Fuzzy Inference Rules Research Articles

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.

Forecasting the orbital-climate diagram dynamics based on the wavelet analysis and fuzzy neural networks

Based on the new concept of the V. Bolshakov's orbital theory of paleoclimate, as well as the multiscale time-series wavelet decomposition method, and neural network fuzzy inference rules, the paper derives a predicted curve for the so-called orbital-climate diagram (OCD) in the ratio of (eccentricity, orbit inclination, precession) within the time interval from -1000 kyr in the past to 100 kyr in the future since modern times. This diagram features the Earth climate change caused by an insolation change to be the principal factor of the climate change, driven by Earth's orbital elements changes. Efficiency of the time-series forecast method is proved by the obtained predicted OCD trajectory verification within the past 100 kyr period and other paleoclimate data with a correlation coefficient 0.93.

Hybrid performance evaluation of sustainable service and manufacturing supply chain management: An integrated approach of fuzzy dematel and fuzzy inference system

SummaryThe aim of this paper is to propose a comprehensive framework for simultaneously measuring the performance of sustainable service and manufacturing supply chain management. Application of the proposed approach also results in reduced uncertainty of the performance measurement process caused by qualitative criteria evaluation. The proposed approach consists of two main steps. First, the fuzzy decision‐making trial and evaluation laboratory (DEMATEL) method has been used to determine important criteria by avoiding low influences; and then a Mamdani fuzzy inference system model has been adopted and applied for performance evaluation of sustainable supply chain management (SSCM). This model is employed in order to cope with the vagueness that exists in the SSCM performance investigation due to the vagueness intrinsic in the evaluation of criteria. In the proposed model, human reasoning has been modelled with fuzzy inference rules and has been set in the system, which is an advantage compared with those models in which fuzzy set theory and multicriteria decision‐making models are integrated. The proposed approach has been implemented in the pipe and fitting industry in order to highlight its application in real life. Sensitivity analysis has been carried out to determine the influence of service and manufacturing criteria on SSCM performance. The findings reveal that sustainable manufacturing criteria compared with sustainable service criteria have more effect on the performance of SSCM.

Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS.

For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively.

Fuzzy based QoS prediction using bayesian network in cloud computing environment

The Quality of Service (QoS) is enforced in discovering an optimal web service (WS).The QoS is uncertain due to the fluctuating performance of WS in the dynamic cloud environment. We propose a Fuzzy based Bayesian Network (FBN) system for Efficient QoS prediction. The novel method comprises three processes namely Semantic QoS Annotation, QoS Prediction, and Adaptive QoS using cloud infrastructure. The FBN employs the compliance factor to measure the performance of QoS attributes and fuzzy inference rules to infer the service capability. The inference rules are defined according to the user preference which assists to achieve the user satisfaction. The FBN returns the optimal WSs from a set of functionally equivalent WS. The unpredictable and extreme access of the selected WS is handled using cloud infrastructure. The results show that the FBN approach achieves nearly 95% of QoS prediction accuracy when providing an adequate number of past QoS data, and improves the prediction probability by 2.6% more than that of the existing approach.

Takagi–Sugeno Dynamic Neuro-Fuzzy Controller of Uncertain Nonlinear Systems

The identification problem incorporated in feedback control of uncertain nonlinear systems exhibiting complex behavior has been solved in different ways. Some of these solutions have used artificial intelligence methods like fuzzy logic and neural networks. However, their individual implementation suffers from certain drawbacks, such as the black-box nature of neural network and the problem of finding suitable membership functions for fuzzy systems. These weaknesses can be avoided by implementing a hybrid structure combining these two approaches, the so-called neuro-fuzzy system. In this paper, a neuro-fuzzy system that implements differential neural networks (DNNs) as consequences of Takagi–Sugeno (T–S) fuzzy inference rules is proposed. The DNNs substitute the local linear systems that are used in the common T–S method. In this paper, DNNs are used to provide an effective instrument for dealing with the identification of the uncertain nonlinear system, while the T–S rules are used to provide the framework of previous knowledge of the system. The main idea is to carry out an online identification process of an uncertain nonlinear system with the aim to design a closed-loop trajectory tracking controller. The methodology developed in this study that supports the identification and trajectory control designs is based on the Lyapunov formalism. The DNN implementation results in a time-varying T–S system. As a consequence, the solution of two time-varying Riccati equations was used to adjust the learning laws in the DNN as well as to adjust the gains of the controller. Two results were provided to justify the existence of positive-definite solutions for the class of Riccati equations used in the learning laws of DNNs. A complete description of the learning laws used for the set of DNN identifiers is also obtained. An autonomous underwater vehicle system is used to demonstrate the performance of the controller on tracking a desired 3-D path by this combination of the DNN and the T–S system.

An extended Takagi\u2013Sugeno\u2013Kang inference system (TSK+) with fuzzy interpolation and its rule base generation

A rule base covering the entire input domain is required for the conventional Mamdani inference and Takagi–Sugeno–Kang (TSK) inference. Fuzzy interpolation enhances conventional fuzzy rule inference systems by allowing the use of sparse rule bases by which certain inputs are not covered. Given that almost all of the existing fuzzy interpolation approaches were developed to support the Mamdani inference, this paper presents a novel fuzzy interpolation approach that extends the TSK inference. This paper also proposes a data-driven rule base generation method to support the extended TSK inference system. The proposed system enhances the conventional TSK inference in two ways: (1) workable with incomplete or unevenly distributed data sets or incomplete expert knowledge that entails only a sparse rule base and (2) simplifying complex fuzzy inference systems by using more compact rule bases for complex systems without the sacrificing of system performance. The experimentation shows that the proposed system overall outperforms the existing approaches with the utilisation of smaller rule bases.

The Decision-making Model for the Stock Market under Uncertainty

<p>The main purpose of this research is developing methods and models of decision-making to assess the stock market state, and predict the possible changes in the RTS index value. This article shows that the analytical models for assessing the stock market state do not give reliable results. The absence of the reliable estimates associated with the high degree of uncertainty, random, nonlinear and non-stationary process with a significant degree of aftereffect. In this paper, to formalize the securities market parameters it’s proposed the fuzzy sets method. To assess the stock market current state and make decisions the fuzzy situational analysis model (situational model) is applied. The analytical prediction results of the stock market and graph of the RTS index expected return changes in 2014-2016 are showed. The model of calculating the fuzzy inference rules truth degree to predict the RTS index is developed. The market parameters linguistic definition is given and the expert’s rules construction to predict the RTS index growth is shown. The program in Matlab environment is designed to perform research. The study result showed that the model allows for the RTS index prediction in the condition of incomplete initial data with a confidence level about 90%.</p>

Heterogeneous classifier ensemble with fuzzy rule-based meta learner

In heterogeneous ensemble systems, each learning algorithm learns a classifier on a given training set to describe the relationship between a feature vector and its class label. As each classifier outputs different result on an observation, uncertainty is introduced. In this paper, we introduce a heterogeneous ensemble system with a fuzzy IF-THEN rule inference engine as the combiner to capture the uncertainty in the outputs of the base classifiers. In our method, fuzzy rules are generated on the outputs of an ensemble of base classifiers, which can be viewed as the class posterior probability of the observations. The performance of our method was evaluated on thirty datasets and in comparison with nine ensemble methods (AdaBoost, Decision Template, Decision Tree on meta-data, and six fixed combiners) and two single learning algorithms (SVM with L2-loss function and Decision Tree), and was shown to significantly outperforms these algorithms in terms of classification accuracy.

The Application of Mamdani Fuzzy Inference System in Evaluating Green Supply Chain Management Performance

Qualitative criteria for assessing green supply chain management (GSCM) performance are influenced by uncertainty, essentially due to the vagueness intrinsic to the evaluation of qualitative factors. This paper aims to decrease the uncertainty which is caused by human judgments in the process of GSCM performance evaluation employing linguistic terms and degrees of membership. In this study, a fuzzy set theory approach has been proposed for handling the linguistic imprecision and the ambiguity of human being’s judgment. It also pioneers applying the fuzzy inference system for evaluating GSCM performance of companies in terms of green criteria. In the proposed model, human reasoning has been modeled with fuzzy inference rules and has been set in the system, which is an advantage when compared to the models that combine fuzzy set theory with multi-criteria decision-making models. To highlight the real-life applicability of the proposed model, an empirical case study has been conducted. Findings reveal the usefulness of the proposed model in evaluating the performance of companies according to GSCM criteria with human linguistic terms. Findings also indicate that green design and green manufacturing dimensions have the highest impact on company performance. The robustness of the proposed FIS model has been proved with different defuzzification methods.

Fuzzy probabilistic seismic hazard analysis with applications to Kunming city, China

China is prone to highly frequent earthquakes due to specific geographical location, which could cause significant losses to society and economy. The task of seismic hazard analysis is to estimate the potential level of ground motion parameters that would be produced by future earthquakes. In this paper, a novel method based on fuzzy logic techniques and probabilistic approach is proposed for seismic hazard analysis (FPSHA). In FPSHA, we employ fuzzy sets for quantification of earthquake magnitude and source-to-site distance, and fuzzy inference rules for ground motion attenuation relationships. The membership functions for earthquake magnitude and source-to-site distance are provided based on expert judgments, and the construction of fuzzy rules for peak ground acceleration relationships is also based on expert judgment. This methodology enables to include aleatory and epistemic uncertainty in the process of seismic hazard analysis. The advantage of the proposed method is in its efficiency, reliability, practicability, and precision. A case study is investigated for seismic hazard analysis of Kunming city in Yunnan Province, People’s Republic of China. The results of the proposed fuzzy logic-based model are compared to other models, which confirms the accuracy in predicting the probability of exceeding a certain level of the peak ground acceleration. Further, the results can provide a sound basis for decision making of disaster reduction and prevention in Yunnan province.

Fuzzy Sliding Mode Variable Structure Control for Brushless DC Motor Position Servo System

To solve the problem of the chattering and strict requirement of the accurate parameter of the model in the sliding mode control, a fuzzy sliding mode controller is proposed in this paper. The new model is constructed by means of introducing fuzzy control into sliding mode control. Parameters of PID control method and reaching law are real-time updated by using fuzzy rule inference. The proposed method utilizes full advantages of fuzzy PID control and sliding mode control so that the position tracking performance and robustness of the system are remarkably improved. In addition, chattering of sliding mode control is reduced. Experiments results are presented to validate the correctness and effectiveness of the proposed method

Development of a Risk-based Methodology for Rock Slope Analysis

The objective of this research is to develop a geotechnical risk assessment methodology to concurrently identify and quantify important risk elements in rock slopes. Thus, important geotechnical issues affecting a high rock slope were categorized into three major critical risk groups after a comprehensive literature review and field observation program. A set of knowledge-based fuzzy inference rules were established to set up membership functions and calculate risk index (RI) for each critical risk element (CRE). The CREs were ranked on the basis of the calculated RI. The developed risk algorithm was employed to assess the geotechnical risks associated with the right abutment slope of Vanyar dam, Iran. The analysis results showed that the presence of high water level (RI 4.47), shear strength of discontinuities (RI 4.27), and presence of shear or fault zones in the slope (RI 4.01) were the most critical elements which affected the slope stability, respectively. According to calculated risk indices the engineers should pay more attention to the underground water level in the dam abutments. The proposed method could provide scientific decision-making basis for varying risk levels associated with rock slope engineering and can be used as an aid in corresponding risk control measures. The obtained results indicated that the developed algorithm is beneficial in providing guidelines for design and remedial measures to improve slope construction and performance. The present study proposes a methodology based on a combination of project management, risk modelling, and geotechnical engineering.

A new adaptive network-based fuzzy inference system with adaptive adjustment rules for stock market volatility forecasting

Forecasting stock volatility is crucial to many fundamental problems of financial field, such as risk management, asset pricing and asset allocation etc. This paper proposes a new Adaptive Network-Based Fuzzy Inference System (ANFIS) which adaptively adjusts fuzzy inference rules by using Fruit Fly Optimization Algorithm (FOA). Empirical analysis is made on the Shanghai A-share sample stocks. Compared with ANFIS, the experimental results reveal that this new model can accurately and successfully forecast the sample stocks' volatility.

A time/space separation based 3D fuzzy modeling approach for nonlinear spatially distributed systems

Spatially distributed systems (SDSs) are usually infinite-dimensional spatio-temporal systems with unknown nonlinearities. Therefore, to model such systems is difficult. In real applications, a low-dimensional model is required. In this paper, a time/space separation based 3D fuzzy modeling approach is proposed for unknown nonlinear SDSs using input-output data measurement. The main characteristics of this approach is that time/space separation and time/space reconstruction are fused into a novel 3D fuzzy system. The modeling methodology includes two stages. The first stage is 3D fuzzy structure modeling which is based on Mamdani fuzzy rules. The consequent sets of 3D fuzzy rules consist of spatial basis functions estimated by Karhunen-Love decomposition. The antecedent sets of 3D fuzzy rules are used to construct temporal coefficients. Going through 3D fuzzy rule inference, each rule realizes time/space synthesis. The second stage is parameter identification of 3D fuzzy system using particle swarm optimization algorithm. After an operation of defuzzification, the output of the 3D fuzzy system can reconstruct the spatio-temporal dynamics of the system. The model is suitable for the prediction and control design of the SDS since it is of low-dimension and simple nonlinear structure. The simulation and experiment are presented to show the effectiveness of the proposed modeling approach.

A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference system

In today’s competitive environment, measuring companies’ performance properly has become a vital subject not only for investors but also for the companies that are working in the same sector. The achieved results of performance measurement can help managers to identify means of improvement, measure progress and find unknown problems in the company. There are many efficiency frontier analysis methods to evaluate performance; but, each of these methods has its strength as well as major limitations. In this article, a fuzzy approach based on Mamdani fuzzy inference system is presented for performance measurement of manufacturing systems. The generation of fuzzy rules is the biggest consideration in designing the proposed model. In fact, fuzzy inference rules model human reasoning and are embedded in the system, which is an advantage when compared to approaches that combine fuzzy set theory with multi-criteria decision-making methods. A fuzzy inference system is constructed and applied to measure the performance or efficiency of manufacturing systems. Implementation of the proposed model is analyzed and discussed using a real case. The results reveal the usefulness of the proposed model in evaluating the performance of manufacturing companies.

Fuzzy Based Personal Color System

This paper, which is based on the research in the personal color diagnoses system, uses the fuzzy logic and fuzzy inference rule to improve the personal color selection method of the existing personal color system and to get more objective and reliable result for personal color. We propose the method which constructs more systematic color selection system using personal color database and fuzzy logic.

APRICOIN: An Adaptive Approach for Prioritizing High-Risk Containers Inspections

Risk evaluation of the containers remains a difficult task, often due to incomplete or ambiguous information on containers. In addition, the evaluation process needs to be adapted on an ongoing basis to cope with emerging risk factors. Furthermore, high-risk container inspection is commonly hindered by a low inspection capacity, which leads to a major issue: how can we prioritize the container inspection if the number of suspect containers exceeds the inspection capacity? Container inspection prioritizing may be the answer. In this paper, we propose a novel approach for adaptively prioritizing container inspection (APRICOIN). First, we enhance the container information flow to alleviate the problem of incomplete information by proposing an enhanced container descriptive. Second, we introduce the APRICOIN algorithm, which combines frequent pattern mining and a fuzzy logic system, to assess the container’s risk score. The frequent pattern growth algorithm is proposed to retrieve the key criteria for evaluating container risk. This is done through mining frequent criteria sets within the historic data set of container inspections by customs. The mined frequent criteria sets are used to assess fuzzy inference rules which are periodically readjusted to integrate new key criteria. Thereafter, the fuzzy logic system uses the obtained fuzzy inference rules to calculate a container’s risk score. Our major contribution consists of providing a new adaptive approach for assessing a container’s risk through combining frequent criteria mining and fuzzy logic. An illustrative study and a comparison with alternative approaches are performed to validate the proposed algorithm.

A t-Norm Fuzzy Logic for Approximate Reasoning

A t-norm fuzzy logic is presented, in which a triangular norm (t-norm) plays the role of a graduated conjunction operator. Based on this fuzzy logic we develop methods for fuzzy reasoning in which antecedents and consequents involve fuzzy conditional propositions of the form “If x is A then y is B”, with A and B being fuzzy concepts (fuzzy sets). In this study, we present a systemic approach toward fuzzy logic formalization for approximate reasoning. We examine statistical characteristics of the proposed fuzzy logic. As the matter of practical interest, we construct a set of fuzzy conditional inference rules on the basis of the proposed fuzzy logic. Important features of these rules are investigated.

FISDeT: Fuzzy Inference System Development Tool

This paper introduces FISDeT, a tool to support the design of Fuzzy Inference Systems, composed of a set of Python modules sharing the standard specification language FCL used for FIS definition. FISDeT includes a graphical user interface that enables easy definition and quick update of elements composing the knowledge base of a FIS. Given the knowledge base, the tool can perform the inference of fuzzy rules, providing the output of a FIS for any given input. Modules for creating a fuzzy rule base for classification and verifying the behavior of the classification system are integrated within the environment. The paper also reports applications of FISDeT to acquire available knowledge bases and create a FIS for an image processing task.