Fuzzy Model Identification Research Articles

An improved adaptive Kriging method for the possibility-based design optimization and its application to aeroengine turbine disk

Possibility-based design optimization (PBDO) can provide theoretical basis for the structural optimal design of fuzzy uncertainty model in engineering, so as to obtain the optimal design variables. The genetic algorithm (GA) based on adaptive Kriging method for PBDO requires high precision in the whole design space, while the region far from the limit state surface (LSS) of the constraint function has little effect on PBDO, thus greatly affects the computational efficiency. In order to improve the efficiency of PBDO, an improved adaptive Kriging method combined with active possibility constraint (I-AK-AC) is proposed in this paper. In I-AK-AC, the enhanced expected improvement learning function is first put forward to promote the convergence efficiency of Kriging model by reducing the accuracy of the region far from the LSS of the constraint function. Whereafter, the active possibility constraint is identified, and only the boundaries of active possibility constraint need to be approximated precisely by Kriging model. By these ways, the convergence speed of Kriging model is further ameliorated without affecting the computational accuracy, and the efficiency of PBDO is significantly improved. Three test examples and an engineering application of aeroengine turbine disk illustrate the validity and accuracy of the proposed I-AK-AC.

Experimental Evaluation of a Takagi–Sugeno Fuzzy Controller for an EV3 Ballbot System

In this paper, experimental results about the performance of a Takagi–Sugeno Fuzzy Controller (TSFC) for an EV3 Ballbot Robotic System (EV3BRS) are reported. The physical configuration of the EV3BRS has the form of an inverted pendulum mounted on a ball. The EV3BRS is an underactuated robotic system with four outputs and two control torques. In this work, following the Takagi–Sugeno (TS) fuzzy control design methodology, the Parallel Distributed Compensation (PDC) approach is used in the design of the TSFC. The EV3BRS’s TS Fuzzy Model (TSFM) design comes from linearization of the nonlinear model around two operation points near the upright position of EV3BRS’s body. The Linear Matrix Inequality (LMI) approach was used to obtain the feedback gains for every local linear controller, guaranteeing, via a conservative stability condition, the global asymptotic stability of the overall fuzzy control system. The main goal of the control task consists of maintaining the EV3BRS’s body at its upright position. Measurement and control data from and to the EV3BRS are transferred via telecontrol and telemetry. The appropriate performance of the controller design is corroborated via experimentation.

Design, analysis, and application of fixed-time convergence fuzzy ZNN model realized by dynamic fuzzy logic system for time-varying Sylvester equation

Due to the strengths of zeroing neural network (ZNN) in dealing with time-varying problems and the advantages of fuzzy logic system (FLS) in uncertain computing, a fixed-time convergence fuzzy ZNN (FTCFZNN) model realized by the FLS with dynamic membership functions is proposed for solving time-varying Sylvester equation (TVSE). The introduction of the dynamic membership functions in the FLS is one of the most meaningful attributes of this paper, and such a FLS is called the dynamic FLS (DFLS). The design parameter of the FTCFZNN model is the fuzzy parameter generated by the DFLS. Meanwhile, the fuzzy parameter is also employed in the proposed adaptive activation function, which makes the FTCFZNN model possess fixed-time convergence with less upper bound and has a better adaptable ability. Moreover, a series of theoretical analyses reflect the superior fixed-time convergence and adaptive robustness of the FTCFZNN model. Numerical simulations demonstrate the predominant performance of the FTCFZNN model and its effectiveness in solving the TVSE compared with serval existing ZNN models. Finally, the FTCFZNN model is applied to realize the consensus of multi-agent systems, which shows the application value of the FTCFZNN model.

Analysis of Spatial and Temporal Variation in Sustainable Water Resources and Their Use Based on Improved Combination Weights

The sustainable use of water resources has become increasingly crucial given the present water supply and demand situation. In this study, the degree of sustainable water resource utilization in Harbin City from 2014 to 2021 was calculated using a fuzzy identification model with a combination of the “sequential relationship analysis method (G1) and coefficient of variation method (CVM)” and 18 evaluation indicators retrieved for water resources, reflecting social, economic, and ecological aspects. The study shows that (1) in terms of the research method, the combined weighting of “G1-CVM” is a feasible approach to avoid the shortcomings of single weighting and (2) in terms of the evaluation of water resources sustainable utilization, the spatial distribution of water resources in each district (county) of Harbin City has been stable over the past 8 years. The spatial distribution pattern is relatively stable, with the three regions of Binxian, Bayan, and Shuangcheng showing better sustainable water resource utilization and the three regions of Tonghe County, including the main urban area and Wuchang City, showing deteriorating sustainable water resource utilization. As a whole, the spatial distribution of sustainable water resources in the 13 districts (counties) of Harbin City from 2014 to 2021 shows a negative correlation, with the main urban area, Wuchang City, Hulan District, Bayan County, Shuangcheng District, and Yilan County showing a clustering type in the local spatial autocorrelation analysis. Based on the evaluation results, the spatial and temporal distribution characteristics of the sustainable use of water resources in Harbin are identified and found to be conducive to the timely adjustment of water resources allocation and the rational use of water resources in each district (county). Meanwhile, the research ideas and methods used in this paper can be applied to research on the sustainable use of water resources in other regions.

MULTI-MOTOR DRIVE NON-PARAMETRIC BLACK-BOX FUZZY MODEL

The multi-motor drives, the typical examples of which are continuous lines (CL) for tension processing of various materials are complex and coupled MIMO higher-order nonlinear systems, which parameters are difficult to identify. The article focuses on a method for the design of a non-parametric black-box fuzzy model of a continuous production line with emphasis on minimal knowledge on the modelled system. The proposed fuzzy model structure is based on the state space representation of the dynamic system in discrete form, which only requires a suitable set of information on its input/output relations. The properties of the proposed CL fuzzy model were verified by experimental measurements on a CL laboratory model. The obtained results have confirmed the rightness and effectiveness of the fuzzy model design method that can be applied not only in the field of industry technologies with CL but also in modeling and control of nonlinear dynamic systems.

Empirical enhancement of EEG classification models for epileptic seizures using a fuzzy statistical perspective

The signals produced by an electrocardiogram (ECG) are made up of intricate pattern se-quences that have a periodic structure. These pattern sequences contain an initial P-wave, which denotes the beginning of an ECG wave, a QRS sequence, which denotes the intensi-ty of the pulse, and a T segment, which denotes the conclusion of the wave. Characteris-tics such as PR interval, QRS interval, QT interval, ST interval, R to R interval, etc. are employed to recognize chronic, ischemic, and other cardiac illnesses. These wave patterns need the simultaneous execution of many high-complexity signal processing operations to be classified into cardiac disorders. Signal pre-processing, feature extraction, feature se-lection, classification into epileptic and non-epileptic seizures, and post-processing are some of these operations. For each of these operations, researchers create a wide range of algorithms. These algorithms' performance differs significantly in terms of the quantity of leads utilized for ECG collection, filtering effectiveness, feature extraction & selection ef-fectiveness, and classifier effectiveness. Thus, researchers and system designers have be-come unclear when choosing the optimum algorithm set for an application. This text of-fers a thorough analysis & design of fuzzy CNN model of a broad range of epileptic & non-epileptic seizure classification techniques to lessen ambiguity. Convolutional neural network (CNN) based models outperform other models in terms of general-purpose per-formance, whereas application-specific deployments need the employment of customized fuzzy CNN models. A significant area of research & development is presented by the ob-servation that fuzzy logic techniques are not observed while constructing ECG classifica-tion models. Based on these findings, a new fuzzy logic-based classification method is provided in this text that employs quantization techniques to transform input ECG signals into fuzzy values. With these parameters and a specially created CNN model, it was pos-sible to achieve an accuracy of 99.5% for diverse ECG datasets. This accuracy was com-pared to a number of cutting-edge models, and it was observed that the suggested model is quite good at categorizing ECG signals. The suggested technique was observed to be quicker than traditional procedures due to the usage of a fuzzy logic model, enhancing its scalability for a broad range of clinical applications.

On prediction of air pollutants with Takagi-Sugeno models based on a hierarchical clustering identification method

In recent years, air pollution has attracted considerable attention worldwide. As an effective air protection method, the accurate prediction of air pollutants can help provide an early warning against harmful air pollutants and plan air pollution prevention and control strategies. Due to its high prediction capability, a novel method using a Takagi-Sugeno fuzzy model is presented to predict air pollutants in this study. This work investigates fuzzy model identification based on a larger dataset, and a hierarchical clustering-based identification method with model structure selection is proposed. The novelty of this work is as follows: First, a novel hierarchical clustering method has been employed in air quality forecasting. This method improves the widely used BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) by introducing a refinement phase for handling clusters with arbitrary shapes. Second, a cluster validity measure automatically determines the number of clusters. Then, the estimation of the model order selection and the model parameters is formulated as a sparse optimization problem, which can be solved using global optimization. Finally, the proposed Takagi-Sugeno model can learn the local trend pattern and spatial-temporal dependencies of multivariate air quality-related time series data. The presented method is demonstrated with air quality measurements in Shanghai, and the results show that the proposed method can achieve acceptable prediction performance.

T-S fuzzy model identification based on an improved interval type-2 fuzzy c-regression model

Fuzzy clustering has been widely applied in T-S fuzzy model identification for nonlinear systems, however, tradition type-1 fuzzy clustering algorithms can’t deal with uncertainties in real world, an improved interval type-2 fuzzy c-regression model (IT2-FCRM) clustering is proposed for T-S fuzzy model identification in this paper. The improved IT2-FCRM adapts a new objective function, which makes the boundary of clustering more clearly and reduces the influence of outliers or noisy data on clustering results. The premise parameters of T-S fuzzy model are upper and lower hyperplanes obtained by improved IT2-FCRM, and the upper and lower hyperplanes are used to build hyper-plane-shaped type-2 Gaussian membership function. Compared with the hyper-sphere-shaped membership function of tradition IT2-FCRM, the hyper-plane-shaped membership function is more coincided with point to plane sample distance described by FCRM clustering. The simulation results of several benchmark problems and a real bed temperature in circulating fluidized bed plant show that the identification algorithm has higher accuracy.

Horizontal Federated Learning of Takagi–Sugeno Fuzzy Rule-Based Models

In this article, we elaborate on a design and realization of fuzzy rule-based model in the horizontal federated learning framework. Traditional machine learning in distributed environment often involves sharing sensitive information with other sites or transferring data to a central server on which a global model is trained. These situations increase the communication overhead and pose serious threats to the privacy of sensitive data. Federated learning opens up the possibility for collaboratively training a global model on a basis of distributed on-site data without sacrificing data privacy. While fuzzy rule-based models have been used in system modeling due to their substantial modeling abilities and good interpretability, the implementation of fuzzy rule-based models in a distributed environment without compromising data privacy still requires careful consideration. This article proposes a two-step federated learning approach to train a global model on a basis of private data located across different sites without their centralization. The first step concerns the determination of the structure of the data through federated collaborative clustering. Subsequently, a shared global model is trained jointly by all the participating clients. An advantage of the proposed method is that it achieves high accuracy without violating data privacy. A series of experimental studies are conducted to gain a detailed insight into the realization steps and demonstrate the effectiveness of the proposed method.

Hemodynamic Analysis for Olfactory Perceptual Degradation Assessment Using Generalized Type-2 Fuzzy Regression

Olfactory perceptual degradation (OPD) refers to the inability of people to recognize the variation in concentration levels of olfactory stimuli. This article attempts to assess the degree of OPD of subjects from their hemodynamic response to olfactory stimuli. This is done in two phases. In the first (training) phase, a regression model is developed to assess the degree of concentration levels of an olfactory stimulus by a subject from her hemodynamic response to the stimulus. In the second (test) phase, the model is employed to predict the possible concentration level experienced by the subject in [0, 100] scale. The difference between the model-predicted response and the oral response (the center value of the qualitative grades) of the subject about her perceived concentration level is regarded as the quantitative measure of the degree of subject’s olfactory degradation. The novelty of the present research lies in the design of a general type-2 fuzzy regression model, which is capable of handling uncertainty due to the presence of intrasession and intersession variations in the brain responses to olfactory stimuli. The attractive feature of the article lies in adaptive tuning of secondary membership functions to reduce model prediction error in an evolutionary optimization setting. The effect of such adaptation in secondary measures is utilized to adjust the corresponding primary memberships in order to reduce the uncertainty involved in the regression process. The proposed regression model has good prediction accuracy and high time efficiency as evident from average percentage success rate (PSR) and runtime complexity analysis, respectively. The Friedman test undertaken also confirms the superior performance of the proposed technique with other competitive techniques at 95% confidence level.

A composite framework coupling FCRM, LSSVM and improved hybrid IHHOMFO optimization for Takagi–Sugeno fuzzy model identification

This paper proposes a novel Takagi–Sugeno fuzzy model identification method by combining fuzzy c-regression model clustering (FCRM), least squares support vector machine (LSSVM) and intelligent optimization algorithm. Firstly, in order to improve the performance of FCRM for the complex nonlinear dataset, in this paper the method of FCRM based on LSSVM (FCRM-LSSVM) is proposed to discover the data structure and obtain the antecedent parameters. And then, a newly developed intelligent optimization algorithm by hybridizing Harris hawks optimization and moth-flame optimization algorithm (IHHOMFO) is proposed to further optimize the antecedent membership function parameters obtained by the FCRM-LSSVM. Finally, the proposed novel T-S fuzzy model identification combines FCRM, LSSVM and IHHOMFO for solving actual model identification problems. Experiments on five different datasets demonstrate that the proposed method is more efficient than conventional methods, such as T-S model identification based on fuzzy c-means (FCM), FCRM and FCRM-LSSVM, in standard measurement indexes. This study thus demonstrates that the proposed method is a credible and competitive fuzzy model identification method. The novel method contributes not only to the theoretical aspects of fuzzy model, but is also widely applicable in data mining, image recognition and prediction problems.

MODELFY: A Model-driven Solution for Decision Making based on Fuzzy Information

There exist areas, such as the disease prevention or inclement weather protocols, in which the analysis of the information based on strict protocols require a high level of rigor and security. In this situation, it would be desirable to apply formal methodologies that provide these features. In this scope, recently, it has been proposed a formalism, fuzzy automaton, that captures two relevant aspects for fuzzy information analysis: imprecision and uncertainty. However, the models should be designed by domain experts, who have the required knowledge for the design of the processes, but do not have the necessary technical knowledge. To address this limitation, this paper proposes MODELFY, a novel model-driven solution for designing a decision-making process based on fuzzy automata that allows users to abstract from technical complexities. With this goal in mind, we have developed a framework for fuzzy automaton model design based on a Domain- Specific Modeling Language (DSML) and a graphical editor. To improve the interoperability and functionality of this framework, it also includes a model-to-text transformation that translates the models designed by using the graphical editor into a format that can be used by a tool for data anal- ysis. The practical value of this proposal is also evaluated through a non-trivial medical protocol for detecting potential heart problems. The results confirm that MODELFY is useful for defining such a protocol in a user-friendly and rigorous manner, bringing fuzzy automata closer to domain experts.

DC MOTOR FUZZY MODEL BASED OPTIMAL CONTROLLER

This paper presents a method for the design of optimal fuzzy controller of a DC motor using a fuzzy model based approach with emphasis on minimal knowledge on the controlled system. In the first part of the paper we describe the method of the black-box fuzzy model design based only on the system´s measured input/output data without the necessity of preliminary knowledge of its internal structure and parameters. This fuzzy model is in the second part used in the design of optimal fuzzy controller based on finding the sequence of input signal values that will transfer the controlled system into the desired state in accordance with the selected optimality criterion. The realized simulation and experimental measurements performed on HIL platform have confirmed the correctness and effectiveness of the proposed design method and also its applicability to others dynamic systems with as little previous knowledge as possible.

An evolving concept in the identification of an interval fuzzy model of Wiener-Hammerstein nonlinear dynamic systems

This paper presents a new approach for identifying interval fuzzy models, which enables estimating fuzzy model structures, parameters, and upper and lower bounds simultaneously and online. It is based on a filtered recursive least squares method combined with an incrementally evolving Gaussian clustering. The proposed method generates interval fuzzy models online, on the fly, and in an evolving manner. This means that the algorithm starts with no a priori information, evolves the structure of the model, adjusts the model parameters simultaneously, and computes the upper and lower intervals simultaneously. The fuzzy partitioning of the input–output data space is based on the eGauss + method; the parameters of the local linear models, which together form the fuzzy model, are determined using a recursive least squares method. The interval fuzzy model, used in a predictive manner as a single or multi-level predictor, can be successfully applied in on-line monitoring, fault detection, and the control of dynamic systems. The proposed identification procedure was used to identify the fuzzy interval model of two different processes: a simplified Hammerstein-type nonlinear dynamic process and a realistic industrial continuously stirred tank reactor.The main contribution and advantage of the proposed new method is the identification of an interval fuzzy model in an online manner, which means that the structural and parametric identification of nonlinear systems is done simultaneously and from the data stream. The structural and parametric uncertainties are modeled and integrated into the upper and lower fuzzy models, which form the fuzzy interval in which the measured data samples of the process output are located with a certain probability. The approach is limited to processes that have Wiener-Hammerstein structures.

A new imputation-based incomplete data-driven fuzzy modeling for accuracy improvement in ubiquitous computing applications

Purpose In real-world decision-making, high accuracy data analysis is essential in a ubiquitous environment. However, we encounter missing data while collecting user-related data information because of various privacy concerns on account of a user. This paper aims to deal with incomplete data for fuzzy model identification, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features. Design/methodology/approach In this work, authors proposed a three-fold approach for fuzzy model identification in which imputation-based linear interpolation technique is used to estimate missing features of the data, and then fuzzy c-means clustering is used for determining optimal number of rules and for the determination of parameters of membership functions of the fuzzy model. Finally, the optimization of the all antecedent and consequent parameters along with the width of the antecedent (Gaussian) membership function is done by gradient descent algorithm based on the minimization of root mean square error. Findings The proposed method is tested on two well-known simulation examples as well as on a real data set, and the performance is compared with some traditional methods. The result analysis and statistical analysis show that the proposed model has achieved a considerable improvement in accuracy in the presence of varying degree of data incompleteness. Originality/value The proposed method works well for fuzzy model identification method, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features with varying degree of missing data as compared to some well-known methods.

System identification of fuzzy relation matrix models by semi-tensor product operations

In order to facilitate the representation of fuzzy relation matrix (FRM) models, a new system identification technique is proposed in this work to recognize the architecture and parameters of FRM models based on the semi-tensor product (STP) operation. Firstly, a fuzzy STP algorithm is defined for fuzzy inference. Secondly, a novel FRM framework is proposed for system parameter identification. Thirdly, the recognized FRM parameters are optimized to improve fuzzy system performance by the use of a hybrid training method based on the least squares estimator and the recursive Levenberg-Marquaedt algorithm. The effectiveness of the proposed structure and parameter identification techniques is verified by simulation of a multi-steps-ahead prediction modeling. Simulation results show that the proposed fuzzy STP technology is efficient for system identification, and the proposed matrix expression can be used to design multi-input multi-output (MIMO) systems with fuzzy FRM models.

Identification of Fuzzy Rule-Based Models With Collaborative Fuzzy Clustering.

Fuzzy rule-based models (FRBMs) are sound constructs to describe complex systems. However, in reality, we may encounter situations, where the user or owner of a system only owns either the input or output data of that system (the other part could be owned by another user); and due to the consideration of data privacy, he/she could not obtain all the needed data to build the FRBMs. Since this type of situation has not been fully realized (noticed) and studied before, our objective is to come up with some strategy to address this challenge to meet the specific privacy consideration during the modeling process. In this study, the concept and algorithm of the collaborative fuzzy clustering (CFC) are applied to the identification of FRBMs, describing either multiple-input-single-output (MISO) or multiple-input-multiple-output (MIMO) systems. The collaboration between input and output spaces based on their structural information (conveyed in terms of the corresponding partition matrices) makes it possible to build FRBMs when input and output data could not be collected and used in unison. Surprisingly, on top of this primary pursuit, with the collaboration mechanism the input and output spaces of a system are endowed with an innovative way to comprehensively share, exchange, and utilize the structural information between each other, which results in their more relevant structures that guarantee better model performance compared with performance produced by some state-of-the-art modeling strategies. The effectiveness of the proposed approach is demonstrated by experiments on a series of synthetic and publicly available datasets.

DESAIN MODEL FUZZY-TSUKAMOTO UNTUK PENENTUAN KUALITAS BUAH PEPAYA CALIFORNIA (CARICA PAPAYA L.) BERDASARKAN BENTUK FISIK

Papaya easily found in the local market with a relatively cheap price, adequate nutrient and vitamin content. The quality of California papaya can be measured by size, color and defect. This research discusses the topic of fuzzy model design regarding the measured of the quality of papaya using fuzzy tsukamoto with input variables major axis, minor axis, red and green intensity color, and defect variables along with the output as a result of determining the quality of California papaya. Based on the tests that have been carried out, the results of the quality is 75%.

Cognitive Modeling of Socio-Economic Systems with Functional Consideration of the Time Factor

The research analyzes scientific and methodological approaches for structural and parametric identification of fuzzy cognitive models based on time-dependent functional dependencies. In the process of constructing and identifying fuzzy cognitive models, the stages of structural identification with the definition of a set of concepts and unclear relationships over this set, parametric identification, which implements the transition to fuzzy identification with the definition of the intensity of influence between factors, are implemented. A specialized software system was developed for the purpose of computer support for modeling and studying the influence of the time factor set by changes in the strength of connections between concepts. A representation of each of the elements of the FCM adjacency matrix is proposed in the form of an additive expression controlled by the alpha parameter, which includes a constant component determined by the expert method and a control function that depends on real or dimensionless model time. To correct the elements of the FCM adjacency matrix, it is proposed to use a 3-parameter parabolic or modified 4-parameter exponential dependence as a function that depends on real or dimensionless model time. The developed modified method for solving the problem of time factor accounting using functional dependencies for the strength of mutual influence of concepts provides for expanding the capabilities of fuzzy cognitive models by taking into account the time factor.

T-S fuzzy systems optimization identification based on FCM and PSO

The division of fuzzy space is very important in the identification of premise parameters, and the Gaussian membership function is applied to the premise fuzzy set. However, the two parameters of Gaussian membership function, center and width, are not easy to be determined. In this paper, based on Fuzzy c-means (FCM) and particle swarm optimization (PSO) algorithm, a novel T-S fuzzy model optimal identification method of optimizing two parameters of Gaussian function is presented. Firstly, we use FCM algorithm to determine the Gaussian center for rough adjustment. Then, under the condition that the center of Gaussian function is fixed, the PSO algorithm is used to optimize another adjustable parameter, the width of the Gaussian membership function, to achieve fine-tuning, so as to complete the identification of prerequisite parameters of fuzzy model. In addition, the recursive least squares (RLS) algorithm is used to identify the conclusion parameters. Finally, the effectiveness of this method for T-S fuzzy model identification is verified by simulation examples, and the higher identification accuracy can be obtained by using the novel identification method described compared with other identification methods.