A new fuzzy logic filter for image enhancement

This paper presents a new fuzzy-logic-control based filter with the ability to remove impulsive noise and smooth Gaussian noise, while, simultaneously, preserving edges and image details efficiently. To achieve these three image enhancement goals, we first develop filters that have excellent edge-preserving capability but do not perform well in smoothing Gaussian noise. Next, we modify the filters so that they perform all three image enhancement tasks. These filters are based on the idea that individual pixels should not be uniformly fired by each of the fuzzy rules. To demonstrate the capability of our filtering approach, it was tested on several different image enhancement problems. These experimental results demonstrate the speed, filtering quality, and image sharpening ability of the new filter.     

Design of an optimal fuzzy logic controller using response surfacemethodology

When the fuzzy logic controller (FLC)-designed based on the plant model-is applied to the real control system, satisfactory control performance may not be attained due to modeling errors from the plant model. Also, a controller that is designed under specific circumstances may not show satisfactory control performance when applied to other circumstances. In such cases, the control parameters of the controller must be adjusted to enhance control performance. Until now, the trial and error method has been used, consuming much time and effort. Also, the set of adjusted values is not guaranteed to be optimal. To resolve such problems, response surface methodology (RSM), a method of adjusting the control parameters of the controller, is suggested. This method is more systematic than the previous trial and error method, thus, optimal solutions can be provided with less tuning. First, the initial values of the control parameters are determined through the plant model and the optimization algorithm. Then designed experiments are performed in the region around the initial values, determining the optimal values of the control parameters that satisfy both the rise time and overshoot simultaneously     

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     

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.     

Board-level partitioning for partial scan using fuzzy logic

We present a board-level partitioning scheme for improved partial scan on the resulting integrated circuits (IC). Fuzzy logic rules and two adaptation techniques allow us to simultaneously minimize four important independent objective functions in the examined problem formulation. The maximum among all sets in the partition are the following quantities: 1) number of scanned nodes in a set; 2) number of incident nets to a set; 3) number of inputs to any set; and finally 4) the period of the global clock. The sets must satisfy upper and lower capacity bounds. We experimented with some ISCAS'89 benchmark circuits and we compared the performance of our tool with four iterative improvement heuristics, each considering only one of the four different functions. Our experimental results indicate that the performance of the proposed tool is very effective     

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.     

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.     

Controlling the power output of a nuclear reactor with fuzzy logic

The application of fuzzy logic control (FLC) in the domain of nuclear industry presents a tremendous challenge. The main reason for this is the public awareness of the risks of nuclear reactors and the very strict safety regulations in force for nuclear power plants. The very same regulations prevent a researcher from quickly introducing novel control methods into this field. On the other hand, the application of FLC has, despite the ominous sound of the word “fuzzy” to nuclear engineers, a number of very desirable advantages over classical control, e.g. its robustness and the capability to include human experience into the controller. In the present paper we describe an FLC for controlling the power level of a nuclear reactor. The study is intended to assess the applicability of FLC in this domain. The final goal is to develop an optimised and intrinsically safe controller. After reviewing the available literature on FLC in nuclear reactors, an FLC is proposed and first tested by comparing it with the classical controller of BR1 (Belgium's first research reactor). In the next step the BR1 at the Belgian Nuclear Research Centre (SCK · CEN) was used as a test bed to implement a PLC-based hardware controller. The BR1 reactor is internationally regarded as a nuclear calibration reference. It therefore provides an excellent environment for this type of experiments, because over the years considerable knowledge of the static and dynamic properties of the reactor has been accumulated. The progress made in these experiments will be presented and discussed.     

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.     

仿手工雕刻的非线性模糊控制器的设计

本文介绍了一种自学习非线性模糊控制器的设计，以及它在雕刻加工中的具体应用。     

Design of a force reflection controller for telerobot systems using neural network and fuzzy logic

This paper presents a new method for selecting the force-reflection gain in a position-force type bilateral teleoperation system. The force-reflection gain greatly affects the task performance of a teleoperation system; too small gain results in poor task performance while too large gain results in system instability. The maximum boundary of the gain guaranteeing the stability greatly depends upon characteristics of the elements in the system such as: a master arm which is combined with the human operator's hand and the environments with which the slave arm contacts. In normal practice, it is, therefore, very difficult to determine such maximum boundary of the gain. To overcome this difficulty, this paper proposes a force-reflection gain selecting algorithm based on artificial neural network and fuzzy logic. The method estimates characteristics of the master arm and the environments by using neural networks and, then, determines the force-reflection gain from the estimated characteristics by using fuzzy logic. In order to show the effectiveness of the proposed algorithm, a series of experiments are conducted under various conditions of teleoperation using a laboratory-made telerobot system.     

Design of output feedback controllers for sampled-data fuzzy systems

In this paper, the design problem of output feedback controllers for sampled-data fuzzy models is considered. We consider the case where the premise variable coincides with the state variable. We give observers and output feedback controllers for an approximation of the original system. We then apply the observers and output feedback controllers to the original system and give sufficient conditions for the asymptotic convergence and stability respectively. A design example is given to illustrate the theory.     

Fault tree based diagnostics using fuzzy logic

Fuzzy set theory is investigated as a tool for the diagnostics of systems described by means of a fault tree. The objective is to diagnose component failures from the observation of fuzzy symptoms using the information contained in a fault tree. A two-step procedure is used to solve the problem. In this first step, causal reasoning is used to diagnose failure modes, consisting of minimal cut-sets of basic events, from the observation of triggered gates treated as symptoms. In the second step, the authors identify the particular components which have failed based on the diagnosed failure modes. To perform this second step, the solution of a fuzzy relational equation a=∧(S ^T αx) connecting failure mode a to basic events x is derived. With this method, the diagnostics equations can be symmetrically generated and solved in terms of the tree's basic events. The systematic nature with which a diagnosis can be generated from a fault tree lends this method to potential application of object-based programming techniques     

Information system project selection using fuzzy logic

The allocation of scarce business resources is becoming a major problem for management nowadays. Limited by lack of resources, management needs to make project selection decisions under the constraints of available information, and often makes decisions based on incomplete information. Traditionally, quantitative techniques dominate decision-making in selecting management information systems (MIS) projects. However, encapsulating or simply discarding the qualitative concerns makes a project economically sound but not operationally sound. This is often the reason that causes a project to fail. This paper establishes a model by incorporating fuzzy logic as a decision tool, which smoothly aids decision makers dealing with uncertain or incomplete information without losing existing quantitative information     

Design of Mamdani fuzzy logic controllers with rule base minimisation using genetic algorithm

This paper presents a design procedure for Mamdani fuzzy logic controller including rule base minimisation. The rules are modelled with binary weights on which constraints are imposed in order to ensure consistency. A genetic algorithm is used for finding stabilising controllers that minimise the number of rules. The cost function includes a stability/performance coefficient which insures that stable, performance satisfying controllers are given the highest possible fitness. The number of fuzzy sets for the input and the control variables are set by the user and the design procedure is concerned only with the rule base and the distribution of the fuzzy sets in the universes of discourses. Two examples were studied: the control of the pole and cart system and the control of the concentration in CSTR. In both cases, the fuzzy sets were isosceles triangles evenly distributed, in the universe of discourses.     

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.     

Design and analysis of a fuzzy logic controller

The fuzzy logic controller (FLC) presented by Siler and Ying (1989) is discussed here and is proved to be equivalent to a non-fuzzy, nonlinear, proportional-integral (PI) controller. Some characteristic properties of this fuzzy logic controller are then investigated. The achievable performance of such a specific fuzzy controller is examined and found to be not necessarily better than that of the conventional, linear, non-fuzzy PI controller. Various extended designs of the basic FLC, including the FLC with dual control laws and the three-piece FLC, are then presented to enhance control performance. These extensions can provide servo-control performance. These extensions can provide servo-control performance superior to that of the basic FLC design, as illustrated by simulation results. Finally a highly nonlinear neutralization process is advanced to demonstrate the applicability of the various FLCs to industrial process control.     

Designing of a type-2 fuzzy logic filter for improving edge-preserving restoration of interlaced-to-progressive conversion

This paper focuses on recently advanced fuzzy models and the application of type-2 fuzzy sets in video deinterlacing. The final goal of the proposed deinterlacing algorithm is to exactly determine an unknown pixel value while preserving the edges and details of the image. To begin, we will discuss some artefacts of spatial, temporal, and spatio-temporal domain deinterlacing methods. In order to address the aforementioned issues, we adopted type-2 fuzzy sets concepts to design a weight evaluating approach. In the proposed method, the upper and lower fuzzy membership functions of the type-2 fuzzy logic filters are derived from the type-1 (or primary) fuzzy membership function. The weights from upper and lower membership functions are considered to be multiplied with the candidate deinterlaced pixels. Experimental results proved that the performance of the proposed method was superior, both objectively and subjectively to other different conventional deinterlacing methods. Moreover, the proposed method preserved the smoothness of the original image edges and produced a high-quality progressive image.