Type‐3 Fuzzy System‐Based Intelligent Control Approaches and Applications

The fuzzy systems and control are regarded as the most widely used application of fuzzy logic systems in recent years (Jang, 1993; John & Coupland, 2007; Lin & Lee, 1006; Mendel, 2001; Wang, 1994). The structure of traditional fuzzy system models that is characterized by using type 1 fuzzy sets, which are defined on a universe of discourse, map an element of the universe of discourse onto a precise number in the unit interval [0, 1]. The concept of type-2 fuzzy sets was initially proposed by Zadeh as an extension of typical fuzzy sets (called type1) (Zadeh, 1975). Mendel and Karnik developed a complete theory of interval type-2 fuzzy logic systems (iT2FLSs) (Karnik et al, 1999; Liang & Mendel, 2000; Mendel, 2001). Recently, T2FLSs have attracted more attention in many literatures and special issue of IEEE Transactions on Fuzzy systems (Baldwin & Karake, 2003; John & Coupland, 2007; Lee & Lin, 2005; Liang & Mendel, 2000; Mendel, 2001, Hagras, 2007; Ozen & Garibaldi, 2004; Pan et al, 2007; Wang et al, 2004). T2FLSs are more complex than type-1 ones, the major difference being the present of typeis their antecedent and consequent sets. T2FLSs result better performance than type-1 Fuzzy Logic Systems (T1FLSs) on the applications of function approximation, modeling, and control. In addition, neural networks have found numerous practical applications, especially in the areas of prediction, classification, and control (Lee & Teng, 2000; Lin & Lee, 1996; Narendra & Parthasarathy, 1990). The main aspect of neural networks lies in the connection weights which are obtained by training process. Based on the advantages of T2FLSs and neural networks, the type-2 neural fuzzy systems are presented to handle the system uncertainty and reduce the rule number and computation (Castillo & Melin, 2004; Lee & Lin, 2005; Mendel, 2001; Pan et al, 2007; Wang et al, 2004). Besides, recurrent neural network has the advantages of store past information and speed up convergence (Lee & Teng, 2000). The design of a fuzzy partition and rules engine normally affects system performance. To simplify the design procedure, we usually use the symmetric and fixed membership functions (MFs), such as Gaussian, triangular. However, a large rule number should be used to achieve the specified approximation accuracy (or result larger approximated error) (Lee & Teng, 2001; Lotfi & Tsoi, 1996). Several approaches have been introduced to optimize fuzzy MFs and choose an efficient scheme for structure and parameter learning. Nevertheless, asymmetric fuzzy MFs (AFMFs) has been discussed and analyzed for this problem (Baldwin O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

This article presents a system for enhancing adaptive management through the integration of fuzzy logic decision-making system in backed by blockchain supply chain smart-contracts of an enterprise. The adaptive management system is based on two main components: risk assessment and supply chain optimization. To assess risk, fuzzy logic models analyze input variables such as supply chain risks, financial risks, and operational risks. An adaptive resource management system is characterized by the ability to respond to changes in the external environment and internal processes. This system should integrate advanced technologies for effective resource management and ensure strategic stability. The system utilizes blockchain’s immutable ledger and smart contracts to automate key processes such as manufacturing processes, inventory management, and regulatory compliance, thus addressing issues like communication gaps, delays, and counterfeit risks. However, the inherent rigidity of blockchain systems in adapting to dynamic manufacturing environments prompts the incorporation of fuzzy logic. Fuzzy logic offers a solution to this limitation by enabling more nuanced decision-making through the processing of uncertain or imprecise data. The article details the integration of fuzzy logic with blockchain, wherein fuzzy inference systems (FIS) are employed to evaluate and interpret operational data under variable conditions. This combination allows for adaptive responses to supply chain disruptions, such as supplier delays or inventory shortages. The fuzzy logic system applies rules to determine the optimal course of action, which is then executed through blockchain-based smart contracts. Key advancements include the development of a modified smart contract framework that uses fuzzy logic to adjust supply chain parameters dynamically. For example, supplier reliability is assessed using fuzzy membership functions, leading to adjustments in pricing and supply quantities based on real-time evaluations. This approach enhances the flexibility and responsiveness of manufacturing operations, ensuring that decisions are based on comprehensive data analysis rather than static rules. A fuzzy logic system processes ambiguous information using linguistic variables and fuzzy sets that help interpret uncertainties in operational data. The key element of the system is the fuzzy inference system, which performs basic steps such as fuzzification, rule evaluation, aggregation, and defuzzification. This results in more refined decision outputs based on fuzzy rules that can take into account different conditions such as supply quantity and supplier reliability. Combining fuzzy logic with smart contracts facilitates dynamic adjustments in supply management, such as pricing and modification of supply quantity based on supplier reliability. It is evaluated how residual networks and deep multi-level transformations can be used in combination with a fuzzy logic system to improve performance. The concept of global mean pooling and fully connected levels is applied to classification tasks, and cross-entropy loss functions improve model accuracy. Additionally, the use of membership functions such as trapezoidal and triangular sets allows for accurate modeling of factors such as delivery timeliness and product quality.The proposed system provides a robust solution for managing production processes amidst fluctuating conditions, combining the transparency and security of blockchain with the adaptive capabilities of fuzzy logic. This integration aims to optimize production efficiency and maintain operational continuity in the face of unpredictable challenges.

Foreword by Lotfi Zadeh Preface INTRODUCTION Background. Why fuzzy logic? FUNDAMENTAL CONCEPTS OF FUZZY COMPUTATION Vagueness and uncertainty: Theories of vagueness. Theories of uncertainty. Fuzzy sets: concepts and characterizations: Introduction. Operations on fuzzy sets. Interpretations of fuzzy sets. Fuzzy relations. Characterization of fuzzy sets. Fuzzy measure and integral. Fuzzy mathematical objects. Extension principle. Fuzzy set calculus: Introduction. Membership function elicitation. Fuzzy relational calculus. Fuzzy arithmetic. Possibility theory. Fuzzy reasoning: Introduction. Fuzzy inference. Defuzzification. FUZZY MODELS Fuzzy models. Modeling and simulation: Granule-based models. Logical aspects of fuzzy models. Statistical models. Fuzzy Petri Net model. Model acquisition. Approximation aspects of fuzzy models. HYBRID APPROACHES Introduction: motivation for hybrid approaches. Neuro-fuzzy systems. Fuzzy-evolutionary systems. FUZZY COMPUTATION ENVIRONMENTS Software approaches: Programming languages. Knowledge-based systems. Database management, information retrieval, and decision support systems. Hardware approaches: Desirable features. Adapting existing hardware to fuzzy computation. Analog approaches. Digital approaches. Hybrid (digital-analog) approaches. APPLICATIONS OF FUZZY COMPUTATION Knowledge based systems: Knowledge representation. Inference methods. Control methods. Design methods. Control. Principles of fuzzy controllers. Fuzzy control approaches: General design schemes. Cell maps. Sliding mode control. Predictive control. Hierarchical control. Model-based control. Optimal fuzzy control. Machine learning: Introduction: learning fuzzy concepts. Supervised learning. Reinforcement learning. Data and information management: Fuzzy databases. Information retrieval. Case-based reasoning. Decision making and optimization: Decision-making models. Optimization. Pattern analysis. Computer vision. FUZZY COMPUTATION IN PRACTICE Aerospace: Proximity operations spacecraft controller: a case study in fuzzy logic control. Systems control: DC/DC converters fuzzy control. Fuzzy control in telecommunications. Fuzzy-neural traffic control and forecasting. Systems control. Backlash compensation using fuzzy logic. Neurofuzzy modeling for nonlinear system identification. Nuclear engineering: Application of fuzzy logic control system for nuclear reactor control. Manufacturing: Applications of fuzzy set methodologies in manufacturing. Compensation of friction in mechanical positioning systems. Diagnostics: Possibilistic handling of uncertainty in fault diagnosis. Robotics: Autonomous mobile robot control. Chemical engineering: Chemical engineering application. Water treatment: Water treatment application. Automotive: Improvement of the relationship between driver and vehicle using fuzzy logic. Traffic engineering: Traffic engineering application. Civil engineering: Civil engineering application. Engineering design: A fuzzy sets application to preliminary passenger vehicle structure design. Oil refining: Neuro-fuzzy hybrid control system in petroleum plant. Medicine: CADIAG2: hospital-based computer-assisted differential diagnosis in internal medicine. Neural networks for ECG diagnostic classification. Information science: Case-based reasoning. Information retrieval: a case study of the CASHE: PVS systems. Economics, finance and business. Decision support system for foreign exchange trade (FOREX). Operations research: Scheduling. Fuzzy sets in operation research: forecasting, a case study. Quality design using possibilistic regression and optimization. Inventory control. Time series prediction. FUZZY COMPUTATION RESEARCH Directions for future research. APPENDICES

Whereas type-1 and type-2 membership functions (MFs) are the core of any fuzzy logic system, there are no performance criteria available to evaluate the goodness or correctness of the fuzzy MFs. In this paper, we make extensive analysis in terms of the capability of type-2 elliptic fuzzy MFs in modeling uncertainty. Having decoupled parameters for its support and width, elliptic MFs are unique amongst existing type-2 fuzzy MFs. In this investigation, the uncertainty distribution along the elliptic MF support is studied, and a detailed analysis is given to compare and contrast its performance with existing type-2 fuzzy MFs. Furthermore, fuzzy arithmetic operations are also investigated, and our finding is that the elliptic MF has similar features to the Gaussian and triangular MFs in addition and multiplication operations. Moreover, we have tested the prediction capability of elliptic MFs using interval type-2 fuzzy logic systems on oil price prediction problem for a data set from 2nd Jan 1985 till 25th April 2016. Throughout the simulation studies, an extreme learning machine is used to train the interval type-2 fuzzy logic system. The prediction results show that, in addition to their various advantages mentioned above, elliptic MFs have comparable prediction results when compared to Gaussian and triangular MFs. Finally, in order to test the performance of fuzzy logic controller with elliptic interval type-2 MFs, extensive real-time experiments are conducted for the 3D trajectory tracking problem of a quadrotor. We believe that the results of this study will open the doors to elliptic MFs’ wider use of real-world identification and control applications as the proposed MF is easy to interpret in addition to its unique features.

<div class="htmlview paragraph">Semi-active suspension has been widely applied in commercial vehicle suspension in order to get good riding comfortableness. Fuzzy logic control (FLC) has been widely applied in the field of kinetic control because control rule of FLC is easy to understand. But the gain of fuzzy rules and adjustment of membership functions usually depend on experts' experiences and repeated experiments, thus the fuzzy rules and membership functions has strong subjectivity, also are easily affected by environment of experiments, so the main problem of fuzzy logic controller design is selection and optimization of fuzzy rules and membership functions. Genetic Algorithms (GA) is the algorithm that searches the optimal solution through simulating natural evolutionary process and is one of the evolution algorithms which have most extensive impact. Because GA has some features such as group search and parallel operation that GA is fit for the group optimization of fuzzy rules and membership functions to get overall optimal solution.</div> <div class="htmlview paragraph">In this paper the research subject is heavy commercial vehicle cab suspension system with air-spring. The air-spring deformation parameters are gained from the platform test of air-spring. According to these parameters and parameters of truck, non-linear cab suspension model is set up in ADAMS. The road excitations are generated according to Chinese National Standard. Selecting vibration amplitude and vibration acceleration of cab as the input variable, using rate of air spring as output variable, we build the initial fuzzy logic controller model in MATLAB/Simulink based on professional knowledge and experience. Initial fuzzy rules and membership functions are optimized respectively through GA. By means of MATLAB-ADAMS union simulation comparison, the result shows that the GA-optimized fuzzy controller has better control performance for different road situations, which can help suspension isolating vibration more effectively and improving riding comfortableness.</div>

Artificial intelligence is becoming an increasingly popular tool in more and more areas of technology. New challenges in control systems design and application are related to increased productivity, control flexibility, and processing of big data. Some kinds of systems require autonomy in real-time decision-making, while the other ones may serve as an essential factor in human-robot interaction and human influences on system performance. Naturally, the complex tasks of controlling technical systems require new modern solutions, but there remains an inextricable link between control theory and artificial intelligence. The first part of the present survey is devoted to the main intelligent control methods in technical systems. Among them, modern methods of adaptive and optimal control, fuzzy logic, and machine learning are considered. In its second part, the crucial achievements in intelligent control applications in robotic and mechatronic systems over the past decade are considered. The references are structured according to the type of such common control problems as stabilization, controller tuning, identification, parametric optimization, iterative learning, and prediction. In the conclusion, the main problems and tendencies toward intelligent control methods improvement are outlined.

An intelligent control implementation is proposed for sample differentiation with Raman spectroscopy, which can be used to characterize various samples for decision-making and medical diagnosis. Raman spectra are weak signals whose features are inevitably affected by numerous noises during the calibration process. These noises must be eliminated to an acceptable level. Fuzzy control has been widely used to solve uncertainty, imprecision and vague phenomena, so fuzzy logic can be used for noise filtering. The resulting intrinsic Raman spectrum has been trained using artificial neural networks. Both unsupervised learning and supervised learning are to be conducted in this preliminary research on sample identification. For unsupervised training, principal component analysis (PCA) is exploited, which is based on Hebbian rule and single value decomposition (SVD) approach, respectively. For supervised training, radial basis function (RBF) is presented. A complete procedure for sample identification consists of Raman spectra calibration, noise filtering, unsupervised classification and supervised neural network training. A systematic intelligent control approach is formulated in consequence for sample identification. The long-term objective is to create a real-time approach for sample analysis using a Raman spectrometer directly mounted at the end-effector of the medical robots to enhance robotic remote surgery

In this paper, we introduce a new method for packet transmission delay analysis and prediction in mobile ad hoc networks. We apply a fuzzy logic system (FLS) to coordinate physical layer and data link layer. We demonstrate that type- 2 fuzzy membership function (MF), i.e., the Gaussian MFs with uncertain variance is most appropriate to model BER and MAC layer service time. Two FLSs: a singleton type-1 FLS and an interval type-2 FLS are designed to predict the packet transmission delay based on the BER and MAC layer service time. Simulation results show that the interval type-2 FLS performs much better than the type-1 FLS in transimission delay prediction. We use the forecasted transimission delay to adjust the transmission power, and it shows that the interval type-2 FLS performs much better than a type-1 FLS in terms of energy consumption, average delay and throughput. Besides, we obtain the performance bound based on the actual transmission delay.

The objective of the work is to predict heart disease using computing techniques like an oppositional firefly with BAT and rule-based fuzzy logic (RBFL). The system would help the doctors to automate heart disease diagnosis and to enhance the medical care. In this paper, a hybrid OFBAT-RBFL heart disease diagnosis system is designed. Here, at first, the relevant features are selected from the dataset using locality preserving projection (LPP) algorithm which helps the diagnosis system to develop a classification model using the fuzzy logic system. After that, the rules for the fuzzy system are created from the sample data. Among the entire rules, the important and relevant group of rules are selected using OFBAT algorithm. Here, the opposition based learning (OBL) is hybrid to the firefly with BAT algorithm to improve the performance of the FAT algorithm while optimizing the rules of the fuzzy logic system. Next, the fuzzy system is designed with the help of designed fuzzy rules and membership functions so that classification can be carried out within the fuzzy system designed. At last, the experimentation is performed by means of publicly available UCI datasets, i.e., Cleveland, Hungarian and Switzerland datasets. The experimentation result proves that the RBFL prediction algorithm outperformed the existing approach by attaining the accuracy of 78%.

The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. In this paper, high-performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of a spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.

Fuzzy logic and neural network system identification for high alpha delta wing maneuvers with deployable control surfaces

Designing Unsupervised Hierarchical Fuzzy Logic Systems

Designing Unsupervised Hierarchical Fuzzy Logic Systems

In this chapter, basic constructions of fuzzy logic systems with uncertain membership functions are presented. We begin with historical approaches to reasoning with interval-valued fuzzy sets and known formulations of general type-2 fuzzy logic systems. Next we provide new formulations grounded in non-singleton fuzzification. In the context of ordinary fuzzy systems, we demonstrate that variously interpreted non-singleton fuzzification, for typical structures fuzzy logic systems, can be implemented by the classical singleton structures only using modified antecedent fuzzy sets. The first approach to fuzzification of premises is done by the interpretation in terms of possibility distributions of actual inputs. Consequently, the possibility and necessity measures of antecedent fuzzy sets create boundaries for the interval-valued antecedent membership function. The second approach applies rough approximations to antecedent fuzzy sets by non-singleton fuzzy premise sets considered as fuzzy-rough partitions. Two known definitions, the one of Dubois and Prade, and the second proposed by Nakamura, lead to different formulations of fuzzy logic systems. Employing fuzzy-rough sets of Dubois and Prade, we obtain the interval-valued fuzzy logic system. Then, it can be immediately proved that upper approximations in fuzzy-rough systems are concurrent to fuzzification in conjunction-type fuzzy systems. Unexpectedly, lower approximations in fuzzy-rough systems coincide with fuzzification in logical-type fuzzy systems. Therefore, the proposed methods can be viewed as extensions to the conventional non-singleton fuzzification method. Fuzzyrough sets in the sense of Nakamura result with a formulation of a general fuzzy-valued fuzzy logic system. For this purpose, three realizations of general fuzzy-valued fuzzy systems: triangular, trapezoidal and Gaussian, are presented in details.

In this paper, we study the security assurance in application layer in wireless acoustic sensors via event forecasting and detection. In order to perform event forecasting and detection, we try to answer several challenging questions in acoustic signal research based on wireless acoustic sensors: (i) Are acoustic signals predictable? (ii) How are acoustic signals predicted? (iii) Are there any event‐forecasting applications for the security in wireless acoustic sensors? We study these questions based on Xbow acoustic sensors and demonstrate that real‐world acoustic signals are self‐similar, which means that they are predictable. We propose an acoustic signal prediction scheme using interval type‐2 fuzzy logic system (FLS). We show that a type‐2 fuzzy membership function (MF); that is, a Gaussian MF with uncertain mean is appropriate to model the acoustic signal strength. Two FLSs, a type‐1 FLS, and an interval type‐2 FLS are designed for signal strength forecasting. Furthermore, we propose a double sliding window scheme for event detection based on the forecasted signals. Simulation results show that the interval type‐2 FLS outperforms the type‐1 FLS in signal strength forecasting and the performance of event detection based on the forecasted signal from type‐2 FLS is much better than that based on type‐1 FLS. Copyright © 2012 John Wiley & Sons, Ltd.