Applying fuzzy logic to codesign partitioning

We propose a tool that will allow designers using the codesign approach to partially automate the development of embedded systems. The framework takes advantage of tools already available on the market for VLSI CAD as well as soft computing techniques. We focus our work mainly on evaluation of cost and partitioning, because this is the area in which soft computing seems to have great advantages over traditional approaches. The main novelty of our approach is our use of artificial intelligence techniques to imitate the behavior of a human in defining a system's partitioning. We hope to devote further studies to techniques to optimize the genetic algorithm, in both the representation and processing of data. We are also working on the use of formal techniques to describe our system     

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.     

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.     

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.     

Design of output filter for inverters using fuzzy logic

This paper proposes a design method to improve the harmonic of output voltage of a single phase inverter with an L-C output filter using fuzzy logic controller (FLC). In practice, the harmonic characteristics of circuits are complicated and entangled. There are two kinds of harmonic sources that cause inverter output voltage waveform distortion: One is the PWM switching of inverter and the other is the nonlinear characteristics of the load. In general, PI feedback control by coefficient diagram method (CDM) is used to design the output voltage filter. The relation between the L-C value and the system time constant are described with the closed form and the filter values must be calculated repeatedly to satisfy the prescribed voltage total harmonic distortion (THD) of the system. Therefore, the MATLAB Fuzzy Logic Toolbox for the fuzzy logic control algorithm is proposed. The L-C value of the filter can be set to a fixed range in the nonlinear characteristic of the practical condition, to improve the harmonic of output voltage more effectively and to avoid repeated calculation.     

Freeway traffic control using fuzzy logic controllers

A major cause of freeway congestion before the traffic density becomes critical is the shock wave due to the speed differences between consecutive vehicles. Such disturbance can be reduced if we can impose homogeneous speed control on the vehicles. In this paper, a two-level model-free control scheme using neural-network-based fuzzy logic controllers is proposed which regulates the speed of the freeway through speed advisory boards. Using information from both measurement data and expert knowledge (e.g., environmental information and psychological factors), it is expected that this controller will outperform the conventional ones.     

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     

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.     

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     

Energy management strategies for parallel hybrid vehicles using fuzzy logic

This paper presents a fuzzy-logic-based energy management and power control strategy for parallel hybrid vehicles (PHV). The main objective is to optimize the fuel economy of the PHV, by optimizing the operational efficiency of all its components. The controller optimizes the power output of the electric motor/generator and the internal combustion engine by using vehicle speed, driver commands from accelerator and braking pedals, state of charge (SOC) of the battery, and the electric motor/generator speed. Separate controllers optimize braking and gear shifting. Simulation results show potential fuel economy improvement relative to other strategies that only maximize the efficiency of the combustion engine.     

Robust spacecraft attitude control using adaptive fuzzy logic

In this paper, we propose to use adaptive fuzzy logic to tackle the spacecraft attitude control problem. The advantage is that no linearity in the system parameter assumption is needed. An on-line tuning scheme with no off-line training phase is used to update the weights in the fuzzy logic controller. Attitude tracking errors are guaranteed to be bounded.     

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.     

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.     

An efficient posture recognition method using fuzzy logic

Computer-human interaction plays an important role in virtual reality. Glove-based input devices have many desirable features which make direct interactions between the user and the virtual world possible. However, due to the complexity of the human hand, recognising hand functions precisely and efficiently is not an easy task. Existing algorithms are either imprecise or computationally expensive, making them impractical to integrate with VR applications, which are usually very CPU intensive.In the problem of posture and gesture recognition, both the sample patterns stored in the database and the ones to be recognised may be imprecise. This kind of imprecise knowledge can be best dealt with using fuzzy logic. A fast and simple posture recognition method using fuzzy logic is presented in this paper. Our model consists of three components: the posture database, the classifier and the identifier. The classifier roughly classifies the sample postures before they are put into the posture database. The identifier compares an input posture with the records in the identified class and finds the right match efficiently. Fuzzy logic is applied in both the classification and identification processes to cope with imprecise data. The main goal of this method is to recognise hand functions in an accurate and efficient manner. The accuracy, efficiency and the noise tolerance of the model have been examined through a number of experiments.     

Modeling gene expression networks using fuzzy logic.

Gene regulatory networks model regulation in living organisms. Fuzzy logic can effectively model gene regulation and interaction to accurately reflect the underlying biology. A new multiscale fuzzy clustering method allows genes to interact between regulatory pathways and across different conditions at different levels of detail. Fuzzy cluster centers can be used to quickly discover causal relationships between groups of coregulated genes. Fuzzy measures weight expert knowledge and help quantify uncertainty about the functions of genes using annotations and the gene ontology database to confirm some of the interactions. The method is illustrated using gene expression data from an experiment on carbohydrate metabolism in the model plant Arabidopsis thaliana. Key gene regulatory relationships were evaluated using information from the gene ontology database. A new regulatory relationship concerning trehalose regulation of carbohydrate metabolism was also discovered in the extracted network.     

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     

Software source code sizing using fuzzy logic modeling

Knowing the likely size of a software product before it has been constructed is potentially beneficial in project management: for instance, size can be an important factor in determining an appropriate development/integration schedule, and it can be a significant input in terms of the allocation of personnel and other resources. In this study we consider the applicability of fuzzy logic modeling methods to the task of software source code sizing, using a previously published data set. Our results suggest that, particularly with refinement using data and knowledge, fuzzy predictive models can outperform their traditional regression-based counterparts.