Intuitionistic Fuzzy Inference System Research Articles

A New Hybrid Supervisory Control System for Cabinet-Type Firebox Furnaces

In this paper, an intelligent hybrid Industrial Control System (ICS) and a Supervisory Control System (SCS) are proposed to improve the efficiency, safety, availability, and control capabilities of industrial furnaces. The main components of ICS are process control systems and advanced control systems that consist of overheating protection and load control. New soft sensors are designed as a combination of Laguerre filters and an artificial neural network to estimate the surface temperature of the furnace’s tubes, which allows the protection system to adjust fuel flow rate via overriding commands. Model-based fault detection systems are developed to detect faults in the combustion system and fouling in the furnace tubes and prepare features for the supervisory system. The supervisory control system is responsible for interfering between different components, evaluating the situation, and decision making based on the unit status and process conditions. An intuitionistic fuzzy inference system is employed as the core of the supervisory controller to tolerate disturbance and faults by switching the control modes. Test studies using experimental data of the furnace indicate the capability of the proposed monitoring and control system to operate in various loading situations and recover the system from abnormal conditions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In petrochemical industries, several reports have been issued about the load reduction of fired-heater furnaces imposed by combustion system faults and emergency shutdowns to carry out un-planned repairs due to fouling and wax-formation in tubes. Different activities such as detecting abnormal conditions, identifying faults, and enforcing corrective action can be performed by operators through manual actions. This paper is focused on designing a new supervisory control system (SCS) to be able to recover the fired heater furnace from abnormal conditions and keep running the plant. The SCS evaluates the condition of the unit by acquiring information from main variables, sensors, actuators, operating status of components and utilities, and operator commands. By identifying the root cause of faults, SCS makes decision on recognizing hazard degree, raising alarms, and applying automatic corrective actions.

A Novel Intuitionistic Fuzzy Inference System for Feature Subset Selection in Weather Prediction

A Novel Intuitionistic Fuzzy Inference System for Feature Subset Selection in Weather Prediction

A novel intuitionistic fuzzy inference system for sentiment analysis

A novel intuitionistic fuzzy inference system for sentiment analysis

Enriched intuitionistic fuzzy rule pruning using butterfly optimization for rule pruning in intrusion detection

The process of detecting and reacting to hostile activities against the computer and network resources is known as intrusion detection. Rather of relying just on expertise and experience, a systematic and automated IDS creation process is required. This drives researchers to look into data mining-based intrusion detection frameworks. Though there are many algorithms are developed for detection intrusion, the issue of inconsistency in rule-based classification model is controlling the volume of data and removing irrelevant rules are the major problems focused in this work to improve the process of IDS. This paper proposed a framework which handles the hesitancy in classifying the unknown traffic pattern data packets by designing evolutionary algorithm optimized intuitionistic fuzzy inference system. The Intuitionistic fuzzy inference system represents each instances of KDD cup 99 dataset with the degree of membership and non-membership. With these two degrees, the hesitancy degree is computed to overcome the issue of inconsistency and uncertainty in analysing the unknown pattern of abnormal packets. In conventional Intuitionistic fuzzy System (IFS), the rules generated by the inference engine is applied for classification directly without determine the validation of it.

Intuitionistic fuzzy inference system with weighted comprehensive evaluation considering standard deviation-cosine entropy: a fused forecasting model

Intuitionistic fuzzy inference system with weighted comprehensive evaluation considering standard deviation-cosine entropy: a fused forecasting model

Multivariate intuitionistic fuzzy inference system for stock market prediction: The cases of Istanbul and Taiwan

Many of decision-making and policy planning processes involve a time-series prediction problem and so this area has extensive literature including a great variety of time-series prediction tools and inferences systems. An important part of these is based on fuzzy sets. However, it is known that fuzzy sets may fail to satisfy or characterize the uncertainty of the data in a comprehensive manner because they cannot depict the neutrality degree of time-series. Another important and decisive deficiency of current inference systems is to based on the univariate structure. However, the time series dealt with in a prediction problem generally interact with other time series. Considering these issues, creating an inference system based on intuitionistic fuzzy sets and multivariate relationships for a time series prediction problem is a requirement even an obligation. With these regards, this study presents a multivariate intuitionistic fuzzy time-series definition and its prediction models and introduces a multivariate intuitionistic fuzzy inference system (M-IFIS). The basic novelty of the article can be expressed as the definition of a multivariate intuitionistic fuzzy time series, as well as the creation of a relevant analysis mechanism, first-time in the literature. Sigma-pi neural network is used as an inference tool in M-IFIS and membership and non-membership values and lagged crisp observations of multivariable time-series are used as inputs of it. In order to reveal the performance of the proposed system, Istanbul Stock Exchange (IEX) and Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) are analysed and the results are evaluated as comprehensive and comparative. All findings reveal the superiority M-IFIS in predictive accuracy.

An assessment of the banking industry performance based on Intuitionistic fuzzy Best-Worst Method and fuzzy inference system

COVID-19’s impact on individuals, enterprises, and organizations is undeniable. Many markets, in particular, financial markets, have experienced considerable shock and suffered massive losses. Supply chain networks have encountered significant problems, especially in terms of financing. Supply chain finance (SCF), responsible for financing the supply chain components and improving supply chain performance, received a significant impact of COVID-19 consequences. Financial providers are the leading resource for supply chain finance. The banking sector is known as the main element of financing among financial providers. Any disruption in the banking system procedures can have a significant impact on the financing process. In this paper, we have tried to understand the essential effects of the COVID-19 pandemic on the banking sector. A hybrid approach consists of a novel and extended intuitionistic fuzzy best-worst method (IFBWM) to determine the critical factors’ weights, and a fuzzy inference system to get the performance index of the banking sector is utilized. The proposed method investigates the banking sector’s role in supply chain finance, specifies the principal risks of this sector in emergencies such as pandemics. The Turkish banking sector’s performance in three different periods is considered to demonstrate the validity and applicability of the proposed approach. The results depict that credit risk, loss of income, and liquidity were the most critical factors of the COVID-19 outbreak in the banking sector. Also, the Turkish banking sector’s performance has worsened with the spread and continuation of the COVID-19. Moreover, the cogency of the proposed method is verified through a brief comparative analysis.

An Optimized Hybrid Forecasting Model and Its Application to Air Pollution Concentration

Previous literature suggested that intuitionistic fuzzy inference systems (IFISs) can offer a good forecasting model and intimately linked to the notion of uncertain parameters. However, their performance can be severely degraded by the presence of missing data and less regulated local optima. This study proposes a hybrid IFIS model by assimilating the probabilistic principal component analysis (PPCA) to enhance preprocessing data and particle swarm optimization (PSO) algorithm to optimize the performance of the forecasting model. The main purpose of the PPCA is to diminish outliers affected by defective values and missing values within experimental data. The PSO optimization algorithm is used to tune the parameters of IFIS and thus elevate the prediction performance of the IFIS. Extensive experimental data on meteorological parameters that are recognized as driving factors of tropospheric pollution were employed to study the benefits of the proposed hybrid model. Comparable three error measures are presented to check the performance of the proposed model against the other models. The error analysis result clearly highlights that the proposed hybrid model is performed better compared to the other IFIS-based models and the well-known existing models.

An Intuitionistic Fuzzy Based Novel Approach to CPU Scheduler.

The extension of CPU schedulers with fuzzy has been ascertained better because of its unique capability of handling imprecise information. Though, other generalized forms of fuzzy can be used which can further extend the performance of the scheduler. This paper introduces a novel approach to design an intuitionistic fuzzy inference system for CPU scheduler. The proposed inference system is implemented with a priority scheduler. The proposed scheduler has the ability to dynamically handle the impreciseness of both priority and estimated execution time. It also makes the system adaptive based on the continuous feedback. The proposed scheduler is also capable enough to schedule the tasks according to dynamically generated priority. To demonstrate the performance of proposed scheduler, a simulation environment has been implemented and the performance of proposed scheduler is compared with the other three baseline schedulers (conventional priority scheduler, fuzzy based priority scheduler and vague based priority scheduler). Proposed scheduler is also compared with the shortest job first CPU scheduler as it is known to be an optimized solution for the schedulers. Simulation results prove the effectiveness and efficiency of intuitionistic fuzzy based priority scheduler. Moreover, it provides optimised results as its results are comparable to the results of shortest job first.

A Parameterized Intuitionistic Type-2 Fuzzy Inference System with Particle Swarm Optimization

The aim of this study was to develop a novel intuitionistic Type-2 fuzzy inference system (IT-2 FIS) which adopts a parameterized Yager-generating function and particle swarm optimization (PSO). In IT-2 FIS, the intuitionistic Type-2 is set as a fuzzy symmetrical triangular number in which the hesitation degree adopts the Yager-generating function, and the parameters of the proposed IT-2 FIS adopting the PSO are tuned. The intuitionistic and Type-2 fuzzy sets have been proven to be the most effective for handling more uncertainty. Therefore, this study proposes an intuitionistic Type-2 set with a Yager-generating function to enhance the conventional fuzzy inference system. Moreover, PSO can improve the fuzzy inference system by searching for the optimal parameters of IT-2 FIS. In this study, linguistic variables were represented by triangular fuzzy numbers (TFS). Two numerical examples were examined: capacity-planning and medical diagnosis problems. An approaching capacity-loadings example was used to verify that the proposed IT-2 FIS could effectively estimate the results of the capacity loadings. In the medical diagnosis problem, IT-2 FIS could obtain a higher correct rate by revealing experts’ knowledge. In both examples, the proposed IT-2 FIS provided more objective estimated values than traditional fuzzy inference systems (FIS) and Type-2 FIS.

Intuitionistic fuzzy time series functions approach for time series forecasting

Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods.

Iot based Agriculture Drought Prediction using Chaotic Genetic Algorithm Integrated Intuitionist Fuzzy Subtractive Clustering

The exponential demand in usage of internet of Things (IoT) devices, there is a vast effective improvement in commination among different things. Especially in the field of agriculture, IoT based applications plays a vital role to make the functionalities more reliable. With the perception of IoT and wireless sensor network, smart intelligent farming system has become a significant research area for researchers. Several researchers have developed automation and monitoring system for various agricultural functionalities. One of the serious issues is agricultural droughts which affect crop production or the ecology of the range. This research work aims to overwhelm this issue positively by enhancing the agriculture drought prediction in India. This proposed technique enriches the quality of the dataset by finding the similar patterns using chaos genetic algorithm based Intuitionistic fuzzy Subtractive Clustering. The uncertainty in drought prediction is greatly handled by representing the dataset in the form of intuitionistic fuzzy domain which gives more importance to the degree of indeterminacy. Intuitionistic fuzzy inference system is enhanced with the knowledge of subtractive clustering. The cluster centroids are selected by the chaotic genetic algorithm,which overcomes the earlier convergence and increase the search space in a parallel manner to handle voluminous agriculture dataset. Feed forward neural network is used for predicting the clustered agriculture dataset to provide intelligent smart solution for drought prediction and to improve the crop growth monitoring task by farmers.

Intuitionistic time series fuzzy inference system

Although adaptive network fuzzy inference system and fuzzy functions approach can be utilized as a prediction tool, they have been not designed for prediction problem and they ignore the dependency structure of time series observations. From this viewpoint, making a design of the method that considers the dependency structure of observations will provide to get more accurate prediction. In this study, an intuitionistic time series fuzzy inference system (I-TSFIS) has been proposed. In the I-TSFIS, in just the same way as in the intuitionistic fuzzy inference systems, not only the membership values and crisp observations but also the non-membership values are used as inputs. Moreover, due to the use of crisp values as targets and outputs, the output does not need to be deffuzzified. Non-linear relationships between inputs and outputs of the proposed I-TSFIS are determined by Sigma-Pi neural network (SP-NN). The obtaining of optimal weights of SP-NN is performed by modified particle swarm optimization. And also I-TSFIS uses intuitionistic fuzzy C-means to obtain fuzzy clusters, membership and non-membership values of observations for these clusters. To evaluate the prediction performance of the proposed I-TSFIS, various real-life time series data sets have been analyzed and the results demonstrate the superior prediction ability of the proposed I-TSFIS.

Intuitionistic neuro-fuzzy network with evolutionary adaptation

Intuitionistic fuzzy inference systems (IFISs) incorporate imprecision in the construction of membership functions present in fuzzy inference systems. In this paper we design intuitionistic neuro-fuzzy networks to adapt the antecedent and consequent parameters of IFISs. We also propose a mean of maximum defuzzification method for a class of Takagi–Sugeno IFISs and this method is compared with the basic defuzzification distribution operator. On both real-life credit scoring data and seven benchmark regression datasets we show that the intuitionistic neuro-fuzzy network trained with particle swarm optimization outperforms traditional ANFIS methods (hybrid and backpropagation) and ANFIS trained with evolutionary algorithms (genetic algorithm and particle swarm optimization), respectively. A set of nonparametric tests for multiple datasets is performed to demonstrate statistical differences between the algorithms. In the task of adapting the intuitionistic neuro-fuzzy network, we show that particle swarm optimization provides a higher prediction accuracy compared with traditional algorithms based on gradient descent or least-squares estimation.

Medical diagnosis with the aid of using fuzzy logic and intuitionistic fuzzy logic

The objective of the present study is to develop/establish a web-based medical diagnostic support system (MDSS) by which health care support can be provided for people living in rural areas of a country. In this respect, this research provides a novel approach for medical diagnosis driven by integrating fuzzy and intuitionistic fuzzy (IF) frameworks. Subsequently, based on the proposed approach a web-based MDSS is developed. The proposed MDSS comprises of a knowledge base (KB) and intuitionistic fuzzy inference system (IFIS). Based on the observation that medical data cannot be described with both precision and certainty, a medical KB is constructed in the form of a set of if-then decision rules by employing both fuzzy and IF logics. After constructing the medical KB, a new set of patients is considered for diagnosing the diseases. For each patient, linguistic values of the patients' symptoms are considered as inputs of the proposed IFIS and modeled by using the generalized triangular membership functions. Subsequently, integrated fuzzy and IF rule-based inference system is used to find a valid conclusion for the new set of patients. In a nutshell, in this paper fuzzy rule-based and IFS based inference systems are combined for better and more realistic representation of uncertainty of the medical diagnosis problem and for more accurate diagnostic result. The method is composed of following four steps: (1) the modeling of antecedent part of the rules, which consist of linguistic assessments of the patients' symptoms provided by the doctors/medical experts with their corresponding confidence levels, by using generalized fuzzy numbers; (2) the modeling of consequent part, which reveals the degree of association and the degree of non-association of diseases into the patient, by using IFSs; (3) the use of IF aggregation operator in inference process; (4) the application of relative closeness function to find the final crisp output for a given diagnosis. Finally, the applicability of the proposed approach is illustrated with a suitable case study. This article has also justified the proposed approach by using similarity measurement.

Intuitionistic Fuzzy Logic Control for Washing Machines

In this paper, we describe the development of an intuitionistic fuzzy logic controller for washing machine on the basis of intuitionistic fuzzy systems. Intuitionistic fuzzy inference systems and defuzzification techniques are used to obtain crisp output (i.e., wash time of the washing machine) from an intuitionistic fuzzy input (i.e., type of dirt and degree of dirt). The wash time is calculated using intuitionistic fuzzy rules applied to an inference engine using defuzzification methods.

Intuitionistic Fuzzy Logic Control for Heater Fans

The concept of intuitionistic fuzzy systems, including intuitionistic fuzzy sets and intuitionistic fuzzy logic, was introduced by Atanassov as a generalization of fuzzy systems. Intuitionistic fuzzy systems provide a mechanism for communication between computing systems and humans. In this paper, we describe the development of an intuitionistic fuzzy logic controller for heater fans, developed on the basis of intuitionistic fuzzy systems. Intuitionistic fuzzy inference systems and defuzzification techniques are used to obtain crisp output (i.e., speed of the heater fan) from an intuitionistic fuzzy input (i.e., ambient temperature). The speed of the heater fan is calculated using intuitionistic fuzzy rules applied in an inference engine using defuzzification methods.

Ozone prediction on the basis of neural networks, support vector regression and methods with uncertainty

The article presents modeling of daily average ozone level prediction by means of neural networks, support vector regression and methods based on uncertainty. Based on data measured by a monitoring station of the Pardubice micro-region, the Czech Republic, and optimization of the number of parameters by a defined objective function and genetic algorithm a model of daily average ozone level prediction in a certain time has been designed. The designed model has been optimized in light of its input parameters. The goal of prediction by various methods was to compare the results of prediction with the aim of various recommendations to micro-regional public administration management. It is modeling by means of feed-forward perceptron type neural networks, time delay neural networks, radial basis function neural networks, ε-support vector regression, fuzzy inference systems and Takagi–Sugeno intuitionistic fuzzy inference systems. Special attention is paid to the adaptation of the Takagi–Sugeno intuitionistic fuzzy inference system and adaptation of fuzzy logic-based systems using evolutionary algorithms. Based on data obtained, the daily average ozone level prediction in a certain time is characterized by a root mean squared error. The best possible results were obtained by means of an ε-support vector regression with polynomial kernel functions and Takagi–Sugeno intuitionistic fuzzy inference systems with adaptation by means of a Kalman filter.