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.     

Estimation and diagnosis using multi-models with application to a wastewater treatment plant

Process diagnosis is still considered a challenging engineering problem. Technological and also environmental systems have complex behaviors often involving nonlinear relationships. When confronted to such systems, there is a need to build systems that can operate over a wide range of operating conditions. For that it is very attractive to appeal to a decomposition of the system model into a number of simpler linear models. This paper mainly focuses on the use of multi-models for process diagnosis. It is shown how the traditional tools of the linear automatic can be wide and applied to multi-model structures. A proportional multi-integral observer is used for fault diagnosis using banks of observers to generate structured residuals. The performances of the proposed diagnosis method are highlighted through the application to a wastewater treatment plant model (WWTP), which is an uncertain nonlinear system affected by unknown inputs.     

Optimisation of garment design using fuzzy logic and sensory evaluation techniques

The ease allowance is an important criterion in garment design. It is often taken into account in the process of construction of garment patterns. However, the existing pattern generation methods cannot provide a suitable estimation of ease allowance, which is strongly related to wearer's body shapes and movements and used fabrics. They can only produce 2D patterns for fixed standard values of ease allowance. In this paper, we present a new method for optimizing the estimation of ease allowance of a garment using fuzzy logic and sensory evaluation. Based on the optimized values of ease allowance generated from fuzzy models related to different key body positions and different wearer's movements, we obtain an aggregated ease allowance using the OWA operator. This aggregated result can further improve the wearer's fitting perception of a garment and adjust the compromise between the style of garments and the fitting comfort sensation of wearers. The related weights of the OWA operator are determined according to designer's linguistic criteria on comfort and garment style. The effectiveness of our method has been validated in the design of trousers of jean type. It can be also applied for designing other types of garment.     

Modeling uncertainty in clinical diagnosis using fuzzy logic.

This paper describes a fuzzy approach to computer-aided medical diagnosis in a clinical context. It introduces a formal view of diagnosis in clinical settings and shows the relevance and possible uses of fuzzy cognitive maps. A constraint satisfaction method is introduced that uses the temporal uncertainty in symptom durations that may occur with particular diseases. The method results in an estimate of the stage of the disease if the temporal constraints of the disease in relation to the occurrence of the symptoms are satisfied. A lightweight fuzzy process is described and evaluated in the context of diagnosis of two confusable diseases. The process is based on the idea of an incremental simple additive model for fuzzy sets supporting and negating particular diseases. These are combined to produce an index of support for a particular disease. The process is developed to allow fuzzy symptom information on the intensity and duration of symptoms. Results are presented showing the effectiveness of the method for supporting differential diagnosis.     

RENOIR: An expert system using fuzzy logic for rheumatology diagnosis

A new expert system (ES) to aid the nonspecialist physician in diagnosing arthritis and collagen diseases has been developed. Here we present the structure of RENOIR and the results of its implementation. This rule-based ES has been programmed using the MILORD environment. This is a shell to develop ES using a closed set of linguistic labels to express uncertainty. A feature of RENOIR is its five levels of knowledge representation, which permits to build a very flexible knowledge base (KB) and express knowledge with high accuracy. Those rules directed to similar goals are grouped in modules to improve computational performance and for higher clarity of the KB. Control of the reasoning process is assured by several mechanisms, one of the main being metarules specifically designed for almost all the knowledge levels of the KB. We have used public domain knowledge (books, criteria tables) and personal heuristics from one of the authors (Belmonte-Serrano) to implement the KB of RENOIR. In its present form, our KB comprises 1 058 rules, 978 facts, 220 metarules, and 34 modules. A first validation process has shown good performance of the ES compared to 12 physicians with diverse levels of experience in rheumatic diseases. New ongoing versions of the system with improved interfaces and reasoning capabilities are expected before verifying RENOIR's clinical acceptability. © 1994 John Wiley & Sons, Inc.     

Designing fuzzy logic controllers using a multiresolutional searchparadigm

A multiresolutional search paradigm is employed to design optimal fuzzy logic controllers in a variable structure simulation environment. The initial search space is evaluated with a coarse resolution and some of the subspaces are selected as candidate regions for global optimum. New optimization processes are then created to investigate the candidate search spaces in detail, a process which continues until a solution is found. This search paradigm was implemented using hierarchical distributed genetic algorithms (HDGAs)-search agents solving different degrees of abstracted problems. Creation/destruction of agents is executed dynamically during the operation based on their performance. In the application to fuzzy systems, the HDGA investigates design alternatives such as different types of membership functions and the number of the fuzzy labels, as well as their optimal parameter settings, all at the same time. This paradigm is demonstrated with an application to the design of a fuzzy controller for an inverted pendulum     

A methodology to design a fuzzy logic based supervision of Hybrid Renewable Energy Systems

Hybrid Renewable Energy Systems (HRES) are increasingly used to improve the grid integration of wind power generators. The goal of this work is to propose a methodology to design a fuzzy logic based supervision of this new kind of production unit. A graphical modeling tool is proposed to facilitate the analysis and the determination of fuzzy control algorithms adapted to complex hybrid systems. To explain this methodology, the association of wind generators, decentralized generators and storage systems are considered for the production of electrical power. The methodology is divided in six steps covering the design of a supervisor from the system work specifications to an optimized implementation of the control. The performance of this supervisor is shown with the help of simulations. Finally, the application of this methodology to the supervision of different topologies of HRES is also proposed to bring forward the systematic dimension of the approach.     

Rule-based Mamdani type fuzzy logic model for the prediction of compressive strength of silica fume included concrete using non-destructive test results

In this study, a fuzzy logic model for predicting compressive strength of concretes containing silica fume (SF) (0, 5, 10%) has been developed using non-destructive testing results [ultrasonic pulse velocity (km/s) and Schmidt hardness (R)]. Experimental results of non-destructive tests and the amount of the SF were used to construct the model. Result have shown that fuzzy logic systems have strong potential for predicting 7, 28, and 90 days compressive strength using ultrasonic pulse velocity (km/s), Schmidt hardness (R), and silica fume content (%) as inputs.     

Expert system for nuclear power plant accident diagnosis using a fuzzy inference method

Huge and complex systems such as nuclear power generating stations are likely to cause the operators to make operational mistakes for a variety of inexplicable reasons and to produce ambiguous and complicated symptoms in the case of an emergency. Therefore, a safety protection system to assist the operators in making proper decisions within a limited time is required. In this paper, we develop a reliable and improved diagnosis system using the fuzzy inference method so that the system can classify accident symptoms and identify the most probable causes of accidents in order for appropriate actions to be taken to mitigate the consequences. In the computer simulation, the proposed system proved to be able to classify accident types within only 20–30 s. Therefore, the corresponding operation guidelines can be determined in a very short time to put the nuclear power plant in a safe state immediately after the accident.     

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.     

Building a financial diagnosis system based on fuzzy logic production system

The purpose of this study is to build a financial expert system based on fuzzy theory and Fuzzy LOgic Production System (FLOPS), which is an expert tool for processing the ambiguity. The study consists if four parts. For the first part, the basic features of expert systems are presented. For the second part, fizzy concepts and the evaluation of classical expert systems to fuzzy expert systems will be presented. For the third part, the expert system shell (FLOPS) used in this study will be described. For the last part, it will be presented the financial diagnosis system, developed by using the Wall's seven ratios, traditional seven ratios and also 34 ratios selected by a financial expert. After analyzing and investigating these three kinds of methods, financial diagnosis system will be developed as a fuzzy expen system which used a membership function based on averages and standard deviation. At the last step, the new approach will be tried by increasing the fuzzy sets for five membership functions. Some practical examples will be given. Throughout the paper, the way of building a financial diagnosis system based on fuzzy expert system is stressed.     

Adaptive management of natural systems using fuzzy logic

Hypotheses about how management practices influence ecosystem services can be tested using a crisp, probability-based, or fuzzy decision rule. The correct decision rule depends on whether: (1) the observed state of an ecosystem service (x) is non-stochastic or stochastic; (2) the true state of the ecosystem service (y) is non-stochastic or stochastic; and (3) the relationship between x and y is deterministic, stochastic, or uncertain. Crisp and probability-based decision rules are not appropriate when the relationship between y and x is uncertain in the sense that the decision maker is unable or unwilling to specify conditional probabilities of y given x. Under these conditions, a fuzzy decision rule is appropriate. A hypothetical case study is used to illustrate how a fuzzy decision rule is used to test hypotheses about whether selective cutting of timber provides greater or less forest biodiversity than clearcutting of timber. The case study describes how to incorporate the decision rule in an active adaptive management framework to sequentially test the extent to which changes over time in other factors influencing ecosystem services, such as greater spread of invasive species due to global warming, alter the efficacy of timber management practices. The fuzzy adaptive management decision rule can be generalized to account for the effects of management practices on multiple ecosystem services.     

Modeling hardness of Nb-microalloyed steels using fuzzy logic

The paper presents some results of the research connected with the development of new approach based on the fuzzy logic of predicting the Vickers microhardness of the phase constituents occurring in five steels after continuous cooling. The independent variables in the model are chemical compositions, initial austenite grain size, and cooling rate over the temperature range of the occurrence of phase transformations. For purpose of constructing these models, 114 different experimental data were gathered from the literature. The data used in the fuzzy logic model are arranged in a format of twelve input parameters that cover the chemical compositions, initial austenite grain size, and cooling rate, and output parameter which is Vickers microhardness. In this model, the training and testing results in the fuzzy logic systems have shown strong potential for prediction of effects of chemical compositions and heat treatments on hardness of microalloyed steels.     

Control rules of aeration in a submerged biofilm wastewater treatment process using fuzzy neural networks

The present work is part of a global development of reliable real-time control and supervision tools applied to wastewater pollution removal processes. In these processes, oxygen is a key substrate in animal cell metabolism and its consumption is thus a parameter of great interest for the monitoring. In this paper, an integrated neural-fuzzy process controller was developed to control aeration in an Aerated Submerged Biofilm Wastewater Treatment Process (ASBWTP). In order to improve the fuzzy neural network performance, the self-learning ability embedded in the fuzzy neural network model was emphasized for improving the rule extraction performance. The fuzzy neural network proves to be very effective in modeling the aeration performs better than artificial neural networks (ANN).For comparing between operation with and without the fuzzy neural controller, an aeration unit in an Aerated Submerged Biofilm Wastewater Treatment Process (ASBWTP) was picked up to support the derivation of a solid fuzzy control rule base. It is shown that, using the fuzzy neural controller, in terms of the cost effectiveness, it enables us to save almost 33% of the operation cost during the time period when the controller can be applied. Thus, the fuzzy neural network proved to be a robust and effective DO control tool, easy to integrate in a global monitoring system for cost managing.     

Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network

Recent studies into the estimation and control of an activated sludge process (ASP) at a wastewater treatment plant suggest that artificial-intelligence methods, such as neural networks, fuzzy systems and genetic algorithms, can improve the plant performance in terms of reduced operation costs and improved effluent quality. In this paper, a neural-network-based soft sensor is developed for the on-line prediction of effluent concentrations in an ASP in terms of primary hard-to-measure variables, such as chemical oxygen demand, total nitrogen content and total suspended solids, starting from secondary on-line easy-to-measure variables, such as oxygen and nitrogen compound concentrations in biological tanks, input flow rate and alkalinity, among others. An algorithm based on principal component analysis is applied to select the optimal net input vectors for the soft sensor, using an appropriated number of samples of the secondary variables set. The proposed soft sensor is tested on the ASP of a large-scale municipal wastewater treatment plant running under the GPS-X simulation frame and validated with operational gathered data. Satisfactory low values for mean and maximum absolute prediction errors are obtained, even when high values of sampling time of primary variables are set, as it is frequently done during monitoring operation. In this way, data-driven soft-sensors based on neural networks can become valuable tools for plant operators for the recognition of operational states in terms of low cost and efficient prediction of primary process variables such as chemical oxygen demand, total nitrogen content and total suspended solids, therefore avoiding the acquisition of expensive and sometimes unreliable instruments for measuring nutrient concentrations in plant.     

Microcalcification detection using fuzzy logic and scale space approaches

Breast cancer is one of the leading causes of women mortality in the world. Since the causes are unknown, breast cancer cannot be prevented. It is difficult for radiologists to provide both accurate and uniform evaluation over the enormous number of mammograms generated in widespread screening. Computer-aided mammography diagnosis is an important and challenging task. Microcalcifications and masses are the early signs of breast carcinomas and their detection is one of the key issues for breast cancer control. In this study, a novel approach to microcalcification detection based on fuzzy logic and scale space techniques is presented. First, we employ fuzzy entropy principal and fuzzy set theory to fuzzify the images. Then, we enhance the fuzzified image. Finally, scale-space and Laplacian-of-Gaussian filter techniques are used to detect the sizes and locations of microcalcifications. A free-response operating characteristic curve is used to evaluate the performance. The major advantage of the proposed method is its ability to detect microcalcifications even in the mammograms of very dense breasts. A data set of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications is studied. Experimental results demonstrate that the microcalcifications can be accurately and efficiently detected using the proposed approach. It can produce lower false positives and false negatives than the existing methods.     

Designing a hierarchical fuzzy logic controller using the differential evolution approach

In conventional fuzzy logic controllers, the computational complexity increases with the dimensions of the system variables; the number of rules increases exponentially as the number of system variables increases. Hierarchical fuzzy logic controllers (HFLC) have been introduced to reduce the number of rules to a linear function of system variables. However, the use of hierarchical fuzzy logic controllers raises new issues in the automatic design of controllers, namely the coordination of outputs of sub-controllers at lower levels of the hierarchy. In this paper, a method is described for the automatic design of an HFLC using an evolutionary algorithm called differential evolution (DE).The aim in this paper is to develop a sufficiently versatile method that can be applied to the design of any HFLC architecture. The feasibility of the method is demonstrated by developing a two-stage HFLC for controlling a cart–pole with four state variables. The merits of the method are automatic generation of the HFLC and simplicity as the number of parameters used for encoding the problem are greatly reduced as compared to conventional methods.