Fuzzy Model Research Articles

Mathematical optimization strategy for online scheduling of complex manufacturing systems based on thermal energy optimization and fuzzy mathematical model

With the rapid development of the manufacturing industry and the advancement of the intelligent process, the scheduling problem of complex manufacturing systems becomes more and more complicated, especially in the aspect of energy management and optimization. This study aims to propose an online scheduling mathematical optimization strategy based on thermal energy optimization and fuzzy mathematical model, so as to improve the scheduling efficiency and resource allocation rationality of complex manufacturing systems under different production conditions. A scheduling model considering thermal energy utilization rate, production priority and real-time demand is established by using fuzzy mathematics. The model deals with the uncertain factors by fuzzy logic and solves them by genetic algorithm to realize the dynamic adjustment and optimal scheduling of production resources. Through the experimental verification of a complex manufacturing system, the improvement of scheduling efficiency not only reduces the operating cost, but also improves the flexibility and response speed of the system. The fuzzy mathematical model based on thermal energy optimization provides an effective on-line scheduling strategy for complex manufacturing systems, which can realize efficient scheduling and energy management in dynamic environment.

Comfort evaluation of carrier-based aircraft missile handling operation based on fuzzy analytic hierarchy process

The analysis and evaluation of missile handling operation are crucial for ensuring safety and reliability during these processes. This paper proposes a novel assessment method for missile handling motions based on the Fuzzy Analytic Hierarchy Process (FAHP). The methodology comprises several stages: Initially, an analysis of missile handling motions is conducted. Subsequently, joint evaluation indicators influencing operational comfort are selected. Following this, membership functions and a comprehensive evaluation matrix are constructed. Finally, a fuzzy comprehensive evaluation model for the comfort level of carrier-based aircraft ammunition handling operations is established. A typical case study of carrier-based aircraft missile handling operation is presented to illustrate the fuzzy comprehensive evaluation process. The evaluation results are then compared with those obtained from the Rapid Upper Limb Assessment (RULA) and Ovako Working Posture Analysis System (OWAS) models. Comparative analysis indicates that the results derived from the fuzzy comprehensive evaluation model demonstrate high reliability.

Integrated Fault-Tolerant Control Design With Sampled-Output Measurements for Interval Type-2 Takagi-Sugeno Fuzzy Systems.

This article is concerned with the integrated design of fault estimation (FE) and fault-tolerant control (FTC) for uncertain nonlinear systems suffering from actuator faults and external disturbance. The uncertain nonlinear systems are characterized as the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model, and IT2 membership functions are employed to effectively handle uncertainties. A fuzzy observer, utilizing only sampled-output measurements, is applied to simultaneously estimate actuator faults and system states. Based on the estimation, the fault-tolerant controller is designed to ensure the system stability under a predefined H∞ performance. The sampling behavior complicates the system dynamics and makes the integrated FTC design more challenging. To confront this issue, the discontinuous Lyapunov functional technique is exploited to enhance stability results by considering the sampling characteristic, upon which FE and FTC units are co-designed in the linear matrix inequality (LMI) framework. To further relax stability criteria, the analysis process incorporates the bound information of membership functions through the membership-function-dependent (MFD) method. Additionally, the relationship of mismatched premise variables resulting from the sampling scheme is also taken into account. Moreover, considering the imperfect premise matching (IPM) framework, the proposed fault-tolerant controller provides greater flexibility in selecting the shapes of membership functions and number of fuzzy rules that can vary from the counterpart of the fuzzy system. Finally, the efficacy of the proposed FTC technique is validated through a detailed numerical example.

Reliability dependent production-inventory model for redundancy allocation via fuzzy logic

This study deals with a reliability dependent production-inventory model in two scenarios: a redundancy allocation with crisp structure and the other with fuzzy logic. Here, a manufacturer purchases some raw-materials/components in variable cycles and arranges them as series-parallel system to produce a single item with production cost dependent on system reliability. In this model, a retailer gets the opportunity of warranty period and credit period offered by the manufacturer. Also, the retailer’s demand is dependent on system reliability, credit period and selling price. In the crisp model, the component reliability is exponentially dependent on known failure rate and failure time. However, there is no dependent relationship between these two parameters. Actually, the real world is full of uncertainty and these two parameters may depend on each other following some uncertain nature which can be expressed as fuzzy logic. So, in the fuzzy model, failure time has been considered as dependent on failure rate following fuzzy logic and these fuzzy relations are defuzzified by using three fuzzy inference techniques: Mamdani, Sugeno and Tsukamoto. Main goal of this article is to determine the optimum number of cycles and components to maximize manufacturer’s profit and system reliability with some constraints. The model is solved by using elitist non-dominated sorting genetic algorithm (NSGA-II) and some numerical examples closed to real-world have been executed. Comparative analyses are done for different cases; different fuzzy inference techniques and for active and mixed strategies. Finally, some sensitivity analyses and managerial insights are drawn.

Valuation of Memory Effect of Fuzzy EOQ Model with Constant Demand Rate

Valuation of Memory Effect of Fuzzy EOQ Model with Constant Demand Rate

Sqrt-Loglogish CNN and Markov model for 5G spectrum sensing application

The research presents innovative methods for spectrum sensing in 5G networks, using the Sqrt-Loglogish convolutional neural network (SL-CNN) and hidden orthogonal intuitionistic fuzzy Markov model (HOIFMM). The proposed methods aim to tackle issues related to detecting principal user signals accurately, mitigating interference, and efficiently utilizing the spectrum in wideband spectrum environments due to their diverse and ever changing characteristics. The Sqrt-Loglogish CNN improves spectrum sensing by addressing static threshold dependency and potential overfitting. The HOIFMM offers a complex framework for predicting sparsity levels and primary user patterns. The results highlight the effectiveness of the suggested techniques in differentiating primary user signals from noise and interference, resulting in enhanced interference management tactics and overall network performance. MATLAB simulation is performed and compared the proposed methods performance with existing state-of-the-art methods such as CNN, deep neural network (DNN), long short-term memory (LSTM) and artificial neural networks (ANN). The proposed method has outperformed existing methods in terms of sensitivity, accuracy, and precision. Future endeavors include improving these methods, investigating sophisticated machine learning algorithms, and doing real world validations to guarantee scalability and resilience in various 5G deployment situations. This research advances the spectrum sensing capabilities in 5G networks, potentially improving efficiency, reliability, and quality of service.

A fuzzy multi-objective model for aggregate production planning considering energy consumption, carbon emission, chemical, solid waste, and wastewater

With a growing focus on environmental conservation, enterprises continuously reevaluate their conventional profit-driven business models. This shift has led to the development of green production planning models, attracting considerable attention in recent years. Previously, energy consumption or carbon emission were the most considered green parameters in the production planning problems. Nevertheless, several other essential green aspects such as reductions in chemical, solid waste, and wastewater are usually neglected. Therefore, this study proposes a multi-objective model for environmentally conscious aggregate production planning, simultaneously considering the above-mentioned green objectives. Additionally, the uncertain parameters of the model, including the green parameters, were modelled through fuzzy programming. Furthermore, this study incorporated a contradicting relationship between energy consumption cost and associated carbon emission, which was previously presented as non-contradicting. This relationship was illustrated by utilizing the time-of-use electricity tariff scheme. To solve the model, the initial step involved the defuzzification of uncertain attributes, followed by the transformation of the multi-objective model into a single-objective model. Finally, the IBM ILOG CPLEX Optimization Studio solver was utilized to solve the model based on the data collected from a precision engineering company. The generated production plan achieved an overall satisfaction level of 71%, which is reasonably acceptable to the decision maker. Moreover, sensitivity analysis has been conducted for the degree of optimism of the objective functions and the feasibility degree of the constraints. A general overview of the results associated with the performed sensitivity analysis indicated that the optimum values of the objective functions deteriorate as the parameters increase, though all remain within an acceptable range. The proposed study will assist decision makers in generating production plans that are both economically and environmentally feasible.

GIFTWD: A Prospect Theory-Based Generalized Intuitionistic Fuzzy Three-Way Decision Model

GIFTWD: A Prospect Theory-Based Generalized Intuitionistic Fuzzy Three-Way Decision Model

Fuzzy uncertainty modeling of generalized Hirota–Satsuma coupled Korteweg–de Vries equation

This article explores the solitary wave solutions of a generalized Hirota–Satsuma Coupled Korteweg–de Vries (HSCKdV) equation. The HSCKdV equation is a mathematical model that describes certain types of long waves, particularly those found in shallow water. The generalized HSCKdV equation is solved exactly using the Homotopy Perturbation Transform Method (HPTM). By applying this technique, the authors obtain solutions in the form of a convergent power series. These solutions offer an understanding of the characteristics of solitary waves within the domain of shallow water waves. The HSCKdV equation has been solved using the adomian decomposition method, and the results have been compared with those obtained from the HPTM. This comparison demonstrates the effectiveness of the HPTM in solving such nonlinear equations. Further, the HSCKdV equation is extended to a fuzzy version considering the initial condition as a fuzzy parameter. Uncertainty in the initial condition is addressed by representing it using triangular and trapezoidal fuzzy numbers. The generalized fuzzy HSCKdV equation is subsequently tackled using the fuzzy HPTM (FHPTM) providing fuzzy bound solutions. Using the FHPTM, we explain the fuzzy results, highlighting how the solitary wave splits into two solitary waves and noting that the lower and upper bound solutions are interchanged due to negative fuzzy results.

Fuzzy Model of Diagnostics of Technological Processes

Fuzzy Model of Diagnostics of Technological Processes

A Robust Fuzzy Model for Evaluating Defects in Building Elements

A Robust Fuzzy Model for Evaluating Defects in Building Elements

An Improved Type-1 Fuzzy Regression Function for COVID-19 Cases Prediction

The deficiency in data collection and reporting has led to the emergence of uncertainty in the data in the pandemic process. These matters cause that traditional statistical and mathematical models have been functionless and unreliable in this issue. In this respect, this study presents an ensemble prediction model with innovative and contemporary properties to model COVID-19 cases in the UK and USA inclusively throughout the whole pandemic process. The proposed ensemble prediction model is composed of an assembly of Type-1 fuzzy regression functions with elastic net regularization (E-T1FRF) and radial basis function neural networks (RBFNNs). Thus, the proposed ensemble model can successfully model the uncertainty in the data with the fuzzy modelling perspective of T1FRF and also thoroughly adapt to the patterns in the data thanks to the flexible modelling ability of RBFNN based on data. With the proposed ensemble prediction model, the cases in the entire pandemic process from the beginning of March 2020 to the end of June 2022 were modelled and predicted for 23 different periods in one-month prediction steps. The proposed model produced predictions with MAPE values below 3% in all but periods except for three periods. Also, the average MAPE values for all periods were obtained at around 2% for the UK and only 1.5% for the US. These results, at a reasonable level of error, demonstrated the practicality of the usage of the proposed community model for other countries and provided valuable information for future action.

Relaxed TS Fuzzy Model Transformation to Improve the Approximation Accuracy/Complexity Tradeoff and Relax the Computation Complexity

Relaxed TS Fuzzy Model Transformation to Improve the Approximation Accuracy/Complexity Tradeoff and Relax the Computation Complexity

A fine-grained RDF graph model for fuzzy spatiotemporal data

The uncertainty and spatiotemporal dynamics of information necessitate the urgent modeling of fuzzy spatiotemporal knowledge across various applications, with the Resource Description Framework (RDF) serving as a widely recognized data representation model. Existing models suffer from incomplete semantic representation and poor robustness in modeling fuzzy spatiotemporal data, e.g., lack of fuzziness in spatiotemporal semantics; lack of altitude in spatial semantics. Meanwhile, the algebraic framework regarding the model has not been investigated. Thus, in this paper, we first propose a new fine-grained fuzzy spatiotemporal RDF model. This model can represent fine-grained uncertain spatiotemporal semantics that may be associated with any element of a spatiotemporal RDF. We further define its graph algebraic operations. Note that we demonstrate the use of the algebraic operations for fuzzy spatiotemporal RDF querying. Finally, we establish a set of transformation rules for SPARQL query syntax to algebraic operations in fuzzy spatiotemporal RDF. In addition, we used experiments to evaluate the validity and rationality of our model.

Chameleon Sequences-Structural Effects in Proteins Characterized by Hydrophobicity Disorder.

Repeated protein folding processes both in vivo and in vitro leading to the same structure for a specific amino acid sequence prove that the amino acid sequence determines protein structuring. This is also evidenced by the variability of structuring, dependent on the introduced mutations. An important phenomenon in this regard is the presence of a differentiated secondary structure for chain fragments of identical sequence representing distinct forms of the secondary-order structure. Proteins termed chameleon proteins contain polypeptide chain fragments of identical sequence (length 6-12 aa) showing structural differentiation: helix versus β-structure. In the present paper, it was shown that these fragments represent components matching the structural status dictated by the physicochemical properties of the entire structural unit. This structural matching is related to achieving the goal of the biological function of the structural unit. The corresponding secondary structure represents a means to achieving this goal, not an end in itself. A selected set of proteins from the ChSeq database have been analyzed using a fuzzy oil drop model (FOD-M) identifying the uniqueness of the hydrophobicity distribution taken as a medium for recording the specificity of a given protein and a given chameleon section in particular. It was shown that in the vast majority, the status of chameleon sections turns out to be comparable regardless of the represented secondary structure.

Development of a fuzzy logic-based model for assessing the reliability of relay protection systems

This article presents a reliability assessment model for relay protection devices using fuzzy logic. It introduces an algorithm for simulating these devices, employing the Mamdani model with an "if-then" rule base. Inputs include the percentage of correct operation and the frequency of correct and incorrect operations. Implemented in Matlab with 21 rules, the fuzzy logic model uses triangular membership functions for input and output variables, with defuzzification via the center of gravity method. The model was tested using statistical data from SIEMENS SIPROTEC terminals, specifically distance protection and differential protection devices for transformers and autotransformers. The assessment considers operation frequencies for 1 to 3 devices, combining statistical and simulation data for a comprehensive analysis. Results show that including operation frequencies improves evaluation accuracy. The proposed model not only assesses current reliability but also predicts future behavior, aiding in the planning and optimization of relay protection systems. This model is valuable for professionals in generating companies, grid organizations, and operational dispatch control entities, helping them analyze relay protection performance and develop strategies to ensure reliable operation. The research focuses on the reliability of relay protection devices in power systems, addressing the need for a more accurate and comprehensive evaluation method. Traditional methods may not fully account for the complexities in modern microprocessor-based protections. This study aims to enhance reliability assessments through a model that integrates statistical data and simulation techniques, ultimately supporting better planning and optimization of relay protection systems

Building a fuzzy model for determining the level of social well-being of the population

This paper considers objects that affect the social security of the country. The complexity of such objects makes the development of computer systems in sociological research a difficult algorithmic task because of information uncertainty. Human thinking is based on inaccurate, approximate data, the analysis of which makes it possible to formulate clear decisions. In practice, there are usually no precise mathematical models that describe social objects. In such cases, it is advisable to use fuzzy mathematics as a tool for solving this problem. The main advantage of this approach compared to other artificial intelligence methods is the ability to interpret the results obtained. To assess the level of social well-being of the population, we used the mathematical apparatus of fuzzy set theory and fuzzy inference. The study is based on the OECD Better Life Index, which was developed by the Organization for Economic Cooperation and Development (OECD) to help countries assess and improve the quality of life of their citizens. In the course of the study, a fuzzy inference system was built to measure the social well-being of the population based on the indicators of the OECD Better Life Index. Since determining the level of social well-being is a complex task, a hierarchical structure with two main groups of social well-being indicators was constructed to simplify it. The resultant system evaluates each social indicator included in the OECD’s Better Life Index. Using the fuzzy inference model built, it was possible to assess the social well-being of the country’s population in a simple and transparent way in comparison with the OECD member countries. The results of the study make it possible to understand which indicators of social well-being of the country’s population are desirable or need to be improved in the future

Hybrid intelligent framework for designing band gap-rich 2D metamaterials

An artificial intelligence machine learning-based design framework is proposed to design lattice-based metamaterials with hexagonal symmetry that deliver wide band gaps at user-desired frequency ranges between 0 and 1000 kHz. The design approach starts by selecting a traditional, easy-to-manufacture parent lattice-based material that does not necessarily exhibit wide or functional band gaps. Subsequently, the parent lattice is transformed into a band-gap-rich lattice by superposing periodic triangular-shaped perturbations (i.e., zigzag-sine-based curvatures) with controllable frequencies and magnitudes on its ligaments. Finally, the frequency and magnitude parameters needed to deliver a specific band gap between 0 and 1000 kHz are determined using a hybrid intelligent framework, developed based on an Adaptive Neuro-Fuzzy Inference Systems (ANFIS). The ANFIS network integrates fuzzy logic expert models and artificial neural networks’ machine learning capabilities. Such a hybrid network is known for its ability to model strongly nonlinear and complex data. The data used in training the ANFIS models is generated using parametric finite element-based simulations where band gaps corresponding to a wide range of perturbation frequencies and magnitudes are computationally determined. The parametric study showed a nonlinear and complex topology-band gap characteristic relation; however, the Adaptive Neuro-Fuzzy Inference System (ANFIS) proved capable of modeling the observed complex topology-band gap behavior efficiently. The accuracy of the ANFIS models exceeded 99 % in several design ranges (i.e., perturbation parameters ranges). These were designated as high-accuracy design regions and were highlighted in the proposed design approach. Using multiple case studies with different band gap requirements, the ANFIS-based design framework proved effective in delivering customized lattice-based metamaterials with user-defined band gap frequencies.

Multi-population genetic algorithm with crowding-based local search for fuzzy multi-objective supply chain configuration

Supply chain configuration is often fuzzy and involves multiple objectives in real-world scenarios, but existing researches lack the exploration in the fuzzy aspect. Therefore, this paper establishes a fuzzy multi-objective supply chain configuration problem model to minimize the lead time and product cost oriented towards real supply chain environments. To solve the fuzzy problem, the theories of membership and closeness degree in fuzzy mathematics are adopted, and a multi-population genetic algorithm (MPGA) with crowding-based local search method is proposed. The MPGA algorithm uses two populations for optimizing the two objectives separately and effectively, and is characterized by three main innovative aspects. Firstly, a radical-and-radial selection operator is designed to balance the convergence speed and diversity of population. In the early stage of the algorithm, two populations are both optimized towards the ideal knee point, and then are separately optimized towards the two ends of the Pareto front (PF). Secondly, an elitist crossover operator is devised to promote information exchange within two populations. Thirdly, a crowding-based local search is proposed to speed up convergence by improving the crowded solutions, and to enhance diversity by obtaining new solutions around the uncrowded ones. Comprehensive experiments are tested on a fuzzy dataset with different sizes, and the integral of the hypervolume of PF is used for the evaluation of the fuzzy PF. The results show that MPGA achieves the best performance over other comparative algorithms, especially on maximum spread metric, outperforming all others by an average of 39 % across all test instances.

Holistic evaluation of energy transition technology investments using an integrated recommender system and artificial intelligence-based fuzzy decision-making approach

The most essential criteria should be determined in the selection of the suitable energy transition technologies due to budget deficit problem. Therefore, it is necessary to identify the most important criteria in energy transition technology selection. Therefore, a new study is needed to determine the most prominent issues in the correct selection of energy transition technologies. The purpose of this study is to identify the most appropriate energy transition technology alternative. Within this framework, a novel artificial intelligence (AI)-based fuzzy decision-making model has been presented. In the first part, the experts are prioritized by the help of AI methodology. In the next section, missing evaluations of energy transition technology investments are estimated via expert recommender system. Thirdly, the weights of the criteria for energy transition technology selection are computed by quantum picture fuzzy rough sets (QPFR) M-Stepwise Weight Assessment Ratio Analysis (SWARA). At the final stage, selected energy transition technology alternatives are ranked via QPFR-Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR). The main contribution of this study is the integration of AI technique to the proposed model. Similar to this issue, using M-SWARA methodology in the process of criteria weighting increases the quality of the findings. This methodology helps to consider the impact relation map of the criteria. The findings demonstrate that the most important factor is cost-effectiveness of energy transition. Similarly, it is also found that the local ecosystem is the second most significant issue. On the other side, the ranking results denote that compact renewable systems for small scale production is the most optimal solution of energy transition technology alternatives.