Fuzzy If-then Rules Research Articles

Investigation of thermal performance of SAC variables using fuzzy logic based expert system

The thermal performance of a solar air collector (SAC) is investigated experimentally under the different climatic conditions of north eastern India using fuzzy logic based expert system (FLES). The FLES based on subtractive clustering (SC) with the fuzzy logic method where here, SC is used for extraction of optimal fuzzy IF-THEN rules while a fuzzy logic is used for modeling of SAC variables. This work considered four input variables [like mass flow rate (m), collector tilt angles (θ), solar radiation (Q), temperature (T)] and the four output variables [i.e. efficiency (η), exergetic efficiency (ηII), temperature rise (∆T), and pressure drop (∆P)]. First, 272 trials of experimentation on SAC are performed by varying m from 0.0078 to 0.0118 kg/s and θ from 30 to 60°, whereas the variation of metrological data is obtained in different working days. Then modeling and parametric analysis is carried out for SAC. Experimental results reveal that the value of η increases with the increase in m, Q, T and θ up to 45°. The higher value of m results in a higher value of ∆P and that reduces the value of ηII. Also, FLES model provides comparable and acceptable values for SAC. At last, validation of the FLES model is done via published data to confirm the results.

Ontology-based representation and reasoning about precise and imprecise temporal data: A fuzzy-based view

Temporal representation and reasoning are important facets in the design of many Semantic Web applications. Several approaches exist to represent and reason about precise temporal data in ontology. However, most of them handle only time intervals and associated qualitative relations. Besides, to the best of our knowledge, there is no approach devoted to handle imprecise temporal data (e.g., “late 1970s”). In this paper, we propose an ontology-based approach for representing and reasoning about precise and imprecise temporal data. Quantitative temporal data (i.e., time intervals and points) and qualitative ones (i.e., relations between time intervals, relations between a time interval and a time point and relations between time points) are taken into consideration. Our approach is three folds: (i) extending the 4D-fluents approach with new crisp and fuzzy components, to represent precise and imprecise temporal data, (ii) extending the Allen’s interval algebra to enable reasoning about precise and imprecise temporal data, and (iii) creating a Fuzzy-OWL 2 ontology TimeOnto that, based on the extended Allen’s interval algebra, instantiates our 4D-fluents-based representation. The extension that we propose for the Allen’s interval algebra handles precise and imprecise time intervals. Indeed, it enables expressing precise (e.g., “before”) and imprecise (e.g., “just before”) temporal relations. Compared to related work, our imprecise relations are personalized, in the sense that they are not limited to a defined set of interval relations and their meanings are determined by the domain expert. For instance, the classic Allen’s relation “Before” may be generalized in 5 imprecise relations, where “Before(1)” means “just before” and gradually the time gap between the two intervals increases until “Before(5)” which means “very long before”. To enable this representation, we propose an extension of the Vilain and Kautz’s point algebra and redefined the Allen’s relations by means of this extended algebra. We show in this paper that, unlike most related work, the resulting relations preserve many of the desirable properties of the Allen’s interval algebra. The definitions of the resulting interval relations are adapted to allow relating a time interval and a time point, and two time points, where time intervals and points maybe both precise or both imprecise. These relations can be used for temporal reasoning by means of four transitivity tables. Finally, we describe a prototype based on “TimeOnto” that infers new relations using a set of SWRL and fuzzy IF-THEN rules. This prototype was integrated in an ontology-based memory prosthesis for Alzheimer’s patients.

The Role of Supply Chain Features in the Effectiveness of Sustainability Practices in the Service Supply Chain: Application of Fuzzy Rule-Based System

This study analyzes the affecting variables on the relation of sustainability practice-performance in the service supply chain. The identified factors (supply chain features) are classified in a hierarchical structure of Fuzzy Analytic Hierarchy Process (FAHP): Macro Environment Features, Service Provider Features, Supplier Features, Employee Features, and Customer Features. Then, the FAHP is applied to identify the importance of each factor in the hospital supply chain, as an example of the service sector. To extract and predict the factors’ interactions, the knowledge base of the fuzzy if-then rules is constructed. The developed Fuzzy Inference System (FIS) gives insights on the supply chain features with significant impact on the effectiveness of sustainability initiatives. So, by predicting and strategically managing the affecting factors, service supply chain managers can have more effective and sustainable performance. The fuzzy surfaces indicate that the features of the macro environment, especially the government regulations, are crucial factors influencing the movement toward sustainability.

Design and implementation of an expert system for periodic and emergency control under uncertainty: A case study of city gate stations

Safety analysis is essential in the natural gas transmission industry to guarantee effective hazard identification and to prevent the failure of components in advance. The overall aim of this research is to introduce a new hybrid expert system and fuzzy logic to monitor city gate stations as one of the most vital installations of gas distribution networks. The proposed model utilizes fuzzy if-then rules based on multiple experts' opinions to study the compound interrelations between the mechanical and physical elements of city gate stations. The presented expert system accounts for uncertainty associated with the experts’ judgments by fuzzy sets theory. The expert system is implemented in an object-oriented platform and is programmed with C#. The validity of the expert system is confirmed using simulation experiments through a case study of city gate stations.

A fast learning algorithm based on extreme learning machine for regular fuzzy neural network

The regular fuzzy neural network (RFNN) is a kind of fuzzy neural network by fuzzifying the feed-forward neural network. The RFNN can directly deal with the language information and it has the merits of fuzzy system and neural network. It is presented a fast learning algorithm based on the extreme learning machine (ELM) for the RFNN in this paper. The RFNN referred here is a three-layer feed-forward fuzzy neural network and the connected weights in the RFNN are all fuzzy numbers. A simulation example is given to approximately realize the fuzzy if-then rules by the RFNN. The results show that the RFNN trained by the proposed algorithm has good performance and approximation ability.

Multi-objective optimisation of risk and business strategy in real-world supply networks in the presence of uncertainty

Selection of suppliers is very important for a strategic supply network (SN) design. This paper presents a novel multi-objective optimisation model for supplier selection and order allocation. In addition to a standard objective of total SN cost minimisation, two new objectives are considered: minimisation of suppliers’ risk and maximisation of achievement of a manufacturer business strategy. Uncertainty in supply lead times and non-conformance rates of delivered components causes uncertainty in the SN cost objective. These parameters are described using imprecise linguistic terms and modelled using fuzzy numbers. Risk classification of suppliers is carried out using imprecise knowledge which is modelled using fuzzy If-Then rules and embedded in the risk objective. Various experiments are carried out to analyse the trade-off between the considered objectives and the impact of SN network parameters on the suppliers’ selection and order allocation. The size of the problem that the model can handle is analysed also.

Robust synchronization of chaotic systems with novel fuzzy rule-based controllers

This paper proposes a set of novel fuzzy controllers with adjustable control forces for the robust synchronization of different chaotic systems that include uncertain terms or unexpected disturbances. The proposed fuzzy rule-based control system is developed through Lyapunov stability theory and the fuzzy logic concept to form a multi-input multi-output (MIMO) system. This system provides flexible control inputs based on the error dynamics of the system, and the inputs satisfy the synchronization condition of being asymptotically stable. In this case, the error derivatives always have signs opposite those of the error states, which yields negative definite first-order Lyapunov function derivatives. Furthermore, the proposed system appropriately adjusts the control inputs through the constructed fuzzy if-then rules. This method is robust to both unmodeled terms and unexpected interference signals during the synchronization process. The simulation results demonstrate the feasibility and effectiveness of the proposed fuzzy rule-based control system.

Formalization of Fuzzy Control in Possibility Theory via Rule Extraction

Possibility system has been recently recognized as a potential foundational theory for fuzzy set theory though the concept of possibility originated from the membership function of fuzzy sets. As an application of fuzzy set theory, fuzzy control has been widely used in engineering practices, where control laws are described by fuzzy if-then rules. This paper intends to formulate fuzzy control in the framework of possibility theory by regarding the fuzzification procedure of fuzzy control as extraction process of the fuzzy variable from crisp input, and the fuzzy if-then rules as extracted rules between fuzzy input/output. Typical procedures of fuzzy control, such as fuzzification, implication, aggregation, and defuzzification, are eventually formulated as a series of conditional possibilities operated by “max-product” operators. The reformulated fuzzy controller has a more general form, which encompasses the Mamdani fuzzy controller as a special case. Some fundamental concepts of possibility theory, such as randomness/fuzziness, possibility, and conditional possibility, are also discussed, which may be helpful for the correct understanding of possibility theory. Efforts have been made to bridge the two systems of possibility theory and fuzzy sets by the derivation of composition rule of fuzzy relations from conditional possibility. All results are derived for both normalized possibility and non-normalized possibility. This paper strengthened the role of possibility theory as a foundation for fuzzy sets, and as a complementary method to probability theory for handling information with fuzzy uncertainty.

SISO Intuitionistic Fuzzy Systems: IF-${t}$ -Norm, IF-${R}$ -Implication, and Universal Approximators

Fuzzy system, as the main method of approximate reasoning, has been widely used to solve practical problems. The existing results on fuzzy systems were based on fuzzy sets, fuzzy if-then rule, and fuzzy inference. In this work, we investigate the component of the intuitionistic fuzzy systems, such as the intuitionistic fuzzy triangular norms (IF-t-norm), the intuitionistic fuzzy residual implications (IF-R-implication), the intuitionistic fuzzy if-then rule, the intuitionistic fuzzy inference, and the intuitionistic defuzzification. Accordingly, the single input single output (SISO) intuitionistic fuzzy systems are built. Some examples are paid to illustrate the methods and their application. Moreover, the approximation properties of the intuitionistic Mamdani, the intuitionistic Larsen, the IF-t-norm, and the intuitionistic triple-I fuzzy systems are obtained, which show that the SISO intuitionistic fuzzy systems are the universal approximators.

A priority list-based fuzzy logic controller for short-term unit commitment problem

This paper presents a fuzzy logic-based priority list method to solve the unit commitment problem. The priority list order is selected according to the maximum output power rating of the generating units while satisfying all the constraints over a period of time. The turn on (commitment) and turn off (de-commitment) decision of thermal generating units is handled by priority list order with fuzzy logic algorithm. A fuzzy optimisation-based approach is developed to find the optimal solution by using fuzzy operations and if-then rules. The objective of the optimisation is to achieve minimum operating cost by scheduling of generator units. In solving the unit commitment problem, reliability and feasibility constraints are checked for the planning interval. This method is implemented on standard IEEE 69-bus, 11-thermal generator system. Use of the fuzzy logic controller with priority list method enables the handling of uncertainties in load forecast in an efficient manner.

Mamdani fuzzy-based vehicular grouping at the intersection of roads for smart transportation system

Smart transportation system is an innovative solution to the traffic congestion at the real-time traffic environment. A smart transportation system based on vehicular grouping technique with length as a parameter has proposed in this research contribution to avoid traffic congestion at the intersection of roads. In this method, vehicles which are waiting at the intersection of roads are divided into small groups with highest priority given to the emergency vehicles. Owing to the proposed work, the average number of vehicles which can cross the junction is increased to 2.4 times compare with existing traffic statistics of Hyderabad City, India. Therefore the average waiting time of an emergency vehicle is diminished up to 38%. Mamdani fuzzy inference system has used to frame the fuzzy IF-THEN rules and MATLAB 2014a has used to simulate the proposed work.

Mamdani fuzzy-based vehicular grouping at the intersection of roads for smart transportation system

Smart transportation system is an innovative solution to the traffic congestion at the real-time traffic environment. A smart transportation system based on vehicular grouping technique with length as a parameter has proposed in this research contribution to avoid traffic congestion at the intersection of roads. In this method, vehicles which are waiting at the intersection of roads are divided into small groups with highest priority given to the emergency vehicles. Owing to the proposed work, the average number of vehicles which can cross the junction is increased to 2.4 times compare with existing traffic statistics of Hyderabad City, India. Therefore the average waiting time of an emergency vehicle is diminished up to 38%. Mamdani fuzzy inference system has used to frame the fuzzy IF-THEN rules and MATLAB 2014a has used to simulate the proposed work.

Liver Hepatitis Diagnosing based on Fuzzy Inference System

Hepatitis is considered a liver disease that is difficult to diagnose at an early stage. The number of infected people exceeds two billion, with one million deaths and more than four million infected people registered per year. Therefore, there is a great need for a system to diagnose this disease. Hepatitis is a critical inflammatory liver disease with different causes, including viral infection, alcohol, and the autoimmune system. Several systems were proposed to diagnose and classify this disease, using numerical, rigid, and low level methods such as color histogram, standard deviation, and entropy. In our research, we leveraged these to linguistic, flexible, and high level by applying Fuzzy Logic theory using a Fuzzy Inference System (FIS). In this paper, a model is implemented through many stages where 3D-Discrete Wavelet is applied to remove noise from liver biopsy images. Then the Normalized Mean Color Histogram (NMCH) is extracted as a visual feature, and a FIS is built for diagnosing the class of hepatitis using 45 fuzzy IF-THEN rules. The system is evaluated by calculating precision and accuracy, and the results were both very accurate and interesting. Diagnosis accuracy reaches 96%, with the corresponding approximated time ranging between 0.10 – 0.15 seconds.

DETERMINATION OF OPTIMAL WELL INTERVENTIONS USING DATA MINING

Performing of well interventions is an important stage of the optimal brownfield development. The implementation of Data Mining tech niques allows increasing the efficiency of the well workover planning in case of insufficient data quantity and quality for the mathematical mod- eling of the fluid flow. In order to improve the efficiency of the determi- nation of optimal well interventions, a fuzzy logic algorithm was devel- oped. The proposed algorithm can increase the accuracy of the predic- tion of well interventions production rates, and reduce uncertainties and risks related to the brownfield development. It involves using trapezoidal membership functions for the terms of linguistic variables, a set of IF-THEN fuzzy rules with one or several conditions and a conclusion, Mamdani implication and aggregation functions, and defuzzification based on the Center of Gravity method.

A Genetic Algorithm Based Fuzzy Inference System for Pattern Classification and Rule Extraction

Setting fuzzy rules is one of the paramount techniques in the design of a fuzzy system. For a simple system, fuzzy if-then rules are usually derived from the human experts. However, in the event of having multiple variables coupled with a few features, the classification problem will be getting more sophisticated, as a result human expert may not be able to derive proper rules. This paper presents a genetic-algorithm-based fuzzy inference system for extracting highly comprehensible fuzzy rules to be implemented in human practices without detailed computation (hereafter denoted as GA-FIS). The impetus for developing a new and efficient GA-FIS model arises from the need of constructing fuzzy rules directly from raw data sets that combines good approximation and classification properties with compactness and transparency. Therefore, our proposed GA-FIS method will first define the membership functions with logical interpretation which is amendable by domain experts to human understanding, and then genetic algorithm serves as an optimization tool to construct the best combination of rules in fuzzy inference system that can achieve higher classification accuracy and gain better interpretability. The proposed approach is applied to various benchmark and real world problems and the results show its validity.

Assessment of fuzzy failure mode and effect analysis (FMEA) for reach stacker crane (RST): A case study

FMEA (Failure Mode and Effect Analysis) refers to a proactive quality tool that enables the identification and prevention of the potential failure modes of a product or process. However, in executing traditional FMEA, the difficulties such as vague information, relative importance ratings, decisions on same ratings, and opinion difference among experts arise which reduce the validity of the results. This paper presents a fuzzy logic based FMEA depending on fuzzy IF-THEN rules over traditional FMEA to make it precise and give proper maintenance decision. Here, the Risk Priority Number (RPN) is calculated and compared to the Fuzzy Risk Priority Number (FRPN) to give maintenance decision. Furthermore, the FMEA of Reach Stacker Crane (RST) is presented to demonstrate the proposed Fuzzy FMEA.

Remote sensing image fusion using fuzzy logic and gyrator transform

ABSTRACTImage fusion is a way to merge high spatial resolution image with low-resolution image, which is extensively utilized in several vision-based applications. The primary objective is to improve the radiometric quality of fused image compared to multi-spectral images with low resolution. Existing methods are found to be efficient, but if the similar radiometric information is blended into every image, it produces redundant high frequency of pixels. Therefore, to overcome this issue, in this paper a Fuzzy and Gyrator Transform (GT) based image fusion technique is proposed. It decomposes remotely sensed image into the GT domain; then it preserves the radiometric data by using fuzzy if-then rules. In the last phase, proposed technique injects high-frequency blocks from input images and returns a high-resolution multi-spectral image with maximum radiometric data. In this work, to efficiently tune the required fuzzification parameters we have utilized well-known Genetic algorithm. Genetic algorithm iteratively tune the required parameters for fuzzification in such a way that the overall entropy is maximized. Therefore, entropy is used as fitness function. Quantitative analysis has shown that the proposed method has better structural detail, spatial resolution, and spectral information than existing methods.

Three phases segmentation from Ni/YSZ anode optical microscopy images using quantum-inspired mixture clustering model

Three-phase segmentation of solid oxide fuel cell (SOFC) anode image is essential for its microstructure quantitative analysis. A quantum-inspired mixture clustering model is developed for Ni/YSZ anode optical microscopy (OM) images. The proposed mixture clustering model focuses on combining Markov random field (MRF)-based fuzzy logic model with Gaussian mixture model (GMM). In the premise part of fuzzy if-then rules, the clique potential MRF function is defined by multiplication of both prior distributions and fuzzy membership functions with a quantum-inspired adaptive fuzzy degree, which takes prior statistics properties and spatial contextual information of SOFC anode OM image into consideration. In addition, GMM is introduced into the consequent part to design a negative log-prior function as the pixel distance metric. The proposed method has been compared to other state-of-the-art segmentation algorithms on both simulated images and real SOFC anode OM images. The experimental results demonstrate that the proposed method is able to achieve a higher segmentation accuracy with a faster convergence speed, which lays firm foundation for microstructural parameters extraction from SOFC electrodes image datasets.

Memetic fuzzy clustering protocol for wireless sensor networks: Shuffled frog leaping algorithm

The importance of Wireless Sensor Networks (WSNs) has increased owing to their extensive advances, allowing integration of nano-sensors, wireless networks, and smart software. The main challenge in WSNs is the fast sensor energy discharging. One of the most effective approaches to deal with this issue is clustering and selecting appropriate cluster heads. This study presents a Fuzzy Shuffled Frog Leaping Algorithm (FSFLA), which employs the memetic Shuffled Frog Leaping Algorithm (SFLA) to optimize the Mamdani fuzzy rule-base table based on the application specifications. In addition to automatically adjusting the if-then fuzzy rules, this protocol optimizes five controllable parameters associated with the inputs to the fuzzy system in an offline procedure prior to launching the network. The inputs of the fuzzy systems include remaining energy, distance from the base station, the number of neighboring nodes, and node histories. The proposed clustering algorithm can be adjusted according to the application due to having two determined thresholds for turning candidate nodes to final cluster heads. The proposed FSFLA protocol is compared to various protocols such as LEACH, LEACH-DT, SIF, and ASLPR, in terms of the number of the network lifetime, remaining energy, the number of packets successfully received at the base station and intra-cluster distance. The simulation results indicate that the proposed FSFLA clustering protocol, which is implemented in two versions, significantly outperforms other protocols in all scenarios.

An Optimized Multiple Fuzzy Membership Functions based Image Contrast Enhancement Technique

Image enhancement is an emerging method for analyzing the images clearer for interpretation and analysis in the spatial domain. The goal of image enhancement is to serve an input image so that the resultant image is more suited to the particular application. In this paper, a novel method is proposed based on Mamdani fuzzy inference system (FIS) using multiple fuzzy membership functions. It is observed that the shape of membership function while converting the input image into the fuzzy domain is the essential important selection. Then, a set of fuzzy If-Then rule base in fuzzy domain gives the best result in image contrast enhancement. Based on a different combination of membership function shapes, a best predictive solution can be determined which can be suitable for different types of the input image as per application requirements. Our result analysis shows that the quality attributes such as PSNR, Index of Fuzziness (IOF) parameters give different performances with a selection of numbers and different sized membership function in the fuzzy domain. To get more insight, an optimization algorithm is proposed to identify the best combination of the fuzzy membership function for best image contrast enhancement.