Fuzzy Reasoning Method Research Articles

Supply Chain Risk Management Analysis using the Development of Fuzzy Reasoning Methods and Analytical Network Process (ANP) at Wooden Toys Industries

The supply chain is the relationship between material flows or services, money flow and information ranging from suppliers, producers, distributors, warehouses, retailers to customers. The smooth flow of material in the supply chain affected in the fulfillment of customer needs. If the problem occurs, it caused losses and risks. To minimize risks, it can be handled with Supply Chain Risk Management. This research was conducted in the supply chain in Wooden Toys Industries by identifying risks using the Fuzzy Reasoning method to calculate the amount of opportunity and impact that would arise if the risk occurred. To reduce the opportunity and impact of risk, a mitigation strategy is needed to consider and can be done using the Analytical Network Process method. Followed the results, the competitor’s moves have a large chance and risk. Thus, risk mitigation can be proposed is necessary to conduct market observations and identify competitors at (31.3%).

Adaptive fuzzy interpolative reasoning based on similarity measures of polygonal fuzzy sets and novel move and transformation techniques

In this paper, we propose a new adaptive fuzzy interpolative reasoning (AFIR) method based on similarity measures of polygonal fuzzy sets (PFSs) and novel move and transformation techniques for solving contradictory fuzzy interpolative reasoning (FIR) results that occur at FIR components. The proposed AFIR method is applied for dealing with the diarrheal disease prediction problem. The experimental results show that the proposed AFIR method obtains a higher consistency degree between the FIR results compared to the ones obtained by Chen and Adam's AFIR method (2017), Chen and Adam's AFIR method (2018), Chen and Barman's AFIR method (2019), Cheng et al.’s AFIR method (2016), Yang et al.’s AFIR method (2017) and Yang and Shen's AFIR method (2011).

Improving the Performance of Fuzzy Rule-Based Classification Systems Based on a Nonaveraging Generalization of CC-Integrals Named $C_{F_1F_2}$-Integrals

A key component of fuzzy rule-based classification systems (FRBCS) is the fuzzy reasoning method (FRM) since it infers the class predicted for new examples. A crucial stage in any FRM is the way in which the information given by the fired rules during the inference process is aggregated. A widely used FRM is the winning rule, which applies the maximum to accomplish this aggregation. The maximum is an averaging operator, which means that its result is within the range delimited by the minimum and the maximum of the aggregated values. Recently, new averaging operators based on generalizations of the Choquet integral have been proposed to perform this aggregation process. However, the most accurate FRBCSs use the FRM known as additive combination that considers the normalized sum as the aggregation operator, which is nonaveraging. For this reason, this paper is aimed at introducing a new nonaveraging operator named $C_{F_1F_2}$ -integral, which is a generalization of the Choquet-like Copula-based integral (CC-integral). $C_{F_1F_2}$ -integrals present the desired properties of an aggregation-like operator since they satisfy appropriate boundary conditions and have some kind of increasingness property. We show that $C_{F_1F_2}$ -integrals, when used to cope with classification problems, enhance the results of the previous averaging generalizations of the Choquet integral and provide competitive results (even better) when compared with state-of-the-art FRBCSs.

A practical approach for estimating human daily water intake

The proportion of water in the human body must keep at a proper level to maintain normal physiological action. Daily water consumption needs to be properly supplemented in time to protect health of human beings. Water consumption is related with age, body weight, temperature, physical activity, etc. These factors are generally difficult to express with crisp data to get accurate value of recommended daily water intake. The main objective of this paper is to build a recommended daily water intake system and a practical application using fuzzy reasoning methods. We also compared the actual recommended values with our system output values to find the differences. By designing an application on the Arduino platform and using Bluetooth electronic scale to connect to smart phones, we help people control daily water intake. The experiment results proved that the recommendation system is effective in actual applications.

A practical approach for estimating human daily water intake

The proportion of water in the human body must keep at a proper level to maintain normal physiological action. Daily water consumption needs to be properly supplemented in time to protect health of human beings. Water consumption is related with age, body weight, temperature, physical activity, etc. These factors are generally difficult to express with crisp data to get accurate value of recommended daily water intake. The main objective of this paper is to build a recommended daily water intake system and a practical application using fuzzy reasoning methods. We also compared the actual recommended values with our system output values to find the differences. By designing an application on the Arduino platform and using Bluetooth electronic scale to connect to smart phones, we help people control daily water intake. The experiment results proved that the recommendation system is effective in actual applications.

Adaptive weighted fuzzy interpolative reasoning based on representative values and similarity measures of interval type-2 fuzzy sets

In this paper, we propose an adaptive weighted fuzzy interpolative reasoning (AWFIR) method based on representative values (RVs) and similarity measures of interval type-2 polygonal fuzzy sets (IT2PFSs). The proposed AWFIR method can overcome the shortcomings of Cheng et al.’s adaptive fuzzy interpolative reasoning (AFIR) method (2016), Chen and Adam's AFIR method (2018), Yang and Shen's AFIR method (2011) and Yang et al.’s AFIR method (2017) to deal with the diarrheal disease prediction problem. The advantages of the proposed AWFIR method are (1) it produces higher degrees of consistency between the FIR results and (2) it deals with AWFIR using IT2PFSs rather than type-1 fuzzy sets (T1FSs).

PROPERTY ANALYSIS OF TRIPLE IMPLICATION METHOD FOR APPROXIMATE REASONING ON ATANASSOVS INTUITIONISTIC FUZZY SETS

Firstly, two kinds of natural distances between intuitionistic fuzzy sets are generated by the classical natural distance between fuzzy sets under a unified framework of residual intuitionistic implication operators. Secondly, the continuity and approximation property of a method for solving intuitionistic fuzzy reasoning are defined. It is proved that the triple implication method for intuitionistic fuzzy modus ponens has both continuity and approximation property with respect to these two kinds of natural distances based on {L}ukasiewicz implication, meanwhile the triple implication method for intuitionistic fuzzy modus tollens possesses unconditional continuity and conditional approximation property. Finally, some robustness results about the triple implication method of intuitionistic fuzzy reasoning are given.

GSI method of fuzzy reasoning 1

GSI method of fuzzy reasoning 1

Logical foundation of the quintuple implication inference methods

The quintuple implication inference methods for fuzzy reasoning characterizes the solution B⁎(A⁎) for the fuzzy modus ponens (fuzzy modus tollens) as the formula that is best supported by A→B, A⁎→A and A⁎(A→B,B→B⁎ and B⁎). In this study, we provide a predicate formal representation of the solution for the quintuple implication inference methods based on the many-sorted first-order formal system Monoidal t-norm based logic MTL∀ms, including detailed logic proofs. We bring the quintuple implication inference methods within a logical framework and provide a sound logic foundation for the quintuple implication inference methods of fuzzy reasoning.

Adaptive fuzzy interpolation based on ranking values of interval type-2 polygonal fuzzy sets

In recent years, some adaptive fuzzy interpolative reasoning (AFIR) methods have been proposed to deal with contradictions occurred during fuzzy interpolation processes. However, the degrees of consistency of the AFIR results of the existing AFIR methods are not good enough. Moreover, the existing AFIR methods are based on type-1 fuzzy sets (T1FSs), which cannot deal with AFIR using interval type-2 fuzzy sets (IT2FSs), where IT2FSs are more suitable to represent the fuzziness of information than T1FSs. In this paper, we propose a new AFIR method based on the ranking values of interval type-2 polygonal fuzzy sets (IT2PFSs). We also apply the proposed AFIR method based on IT2PFSs to deal with the diarrheal disease prediction problem. The proposed AFIR method based on the ranking values of IT2PFSs can overcome the shortcomings of Yang and Shen's AFIR method (2011) and Cheng et al.’s AFIR method (2016) because it gets a higher consistency of the AFIR results in terms of the degree of similarity between the AFIR results and it deals with AFIR based on IT2PFSs rather than T1FSs.

ROBUSTNESS OF THE TRIPLE IMPLICATION INFERENCE METHOD BASED ON THE WEIGHTED LOGIC METRIC

This paper focuses on the robustness problem of full implication triple implication inference method for fuzzy reasoning. First of all, based on strong regular implication, the weighted logic metric for measuring distance between two fuzzy sets is proposed. Besides, under this metric, some robustness results of the triple implication method are obtained, which demonstrates that the triple implication method possesses a good behavior of robustness.

Weighted fuzzy interpolated reasoning based on ranking values of polygonal fuzzy sets and new scale and move transformation techniques

In this paper, we propose a new transformation-based weighted fuzzy interpolative reasoning (FIR) method based on ranking values of polygonal fuzzy sets (PFSs) and the proposed new scale and move transformation techniques. The proposed weighted FIR method gets more reasonable FIR results than the ones of the existing methods, where the weight of each antecedent variable and the weight of each fuzzy rule are generated automatically. Moreover, the proposed new scale and move transformation techniques can deal with FIR using singleton fuzzy sets and PFSs. We also apply the proposed weighted FIR method to predict the diarrheal disease rates in remote villages. The proposed weighted FIR method provides us with a very useful way for weighted FIR in sparse fuzzy rule-based systems.

CF-integrals: A new family of pre-aggregation functions with application to fuzzy rule-based classification systems

This paper introduces the family of CF-integrals, which are pre-aggregations functions that generalizes the Choquet integral considering a bivariate function F that is left 0-absorbent. We show that CF-integrals are 1→-pre-aggregation functions, studying in which conditions they are idempotent and/or averaging functions. This characterization is an important issue of our approach, since we apply these functions in the Fuzzy Reasoning Method (FRM) of a fuzzy rule-based classification system and, in the literature, it is possible to observe that non-averaging aggregation functions usually provide better results. We carry out a study with several subfamilies of CF-integrals having averaging or non-averaging characteristics. As expected, the proposed non-averaging CF-integrals obtain more accurate results than the averaging ones, thus, offering new possibilities for aggregating accurately the information in the FRM. Furthermore, it allows us to enhance the results of classical FRMs like the winning rule and the additive combination.

A Fuzzy Reasoning Method in Fault Diagnosis of House Transformer Simulating System

Fuzzy set and reasoning are studied and used in the fault diagnosis of House Transformer Simulating System in this paper. Fuzzy diagnosis matrix and Fault cause fuzzy vector are firstly constructed according to collection of fault symptoms and the cause set. With maximum membership method performed on the fault fuzzy vector, fuzzy fault diagnosis algorithm is implemented. Experiments on real data about House Transformer from a hydro-electric factory are performed and efficiency of the fuzzy fault diagnosis method is guaranteed.

A knowledge-based system for breast cancer classification using fuzzy logic method

Breast cancer has become a common disease around the world. Expert systems are valuable tools that have been successful for the disease diagnosis. In this research, we accordingly develop a new knowledge-based system for classification of breast cancer disease using clustering, noise removal, and classification techniques. Expectation Maximization (EM) is used as a clustering method to cluster the data in similar groups. We then use Classification and Regression Trees (CART) to generate the fuzzy rules to be used for the classification of breast cancer disease in the knowledge-based system of fuzzy rule-based reasoning method. To overcome the multi-collinearity issue, we incorporate Principal Component Analysis (PCA) in the proposed knowledge-based system. Experimental results on Wisconsin Diagnostic Breast Cancer and Mammographic mass datasets show that proposed methods remarkably improves the prediction accuracy of breast cancer. The proposed knowledge-based system can be used as a clinical decision support system to assist medical practitioners in the healthcare practice.

A t-Norm Fuzzy Logic for Approximate Reasoning

A t-norm fuzzy logic is presented, in which a triangular norm (t-norm) plays the role of a graduated conjunction operator. Based on this fuzzy logic we develop methods for fuzzy reasoning in which antecedents and consequents involve fuzzy conditional propositions of the form “If x is A then y is B”, with A and B being fuzzy concepts (fuzzy sets). In this study, we present a systemic approach toward fuzzy logic formalization for approximate reasoning. We examine statistical characteristics of the proposed fuzzy logic. As the matter of practical interest, we construct a set of fuzzy conditional inference rules on the basis of the proposed fuzzy logic. Important features of these rules are investigated.

CC-integrals: Choquet-like Copula-based aggregation functions and its application in fuzzy rule-based classification systems

This paper introduces the concept of Choquet-like Copula-based aggregation function (CC-integral) and its application in fuzzy rule-based classification systems. The standard Choquet integral is expanded by distributing the product operation. Then, the product operation is generalized by a copula. Unlike the generalization of the Choquet integral by t-norms using its standard form (i.e., without distributing the product operator), which results in a pre-aggregation function, the CC-integral satisfies all the conditions required for an aggregation function. We build some examples of CC-integrals considering different examples of copulas, including t-norms, overlap functions and copulas that are neither t-norms nor overlap functions. We show that the CC-integral based on the minimum t-norm, when applied in fuzzy rule-based classification systems, obtains a performance that is, with a high level of confidence, better than that which adopts the winning rule (maximum). We concluded that the behavior of CC-integral is similar to the best Choquet-like pre-aggregation function. Consequently, the CC-integrals introduced in this paper can enlarge the scope of the applications by offering new possibilities for defining fuzzy reasoning methods with a similar gain in performance.

Preaggregation Functions: Construction and an Application

In this paper, we introduce the notion of preaggregation function. Such a function satisfies the same boundary conditions as an aggregation function, but, instead of requiring monotonicity, only monotonicity along some fixed direction (directional monotonicity) is required. We present some examples of such functions. We propose three different methods to build preaggregation functions. We experimentally show that in fuzzy rule-based classification systems, when we use one of these methods, namely, the one based on the use of the Choquet integral replacing the product by other aggregation functions, if we consider the minimum or the Hamacher product t-norms for such construction, we improve the results obtained when applying the fuzzy reasoning methods obtained using two classical averaging operators such as the maximum and the Choquet integral.

Using DEVS for modeling and simulation of ambient objects in intelligent buildings

Modern intelligent buildings should be designed to provide comfort and optimal quality of life for occupants. Therefore, smart home devices that allow for the control of actual indoor ambience, which is critical to minimizing dissatisfaction, and simultaneously reduce the energy consumption must be modeled. In this paper, we propose an original model of the contribution of objects, where each object contributes to a type of ambience, namely, thermal, visual, acoustic and air quality. In other words, a given indoor ambience is defined by the contribution of objects. The discrete event system specification (DEVS) is a formalism for describing simulation models in a modular way. The modular nature of the DEVS formalism is exploited in this study by forming submodels of smart objects that allow domain experts to develop simulation techniques independently and later combine their work for reducing energy consumption and improving comfort. We have incorporated fuzzy reasoning methods in the DEVS formalism to account for the human perception error in an indoor environment, allowing for improved indoor environmental quality management in buildings.

A Fuzzy Rule-Based Bayesian Reasoning Method for Analysing the Necessity of Super Slow Steaming under Uncertainty: Containership

The global economic and financial conditions in 2010 and 2011 were positive and the business trade grew at about twice of the rate of that in 2009. The container shipping players started to enjoy a new chapter of international business trade having struggled to operate their vessels since 2008. So we now have to consider if the shipping business will return to its old strategy? What will happen to the container shipping sector in 10 years from now still remains uncertain. Recently, the uncertain situation globally has been giving shipping companies in difficulty the opportunity to make decision as to whether it is necessary to use super slow steaming for containerships. Therefore, the aim of this study is to analyse the necessity of super slow steaming on containerships despite such uncertainty. A Fuzzy Rule-based Bayesian Reasoning method has been used which incorporates the membership function and 14 selected nodes. Finally, the outcome of this study is 48 rules which have been proposed to assist shipping companies in their decision making processes when dealing with the dynamic business environment. Each rule gives a clear-cut understanding of the result which is able to be applied to real situations the containership industry faces.