Expert system design for credit risk evaluation using neuro‐fuzzy logic

Over the past few years, the credit risk evaluation of micro‐, small‐ and medium‐scale enterprises by banks and financial institutions has been an active area of research under the joint pressure of regulators and shareholders. The credit rating assessment forms an important part of credit risk assessment, involving risk parameters such as financial, business, industry and management areas. The mathematical models of evaluation are at the core of modern credit risk management systems. This paper focuses on the use of fuzzy logic and neural network techniques to design a methodology for evaluating the credit worthiness of the entrepreneur. The neuro‐fuzzy logic approach takes into account the minute details of credit rating expert's thought process to arrive at the final decision. A flexible credit rating framework (CRF) has been designed to organize all the facts of the client in a hierarchical fashion. The neural networks provide self‐learning capability to the CRF. The CRF can be customized to suit different business and industrial interests.     

One-and-only item recommendation with fuzzy logic techniques

Recommender systems anticipate users’ needs by suggesting items that are likely to interest them. Most existing systems employ collaborative filtering (CF) techniques, searching for regularities in the way users have rated items. While in general a successful approach, CF cannot cope well with so-called one-and-only items, that is: items of which there is only one single instance (like an event), and which as such cannot be repetitively “sold”. Typically such items are evaluated only after they have ceased being available, thereby thwarting the classical CF strategy. In this paper, we develop a conceptual framework for recommending one-and-only items. It uses fuzzy logic, which allows to reflect the graded/uncertain information in the domain, and to extend the CF paradigm, overcoming limitations of existing techniques. A possible application in the context of trade exhibition recommendation for e-government is discussed to illustrate the proposed conceptual framework.     

Medical diagnosis with the aid of using fuzzy logic and intuitionistic fuzzy logic

The objective of the present study is to develop/establish a web-based medical diagnostic support system (MDSS) by which health care support can be provided for people living in rural areas of a country. In this respect, this research provides a novel approach for medical diagnosis driven by integrating fuzzy and intuitionistic fuzzy (IF) frameworks. Subsequently, based on the proposed approach a web-based MDSS is developed. The proposed MDSS comprises of a knowledge base (KB) and intuitionistic fuzzy inference system (IFIS). Based on the observation that medical data cannot be described with both precision and certainty, a medical KB is constructed in the form of a set of if-then decision rules by employing both fuzzy and IF logics. After constructing the medical KB, a new set of patients is considered for diagnosing the diseases. For each patient, linguistic values of the patients’ symptoms are considered as inputs of the proposed IFIS and modeled by using the generalized triangular membership functions. Subsequently, integrated fuzzy and IF rule-based inference system is used to find a valid conclusion for the new set of patients. In a nutshell, in this paper fuzzy rule-based and IFS based inference systems are combined for better and more realistic representation of uncertainty of the medical diagnosis problem and for more accurate diagnostic result. The method is composed of following four steps: (1) the modeling of antecedent part of the rules, which consist of linguistic assessments of the patients’ symptoms provided by the doctors/medical experts with their corresponding confidence levels, by using generalized fuzzy numbers; (2) the modeling of consequent part, which reveals the degree of association and the degree of non-association of diseases into the patient, by using IFSs; (3) the use of IF aggregation operator in inference process; (4) the application of relative closeness function to find the final crisp output for a given diagnosis. Finally, the applicability of the proposed approach is illustrated with a suitable case study. This article has also justified the proposed approach by using similarity measurement.     

Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic

In this paper a new method for dynamic parameter adaptation in particle swarm optimization (PSO) is proposed. PSO is a metaheuristic inspired in social behaviors, which is very useful in optimization problems. In this paper we propose an improvement to the convergence and diversity of the swarm in PSO using fuzzy logic. Simulation results show that the proposed approach improves the performance of PSO. First, benchmark mathematical functions are used to illustrate the feasibility of the proposed approach. Then a set of classification problems are used to show the potential applicability of the fuzzy parameter adaptation of PSO.     

Rule-based diagnosis and supervision of a pilot-scale wastewater treatment plant using fuzzy logic techniques

This paper is focused on the development of a fuzzy expert system capable to diagnose the state of a pilot-scale wastewater treatment plant, its trend and also to be able to decide the best commands to be sent to the final control elements to recover the stable operation in case of disturbances. The development of the fuzzy expert system was carried out by selecting the on-line variables to be used, building the fuzzy membership functions for each input and output variable and developing a knowledge based rules structure. Finally, the fuzzy expert system was carefully tested and adjusted by performing some experiments.     

Word recognition using fuzzy logic

This paper presents an offline word-recognition system based on structural information in the unconstrained written word. Oriented features in the word are extracted with the Gabor filters. We estimate the Gabor filter parameters from the grayscale images. A two-dimensional fuzzy word classification system is developed where the spatial location and shape of the membership functions are derived from the training words. The system achieves an average recognition rate of 74% for the word being correctly classified in the top position and an average of 96% for the word being correctly classified within the top five positions     

Leaving inconsistency using fuzzy logic

Current software development methods do not provide adequate means to model inconsistencies and therefore force software engineers to resolve inconsistencies whenever they are detected. Certain kinds of inconsistencies, however, are desirable and should be maintained as long as possible. For instance, when multiple conflicting solutions exist for the same problem, each solution should be preserved to allow further refinements along the development process. An early resolution of inconsistencies may result in loss of information and excessive restriction of the design space. This paper aims to enhance the current methods by modeling and controlling the desired inconsistencies through the application of fuzzy logic-based techniques. It is shown that the proposed approach increases the adaptability and reusability of design models.     

Simplifying the automatic design of a fuzzy logic controller using evolutionary programming

With the availability of a wide range of Evolutionary Algorithms such as Genetic Algorithms, Evolutionary Programming, Evolutionary Strategies and Differential Evolution, every conceivable aspect of the design of a fuzzy logic controller has been optimized and automated. Although there is no doubt that these automated techniques can produce an optimal fuzzy logic controller, the structure of such a controller is often obscure and in many cases these optimizations are simply not needed. We believe that the automatic design of a fuzzy logic controller can be simplified by using a generic rule base such as the MacVicar-Whelan rule base and using an evolutionary algorithm to optimize only the membership functions of the fuzzy sets. Furthermore, by restricting the overlapping of fuzzy sets, using triangular membership functions and singletons, and reducing the number of parameters to represent the membership functions, the design can be further simplified. This paper describes this method of simplifying the design and some experiments performed to ascertain its validity.     

Digital fuzzy logic controller: design and implementation

In this paper, various aspects of digital fuzzy logic controller (FLC) design and implementation are discussed, Classic and improved models of the single-input single-output (SISO), multiple-input single-output (MISC), and multiple-input multiple-output (MIMO) FLCs are analyzed in terms of hardware cost and performance. A set of universal parameters to characterize any hardware realization of digital FLCs is defined. The comparative study of classic and alternative MIMO FLCs is presented as a generalization of other controller configurations. A processing element for the parallel FLC architecture realizing improved inferencing of MIMO system is designed, characterized, and tested. Finally, as a case feasibility study, a direct data stream architecture for complete digital fuzzy controller is shown as an improved solution for high-speed, cost-effective, real-time control applications     

基于模糊逻辑的信用评判模型

为了解决个人对个人(C2C)交易平台信用评价体系不能正确识别信用炒作行为导致交易数据不能真实反映卖方信用的问题,设计了一种基于模糊逻辑的可信度评判模型.该模型为信用影响因素建立论域以及论域上的模糊集,通过隶属度函数对各个时段交易进行模糊评价,得到信用可信度评判结果.实例数据分析表明:该模型完善了信用评价系统,为用户的交易决策提供了较可靠的评判依据,降低了交易中的信用风险.     

Multiple network fusion using fuzzy logic

Multiplayer feedforward networks trained by minimizing the mean squared error and by using a one of c teaching function yield network outputs that estimate posterior class probabilities. This provides a sound basis for combining the results from multiple networks to get more accurate classification. This paper presents a method for combining multiple networks based on fuzzy logic, especially the fuzzy integral. This method non-linearly combines objective evidence, in the form of a network output, with subjective evaluation of the importance of the individual neural networks. The experimental results with the recognition problem of on-line handwriting characters show that the performance of individual networks could be improved significantly.     

Astronomical pipeline processing using fuzzy logic

Fundamental astronomical questions on the composition of the universe, the abundance of Earth-like planets, and the cause of the brightest explosions in the universe are being attacked by robotic telescopes costing billions of dollars and returning vast pipelines of data. The success of these programs depends on the accuracy of automated real time processing of images never seen by a human, and all predicated on fast and accurate automatic identifications of known astronomical objects and new astronomical transients. In this paper the needs of modern astronomical pipelines are discussed in the light of fuzzy-logic based decision-making. Several specific fuzzy-logic algorithms have been develop for the first time for astronomical purposes, and tested with excellent results on a test pipeline of data from the existing Night Sky Live sky survey.     

Application of fuzzy logic in computer-aided VLSI design

Application of fuzzy logic structures in CAD of digital electronics substantially improves quality of design solutions by providing designers with flexibility in formulating goals and selecting tradeoffs. In addition, the following aspects of a design process are positively impacted by application of fuzzy logic: utilization of domain knowledge, interpretation of uncertainties in design data, and adaptation of design algorithms. We successfully applied fuzzy logic structures in conjunction with constructive and iterative algorithms for selecting of design solutions for different stages of the design process. We also introduced fuzzy logic software development tool to be used in CAD applications     

The evaluation of Learning Management Systems using an artificial intelligence fuzzy logic algorithm

There are many open source and commercially available Learning Management System (LMS) on the Internet and one of the important problems in this field is how to choose an LMS that will be the most effective one and that will satisfy the requirements. In order to help in the solution of this problem, the author has developed a computer program to aid in the selection of an LMS. The developed system is web-based and can easily be used over the Internet any where over the world at any time. The developed system is basically a web-based decision support system used to evaluate LMSs by using a flexible and smart algorithm derived from artificial intelligent concepts with fuzzy logic values. The paper describes the development of the LMS evaluation system. The individuals who are most likely to be interested in the LMS evaluation process are teachers, students, and any educational organizations such as: universities, schools, institutes, and anyone else who seeks to have a LMS.     

Formulation of qualitative models using fuzzy logic

Formulation of qualitative models for complex decision problems exhibiting less structure, more imprecision and uncertainty is not adequately addressed in DSS research. Typical characteristics and requirements of such problems prohibit the development of DSS using knowledge based system development methodologies. This paper presents a methodology for formulation of qualitative models using fuzzy logic to handle the imprecision and uncertainty in the problem domain. The problem domain, in this methodology, is represented using problem-solving knowledge, environmental knowledge, and control knowledge components. A high level non-procedural language for representing these components of knowledge is illustrated using a project selection and resource allocation problem. The paper also describes the implementation of a prototype decision support environment based on this methodology.     

基于监督模糊聚类的缝纫平整度客观评价系统

提出了一种基于改进型模糊聚类的缝纫平整度客观评价系统。首先,用FAST仪器测量得到服装面料的各项力学性能指标,并根据主因子分析法提取6个贡献最大的综合指标;其次,通过基于输出空间的SFCM算法对获取的综合指标进行模糊聚类,得到相应的各聚类中心;最后,根据聚类结果确定径向基神经网络的节点中心和宽度。经过大量实验,系统可以根据中厚毛型面料的不同结构及物理性能快速准确地给出该织物成衣后的缝纫性能评价指标。     

Genetic algorithm design of neural network and fuzzy logic controllers

This paper discusses the design of neural network and fuzzy logic controllers using genetic algorithms, for real-time control of flows in sewerage networks. The soft controllers operate in a critical control range, with a simple set-point strategy governing “easy” cases. The genetic algorithm designs controllers and set-points by repeated application of a simulator. A comparison between neural network, fuzzy logic and benchmark controller performance is presented. Global and local control strategies are compared. Methods to reduce execution time of the genetic algorithm, including the use of a Tabu algorithm for training data selection, are also discussed. The results indicate that local control is superior to global control, and that the genetic algorithm design of soft controllers is feasible even for complex flow systems of a realistic scale. Neural network and fuzzy logic controllers have comparable performance, although neural networks can be successfully optimised more consistently.     

A design for a class of nonlinear systems with delay time using fuzzy logic

 The goal of this paper is to design a controller for a class of nonlinear systems with delay time using fuzzy logic. The control scheme considered in this paper integrates a fuzzy component and a sliding control component. In the former, the fuzzy system can be considered as a universal approximator to approximate the unknown functions in plant. In the latter, a variable structure control with a sector guarantees the global stability of the closed-loop system when a variable, involving tracking error, travels outside of the sector. The adaptive laws to adjust the parameters in the system are developed based on the Lyapunov synthesis approach. It is shown that the proposed adaptive controller guarantees tracking error, between the outputs of the considered system and desired␣values, to be asymptotical in decay.