Using the transformed data to construct an extension-based fuzzy inference model

Anemia is considered one of the most common diseases that affect the human body. In case of prolonged existence, it can lead to the development of chronic diseases due to excessive load on the vital organs. Effective treatments are available with early detection of the disease and largely depend on its underlying cause. In terms of severity, anemia can be divided into mild, moderate, and severe according to the level of hemoglobin. The morphological classification provides for the division of anemia into microcytic, normocytic, and macrocytic depending on the average volume of red blood cells. According to the hemoglobin, content anemia can be hypochromic, normochromic and hyperchromic. The article explores scientific and technical information on classification and fuzzy logic as well as describes the development of a decision support system for anemia diagnosis using a fuzzy logic inference model. During the analysis of existing literature, it was found that the use of naive Bayesian methods is optimal for solving the problem of determining anemia. But there is a possibility of minor errors in measuring features, so it is necessary to use the theory of fuzzy sets. Modulation of fuzzy logic inference was implemented using the Mamdani algorithm. The developed information system has two levels. The first level determines the severity of anemia. The second level is responsible for belonging to one of the classes: microcytic hypochromic, normocytic normochromic, and macrocytic. The software was written in Java on the basis of its own library to implement a computational algorithm for morphological classification of anemia and classification by severity according to the indicators of hematological laboratory tests. The application of the proposed architectural design of the system saves time for decision-making and eliminates the need for additional manpower to solve the problems of information and analytical support for management decision-making in support of business processes of anemia monitoring

Environmental comprehensive assessment of agricultural systems at the farm level using fuzzy logic: A case study in cane farms in Iran

In an attempt to overcome several hurdles that exist in organ segmentation approaches, the authors previously described a general automatic anatomy recognition (AAR) methodology for segmenting all major organs in multiple body regions body-wide [J. K. Udupa et al., "Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images," Med. Image Anal. 18(5), 752-771 (2014)]. That approach utilized fuzzy modeling strategies, a hierarchical organization of organs, and divided the segmentation task into a recognition step to localize organs which was then followed by a delineation step to demarcate the boundary of organs. It achieved speed and accuracy without employing image/object registration which is commonly utilized in many reported methods, particularly atlas-based. In this paper, our aim is to study how registration may influence performance of the AAR approach. By tightly coupling the recognition and delineation steps, by performing registration in the hierarchical order of the organs, and through several object-specific refinements, the authors demonstrate that improved accuracy for recognition and delineation can be achieved by judicial use of image/object registration. The presented approach consists of three processes: model building, hierarchical recognition, and delineation. Labeled binary images for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The hierarchical relation and mean location relation between different organs are captured in the model. The gray intensity distributions of the corresponding regions of the organ in the original image are also recorded in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connectedness delineation method is then employed to obtain the final segmentation result of organs with seed points provided by recognition. The authors assess the performance of this method for both nonsparse (compact blob-like) and sparse (thin tubular) objects in the thorax. The results of eight thoracic organs on 30 real images are presented. Overall, the delineation accuracy in terms of mean false positive and false negative volume fractions is 0.34% and 4.02%, respectively, for nonsparse objects, and 0.16% and 12.6%, respectively, for sparse objects. The two object groups achieve mean boundary distance relative to ground truth of 1.31 and 2.28 mm, respectively. The hierarchical structure and location relation integrated into the model provide the initial pose for registration and make the recognition process efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both nonsparse and sparse organs. Tailoring the registration process for each organ by specialized similarity criteria and updating the organ intensity properties based on refined recognition improve the overall segmentation process.

A new approach is presented to enhance fuzzy modeling using multidimensional fuzzy sets directly in the fuzzy model in place of decomposed fuzzy sets by projection. The antecedent fuzzy sets in the form of multivariable functions are represented in continuous form. This is accomplished by using a multi input and multi output neural network, which is in particular radial basis functions (RBF) network providing the required multivariable function approximation properties in a fuzzy model. The inputs of the network are fuzzy model inputs, outputs are the membership function values as to the multivariable fuzzy sets involved in the model. Thus, each output is restricted to one multivariable fuzzy set. The RBF network is trained according to the multidimensional fuzzy sets which are identified after fuzzy clustering of the data. Based on this, the linear model parameters are determined by the method of least squares in a straightforward manner. The transparency issues can be handled by means of projection process to have information about the shape and locations of the membership functions pertinent to each variable at the input. The accurate fuzzy modeling having been thus guaranteed the information on the linear model parameters are used to establish the multivariable fuzzy rules with their respective validity regions. The redundancy of the number of fuzzy sets can be circumvented by classical cluster merging algorithms available. In this approach, the fuzzy rules are determined from the local linear models, however in contrast with the conventional fuzzy modeling, the model outputs are determined by the multivariable fuzzy sets. To obtain regular sets, RBF network is trained by means of orthogonal least squares (OLS) method so that at the same time the convergence issues of general neural network training is eliminated.

A broad-deep fusion network-based fuzzy emotional intention inference model for teaching validity evaluation

Design of a two-stage fuzzy classification model

Describes an approach to automatically build a Takagi-Sugeno fuzzy model (TSK-model) based on a set of input-output data. Identifying rule-based fuzzy models consists of two parts: structure modeling and parameter optimization. For structure modeling, we investigate several search heuristics. In order to find a good model structure, such search heuristics make it necessary to optimize and then evaluate a large number of different candidate models. To be applicable to real world problems, the parameter optimization must be highly efficient. For this, we investigate the use of two gradient descent algorithms: standard gradient descent (backpropagation) and resilient propagation (RPROP). The combination of a structure search with a fast parameter optimization yields a powerful modeling algorithm that is capable to identify large real world systems. We evaluate a number of varieties of TSK-like fuzzy models by performing a nonlinear regression benchmark of Frank (1995). We compare several types of fuzzy models that are constructed as combinations of different fuzzy sets, structuring algorithms, and parameter optimization techniques.

A new fuzzy inference model, SIRMs (Single Input Rule Modules) Connected Fuzzy Inference Model, is proposed for plural input fuzzy control. For each input item, an importance degree is defined and single input fuzzy rule module is constructed. The importance degrees control the roles of the input items in systems. The model output is obtained by the summation of the products of the importance degree and the fuzzy inference result of each SIRM. The proposed model needs both very few rules and parameters, and the rules can be designed much easier. The new model is first applied to typical secondorder lag systems. The simulation results show that the proposed model can largely improve the control performance compared with that of the conventional fuzzy inference model. The tuning algorithm is then given based on the gradient descent method and used to adjust the parameters of the proposed model for identifying 4-input 1-output nonlinear functions. The identification results indicate that the proposed model also has the ability to identify nonlinear systems.

This paper introduces a novel fuzzy model - transductive neural-fuzzy classifier with weighted data normalization (TWNFC). While inductive approaches are concerned with the development of a model to approximate data in the whole problem space (induction), and consecutively - using this model to calculate the output value(s) for a new input vector (deduction), in transductive systems a local model is developed for every new input vector, based on some closest data to this vector from the training data set. The weighted data normalization method (WDN) optimizes the data normalization ranges for the input variables of a system. A steepest descent algorithm is used for training the TWNFC model. The TWNFC is illustrated on a case study: a real medical decision support problem of estimating the survival of haemodialysis patients. This personalized modeling can also be applied to other distance-based, prototype learning neural network or fuzzy inference models

Chaotic systems are nonlinear dynamic systems, the main feature of which is high sensitivity to initial conditions. To initiate a design process in fuzzy model, chaotic systems must first be represented by T-S fuzzy models. In this paper, a new fuzzy modeling method based on sector nonlinearity approach has been recommended for chaotic systems relating to initial condition variations using the interval type-2 Takagi–Sugeno (IT2 T-S) fuzzy model. Examining many famous chaotic systems, it can be seen that nonlinear terms in chaotic systems are composed of just one variable or more. In the process of constructing an IT2 T-S fuzzy model which represents the chaotic systems, authors will focus on nonlinear terms of the chaotic systems. The proposed interval type-2 Takagi-Sugeno fuzzy modeling method is employed for two kinds of nonlinear terms; at first, a uni-variable nonlinear term is presented and then a multi-variable one will be introduced. So, it will be shown how many famous chaotic systems are represented by IT2 T-S fuzzy model. Then the proposed approach is applied to Genesio-Tesi and Rossler systems. Numerical simulations are given to demonstrate the efficiency of the proposed method in MATLAB environment.

The inventory problem associated with trade credit is a popular topic in which interest income and interest payments are important issues. Most studies related to trade credit assume that the interest rate is both fixed and predetermined. However, in the real market, many factors such as financial policy, monetary policy and inflation, may affect the interest rate. Moreover, within the environment of merchandise storage, some distinctive factors arise which ultimately affect the quality of products such as temperature, humidity, and storage equipment. Thus, the rate of interest charges, the rate of interest earned, and the deterioration rate in a real inventory problem may be fuzzy. In this paper, we deal with these three imprecise parameters in inventory modeling by utilizing the fuzzy set theory. We develop the fuzzy inventory model based on Chang et al.'s [1] model by fuzzifying the rate of interest charges, the rate of interest earned, and the deterioration rate into the triangular fuzzy number. Subsequently, we discuss how to determine the optimal ordering policy so that the total relevant inventory cost, in the fuzzy sense, is minimal. Furthermore, we show that Chang et al.'s [1] model (the crisp model) is a special case of our model (the fuzzy model). Finally, numerical examples are provided to illustrate these results.

Fuzzy set theory has a remarkable progress in the field of research. It initiates many areas in both practical & theoretical studies. It is really useful for several people engaged in research and development including medical researchers, mathematician, businessman, social scientists, natural researchers etc. This field of mathematics has introduced new life into technical and scientific fields that have been undeveloped for a long period. Thousands of scholars are operating and working with fuzzy set theory and presented a lot of research papers. In this paper latest review of existing literature & numerous types of fuzzy EOQ inventory models under shortage situation. It assists to categorize how the concept of fuzzy sets theory has been applied in inventory models which motivate researchers to concentrating on new technique in study of inventory control models in fuzzy environment. In this review, we study desirable constraints of existing models in fuzzy environment under shortage of supplies. A lot of effort is attempted to deliver the latest review of existing literature of inventory and fuzzy models. The purpose of the work is to obtain a continuous and comprehensive assessment of existing literature and recognize upcoming research guidelines. This review helps other researchers to draft an outcome during the situation of short supplies. This will also help to manage inventory according to the situation & reduce the loss during shortage of supplies, this review also helps to reduce loss with proper inventory management in real-life applications and marketable products.

Small and medium-sized manufacturing enterprises have the characteristics of large numbers and small scales. Problems such as backward manufacturing technology, lack of talents, small amount of information resources, and insufficient product research and development capabilities have severely restricted the development of enterprises. The backward manufacturing design model cannot adapt to the development trend of modern manufacturing informatization. This paper proposes and designs a fuzzy inference model and fuzzy inference engine algorithm with threshold. In order to describe the numerical multiple input and multiple output variables in the industrial manufacturing design industry, the relevant experience is used to make numerical reasoning decisions. Applying fuzzy sets and fuzzy theory to the expert system, a fuzzy rule model containing the membership function information and thresholds of the corresponding fuzzy sets is proposed and established, and a fuzzy reasoning system suitable for numerical and uncertain reasoning decisions is constructed. The improved grey relational analysis method is used to decompose and evaluate the exponential mathematical quantitative process of manufacturing enterprises. Based on the fuzzy Decision Analytic Network Process (DANP) method to calculate the relative weight of the influencing factors in the evaluation system, the evaluation index of the enterprise is obtained. Starting from the industrial manufacturing design process, this article constructs a relatively comprehensive and reasonable enterprise exponential mathematical quantitative process decomposition evaluation system. Considering that there are complex interactions between the various influencing factors in the system, the fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) method is selected to process the direct impact matrix of the evaluation system, and the causal relationship between the indicators is obtained. The fuzzy exponential gray correlation method is used to evaluate the quantitative process of industrial manufacturing design, avoiding the shortcomings of traditional methods that only consider ideal values.

Information imprecision and uncertainty exist in many real-world applications, and for this reason fuzzy data modeling has been extensively investigated in various database models. In particular, Zadeh’s fuzzy set theory has been identified as a successful technique for modeling imprecise and uncertain information in various database models. This has resulted in numerous contributions, mainly with respect to the popular fuzzy conceptual data models (fuzzy ER/EER model, fuzzy UML data model, and etc.) and fuzzy logical database models (fuzzy relational database model, fuzzy object-oriented database model, and etc.). Also, it is shown that fuzzy set theory is very useful in Web-based business intelligence. Therefore, topics related to the modeling of fuzzy data are considered very interesting in XML since it is the current standard data representation and exchange format over the Web. In particular, to manage fuzzy XML data, it is necessary to integrate fuzzy XML and various fuzzy databases, and various fuzzy database models (fuzzy relational database model and fuzzy object-oriented database model) need to be used for mapping to and from the fuzzy XML models. Therefore, in this chapter, we mainly introduce several fuzzy database models, including fuzzy UML data model, fuzzy relational database model, and fuzzy object-oriented database model. Before that, we briefly introduce some notions of fuzzy set theory.

This study aims to (i) develop predictive models using statistical and fuzzy approaches, and (ii) evaluate their forecasting performance. The data were obtained from www.investing.com for the period 1961 to 2017 and focus on five ASEAN countries: Indonesia, Malaysia, the Philippines, Singapore, and Thailand. The statistical models used are Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing, while the fuzzy approaches include Chen and Singh fuzzy time series models. The dataset was divided into training and test sets in a 75%-25% proportion. ARIMA models capture trends and autocorrelations in time series data, while Exponential Smoothing uses exponentially weighted averages. Fuzzy models are designed to handle uncertainty and linguistic patterns in data. The results show that Singh’s fuzzy model yields the lowest error for Indonesia, while exponential smoothing and Chen fuzzy time series model demonstrate the same lowest error for Malaysia. For the Philippines, exponential smoothing is most accurate, whereas ARIMA and Singh fuzzy time series achieve the smallest error for Singapore. For Thailand, exponential smoothing and ARIMA perform equally well. However, the robustness of the forecasting model cannot be determined from either statistical or fuzzy methods, highlighting the challenge in determining the most robust model for inflation in the ASEAN region. The 75%-25% data split may also limit the generalizability of the findings. This study contributes a rare cross-country comparison of statistical and fuzzy forecasting methods in the ASEAN context. It highlights the importance of model selection based on country-specific inflation behavior and provides insights for improving forecasting strategies in macroeconomic applications.