Fuzzy Predictors Research Articles

A learning system-based soft multiple linear regression model

Machine learning applied to regression models offers powerful mathematical tools for predicting responses based on one or more predictor variables. This paper extends the concept of multiple linear regression by implementing a learning system and incorporating both fuzzy predictors and fuzzy responses. To estimate the unknown parameters of this soft regression model, the approach involves minimizing the absolute distance between two lines under three constraints related to the absolute error distance between observed data and their respective predicted lines. A thorough comparative analysis is conducted, showcasing the practical applicability and superiority of the proposed soft multiple linear regression model. The effectiveness of the model is demonstrated through a comprehensive examination involving simulation studies and real-life application examples.

Safety-Critical Cooperative Target Enclosing Control of Autonomous Surface Vehicles Based on Finite-Time Fuzzy Predictors and Input-to-State Safe High-Order Control Barrier Functions

Safety-Critical Cooperative Target Enclosing Control of Autonomous Surface Vehicles Based on Finite-Time Fuzzy Predictors and Input-to-State Safe High-Order Control Barrier Functions

An explainable fused lasso regression model for handling high-dimensional fuzzy data

In machine learning, the fused lasso is a regularization technique that is used to handle problems where the underlying signal has some kind of structure. In this paper, we extend fused lasso estimation for regression models characterized by fuzzy predictors and fuzzy responses. We present the first fused lasso regression model that is able to handle and analyze high-dimensional fuzzy data. The proposed model provides feature selection, leads to an improved predictive performance, and ensures interpretable and explainable models, all while being computationally efficient. Moreover, it can recover the true signal more accurately, find solutions that are structurally sparse, and is robust to noise. We conduct comprehensive comparative analysis and demonstrate the practical applicability of the presented fuzzy regression model through simulation and real-life applications.

Cooperative Target Enclosing of Ring-Networked Underactuated Autonomous Surface Vehicles Based on Data-Driven Fuzzy Predictors and Extended State Observers

This article addresses the cooperative target enclosing problem of ring-networked underactuated autonomous surface vehicles (ASVs). The target velocity is unavailable, and the ASVs are subject to sideslip effects, unknown control gains, and uncertain kinetics. The control objective is to drive a fleet of ASVs to surround a moving target at a desired range and maintain a spaced formation. An integrated distributed guidance and model-free control method is presented based on extended state observers (ESOs) and a data-driven fuzzy predictor. Specifically, by using two ESOs to estimate the unknown relative kinematics induced by the unknown target velocity and unknown sideslip and a distributed target estimator to recover the target position, intermediate range keeping and phase keeping guidance laws are designed to achieve a circular motion and an evenly spaced formation, respectively. Next, a model-free fuzzy control law is developed based on a data-driven fuzzy predictor, which learns the unknown control gains and uncertain kinetics simultaneously. Finally, the closed-loop control system is proven to be input-to-state stable through Lyapunov analysis. The salient feature of the proposed method is that cooperative circumnavigating a maneuvering target with unknown velocity can be achieved without the global target information and knowledge of vehicle kinetics. Simulation results validate the effectiveness of the proposed distributed guidance and control method for cooperative target enclosing of ASVs.

Self-triggered three-dimensional coordinated path following of disk-type autonomous underwater gliders based on low-frequency learning fuzzy predictors

This paper is concerned with a self-triggered three-dimensional coordinated path following problem for a fleet of under-actuated disk-type autonomous underwater gliders (AUGs) subject to input constraints as well as internal model uncertainties and external ocean disturbances. A modular backstepping design approach is employed to devise three-dimensional fuzzy coordinated path following controllers for networked AUGs. Especially, a three-dimensional kinematic control law is firstly constructed by using a line-of-sight guidance scheme. Secondly, a predictor-based self-triggered communication mechanism is proposed to guarantee that each AUG broadcasts information at its own triggering instants, and to listen to and receive the incoming information from the neighboring AUGs at their triggering instants. Finally, an adaptive kinetic control law is designed based on low-frequency learning fuzzy predictors, which are able to extract the high-frequency components of uncertainties and disturbances. The closed-loop system is proven to be input-to-state stable via a cascade stability analysis. Simulation results validate the feasibility and efficacy of the proposed self-triggered three-dimensional coordinated path following controllers for the under-actuated disk-type AUGs subject to uncertainties and disturbances.

Unraveling the fuzzy predictors of stress at work

Purpose This study aims to identify and rank the significant determinants of stress among tourism and hospitality employees. Design/methodology/approach A mixed-method approach is used to identify and rank workplace stressors. Particularly, the synthesis of relative literature and interview with the panel of experts resulted in the preliminary identification of workplace stressors. Underpinned by fuzzy theory, in addition, the fuzzy analytical hierarchy process is used to rank identified criteria and relative sub-criteria. Findings Results of three-wave investigation lead to an index comprising key components and weighted ranking of workplace stressors in the tourism and hospitality industry with job characteristics as the most important criteria and mental demand as the most salient sub-criteria influencing stress at work. Research limitations/implications The pattern of findings enhances the current knowledge regarding significant workplace stressors in the tourism and hospitality industry. Practical implications Compositional framework and the weight-based ranking of identified components may act as a source of strategic solution for managers to reduce and manage stress among employees. Originality/value Workplace stressors have attracted considerable research attention, however, no general consensus yet exists among scholars and practitioners conferring to the key composition and relative importance of workplace stressors.

Elastic Net Oriented to Fuzzy Semiparametric Regression Model With Fuzzy Explanatory Variables and Fuzzy Responses

In the multivariate linear regression model, it is desirable to include the important explanatory variables to achieve maximal prediction. In this context, the present paper is an attempt to extend the conventional elastic net multiple linear regression model adopted with a semiparametric method to fuzzy predictors and responses. For this purpose, kernel smoothing and elastic net penalized methods were combined to construct a novel variable-selection method in a fuzzy multiple regression model. Some common goodness-of-fit criteria were also included to examine the performance of the proposed method. The effectiveness of the proposed method was illustrated through three numerical examples including a simulation study and two practical cases. The proposed method was also compared with several common fuzzy multiple regression models. The numerical results clearly indicated that the proposed method is capable of providing sufficiently accurate results in cases where noninformative explanatory variables are removed from the model.

A partial-robust-ridge-based regression model with fuzzy predictors-responses

This paper extends the conventional semi-parametric partial linear regression model with fuzzy predictors and fuzzy response in cases where 1—outliers occur in data set, 2—multicollinearity exists in predictors. For this purpose, the classical kernel fitting method is first extended for estimating unknown fuzzy smooth function. Then, inspired by the conventional local linear smoothing method, a kernel-based weighted criterion is suggested for determining the exact coefficients on the basis of ridge methodology. Some common goodness-of-fit criteria are also used to examine the performance of the proposed method. The effectiveness of the proposed method is then illustrated through two numerical examples including a simulation study. The proposed method is also compared with some common fuzzy multiple regression models with fuzzy predictors and fuzzy responses. The numerical results clearly indicate that 1—the proposed method is not sensitive to the outliers, 2—the proposed method provides sufficiently accurate results in cases where multicollinearity happens among predictors and 3—comparing to existing fuzzy multiple regressions, the proposed model provides more efficient predictions. • A fuzzy semi-parametric regression model is proposed when multicollinearity and/or outliers occur. • The fuzzy model contains the fuzzy predictors-responses. • The ridge methodology and a novel robust method are applied to evaluate crisp coefficients. • The method is compared with several methods using various goodness-of-fit-measures.

Fuzzy Quantile Linear Regression Model Adopted with a Semi-Parametric Technique based on Fuzzy Predictors and Fuzzy Responses

As an extension to statistical techniques, fuzzy regression analysis estimates the relationship between fuzzy independent variables and non-fuzzy and/or fuzzy dependent variables. However, facing skewed or non-normal data may lead to vague results if the conventional least square models are used. However, there are many situations in applications of intelligence systems where regression components are fuzzy quantities instead of exact values. The present work is aimed to propose a fuzzy semi-parametric quantile regression model with fuzzy predictors, a fuzzy smooth function, exact coefficients and fuzzy responses. The proposed method is based on a new class of novel kernel-based signed-distance measures in space of fuzzy numbers. Main features of the proposed signed-distance including its robustness were studied. It was shown that the proposed signed-distance measure is more reasonable and effective than a common signed-distance which extensively has been used in fuzzy set theory. This is the reason why we decided to apply the proposed signed-distance to extend the classical quantile regression model. Then, a hybrid algorithm was suggested to evaluate optimal bandwidth, non-fuzzy regression coefficients and fuzzy smooth function at a specific quantile level. The proposed method was then compared with some common fuzzy regression models based on two data sets including a simulation study and an applied example. The numerical results clearly indicated that the proposed fuzzy semi-parametric quantile linear regression model performs better than the other available methods in terms of some common goodness-of-fit criteria in cases where the data set includes skewness and/or some outlier data point. The main advantages of the proposed method are as follows: 1-it is not so sensitive to the outlier data points and/or skewed data, 2- it possesses better fitting effect compared to other existing fuzzy regression models. Thus, the proposed quantile regression model could be successfully applied in many practical studies of fuzzy regression model in real-world applications.

Earthquake hazard assessment in the Zagros Orogenic Belt of Iran using a fuzzy rule-based model

Producing accurate seismic hazard map and predicting hazardous areas is necessary for risk mitigation strategies. In this paper, a fuzzy logic inference system is utilized to estimate the earthquake potential and seismic zoning of Zagros Orogenic Belt. In addition to the interpretability, fuzzy predictors can capture both nonlinearity and chaotic behavior of data, where the number of data is limited. In this paper, earthquake pattern in the Zagros has been assessed for the intervals of 10 and 50 years using fuzzy rule-based model. The Molchan statistical procedure has been used to show that our forecasting model is reliable. The earthquake hazard maps for this area reveal some remarkable features that cannot be observed on the conventional maps. Regarding our achievements, some areas in the southern (Bandar Abbas), southwestern (Bandar Kangan) and western (Kermanshah) parts of Iran display high earthquake severity even though they are geographically far apart.

Fuzzy Logic-Based Model to Stratify Cardiac Surgery Risk

Background: Medical practice is usually performed in a context of uncertainty, where expert knowledge is used for efficiencyin the decision-making process.Objective: The aim of this study was to develop and validate a fuzzy logic-based model to predict cardiac surgery mortality risk.Methods: Four hundred and fifty patients undergoing cardiac surgery were prospectively included in the study and mortalityrisk was predicted based on five scores: 1) “clinical expert” opinion, 2) fuzzy logic-based system according to expert knowledge,3) Parsonnet, 4) Ontario and 5) EuroSCORE. The fuzzy logic model was developed in the following stages: expert selectionof different mortality predictive variables, tables of influence among variables, construction of a fuzzy cognitive map (FCM)and its implementation in an artificial neuronal network, expert-determined patient risk score, test set risk calculation basedon fuzzy predictors, validation set risk using calibrated FCM, and comparison with the other scores according to the level ofagreement and precision with ROC curves.Results: The calibrated model was used to predict the outcome of the validation set (360 patients), based on the FCM scoreand risk predicted by Parsonnet, Ontario and EuroSCORE. The ROC areas showed that FCM had at least the same performanceas other scores to predict mortality (ROC=0.793 vs. 0.775, 0.767, 0.741 and 0.701 for EuroSCORE, “expert”, Ontarioand Parsonnet, respectively).Conclusions: A fuzzy logic-based system employing expert knowledge and the implementation of an expert system is postulatedto predict cardiac surgery mortality risk. The model not only mimicked the outcomes obtained by the “expert”, but had thesame performance as others risk scores.

전기자동차용 리튬이온전지를 위한 SOC 추정 및 센서 고장검출

A model based SOC estimation scheme using parameter identification is described and applied to a Lithium-ion battery module that can be installed in electric vehicles. Simulation studies are performed to verify the effect of sensor faults on the SOC estimation results for terminal voltage sensor and load current sensor. The sensor faults should be detected and isolated as soon as possible because the SOC estimation error due to any sensor fault seriously affects the overall performance of the BMS. A new fault detection and isolation(FDI) scheme by which the fault of terminal voltage sensor and load current sensor can be detected and isolated is proposed to improve the reliability of the BMS. The proposed FDI scheme utilizes the parameter estimation of an input-output model and two fuzzy predictors for residual generation; one for terminal voltage and the other for load current. Recently developed dual polarization(DP) model is taken to develope and evaluate the performance of the proposed FDI scheme. Simulation results show the practical feasibility of the proposed FDI scheme.

An alternative approach to estimating the parameters of a generalised Grey Verhulst model: An application to steel intensity of use in the UK

Being able to forecast time series accurately has been quite a popular subject for researchers both in the past and at present. However, researchers have resorted to various forecasting models that have different mathematical backgrounds, such as statistical time series models, causal econometric models, artificial neural networks, fuzzy predictors, evolutionary and genetic algorithms. In this paper, a brief review of a relatively new approach, known as grey system theory is provided. The paper offers an alternative approach to estimating the unknown parameters of the well know GM(1,1) and it is shown that this alternative procedure provides more reliable parameter estimates together with a simple visual framework for assessing whether the properties of the chosen GM(1,1) model are consistent with the actual data. In this paper a flexible generalisation of the Grey–Verhulst model is put forward which when applied to UK steel intensity of use produces very reliable multi step ahead predictions.

Fuzzy time series prediction using hierarchical clustering algorithms

Abstract In many cases, the k-means clustering algorithm has been most frequently used to the field of data mining, fuzzy control systems and prediction since it was designed in simple procedures and excellent ability of classification. However, it sometimes brought about the failed results for non-linear data by classification behavior caused by just considering the statistical characteristics of non-linear data such as distances between data. To overcome the problems above, this paper proposes a new clustering algorithm of which the structure hierarchically classifies non-linear data. The proposed hierarchical classification technique consists of two levels, called upper clusters and lower fuzzy sets, using the cross-correlation clustering algorithm combined with the k-means clustering algorithm (HCKA), and it was able to improve classification accuracy. In addition, this paper constructs multiple model fuzzy predictors (MMFPs) corresponding to difference data of original time series, which was able to reflect the various characteristics of the time series to the proposed system. Simulation results show that the proposed system was effective and useful for modeling and predicting non-linear time series.

Grey system theory-based models in time series prediction

Being able to forecast time series accurately has been quite a popular subject for researchers both in the past and at present. However, the lack of ability of conventional analysis methods to forecast time series that are not smooth leads the scientists and researchers to resort to various forecasting models that have different mathematical backgrounds, such as artificial neural networks, fuzzy predictors, evolutionary and genetic algorithms. In this paper, the accuracies of different grey models such as GM(1,1), Grey Verhulst model, modified grey models using Fourier Series is investigated. Highly noisy data, the United States dollar to Euro parity between the dates 01.01.2005 and 30.12.2007, are used to compare the performances of the different models. The simulation results show that modified grey models have higher performances not only on model fitting but also on forecasting. Among these grey models, the modified GM(1,1) using Fourier series in time is the best in model fitting and forecasting.

러프 집합 기반 적응 모델 선택을 갖는 다중 모델 퍼지 예측 시스템 구현과 시계열 예측 응용

최근 시계열 예측에 결론부에 선형식을 갖는 TS 퍼지 모델이 많이 이용되고 있는데, 이의 예측 성능은 정상성과 같은 데이터의 특성과 밀접한 관련이 있다. 그러므로 본 논문에서는 특히 비정상 시계열 예측에 매우 효과적인 새로운 예측 기법을 제안하였다. 시계열의 패턴이나 규칙성을 잘 끌어내기 위한 데이터 전처리 과정을 도입하고 다중 모델 TS 퍼지 예측기를 구성한 뒤, 러프집합을 이용한 적응 모델 선택 기법에 의해 입력 데이터의 특성에 따라 가변적으로 적합한 예측 모델을 선택하여 시계열 예측이 수행되도록 하였다. 마지막으로 예측 오차를 감소시키기 위하여 오차 보정 메커니즘을 추가함으로써 예측 성능을 더욱 향상시켰다. 시뮬레이션을 통해 제안된 기법의 성능을 검증하였다. 제안된 기법은 예측 모델 구현과 예측 수행 과정에서 시계열 데이터의 특성들을 잘 반영할 수 있으므로 불확실성과 비정상성을 갖는 시계열의 예측에 매우 효과적으로 이용될 수 있을 것이다. Recently, the TS fuzzy models that include the linear equations in the consequent part are widely used for time series forecasting, and the prediction performance of them is somewhat dependent on the characteristics of time series such as stationariness. Thus, a new prediction method is suggested in this paper which is especially effective to nonstationary time series prediction. First, data preprocessing is introduced to extract the patterns and regularities of time series well, and then multiple model TS fuzzy predictors are constructed. Next, an appropriate model is chosen for each input data by an adaptive model selection mechanism based on rough sets, and the prediction is going. Finally, the error compensation procedure is added to improve the performance by decreasing the prediction error. Computer simulations are performed on typical cases to verify the effectiveness of the proposed method. It may be very useful for the prediction of time series with uncertainty and/or nonstationariness because it handles and reflects better the characteristics of data.

Improving the fuzzy system performance by fuzzy system ensemble

This paper proposes a fuzzy system ensemble (FSE) that can improve the system performance in non-linear and complex problems. Each fuzzy system in the FSE is built from two design stages, each stage of which is performed by different genetic algorithms (GAs). The first stage generates a fuzzy rule base that covers as many of the training examples as possible. The second stage builds fine-tuned membership functions that make the system error as small as possible. These two stages are repeated independently upon the different partitions of input-output variables. The system error will be reduced further by invoking the FSE that combines multiple fuzzy systems with an equal system-error weighting method where the weight constant is inversely proportional to the FS's error. Applications of the FSE to both the truck backer-upper control and the Mackey-Glass chaotic time-series prediction are presented. For the truck control problem, control performance of the proposed method is compared with the approach of Wang and Mendel [IEEE Transactions on System, Man, and Cybernetics 22 (1992) 1414] in terms of either the rate of successful controls reaching to the goal or the average traveling distance. For the chaotic time-series prediction problem, prediction accuracy of the proposed method is compared with that of other fuzzy or neural network predictors in terms of non-dimensional error index (NDEI).

Forecasting time series with genetic fuzzy predictor ensemble

This paper proposes a genetic fuzzy predictor ensemble (GFPE) for the accurate prediction of the future in the chaotic or nonstationary time series. Each fuzzy predictor in the GFPE is built from two design stages, where each stage is performed by different genetic algorithms (GA). The first stage generates a fuzzy rule base that covers as many of training examples as possible. The second stage builds fine-tuned membership functions that make the prediction error as small as possible. These two design stages are repeated independently upon the different partition combinations of input-output variables. The prediction error will be reduced further by invoking the GFPE that combines multiple fuzzy predictors by an equal prediction error weighting method. Applications to both the Mackey-Glass chaotic time series and the nonstationary foreign currency exchange rate prediction problem are presented. The prediction accuracy of the proposed method is compared with that of other fuzzy and neural network predictors in terms of the root mean squared error (RMSE).