK SPARSE SUBSPACE LEARNING FOR MOTOR IMAGERY ELECTROENCEPHALOGRAM SIGNAL CLASSIFICATION

The classification of electroencephalogram (EEG) signals derived from motor imagery (MI) has always been a hot topic in the field of brain–computer interfaces. Due to its ability to handle the nonstationary and uncertain information contained in EEG signals, the Takagi–Sugeno–Kang fuzzy system (TSK-FS) has become an advantageous classification algorithm. To train a fuzzy system with strong discrimination capabilities from EEG data interspersed with redundant information, this paper proposes a TSK-FS modeling method based on low-rank sparse subspace learning (TSK-LSSL). This method focuses on consequent parameter learning, which transforms the traditional consequent parameter learning strategy into low-rank subspace and sparse subspace learning processes. Low-rank subspace learning is used to mine the global structural information of data and effectively reduce the number of fuzzy rules. During sparse subspace learning, [Formula: see text]-norm regularization is used to constrain the consequent parameters and causes the number of redundant consequent parameters to be zero, thereby simplifying the fuzzy rules. In addition, a local boundary term based on graph matrices is embedded into the objective function to mine the local structural information of the given data. TSK-LSSL simplifies the number of rules and the consequent part of the fuzzy rules. It exhibits good classification performance on two BCI Competition databases.

Takagi–Sugeno–Kang Fuzzy Systems for High-Dimensional Multilabel Classification

Takagi–Sugeno–Kang Fuzzy Systems for High-Dimensional Multilabel Classification

A Multi-view Semi-supervised Takagi–Sugeno–Kang Fuzzy System for EEG Emotion Classification

A Multi-view Semi-supervised Takagi–Sugeno–Kang Fuzzy System for EEG Emotion Classification

Crude oil production prediction based on an intelligent hybrid modelling structure generated by using the clustering algorithm in big data

Since the behavior of a complex dynamic system for a large oil field in Iraq is significantly influenced by many nonlinearities, its dependent parameters exhibit non-stationary with a very high delay time. Developing white-box modelling approaches for such dynamic oil well production cannot handle these large data sets with all dependent dimensions and their non-linear effects. Therefore, this study adopts the hybrid model that combines white-box and black-box to address such problems because the model outputs require various variable types to achieve optimal fitness to measured values. The hybrid model structure needs to evolve with changes in the physical parameters (white-box part) and Neural Networks' Weights (black-box part). The model structure of the proposed hybrid network relied on converting fuzzy rules in a Takagi–Sugeno–Kang Fuzzy System (TSK-FS) into a multilayer perceptron network (MLP). The hybrid parameters are formulated concerning six-dimensional dependent variables to describe them in matrix form or layer and by which can quantify total model outputs. After mapping categorical variables to tuples of MLP, the Gauss-Newton regression (GNR) provides an optimal update of the hybrid parameters to get the best fitting of the model outputs with the target of the dataset. The clustering technique and GNR promote predictive performance due to reducing uncertainties in the hybrid parameters. Due to time being the most effective of the independent variables for predicting oil production, datasets are classified into different clusters based on time. The actual field dataset for training and validation is collected from Zubair Oil Field (9 oil wells), which is implemented to build the proposed model. The results of the hybrid model indicate that the development of the proposed structure has achieved the high capability to represent such big data which is the most imperative feature of the proposed model. Furthermore, obtained results show its accuracy far outpacing competitors and achieving a significant improvement in predictive performance.

A reinforcement learning fuzzy system for continuous control in robotic odor plume tracking

AbstractIn dynamic outdoor environments characterized by turbulent airflow and intermittent odor plumes, robotic odor plume tracking remains challenging, because existing algorithms heavily rely on manually tuning or learning from expert experience, which are hard to implement in an unknown environment. In this paper, a multi-continuous-output Takagi–Sugeno–Kang fuzzy system was designed and tuned with reinforcement learning to solve the robotic odor source localization problem in dynamic odor plumes. Based on the Lévy Taxis plume tracking controller, the proposed fuzzy system determined the parameters of the controller based on the robot’s observation and guided the robot to turn and move towards the odor source at each searching step. The trained fuzzy system was tested in simulated filament-based odor plumes dispersed by a changing wind field. The results showed that the performance of the proposed fuzzy system-based controller trained with reinforcement learning can achieve a similar success rate and higher efficiency compared with a manually tuned and well-designed fuzzy system-based controller. The fuzzy system-based plume tracking controller was also validated through real robotic experiments.

A hybrid Genetic–Grey Wolf Optimization algorithm for optimizing Takagi–Sugeno–Kang fuzzy systems

Nature-inspired optimization techniques have been applied in various fields of study to solve optimization problems. Since designing a Fuzzy System (FS) can be considered one of the most complex optimization problems, many meta-heuristic optimizations have been developed to design FS structures. This paper aims to design a Takagi–Sugeno–Kang fuzzy Systems (TSK-FS) structure by generating the required fuzzy rules and selecting the most influential parameters for these rules. In this context, a new hybrid nature-inspired algorithm is proposed, namely Genetic–Grey Wolf Optimization (GGWO) algorithm, to optimize TSK-FSs. In GGWO, a hybridization of the genetic algorithm (GA) and the grey wolf optimizer (GWO) is applied to overcome the premature convergence and poor solution exploitation of the standard GWO. Using genetic crossover and mutation operators accelerates the exploration process and efficiently reaches the best solution (rule generation) within a reasonable time. The proposed GGWO is tested on several benchmark functions compared with other nature-inspired optimization algorithms. The result of simulations applied to the fuzzy control of nonlinear plants shows the superiority of GGWO in designing TSK-FSs with high accuracy compared with different optimization algorithms in terms of Root Mean Squared Error (RMSE) and computational time.

A novel heuristic for handover priority in mobile heterogeneous networks based on a multimodule Takagi–Sugeno–Kang fuzzy system

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

Structured Sparse Regularized TSK Fuzzy System for predicting therapeutic peptides.

Therapeutic peptides act on the skeletal system, digestive system and blood system, have antibacterial properties and help relieve inflammation. In order to reduce the resource consumption of wet experiments for the identification of therapeutic peptides, many computational-based methods have been developed to solve the identification of therapeutic peptides. Due to the insufficiency of traditional machine learning methods in dealing with feature noise. We propose a novel therapeutic peptide identification method called Structured Sparse Regularized Takagi-Sugeno-Kang Fuzzy System on Within-Class Scatter (SSR-TSK-FS-WCS). Our method achieves good performance on multiple therapeutic peptides and UCI datasets.

Novel multi-view Takagi–Sugeno–Kang fuzzy system for epilepsy EEG detection

Most intelligent algorithms used for recognizing epilepsy electroencephalogram (EEG) have two major deficiencies. The one is the lack of interpretability and the other is unsatisfactory recognition results. In response to these challenges, we propose a dedicated model called multi-view Takagi–Sugeno–Kang (TSK) fuzzy system (MV-TSK-FS) for the epilepsy EEG detection. Our contributions lie in three aspects. First, TSK-FS is selected as the basic model. As one of the most famous fuzzy systems, TSK-FS has the advantage of nice interpretability and thus meets the requirement of clinic trials and applications. Second, MV-TSK-FS uses a multi-view framework to collaboratively handle the collective feature data extracted from diverse extraction perspectives, which strives to avoid the potential performance degradation commonly incurred with single feature extraction. Third, we propose a view-weighted mechanism based on the quadratic regularization to distinguish the importance of each view. The more important the view, the larger the corresponding weight is. The final decision is consequently figured out with the weighted outputs of all views. Experimental results demonstrate that, compared with other epilepsy EEG detection ones, our proposed method has better classification performance as well as more satisfied interpretability on results.

On the Accuracy–Complexity Tradeoff of Fuzzy Broad Learning System

The fuzzy broad learning system (FBLS) is a recently proposed neuro-fuzzy model that shares the similar structure of a broad learning system (BLS). It shows high accuracy in both classification and regression tasks and inherits the fast computational nature of a BLS. However, the ensemble of several fuzzy subsystems in an FBLS decreases the possibility of understanding the fuzzy model since the fuzzy rules from different fuzzy systems are difficult to combine together while keeping the consistence. To balance the model accuracy and complexity, a synthetically simplified FBLS with better interpretability, named compact FBLS (CFBLS), is developed in this article, which can generate much fewer and more explainable fuzzy rules for understanding. In such a way, only one traditional Takagi–Sugeno–Kang fuzzy system is employed in the feature layer of a CFBLS, and the input universe of discourse is equally partitioned to obtain the fuzzy sets with proper linguistic labels accordingly. The random feature selection matrix and rule combination matrix are employed to reduce the total number of fuzzy rules and to avoid the “curse of dimensionality.” The enhancement layer is kept in the CFBLS that helps to add a nonlinear transformation of input features to the traditional first-order polynomial used in the consequent part of a fuzzy rule. The pseudoinverse is also used to determine the parameters of CFBLS guaranteeing its fast computational nature. The experiments on the popular UCI and KEEL datasets indicate that the CFBLS can generate a smaller set of comprehensible fuzzy rules and achieve much higher accuracy than some state-of-the-art neuro-fuzzy models. Moreover, the advantage of CFBLS is also verified in a real-world application.

Trajectory Tracking of a Flexible Robot Manipulator by a New Optimized Fuzzy Adaptive Sliding Mode-Based Feedback Linearization Controller

This work presents a novel fuzzy adaptive sliding mode-based feedback linearization controller for trajectory tracking of a flexible robot manipulator. To reach this goal, after deriving the dynamical equations of the robot, the feedback linearization approach is utilized to change the nonlinear dynamics to a linear one and find the control law. Then, the sliding mode control strategy is implemented to design a stabilizer for trajectory tracking of the flexible robot. In order to adaptively tune the parameters of the designed controller, the gradient descent approach and the chain derivative rule are employed. Moreover, the Takagi–Sugeno–Kang fuzzy system is applied to regulate the controller gains. Finally, a multiobjective particle swarm optimization algorithm is used to find the optimum fuzzy rules. The conflicting objective functions considered as the integrals of the absolute values of the state error and the control effort should be minimized, simultaneously. The simulation results illustrate the effectiveness and capability of the introduced scenario in comparison with other methods.

Fuzzy Clustering with Self-growing Net

A novel deep feature mapping method self-growing net (SG-Net) is proposed, and its combination with classical fuzzy c-means (FCM) called SG-Net-FCM is further developed. SG-Net is a feedforward learning structure for nonlinear explicit feature mapping and includes four types of layers, i.e., input, fuzzy mapping, hybrid, and output layers. The fuzzy mapping layer maps the data from input layer to a high-dimensional feature space using TSK fuzzy mapping, i.e. the fuzzy mapping of Takagi–Sugeno–Kang fuzzy system (TSK-FS). Afterward, each layer in SG-Net accepts additional inputs from all preceding layers and provides its own distinguished features by using principal component analysis to all subsequent layers. The final output of SG-Net is fed to FCM. Since SG-Net-FCM is developed based on the TSK fuzzy mapping, it is more interpretable than classical kernelized fuzzy clustering methods. The effectiveness of the proposed clustering algorithm is experimentally verified on UCI datasets.

Learning compact zero-order TSK fuzzy rule-based systems for high-dimensional problems using an Apriori + local search approach

Learning fuzzy rule-based systems entails searching a set of fuzzy rules which fits the training data. Even if using fix fuzzy partitions, the amount of rules that can be formed is exponential in the number of variables. Thus, the search must be carried out by means of metaheuristics such as genetic algorithms, and sometimes restricted to the set of candidate rules which reach a minimum support.In this article, we propose and evaluate two methods to learn zero-order Takagi–Sugeno–Kang fuzzy systems in medium-high dimensional domains. First, we introduce the minimum individual error of a rule in the criterion for candidate selection. Then, due to the intrinsic locality of fuzzy rule-based systems, where each rule mainly interacts with adjacent rules, we study the use of local search algorithms to carry out the search of the final rule base.Results show that the proposed scheme for candidate rule selection leads to an improvement in the results, regardless of the subsequent search algorithm. Moreover, local search-based algorithms achieve competitive results, reducing substantially the number of rules of the learnt systems.

Enhanced Knowledge-Leverage-Based TSK Fuzzy System Modeling for Inductive Transfer Learning

The knowledge-leverage-based Takagi--Sugeno--Kang fuzzy system (KL-TSK-FS) modeling method has shown promising performance for fuzzy modeling tasks where transfer learning is required. However, the knowledge-leverage mechanism of the KL-TSK-FS can be further improved. This is because available training data in the target domain are not utilized for the learning of antecedents and the knowledge transfer mechanism from a source domain to the target domain is still too simple for the learning of consequents when a Takagi--Sugeno--Kang fuzzy system (TSK-FS) model is trained in the target domain. The proposed method, that is, the enhanced KL-TSK-FS (EKL-TSK-FS), has two knowledge-leverage strategies for enhancing the parameter learning of the TSK-FS model for the target domain using available information from the source domain. One strategy is used for the learning of antecedent parameters, while the other is for consequent parameters. It is demonstrated that the proposed EKL-TSK-FS has higher transfer learning abilities than the KL-TSK-FS. In addition, the EKL-TSK-FS has been further extended for the scene of the multisource domain.

Recurrent Classifier Based on an Incremental Metacognitive-Based Scaffolding Algorithm

This paper outlines our proposal for a novel metacognitive-based scaffolding classifier, namely recurrent classifier (rClass). rClass is capable of emulating three fundamental pillars of human learning in terms of what-to-learn, how-to-learn, and when-to-learn. The cognitive constituent of rClass is underpinned by a recurrent network based on a generalized version of the Takagi–Sugeno–Kang fuzzy system possessing a local feedback of the rule layer. The main basis of the what-to-learn component relies on the new active learning-based conflict measure. Meanwhile, the when-to-learn learning scenario makes use of the standard sample reserved strategy. The how-to-learn module actualizes the Schema and Scaffolding concepts of cognitive psychology. All learning principles are committed in the single-pass local learning modes and create a plug-and-play learning foundation minimizing additional pre- or post-training phases. The efficacy of rClass has been scrutinized by means of rigorous empirical studies, statistical tests, and benchmarks with state-of-the-art classifiers, which demonstrate the rClass potency in producing reliable classification rates, while retaining low complexity in terms of the rule base burden, computational load, and annotation effort.

A fuzzy approximation to dexterity measures of mobile manipulators

In this paper, a fuzzy system is trained to approximate the manipulability index and the inverted condition number of a mobile manipulator. The kinematic model of a mobile platform with the attached manipulator is fused using twist theory and certain dexterity measures are examined throughout the configuration space. A Takagi–Sugeno–Kang fuzzy system is designed to approximate the dexterity measures and it is evaluated both for its error and its efficiency in computational cost. The proposed methodology is proved as a very efficient approximation that can be utilized in real-time motion planning algorithms without significantly adding computational cost to the controller.

Multi-task TSK fuzzy system modeling using inter-task correlation information

The classical fuzzy system modeling methods have been typically developed for the single task modeling scene, which is essentially not in accordance with many practical applications where a multi-task problem must be considered for the given modeling task. Although a multi-task problem can be decomposed into many single-task sub-problems, the modeling results indeed tell us that the individual modeling approach will not be very suitable for multi-task problems due to the ignorance of the inter-task latent correlation between different tasks. In order to circumvent this shortcoming, a multi-task Takagi–Sugeno–Kang fuzzy system model is proposed based on the classical L2-norm Takagi–Sugeno–Kang fuzzy system in this paper. The proposed model cannot only take advantage of independent information of each task, but also make use of the inter-task latent correlation information effectively, resulting to obtain better generalization performance for the built fuzzy systems. Experiments on synthetic and real-world datasets demonstrate the applicability and distinctive performance of the proposed multi-task fuzzy system model in multi-task modeling scenarios.

A novel approach to fuzzy wavelet neural network modeling and optimization

Abstract In this paper, an efficient approach of combining Takagi–Sugeno–Kang fuzzy system with wavelet based neural network is presented. The model replaces the constant or a linear function of inputs in conclusion part of traditional TSK fuzzy model with wavelet neural network (WNN), thus each rule uses fuzzy set to separate the input space into subspaces spanned by different wavelet functions. For finding the optimal values for parameters of our proposed fuzzy wavelet neural network (proposed-FWNN), a hybrid learning algorithm integrating an improved particle swarm optimization (PSO) and gradient descent algorithm is employed. The two-layer inline-PSO process is proposed in this paper, whose adjustment scheme is more fitting the consequent pattern learning based gradient descent optimization and will locate a good region in the search space. Simulation examples are given to test the efficiency of proposed-FWNN model for identification of the dynamic plants. It is seen that our modeling and optimization approach results in a better performance.

Stability Analysis of Nonlinear Dynamic Systems by Nonlinear Takagi–Sugeno–Kang Fuzzy Systems

This paper proposes a systematic procedure to address the limit cycle prediction of a Nonlinear Takagi–Sugeno–Kang (NTSK) fuzzy control system with adjustable parameters. NTSK fuzzy can be linearized by describing function method. The stability of the equivalent linearized system is then analyzed using the stability equations and the parameter plane method. After that the gain–phase margin (PM) tester has been added, then gain margin (GM) and phase margin for limit cycle are analyzed. Using NTSK fuzzy control system can help to have fewer rules. In order to analyze the stability with the same technique of stability analysis, the results of NTSK fuzzy control system will be compared with Dynamic fuzzy control system [1]. Computer simulations show differences between both systems.