Fuzzy Inference Method Research Articles

Parallel Fuzzy Inference Method for Higher Order Takagi–Sugeno Systems

A fuzzy logic inference method is proposed for higher-order Takagi–Sugeno systems with applying the order reduction operation to rules. The possibility of using hierarchical fuzzy higher-order systems to decrease the number of rules and to parallelize fuzzy inference is analyzed. The method is implemented in an intelligent software system to speedup computations in the problem of evaluating investment projects.

Noise Reduction with Inference Based on Fuzzy Rule Interpolation at an Infinite Number of Activating Points: Toward Fuzzy Rule Learning in a Unified Inference Platform

In order to provide a unified platform for fuzzy inference and fuzzy rule learning with noise-corrupted data, a method is proposed for reducing noise in learning data on the basis of a fuzzy inference method called α-GEMINAS (α-level-set and generalized-mean-based inference with fuzzy rule interpolation at an infinite number of activating points). It is expected to prevent fuzzy rules from overfitting to noise in learning data, especially when there is less learning data available for fuzzy rule optimization. The proposed method is named α-GEMI-ES (α-GEMINAS-based local-evolution toward slight linearity for global smoothness) in this paper. α-GEMI-ES iteratively performs α-GEMINAS and reduces the noise in each iteration. This paper mathematically proves that α-GEMI-ES effectively reduces the noise. The noise-reduction process is decisive and thus relies less on trial-and-error-based progress. The noise is reduced by a large amount in the early iterations and the amount of its reduction is decelerated in the later iterations where the deviation in the learning data is suppressed to a great extent. This property makes it easy to determine the termination conditions for the iterative process. Simulation results demonstrate that α-GEMI-ES properly reduces noise as the mathematical proof suggests. The above-mentioned properties indicate that α-GEMI-ES is feasible in practice for the unified platform.

Position Tracking Control of PMSM Based on Fuzzy PID-Variable Structure Adaptive Control

A novel Fuzzy PID-Variable Structure Adaptive Control is proposed for position tracking of Permanent Magnet Synchronous Motor which will be used in electric extremity exoskeleton robot. This novel control method introduces sliding mode variable structure control on the basis of traditional PID control. The variable structure term is designed according to the sliding mode surface which is designed by system state equation, so it could compensate for the disturbance and uncertainty. Considering the chattering of sliding mode system, the fuzzy inference method is adopted to adjust the parameters of PID adaptively in real time online, which can attenuate chattering and improve control precision and dynamic performance of system correspondingly. In addition, compared with the traditional sliding mode control, this method takes the fuzzy PID control item to replace the equivalent control item of sliding mode variable structure control, which could avoid the control performance reduction resulted from modeling error and parameter error of system. It is proved that this algorithm can converge to the sliding surface and guarantee the stability of system by Lyapunov function. Simulation results show that Fuzzy PID-Variable Structure Adaptive Control enjoys better control precision and dynamic performance compared with traditional control method, and it improves the robustness of system significantly. Finally, the effectiveness and practicability of the algorithm are verified by the method of Rapid Control Prototyping on the semiphysical simulation test bench.

Fuzzy Time Series Models Using Pliant- and Asymptotically Pliant Arithmetic-Based Inference

The fuzzy time series modeling techniques proposed in this study are based on a fuzzy inference method in which the fuzzy output is either a so-called pliant or quasi pliant (q-pliant) number. The novelty of the introduced inference method lies in the fact that its fuzzy output is obtained by fuzzy arithmetic operations; namely, via weighted aggregation of pliant numbers or q-pliant numbers, which are the consequents of the fuzzy rules. These fuzzy inference systems are called the pliant arithmetic-based fuzzy inference system (PAFIS) and the quasi pliant arithmetic-based fuzzy inference system (QPAFIS). The advantage of the defuzzification methods of these two systems is twofold. On the one hand, they do not require any numerical integration to generate the crisp output, on the other hand, they run in a constant time. Here, it is discussed how the pliant arithmetic-based fuzzy time series and the quasi pliant arithmetic-based fuzzy time series models can be established by utilizing the PAFIS and QPAFIS methods. Lastly, the modeling capabilities of the introduced methods are also examined on some real-life time series, and the forecasting results are compared with those of some well-known and recent time series forecasting methods. Based on the experimental results, our methods may be viewed as novel viable time series modeling techniques.

The definition of productive reservoirs by the method of fuzzy inference

The lithological classification and allocation of reservoirs are based on the difference of physical and geophysical parameters of rocks. Finding the values of physical and geophysical parameters in some ranges makes it possible to predict the lithology of the formation. The class of information systems based on fuzzy logic provides an effective use of existing knowledge about a certain object. This allows to apply fuzzy logic in intelligent systems of interpretation of well geophysical research materials.

Fuzzy Identification of the Time- and Space-Dependent Internal Surface Heat Flux of Slab Continuous Casting Mold

To identify the transient and distributed internal surface heat flux of the slab mold in continuous casting process, a fuzzy inference method is proposed in this work. For temporal and spatial distribution characteristics of the internal surface heat flux of continuous casting mold, a decentralized fuzzy inference (DFI) identification scheme possessed of a decoupling characteristic in time and space is established. For each temperature measurement point, the fuzzy inference processes are, respectively, executed from the correspondingly observed temperature sequence through corresponding DFI units. In the time domain, according to sensitivity coefficients, the weighing and synthesizing processes for the decentralized inference results are performed to get the temporal compensation vector for the internal surface heat flux of mold. Then, in the space domain, according to the normal distribution function, the weighing and synthesizing processes for the temporal compensation vectors are performed to get the spatial compensation vector for the internal surface heat flux of mold. Numerical tests are carried out to research the influence of the number of thermocouples and measurement errors on the identification results, which prove the effectiveness of proposed scheme in this work.

L-fuzzy concept analysis for three-way decisions: basic definitions and fuzzy inference mechanisms

In this paper, the basic ideas underlying fuzzy logic are introduced into the study of three-way formal concept analysis. This leads naturally to the notion of L-fuzzy three-way concepts. The L-fuzzy three-way operators and their inverse are defined and their properties are given. Based on these operators, two types of L-fuzzy three-way concepts are defined and the corresponding three-way concept lattices are constructed. A possibility theory reading of L-fuzzy three-way concepts is also provided. Moreover, the corresponding fuzzy inference method is studied. Two coherent fuzzy inference methods, the lower approximate fuzzy inference and the upper approximate fuzzy inference, are proposed, respectively.

Comparison of Solar Radiation Intensity Forecasting Using ANFIS and Multiple Linear Regression Methods

Solar radiation forecasting is important in solar energy power plants (SEPPs) development. The electrical energy generated from the sunlight depends on the weather and climate conditions in the area where the SEPPs are installed. The condition of solar irradiation will indirectly affect the electrical grid system into which the SEPPs are injected, i.e. the amount and direction of the power flow, voltage, frequency, and also the dynamic state of the system. Therefore, the prediction of solar radiation condition is very crucial to identify its impact into the system. There are many methods in determining the prediction of solar radiation, either by mathematical approach or by heuristic approach such as artificial intelligent method. This paper analyzes the comparison of two methods, Adaptive Neuro Fuzzy Inference (ANFIS) method, which belongs into the heuristic methods, and Multiple Linear Regression (MLP) method, which uses a mathematical approach. The performance of both methods is measured using the root mean square error (RMSE) and the mean absolute error (MAE) values. The data of the Swiss Basel city from Meteoblue are used to test the performance of the two methods being compared. The data are divided into four cases, being classified as the training data and the data used as predictions. The solar radiation prediction using the ANFIS method indicates the results which are closer to the real measurement results, being compared to the the use MLP method. The average values of RMSE and MAE achieved are 123.27 W/m2 and 90.91 W/m2 using the ANFIS method, being compared to 138.70 W/m2 and 101.56 W/m2 respectively using the MLP method. The ANFIS method gives better prediction performance of 12.51% for RMSE and 11.71% for MAE with respect to the use of the MLP method.

A case study for application of fuzzy inference and data mining in structural health monitoring

In this study, a system for monitoring the structural health of bridge deck and predicting various possible damages to this section was designed based on measuring the temperature and humidity with the use of wireless sensor networks, and then it was implemented and investigated. A scaled model of a conventional medium sized bridge (length of 50 meters, height of 10 meters, and with 2 piers) was examined for the purpose of this study. This method includes installing two sensor nodes with the ability of measuring temperature and humidity on both side of the bridge deck. The data collected by the system including temperature and humidity values are received by a LABVIEW-based software to be analyzed and stored in a database. Proposed SHM monitoring system is equipped by a novel method of using data mining techniques on the database of climatic conditions of past few years related to the location of the bridge to predict the occurrence and severity of future damages. In addition, this system has several alarm levels which are based on analysis of bridge conditions with fuzzy inference method, so it can issue proactive and precise warnings and alarms in terms of place of occurrence and severity of possible damages in the bridge deck to ensure total productive (TPM) and proactive maintenance. Very low costs, increased efficiency of the bridge service, and reduced maintenance costs makes this SHM system a practical and applicable system. The data and results related to all mentioned subjects were thoroughly discussed .

Method of fuzzy inference for one class of MISO-structure systems with non-singleton inputs

In fuzzy modeling, the inputs of the simulated systems can receive both crisp values and non-Singleton. Computational complexity of fuzzy inference with fuzzy non-Singleton inputs corresponds to an exponential. This paper describes a new method of inference, based on the theorem of decomposition of a multidimensional fuzzy implication and a fuzzy truth value. This method is considered for fuzzy inputs and has a polynomial complexity, which makes it possible to use it for modeling large-dimensional MISO-structure systems.

Analysis and Design of Functionally Weighted Single-Input-Rule-Modules Connected Fuzzy Inference Systems

The single-input-rule-modules (SIRMs) connected fuzzy inference method can efficiently solve the fuzzy rule explosion phenomenon, which usually occurs in the multivariable modeling and/or control applications. However, the performance of the SIRMs connected fuzzy inference system (SIRM-FIS) is limited due to its simple input–output mapping. In this paper, to further enhance the performance of SIRM-FIS, a functionally weighted SIRM-FIS (FWSIRM-FIS), which adopts multivariable functional weights to measure the important degrees of the SIRMs, is presented. Then, in order to show the fundamental differences of the SIRMs methods, properties of the traditional SIRM-FIS, the type-2 SIRM-FIS (T2SIRM-FIS), the functional SIRM-FIS (FSIRM-FIS), the SIRMs model with single-variable functional weights (SIRM-FW), and FWSIRM-FIS are explored. These properties demonstrate that the proposed FWSIRM-FIS has more general and complex input–output mapping than the existing SIRMs methods. Such properties theoretically guarantee that better performance can be achieved by FWSIRM-FIS. Furthermore, based on the least-squares method, a novel data-driven optimization method is presented for the parameter learning of FWSIRM-FIS. It can also be used to optimize the parameters of SIRM-FIS, T2SIRM-FIS, FSIRM-FIS, and SIRM-FW. Due to the properties of the least-squares method, the proposed parameter learning algorithm can overcome the drawbacks of the gradients-based parameter learning methods and obtain both smallest training errors and smallest parameters. Finally, to show the effectiveness and superiority of FWSIRM-FIS and the proposed optimization method, six examples and detailed comparisons are given. Simulation results show that FWSIRM-FIS can obtain better performance than the other SIRMs methods, and, compared with some well-known methods, FWSIRM-FIS can achieve similar or better performance but has much less parameters and faster training speed.

Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Model

A fuzzy identification of the dynamical system model is developed upon a data generated by a software simulator of a hydrogen fuel cell. The data presents a black box model, just composed by inputs and outputs, carry no additional information, and showing a strong nonlinear behavior. The choice for a fuzzy identification is based on the data features, and the malleability of the mathematical fuzzy technique. This approach allows to accomplish the objectives of the research, among which, the validation of the method for it used in other industrial problems. The dynamic system identification process is performed using a fuzzy clustering through the Gustafson and Kessel algorithm, and a Takagi Sugeno fuzzy inference method. Validation tests are performed in terms of the 4-fold technique, confirming the lack of the data over-training. These results make the fuzzy approach looks as a promising tool for black-box identification of non linear dynamic systems.

Black-Box Fuzzy Identification of a Nonlinear Hydrogen Fuel Cell Model

A fuzzy identification of the dynamical system model is developed upon a data generated by a software simulator of a hydrogen fuel cell. The data presents a black box model, just composed by inputs and outputs, carry no additional information, and showing a strong nonlinear behavior. The choice for a fuzzy identification is based on the data features, and the malleability of the mathematical fuzzy technique. This approach allows to accomplish the objectives of the research, among which, the validation of the method for it used in other industrial problems. The dynamic system identification process is performed using a fuzzy clustering through the Gustafson and Kessel algorithm, and a Takagi Sugeno fuzzy inference method. Validation tests are performed in terms of the 4-fold technique, confirming the lack of the data over-training. These results make the fuzzy approach looks as a promising tool for black-box identification of non linear dynamic systems.

A Hybrid of Neural Networks and Genetic Algorithm on Multiple Quality Improvement for Pre-Oriented Yarn

This study is concerned with a four steps approach to optimize the parameter combination of the multiple characteristic quality improvement for the pre-oriented yarn. First, problem is defined through the analytic hierarchy process (AHP) to specify the significant quality characteristics and control factors. Second, to greatly reduce the number of experiments, the orthogonal array of Taguchi method is applied for each single quality characteristic. The signal to noise ratios and the fuzzy inference method for quality characteristics are conducted. The artificial neural networks are implemented in the third step. The Taguchi-ANN-GA and Fuzzy-ANN-GA models are compared in this stage. Finally, the genetic algorithm is a plus to optimize the weights of the neural networks in this step. This study compares the RMSEs for multiple quality characteristics to summarize the best setting for the quality improvement of pre-oriented yarn.

Формирование динамической базы знаний систем нечеткого вывода для оценки объектов, изменяющихся во времени

The article is a continuation of a series of author`s papers of dynamic fuzzy sets, which the main purpose is to process various information about the objects of complex systems represented by dynamic fuzzy descriptions. The factors that influence the change of fuzzy set in time and its membership function, for example, the change of the range of included values of variables in the fuzzy set leads to set for the “old” value of the variable a “new” value of the membership function; transformation of the type of membership function are allocated. The decision-making algorithm based on the fuzzy inference method for estimating objects changing in time is considered. The concept of dynamic membership, dynamic rules and dynamic rule base are determined. We propose to add the process of fuzzy inference with the stage of forming a dynamic rule base, which is able to be updated when the behavior of the dynamic object under study changes, meanwhile, to add and remove rules as a whole, and to change any part of the rule is allowed. The conditions, on the basis of which, occurs a change in fuzzy rules are determined. The process of formation of a linguistic variable in the construction of interval estimates of dynamic objects, which consists of six stages and includes the representation and ordering of known expert control points; search for missing values by interpolation and approximation; determining the number of terms of the linguistic variable and their boundaries and the formation of dynamic term-set of values of indicators is considered. In the same way as the dynamic membership function constitutes a fuzzy surface in “the degree of membership-parameter-time space”, a linguistic variable composed of dynamic terms, represents a graphically broken surface with n - vertices according to the number of terms.

Improvement of Control Response Characteristics for Power Facility using the Adaptive Sizing of Fuzzy Inference Method

Improvement of Control Response Characteristics for Power Facility using the Adaptive Sizing of Fuzzy Inference Method

Fuzzy Matching of OpenAPI Described REST Services

The vast amount of Web services brings the problem of discovering desired services for composition and orchestration. The syntactic service matching methods based on the classical set theory have a difficulty to capture the imprecise information. Therefore, an approximate service matching approach based on fuzzy control is explored in this paper. A service description matching model to the OpenAPI specification, which is the most widely used standard for describing the defacto REST Web services, is proposed to realize the fuzzy service matching with the fuzzy inference method developed by Mamdani and Assilian. An evaluation shows that the fuzzy service matching approach performed slightly better than the weighted approach in the setting context.

An adaptive map-matching algorithm based on hierarchical fuzzy system from vehicular GPS data.

An improved hierarchical fuzzy inference method based on C-measure map-matching algorithm is proposed in this paper, in which the C-measure represents the certainty or probability of the vehicle traveling on the actual road. A strategy is firstly introduced to use historical positioning information to employ curve-curve matching between vehicle trajectories and shapes of candidate roads. It improves matching performance by overcoming the disadvantage of traditional map-matching algorithm only considering current information. An average historical distance is used to measure similarity between vehicle trajectories and road shape. The input of system includes three variables: distance between position point and candidate roads, angle between driving heading and road direction, and average distance. As the number of fuzzy rules will increase exponentially when adding average distance as a variable, a hierarchical fuzzy inference system is then applied to reduce fuzzy rules and improve the calculation efficiency. Additionally, a learning process is updated to support the algorithm. Finally, a case study contains four different routes in Beijing city is used to validate the effectiveness and superiority of the proposed method.

Evolutionary Fuzzy Rule-Based Methods for Monotonic Classification

In data science applications, it is very often to require predictive models satisfying monotonicity with respect to the explanatory variables involved in the dataset. In ordinal classification or regression, this occurs when the output variable or class label do not decrease when input variables increase, or vice versa. This problem is commonly known as monotonic classification, and most existing classification techniques are not able to manage this kind of constraints or they require first to monotonize the data. In the literature, the monotonicity has been considered in linguistic fuzzy models, fuzzy-inference methods, and fuzzy rule-based control systems. However, to the best of our knowledge, there is no fuzzy rule-based system designed to produce monotonic fuzzy rule-based models for classification problems. In this paper, we propose to incorporate some mechanisms based on monotonicity indexes for addressing such problems in two popular and competitive evolutionary fuzzy systems algorithms for classification and regression tasks: FARC-HD and FSmogfs $^e$ + Tun $^e$ . In addition, the proposals are able to handle any kind of classification dataset without the necessity of preprocessing. The quality of our approaches is analyzed using statistical analysis and comparing with well-known monotonic classifiers.

Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study

This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically for satellite image classification. We compare a fuzzy-inference method with two other computational intelligence methods, decision trees and neural networks, using a case study of land cover classification from satellite images. Further, an unsupervised approach based on k-means clustering has been also taken into consideration for comparison. The fuzzy-inference method includes training the classifier with a fuzzy-fusion technique and then performing land cover classification using reinforcement aggregation operators. To assess the robustness of the four methods, a comparative study including three years of land cover maps for the district of Mandimba, Niassa province, Mozambique, was undertaken. Our results show that the fuzzy-fusion method performs similarly to decision trees, achieving reliable classifications; neural networks suffer from overfitting; while k-means clustering constitutes a promising technique to identify land cover types from unknown areas.