Fuzzy Inference Rules Research Articles

Bankruptcy Risk Assessment in Corporate Lending Based on Hybrid Neural Networks and Fuzzy Models

The purpose of this article is the presentation of a novel and unconventional algorithm for bankruptcy risk management in banking technologies catered towards lending to legal entities (enterprises and companies). The challenges of assessing risk in this area primarily relate to the reduction of type I and type II errors when making decisions on the terms of lending (i.e. loan amounts and repayment parameters) on the ostensibly objective basis of a borrower’s creditworthiness assessment.
 
 As such, it is necessary to use a unified procedure to select appropriate economic indicators for any bankruptcy model in order to reduce the high degree of uncertainty and noisiness of publicly available databases, and to take into consideration the specific character of knowledge-intensive, high-tech and “green” manufacturing. In order to approach this challenge, a mix of various methods is presented in this article, including credit scoring, neural simulation, a fuzzy model description, fuzzy inference rules, and a fuzzy Pospelov scale.
 
 The research results are as follows: the authors have developed an unconventional algorithm for diagnosing corporate bankruptcy stages. This algorithm is based on the application of a system-wide law relating to decreases in integrated system entropy, contrasted with the sum of entropies of the relevant collated subsystems. This algorithm has been tested on a series of experimental observations of 30 agricultural enterprises in the Sterlitamak District of the Republic of Bashkortostan. We have thusly assessed the financial condition of borrowing companies, while controlling for the probability of a wide range of indicators.
 
 Using this algorithm, the authors decided not to apply the rigorous requirements of the classical ‘least squares’ method used in regression analyses. A switch to a neural simulation approach in this algorithm necessitated an evaluation of the adequacy of the obtained model on the basis of a Bayesian approach. On the basis of this research, the authors propose that a regularisation of bankruptcy models has been achieved.

An Agent-Based Model Driven Decision Support System for Reactive Aggregate Production Scheduling in the Green Coffee Supply Chain

The aim of this paper is to contribute to the thread of research regarding the need for logistic systems for planning and scheduling/rescheduling within the agro-industry. To this end, an agent-based model driven decision support system for the agri-food supply chain is presented. Inputs in this research are taken from a case example of a Mexican green coffee supply chain. In this context, the decision support agent serves the purposes of deriving useful knowledge to accomplish (i) the decision regarding the estimation of Cherry coffee yield obtained at the coffee plantation, and the Parchment coffee sample verification decision, using fuzzy logic involving an inference engine with IF-THEN type rules; (ii) the production plan establishment decision, using a decision-making rule approach based upon the coupling of IF-THEN fuzzy inference rules and equation-based representation by means of mixed integer programming with the aim to maximize customer service level; and (iii) the production plan update decision using mathematical equations once the customer service level falls below the expected level. Three scenarios of demand patterns were considered to conduct the experiments: increasing, unimodal and decreasing. We found that the input inventory and output inventory vary similar over time for the unimodal demand pattern, not the case for both the increasing and decreasing demand patterns. For the decreasing demand pattern, ten tardy orders for the initial production schedule, an 88% service level, and nineteen tardy orders from the estimated production results, a 77% service level. This value falls below the expected level. Consequently, the updated aggregate production schedule resulted in ten tardy orders and an 88% service level.

Prescribed performance of discrete-time controller based on the dynamic equivalent data model

The equivalent model of a class of unknown discrete-time systems is developed by data-driven approach and fuzzy rules inference network when plant’s control direction can vary for both positive and negation and zero at the dead-zone. The convergence of the modeling error is guaranteed by the proposed learning algorithm. By using the equivalent model, the prescribed performance controller is established when the tracking error is preserved inside the designed boundary at both transient and steady states. The performance of the proposed scheme is validated by theoretical and practical results.

A Fuzzy Based Trust Evaluation Model for Service Selection in Cloud Environment

Cloud computing is a popular computing paradigm among several computing environments, but a deficit in trust among the users and the service providers prevents the large adoption of the cloud in most of the businesses. Cloud service providers should give assurances for providing the reliable services to the cloud consumers. The proposed work explains about the architecture of the trust evaluation model and considered four service measurement indexes (SMI) namely: availability, success rate, turnaround efficiency and feedback about a resource. The trust value for each resource is estimated by the fuzzy evaluation engine in which a fuzzy input set is derived from the SMI parameters. By applying a fuzzy inference rule on fuzzy input sets will yield a fuzzy output set and finally, the most trusted resource value is calculated by defuzzification process called center of gravity. The proposed work is done the implementation by using cloudsim with jfuzzycloud.

Self-Organizing Adaptive Fuzzy Brain Emotional Learning Control for Nonlinear Systems

This paper aims to develop a more efficient adaptive control system for uncertain nonlinear systems. A novel self-organizing fuzzy brain emotional learning controller (FBELC) is proposed. The FBELC neural network is the mathematical replica of human brain emotions incorporated with fuzzy inference rules, which mimics the judgments and emotions of the brain. The FBELC contains two sub-neural networks, namely a sensory neural network and an emotional neural network. These duet networks influence each other, thus improving the learning ability of the system. The proposed control system also merges sliding mode control to simplify the input space dimension of FBELC. In this study, the self-organization algorithm for the structure of FBELC is developed; thus, the growing and pruning of input layers of FBELC can be automatically proceeded to give the most efficient network structure. Moreover, the parameter adaptive law is derived using gradient descent method to achieve effective learning ability of the network. The proposed control system comprises the self-organizing adaptive FBELC (SOAFC) used as the main controller and a fuzzy compensator designed to obtain robust stability of the system for handling uncertain nonlinear systems. The developed SOAFC is then applied to a double inverted pendulum system and a biped robot to illustrate its effectiveness.

Using adaptive neuro-fuzzy inference system and multiple linear regression to estimate orange taste.

In this research, some characteristic qualities of orange fruits such as vitamin C and acid content; weight; fruit and skin diameter; and red (R), green (G), and blue (B) values of the RGB color model for 70 samples were used to predict the taste of orange grown in Darab, southeast of Fars Province, Iran, by multiple linear regression (MLR) and adaptive neuro‐fuzzy inference system (ANFIS). To use MLR, firstly the most important input data were selected, and then, the best model to predict the taste of orange was applied. In this research, methodology of ANFIS consisted of selection of dependent orange taste, fuzzification, fuzzy inference rule, membership function, and defuzzification process. The predictive capability of these models was evaluated by various descriptive statistical indicators such as mean square error (MSE) and determination coefficient (R 2). The results showed that the prediction performance of the MLR model has a strong significant relationship between orange taste and vitamin C (0.897**), red color (0.901**), and blue color (0.713*). Also, the results of ANFIS model showed that with low error for train and check data increased the most accuracy for prediction of orange taste. Moreover, the results indicated that the success rate of taste determination for orange is higher by using ANFIS compared to the MLR. This research was to provide valuable information for orange taste.

Fuzzy-Observer-Based Hybrid Force/Position Control Design for a Multiple-Sampling-Rate Bimanual Teleoperation System

In this paper, a novel fuzzy-observer-based hybrid force/position control method is investigated for a bimanual teleoperation system in the presence of dynamics uncertainties, random network-induced time delays, and multiple sampling rates of remote control signals and local measured data. The system structure consists of two pairs of position observers and contact force/torque estimators. The position observers are designed based on Takagi–Sugeno fuzzy inference rules to estimate the delayed remote state with low sampling rates. The force/torque estimators are designed for estimating the coupled item of uncertain dynamics and contact forces without acceleration information. By adding a compensatory item based on an auxiliary model, the force estimation and motion-tracking errors caused by varying dynamics uncertainties decrease, which is certified by two comparative force estimation techniques. The stability condition for the closed-loop system is also proved by the linear matrix inequality method based on the Lyapunov function. Finally, two simulations verify the effectiveness of the proposed method. The results indicate that the proposed method enables a better motion synchronization effect in soft-handling environment.

Modified Fuzzy Model for Selecting Stakeholder Engagement Strategies of the Company Using Generalized Criterion

The paper proposes a modified fuzzy model for selection of the most appropriate types of engagement strategies of the organization with its different stakeholders. The decision to choose one or another type of strategy is based on fuzzy weighting factors of applicability of the strategies considered as random variables for the most probable scenarios using a fuzzy generalized criterion that combines mathematical expectation and standard deviation. The principal difference of the model from the previously developed ones by the authors is that fuzzy weight factors of strategies applicability are not calculated by the given formulas but are determined on the basis of the given base of fuzzy inference rules. Herewith, when calculating the mathematical expectation and the standard deviation, the weighting factors of strategies applicability are not defuzzificated beforehand. Accordingly, the partial criteria, the generalized criterion, the level of risk tolerance of the decision-maker are fuzzy. In this case, the ranking of strategy types occurs on the basis of various methods for ordering fuzzy numbers.

Optimized bilevel classifier for brain tumor type and grade discrimination usingevolutionary fuzzy computing

In this paper, an optimized bilevel brain tumor diagnostic system for identifying the tumor type at the first level and grade of the identified tumor at the second level is proposed using genetic algorithm, decision tree, and fuzzy rule-based approach. The dataset is composed of axial MRI of brain tumor types and grades. From the images, various features such as first and second order statistical and textural features are extracted (26 features). In the first level, tumor type classification was done using decision tree constructed with all features. Further evolutionary computing using genetic algorithms (GA) was applied to select the optimal discriminating feature set (5 features) and classification using the decision tree constructed with the reduced feature set resulted in better performance. In the second level, grade classification, a fuzzy rule-based approach was used to resolve the uncertainty in discriminating the tumor grades II and III. Membership functions of all grades were defined for all features extracted from brain tumor grade images, to derive the fuzzy inference rules for grade discrimination. Similar to type classification with GA, better grade discrimination performance was exhibited with fuzzy inference rules derived using optimal feature set (13 features) using GA. Overall performance comparison of the proposed bilevel classifier with all features vs GA-based feature selection, shows that evolutionary computing combined with fuzzy rule-based approach is successful in reducing false positives, thereby enhancing classifier performance.

An Algorithm for Severity Estimation of Plant Leaf Diseases by the Use of Colour Threshold Image Segmentation and Fuzzy Logic Inference: A Proposed Algorithm to Update a “Leaf Doctor” Application

This paper explains a proposed algorithm for severity estimation of plant leaf diseases by using maize leaf diseased samples. In the literature, a number of researchers have addressed the problem of plant leaf disease severity estimation, but a few, such as Sannakki et al., have used fuzzy logic to determine the severity estimations of the plant leaf diseases. The present paper aims to update the current algorithm used in the “Leaf Doctor” application that is used to estimate the severities of the plant leaf diseases by introducing the benefits of fuzzy logic decision making rules. This method will contribute to precision agriculture technology as it introduces an algorithm that may be embedded in smartphone devices and used in applications, such as a “Leaf Doctor” application. The applications designed based on the algorithm proposed in this study will help users who are inexperienced and not plant pathologists understand the level of the estimated disease severity. The use of fuzzy logic inference rules along with image segmentation determines the novelty of this approach in comparison with the available methods in the literature.

An efficient security risk estimation technique for Risk-based access control model for IoT

The need to increase information sharing in the Internet of Things (IoT) applications made the risk-based access control model to be the best candidate for both academic and commercial organizations. Risk-based access control model carries out a security risk analysis on the access request by using IoT contextual information to provide access decisions dynamically. Unlike current static access control approaches that are based on predefined policies and give the same result in different situations, this model provides the required flexibility to access system resources and works well in unexpected conditions and situations of the IoT system. One of the main issues to implement this model is to determine the appropriate risk estimation technique that is able to generate accurate and realistic risk values for each access request to determine the access decision. Therefore, this paper proposes a risk estimation technique which integrates the fuzzy inference system with expert judgment to assess security risks of access control operations in the IoT system. Twenty IoT security experts from inside and outside the UK were interviewed to validate the proposed risk estimation technique and build the fuzzy inference rules accurately. The proposed risk estimation approach was implemented and simulated using access control scenarios of the network router. In comparison with the existing fuzzy techniques, the proposed technique has demonstrated it produces precise and realistic values in evaluating security risks of access control operations in the IoT context.

Problems of constructing models of intellectual analysis of states of weakly formalizable processes

The paper discusses various options for constructing models of intellectual analysis of the state of poorly formalizable processes, described using fuzzy inference rules. A hybrid algorithm is proposed that combines fuzzy logic methods with a genetic algorithm that gives a fundamentally new quality.

EFFICIENT ADAPTATION OF FUZZY CONTROLLER FOR SMOOTH SENDING RATE TO AVOID CONGESTION IN MULTICAST NETWORKS

This paper prefers a fuzzy-logic-based sending rate adaption scheme named FSR (Fuzzy Sending Rate) intending to improve the evenness of TCPFriendly Multicast Congestion Control (TFMCC). To mitigate fluctuation of sending rate for TFMCC sender, FSR intends, five actions and link utilization for tuning sending rate and uses a fuzzy controller to determine which operation should be reaped according to the feedback information from CLR (current limiting receiver). Asymmetrical membership functions and biased fuzzy inference rules make FSR as friendly to TCP flows as TFMCC. Simulation results show that FSR has exceptional smoothness and fine TCP Friendliness.

Obtaining a Generalized Index of Bank Competitiveness Using a Fuzzy Approach

Abstract The article is devoted to developing a definition of the indicator of the bank’s competitiveness which based on the theory of fuzzy sets and neural networks techniques. Uncertainties that have a place when considering and analyzing the components of evaluating the success and effectiveness of the bank have been considered and analyzed. The sequence of construction and structure for generalizing parameter of bank competitiveness are presented and grounded. Stages of obtaining an integrated assessment of bank competitiveness by sequential application of fuzzy logic and neural networks approaches are determined and described. Corresponding fuzzy terms, membership functions and fuzzy inference rules are described. Overall sequence and steps to resolve the problem are processed. The practical implementation of the summary fuzzy inference of the bank’s competitiveness is given. In particular, numerical calculations on the proposed model for Ukrainian commercial bank “Khreshchatyk” was carried out. Comparison of obtained evaluation results for the competitiveness of specified bank with available data and other scientific information sources showed their compliance with factual situation. In this way, the expediency of application fuzzy modeling has been confirmed to determine the generalized indicators of bank competitiveness. Adequacy and accuracy of the proposed model and the results of calculations were proved. The proposed approach is quite general. This or similar model can be successfully used in other tasks of building and generalized evaluation of integrated indicators for the presence of several local, individual parameters for different economic processes and tasks.

Knowledge based non-functional software distinguishing quality effort estimation using fuzzy use case point approach

Knowledge management along with data analytic process provides the collection of information which helps to examine both qualitative and quantitative software information. During the knowledge based data examination process, software effort estimation (SEE) is an organised approach to increase the efficiency of a software development process in various organisations. Software effort estimation based on use case point (UCP) methods provides only fixed estimation value which cannot deal with the uncertain and ambiguous conditions of the particular software. This work attempts to provide a fuzzy effort estimation procedure for use case model based on fuzzy inference rules. This paper also proposes a metric to calculate the enhanced UCPs with the fuzzy membership function for examining the software distinguishing quality. The proposed approach has been validated against the goal-driven UCP model, tree-boost model, Regression model and traditional use-case model. The obtained results show the better performance than those obtained by the existing methods.

Knowledge based non-functional software distinguishing quality effort estimation using fuzzy use case point approach

Knowledge management along with data analytic process provides the collection of information which helps to examine both qualitative and quantitative software information. During the knowledge based data examination process, software effort estimation (SEE) is an organised approach to increase the efficiency of a software development process in various organisations. Software effort estimation based on use case point (UCP) methods provides only fixed estimation value which cannot deal with the uncertain and ambiguous conditions of the particular software. This work attempts to provide a fuzzy effort estimation procedure for use case model based on fuzzy inference rules. This paper also proposes a metric to calculate the enhanced UCPs with the fuzzy membership function for examining the software distinguishing quality. The proposed approach has been validated against the goal-driven UCP model, tree-boost model, Regression model and traditional use-case model. The obtained results show the better performance than those obtained by the existing methods.

Fuzzy Logic Based Modelling of Cast Component Properties

Digitalization of manufacturing requires building models to represent accumulated data and knowledge on the products and processes. The use of formal knowledge models allows for increase of the automation level leading to more sustainable manufacturing. Casting is important for different industries because it offers a great freedom of designing for weight reduction. This paper presents an approach to modelling of cast component properties that is based on fuzzy logic. The approach includes learning of the fuzzy inference rules from the data. The constructed fuzzy logic models can be used to tune the manufacturing process to produces cast components with desired properties. The evaluation of the results demonstrates that the accuracy of the two created models are 3.58% and 3.15% respectively with the learned fuzzy inference rules being identical to the manually created ones. The presented approach can help to automate the management of cast component manufacturing.

Efficient Adaptation of Fuzzy Controller for Smooth Sending Rate to Avoid Congestion in Multicast Networks

This paper prefers a fuzzy-logic-based sending rate adaption scheme named FSR(Fuzzy Sending Rate) intending to improve the evenness of TCPFriendly Multicast Congestion Control (TFMCC). To mitigate fluctuation of sending rate for TFMCC sender, FSR intends, five actions and link utilization for tuning sending rate and uses a fuzzy controller to determine which operation should be reaped according to the feedback information from CLR (current limiting receiver). Asymmetrical membership functions and biased fuzzy inference rules make FSR as friendly to TCP flows as TFMCC. Simulation results show that FSR has exceptional smoothness and fine TCP Friendliness.

Contact-Less Real-Time Monitoring of Cardiovascular Risk Using Video Imaging and Fuzzy Inference Rules

Conventional methods for measuring cardiovascular parameters use skin contact techniques requiring a measuring device to be worn by the user. To avoid discomfort of contact devices, camera-based techniques using photoplethysmography have been recently introduced. Nevertheless, these solutions are typically expensive and difficult to be used daily at home. In this work, we propose an innovative solution for monitoring cardiovascular parameters that is low cost and can be easily integrated within any common home environment. The proposed system is a contact-less device composed of a see-through mirror equipped with a camera that detects the person’s face and processes video frames using photoplethysmography in order to estimate the heart rate, the breath rate and the blood oxygen saturation. In addition, the color of lips is automatically detected via clustering-based color quantization. The estimated parameters are used to predict a risk of cardiovascular disease by means of fuzzy inference rules integrated in the mirror-based monitoring system. Comparing our system to a contact device in measuring vital parameters on still or slightly moving subjects, we achieve measurement errors that are within acceptable margins according to the literature. Moreover, in most cases, the response of the fuzzy rule-based system is comparable with that of the clinician in assessing a risk level of cardiovascular disease.

Method of Integral Assessment of Soil Quality in Rural-Urban Areas Based on the Fuzzy Logic

This article proposes a method of integral assessment of soil quality in rural-urban areas based on the fuzzy logic, both from the standpoint of the possibility of living in a given territory, and from the point of view of the possibility of keeping subsidiary farming. There are determined the linguistic variables describing the main soils components of rural-urban areas and terms describing the meaning of these variables. There are constructed the membership function that determine the ratio of the measured soil quality parameters to the terms and the rules of fuzzy inference. The authors developed an algorithm that describes this method.