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This paper studies pricing problem for a closed-loop supply chain consisting of a manufacturer and a retailer in a fuzzy environment. The purpose of this paper is to explore how the manufacturer makes his decisions about wholesale price and transfer price and how the retailer makes her decisions about retail price and collecting price in the expected value standard. Each firm's optimal pricing strategies are established by using game theory under the centralized and decentralized decision cases, respectively. Managerial insights into the economic behavior of firms are also investigated, which can serve as the basis for empirical study in the future. Moreover, we analyze numerically the results and give some insights on the influence of some parameters.

Aircraft approaching is the most dangerous phase in every complete flight. To solve the pressure of air traffic controllers and the landings delayed problems caused by the huge air traffic flow in Terminal Control Area (TCA), an automatic Air Traffic Control (ATC) instructions system is initially designed in this paper. It applies the fuzzy theory to make instant and appropriate decisions which can be transmitted via Controller-Pilot Datalink Communications (CPDLC). By means of the designed system, the decision-making time can be saved and the human factors can be reduced to avoid the flight accidents and further delays in aircraft approaching.

The number of people suffering from diabetes in Taiwan has been increasing in recent years, according to data from the Health Promotion Administration, the prevalence rate of diabetes in Taiwan has reached 5%. In 2019, there were approximately 1.1 million type 1 diabetes patients under the age of 20 in the world, indicating that diabetes is also threatening the health of children and adolescents. Moreover, the vast majority of about 463 million diabetic patients globally between the ages of 20 and 79 suffer from type 2 diabetes. One can see that diabetes is an important public health problem and one of the four major noncommunicable diseases that leaders of all countries should take priority action to address. Type 2 diabetes causes many complications, including cardiovascular disease, impaired vision, amputation, kidney disease, etc. and increases the cost of social medical care. This study takes data from the Data Database of the Health Promotion Administration as the parent population, fuzzy theory and neural network to build predictive models with Matlab tools. The predictive results can be used as a reference for medical personnel in any diagnosis.

Two fractal image compression algorithms based on possibility theory are originally presented in this paper. Fuzzy sets are used to represent the edge character of each image block, and two kinds of membership function are designed. A fuzzy integrated judgement model is also proposed. The model generates an accurate value for each edge block, which would be a label during the search process. The edge possibility distribution function and the edge necessity level are designed to control the quantity of the blocks to be searched. Meanwhile the pre-restriction is proposed, the average intensity value at different locations is used to be a necessary condition before the MSE computations. It is shown by our experiments that the encoding times of our two algorithms, compared to that of Jacquin's approach, are reduced to 60%–70% and 10%–20%, respectively.

In many cases, criterion values are crisp in nature, and their values are determined by economic instruments, mathematical models, and/or by engineering measurement. However, there are situations when the evaluation of alternatives must include the imprecision of established criteria, and the development of a fuzzy multicriteria decision model is necessary to deal with either "qualitative" (unquantifiable or linguistic) or incomplete information. The proposed fuzzy multicriteria decision model (FMCDM) consists of two phases: the CFCS phase - Converting the Fuzzy data into Crisp Scores, and the MCDM phase - MultiCriteria Decision Making. This model is applicable for defuzzification within the MCDM model with a mixed set of crisp and fuzzy criteria. A newly developed CFCS method is based on the procedure of determining the left and right scores by fuzzy min and fuzzy max, respectively, and the total score is determined as a weighted average according to the membership functions. The advantage of this defuzzification method is illustrated by some examples, comparing the results from three considered methods.

The Fuzzy-Product-Limit Estimator (FPLE) is a method for estimating a survival curve and the mean survival time when very few data are available and a high proportion of the data are censored. Considering censored times as vague failure times, the censored values are represented by fuzzy membership functions that represent a belief of continued survival of the associated unit. Associated with any estimate is uncertainty. With the FPLE two distinct types of uncertainty exist in the estimate, the uncertainty due to the randomness in the recorded times and the vague uncertainty in the failure of the censored units. This paper addresses the problem of providing confidence bounds and estimates of uncertainty for the FPLE. Several methods for estimating the vague uncertainty in the estimator are suggested. Among them are the use of Efron's Bootstrap that obtains a confidence interval of the FPLE to quantify random uncertainty and produces an empirical distribution that is used to quantify properties of the vague uncertainty. Also, a method to obtain a graphical representation of the random and vague uncertainties is developed. The new methods provide confidence intervals that quantify statistical uncertainty as well as the vague uncertainty in the estimates. Finally, results of simulations are provided to demonstrate the efficacy of the estimator and uncertainty in the estimates.

Comparison of fuzzy variables is considered one of the most important topics in fuzzy theory. A new approach for ranking fuzzy variable via credibility measure — fuzzy dominance is presented in this paper. Some basic properties of fuzzy dominance are investigated. As an illustration, the cases of fuzzy dominance rule for triangular fuzzy variables are examined.

In this study, a new regression method called Kappa regression is introduced to model conditional probabilities. The regression function is based on Dombi’s Kappa function, which is well known in fuzzy theory. Here, we discuss how the Kappa function relates to the Logistic function as well as how it can be used to approximate the Logistic function. We introduce the so-called Generalized Kappa Differential Equation and show that both the Kappa and the Logistic functions can be derived from it. Kappa regression, like binary Logistic regression, models the conditional probability of the event that a dichotomous random variable takes a particular value at a given value of an explanatory variable. This new regression method may be viewed as an alternative to binary Logistic regression, but while in binary Logistic regression the explanatory variable is defined over the entire Euclidean space, in the Kappa regression model the predictor variable is defined over a bounded subset of the Euclidean space. We will also show that asymptotic Kappa regression is Logistic regression. The advantages of this novel method are demonstrated by means of an example, and afterwards some implications are discussed.

In this paper, the hysteresis characterization in fuzzy spaces is presented by utilizing a fuzzy learning algorithm to generate fuzzy rules automatically from numerical data. The hysteresis phenomenon is first described to analyze its underlying mechanism. Then a fuzzy learning algorithm is presented to learn the hysteresis phenomenon and is used for predicting a simple hysteresis phenomenon. The results of learning are illustrated by mesh plots and input-output relation plots. Furthermore, the dependency of prediction accuracy on the number of fuzzy sets is studied. The method provides a useful tool to model the hysteresis phenomenon in fuzzy spaces.

Nowadays, to cater the increasing green customers, firms have switched to green manufacturing. In a dual-channel green supply chain (DCGSC), customers experience products at offline stores and buy them online (free-riding). Often imprecise cognitive biases (“fairness concern” and “overconfidence”) are observed among the supply chain (SC) members. With these facts, this study introduces the free-riding and above cognitive biases in a DCGSC with a manufacturer selling a green product through own online and offline retail channels and examines their effects. A centralized and four decentralized models (for green and nongreen products) are formulated depending upon channel members’ cognitive biases individually and jointly with and without free-riding. The fuzzy objectives and constraints are made deterministic using expectation and possibility measures, respectively. Models are solved and illustrated numerically. The results indicate that free-riding is harmful and beneficial to retailer and manufacturer, respectively. Manufacturer’s overconfidence enhances the retailer’s profit but decreases or increases own profit depending upon the salvage value. Retailer’s fairness concern is catastrophic for manufacturer but beneficial for her. Product greening increases manufacturer’s profit than the carbon tax regulation for lower emissions. In addition to above observations, for maximum profit, management should not go for greening beyond an optimum level.

The optimal design of linkage mechanisms for path generation and motions with reduced harmonic content is investigated. The designs are carried out using a two-objective optimizer based on the fuzzy theory. As an illustration, the present approach is applied to the optimization of a five-bar hybrid mechanism driven by a constant speed motor and a servo motor. The dynamic characteristics of the servo motor are improved, as the harmonics in the servo motion are reduced.

Spectrum seems to be the lifeblood of wireless communication, which is of high demand as the traffic doubles every year. This increasing demand drives towards 5G NR, which is expected to support 100 folds increase in mobile devices, gigabit user data rate, ultra-low latency, high traffic and ultra-reliability. Since most of the available spectrum has been saturated, new methods that make use of spectrum in efficient manner must be considered. Through flexible 5G NR framework, 5G is aimed to utilize shared/unlicensed spectrum. Cognitive radio is the key enabler for operating through shared/unlicensed spectrum. In this situation, network interference caused by secondary users (SU) in accessing the unlicensed band for cognitive radio can be overcome by implementation of efficient resource management techniques. The interference issue among SU can be minimized to a large extent by efficiently allocating the available spectrum. Fuzzy Analytic hierarchy process (FAHP) seems to be an appropriate solution for spectrum allocation among SU without creating interference among themselves. The mathematical model proves that FAHP allocated the spectrum to the best SU.

In this paper, an Interactively Recurrent Self-evolving Fuzzy Cerebellar Model Articulation Controller (IRSFCMAC) model is developed for solving classification problems. The proposed IRSFCMAC classifier consists of internal feedback and external loops, which are generated by the hypercube cell firing strength to itself and other hypercube cells. The learning process of the IRSFCMAC gets started with an empty hypercube base, and then all of hypercube cells are generated and learned online via structure and parameter learning, respectively. The structure learning algorithm is based on the degree measure to determine the number of hypercube cells. The parameter learning algorithm, based on the gradient descent method, adjusts the shapes of the membership functions and the corresponding fuzzy weights of the IRSFCMAC. Finally, the proposed IRSFCMAC model is tested by four benchmark classification problems. Experimental results show that the proposed IRSFCMAC model has superior performance than traditional FCMAC and other models.

Traditional mathematics is the language of precision. Statements are either true or false. But, in reality there are few things that are truly simply true or false. Life is full of shades of grey. Capturing these shades of grey has been problematic using traditional mathematics. Mathematics can model the uncertainty itself but rarely incorporates it into the model. However, an emerging area of mathematical inquiry known as the mathematics of uncertainty seeks to overcome some of these problems by integrating the uncertainly, the shades of grey, directly into the model. Fuzzy theory is one of those emerging ideas. This paper is simply an overview of some of the basic mechanics of fuzzy theory and recent economic applications in both micro and macro-economics.

The rapid changes in the economic situation and the complex market environment make the cross-border e-commerce industry, as an important part of the market economy, face many challenges and risks in the development process. The particularity of its transaction and the imperfect tax mechanism have virtually increased the financial management risk. The research constructs the financial risk analysis model with the help of the support vector machine (SVM) and fuzzy theory. Through algorithm test and empirical research, it is found that the average accuracy of the optimized SVM on the selected data set is more than 90%, and after parameter optimization, the change of model fitness tends to be stable with the increase of iteration times, which greatly improves the search ability of sample data, and the accuracy of financial data classification with high risk is 46.8%. In the empirical research, the model established by fuzzy SVM can effectively eliminate the irrelevant index data, and the prediction accuracy of investment risk and operation risk has reached 80% or more. In the prediction of financing risk and tax risk, its accuracy has been improved by 12.4% compared to that before use.

According to the newest classification of the Spatial Planning Act of the Land Planning Act in Taiwan, land space is divided into environmental conservation zones, marine resource zones, agricultural development zones, and urban and rural development zones. Among the above zones, environmental conservation zones are the most important for environmental resource conservation. The purpose of this study is to construct evaluation levels for environmental conservation zones, and to construct an evaluation index system after determining the decision criteria and their interrelationships, thereby identifying the key factors based on the evaluation criteria and indexes in decision-making process. The research method combines the multi-criteria decision-making and fuzzy theory. The indexes of optimal scheme and appropriate resource utilization methods are selected. An easy-to-use and concise planning evaluation model is constructed, with the aim to provide references for local governments in formulating policies, so that the utilization of land space can be sustainable.

The “fuzzy” electrical engineering method to online algorithms is defined not only by the evaluation of XML, but also by the theoretical need for reinforcement learning [1,2]. In fact, few scholars would disagree with the development of Moore’s Law, which embodies the structured principles of software engineering. We introduce new multimodal models, which we call AbsorptLydine.

Planning operation of storage reservoir considering water quantity and quality objectives is formulated. An example application is presented for the Barra Bonita reservoir in the state of Sao Paulo, Brazil. It is a multi-purpose reservoir for the basic purposes of energy production, navigation, and recreation. Eutrophication is considered to be a serious problem within this reservoir.

Emphasis is given to optimization and simulation assessment of physical and biological characteristics of the system to achieve the most appropriate operation for long-term planning through the use of statistic, optimization and artificial intelligence (AI) techniques.

Water quality is assessed by the application of the box-type, two-layers model (PAMOLARE) developed by UNEP/ILEC. Due to the enormous number of quality parameters, water quality membership functions are constructed to easier evaluate and compare alternatives.

A Genetic Algorithm (GA) based model is developed for the calibration of the water quality model. The model is constructed using elitism, crossover and mutation operators.

Fuzzy membership functions are to be elaborated based on the operation objectives and goals. A fuzzy deterministic dynamic programming model is developed to handle the water quantity and quality operation for maximum satisfaction and benefit.

In this paper, the authors try to make the fundamentals clear and propose a method to evaluate the effectiveness of Integrated Safeguards Implementation. In addition, the method is shown to be useful for the planning to improve the efficiency.

Image segmentation is an essential research topic in the field of image processing, which primarily functions to separate figures into multiple disjoint regions. In most computer imaging scenarios, segmentation serves as the primary procedure for the further image understanding. In this paper, we propose a novel algorithm based on deep neural network and modified fuzzy clustering model. We enhance the clustering model with the incorporation of Markov random field theory tobuild up the spatial information model. To resolve the challenges influencing the performance of the traditional neural network, we adopt the deep neural structure to enhance the feasibility and robustness of the network. We compare our model with other state-of-the-art algorithms, using numerical and visual simulation to test the effectiveness,. Our experimental results verify that both accuracy and efficiency are promoted simultaneously. We attained a high segmentation accuracy of 93.6% with our proposed method.