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Results for “Fuzzy Logic”

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PubMed2006

Comparison of the applicability of rule-based and self-organizing fuzzy logic controllers for sedation control of intracranial pressure pattern in a neurosurgical intensive care unit.

This paper assesses the controller performance of a self-organizing fuzzy logic controller (SOFLC) in comparison with a routine clinical rule-base controller (RBC) for sedation control of intracranial pressure (ICP) pattern. Eleven patients with severe head injury undergoing different neurosurgeries in a neurosurgical intensive care unit (NICU) were divided into two groups. In all cases the sedation control periods lasted 1 h and assessments of propofol infusion rates were made at a frequency of once per 30 s. In the control group of 10 cases selected from 5 patients, a RBC was used, and in the experimental group of 10 cases selected from 6 patients, a self-organizing fuzzy logic controller was used. A SOFLC was derived from a fuzzy logic controller and allowed to generate new rules via self-learning beyond the initial fuzzy rule-base obtained from experts (i.e., neurosurgeons). The performance of the controllers was analyzed using the ICP pattern of sedation for 1 h of control. The results show that a SOFLC can provide a more stable ICP pattern by administering more propofol and changing the rate of delivery more often when rule-base modifications have been considered.

Algorithms↗
PubMedDate not supplied

Automated sensory nerve conduction testing using fuzzy logic.

Nerve conduction studies continue to be an important tool in the evaluation of peripheral nerve disorders but have come under increased scrutiny because of heightened cost control in health care service delivery. In selected clinical settings, automated nerve conduction studies may be a useful clinical tool replacing conventional testing, but existing instruments are limited and have not generally been accepted into clinical practice. Further advancements in nerve conduction automation may be possible by incorporating expert system approaches into nerve conduction measurement and control algorithms. Using fuzzy logic techniques to duplicate the reasoning strategies of experienced electrodiagnostic clinicians, a software controller was developed to automatically perform sensory nerve conduction studies. The fuzzy logic system successfully performed 88% of 97 sensory studies in a mixed group of normal and patient populations. Sensory nerve action potential latency and amplitude measures obtained with automated testing were the same as determined by clinicians. Failures were related to design limitations of the controller, noise, and artifact. The high negative predictive value and sensitivity of fuzzy logic based testing suggest that its utility is in minimizing the need for unnecessary conventional electrodiagnostic studies in patients with normal nerve function. Fuzzy logic appears to be a useful approach to nerve conduction automation that can model expert reasoning and judgment.

Action Potentials↗
PubMed2002

A fuzzy-logic antiswing controller for three-dimensional overhead cranes.

In this paper, a new fuzzy antiswing control scheme is proposed for a three-dimensional overhead crane. The proposed control consists of a position servo control and a fuzzy-logic control. The position servo control is used to control crane position and rope length, and the fuzzy-logic control is used to suppress load swing. The proposed control guarantees not only prompt suppression of load swing but also accurate control of crane position and rope length for simultaneous travel, traverse, and hoisting motions of the crane. Furthermore, the proposed control provides practical gain tuning criteria for easy application. The effectiveness of the proposed control is shown by experiments with a three-dimensional prototype overhead crane.

Computer Simulation↗
PubMedDate not supplied

Improved sensitivity of fuzzy logic based tumor marker profiles for diagnosis of pancreatic carcinoma versus benign pancreatic disease.

BACKGROUND: The definite diagnosis of pancreatic tumors of unknown origin remains a clinical challenge. Imaging techniques may fail to differentiate malignant tumors from inflammation, especially in chronic pancreatitis. In a considerable number of cases, the definitive diagnosis needs laparotomy. Single tumor markers as CA 19-9 or CEA are of limited value in these cases because of their limited sensitivity and specificity. This study was performed to find out, whether a classification method based on fuzzy logic analysis of tumor marker profiles is feasible in patients with pancreatic carcinoma and benign pancreatic disease. PATIENTS AND METHODS: Tumor markers and other clinical and laboratory parameters of 74 consecutive patients, either with histologically proved pancreatic carcinoma (n = 43) or presumed benign pancreatic disease (n = 31) assessed by ultrasound, endoscopic retrograde pancreatography, or computed tomography were analysed in order to detect their diagnostic value in the multi-dimensional approach of fuzzy logic analysis. RESULTS: Given a specificity of 95%, sensitivity was considerably increased by the fuzzy logic method (83%) compared with the best single tumor marker CA 19-9 (65%). CA 19-9, CYFRA 21-1 and CA 15-3 gave the most considerable contributions to the classification system, whereas CEA, CA 125, CA 72-4, AFP, liver enzymes, lipase, amylase, bilirubin, and ferritin were of no or little value. CONCLUSIONS: Tumor marker profiles analysed with the fuzzy logic method may have an improved sensitivity compared with single standard tumor markers. The diagnostic efficacy should be proved in patients with pancreatic tumors of unknown origin.

Adult↗
PubMed1999

Self-tuning fuzzy logic control for ultrasound hyperthermia with reference temperature based on objective functions.

The purpose of this paper is to develop and evaluate a self-tuning fuzzy logic controller for a scanned focused ultrasound hyperthermia system with the reference temperature (Tr) determined from objective functions. This work employs simulation programs to develop the power deposition for the scanned focused ultrasound system and to solve the responses of temperature profiles based on the transient bioheat transfer equation. A fuzzy logic control algorithm is employed to determine the output power level for the heating system and an observer for blood perfusion variation is used to enhance the capability of the controller to adjust the required output power level for the treatment due to the drastic change of the blood perfusion. The reference temperature (Tr) for the controller is based on objective functions to tune its value during the heating process, while a control temperature (Tc) from the thermosensors located in the tumor region is used as the input for the controller. The objective function based on the entire temperature profile is used to evaluate the appropriateness of the heating temperature distribution for a time-variational blood perfusion. Simulation results demonstrate that the tumor region can be rapidly heated to the desired temperature level and maintained at that level despite blood perfusion variation. The resulting temperature profile, the objective function, and the output power level are related to the magnitude of blood perfusion, but are almost independent of the Tc location and the initial setting value of Tr. The fuzzy logic control algorithm with Tr determined from objective functions can be used for controlling the entire temperature distribution through a single control temperature, and the combination of control and optimization allows appropriate temperature fields to be created during the entire heating process. The control algorithm does not require the accurate prior knowledge of the locations of the thermosensors and the appropriate setting value for Tr.

Fuzzy Logic↗
PubMedDate not supplied

A survey of fuzzy logic monitoring and control utilisation in medicine.

Intelligent systems have appeared in many technical areas, such as consumer electronics, robotics and industrial control systems. Many of these intelligent systems are based on fuzzy control strategies which describe complex systems mathematical models in terms of linguistic rules. Since the 1980s new techniques have appeared from which fuzzy logic has been applied extensively in medical systems. The justification for such intelligent systems driven solutions is that biological systems are so complex that the development of computerised systems within such environments is not always a straightforward exercise. In practice, a precise model may not exist for biological systems or it may be too difficult to model. In most cases fuzzy logic is considered to be an ideal tool as human minds work from approximate data, extract meaningful information and produce crisp solutions. This paper surveys the utilisation of fuzzy logic control and monitoring in medical sciences with an analysis of its possible future penetration.

Artificial Intelligence↗
PubMed2005

Energy saving in a wastewater treatment process: an application of fuzzy logic control.

Many uncertain factors affect the operation of Wastewater Treatment Plants. Due to the complexity of biological wastewater treatment processes, classical methods show significant difficulties when trying to control them automatically. Consequently soft computing techniques and, specifically, fuzzy logic appears to be a good candidate for controlling these ill-defined, time-varying and non-linear systems. This paper describes the development and implementation of a Fuzzy Logic Controller to regulate the aeration in the Taradell Wastewater Treatment Plant. The main goal of this control process is to save energy without decreasing the quality of the effluent discharged. The fuzzy controller integrates the information coming from two different signals: the Dissolved Oxygen and Oxidation-Reduction Potential values. The simulation results proved that fuzzy logic is a good tool for controlling the aeration of the wastewater treatment plant. The results obtained show that energy savings of more than 10% can be achieved using aeration fuzzy control and at the same time still keeping the good removal levels.

Conservation of Energy Resources↗
PubMed2003

Automatic control of volatile fatty acids in anaerobic digestion using a fuzzy logic based approach.

A control law based on fuzzy logic was developed and validated for an anaerobic wastewater treatment process. The controlled variable was the concentration of volatile fatty acids (VFA) in the reactor and the manipulated variable was the input flow rate. In order to use it as the input of the fuzzy sets, the controlled variable was treated using an algorithm of interpolation, extrapolation and filtering. The treatment of VFA values attempted to anticipate the behaviour of the variable and to avoid the inherent delay of the response, associated to the time constant of the system. Furthermore, the controlled variable derivative was used as a second input of the fuzzy sets to increase or decrease the speed of the control action. The control law was applied to a 0.948 m3 fixed-bed anaerobic reactor treating raw and diluted (1:2) industrial distillery vinasses. The validation was performed establishing different transient states between different set points in the range of 0.8 and 1.8 g VFA/l and different concentrations of the influent. The control law proved to be reliable supplying an adequate control action in terms of amplitude and velocity to achieve the desired set point for different types of perturbation and control purposes.

Automation↗
PubMed2005

Fuzzy logic control for intracranial pressure via continuous propofol sedation in a neurosurgical intensive care unit.

The major goal of this paper is to provide automatically continuous propofol sedation for patients with severe head injury, unconsciousness, and mechanical ventilation in order to reduce the effect of agitation on intracranial pressure (ICP) using fuzzy logic control in a neurosurgical intensive care unit (NICU). Seventeen patients were divided into three groups in which control was provided with three different controllers. Experimental control periods were of 60min duration in all cases. Group A used a conventional rule-based controller (RBC), Group B a fuzzy logic controller (FLC), and Group C a self-organizing fuzzy logic controller (SOFLC). The performance of the controllers was analyzed by ICP pattern of sedation. The ICP pattern of errors was analyzed for mean and root mean square deviation (RMSD) for the entire duration of control (i.e., 1h). The results indicate that FLC can easily mimic the rule-base of human experts (i.e., neurosurgeons) to achieve stable sedation similar to the RBC group. Furthermore, the results also show that a SOFLC can provide more stable sedation of ICP pattern because it can modify the fuzzy rule-base to compensate for inter-patient variations.

Adult↗
PubMed1992

A fuzzy logic controller.

This paper describes a fuzzy sets method which is very useful for handling uncertainties and essential for knowledge acquisition of a human expert. Kinetics of a reactor is often complex and not trivial to describe by mathematical equations. Reactor control by traditional control technology is therefore difficult. A novel technology is presented. In the following a fuzzy inference (approximate reasoning) is used for decision making in analogy to human thinking, facilitating a more sophisticated control. Readers of this paper do not need any advanced mathematics beyond the four basic operations in arithmetic (+, -, x, divided by) and using the maximum and minimum values. This fuzzy inference is introduced to construct a fuzzy logic controller which is suitable for a nonlinear, multivariable and time variant system applied to a bioreactor.

Biotechnology↗
PubMed1997

Fuzzy logic in computer-aided breast cancer diagnosis: analysis of lobulation.

This paper illustrates how a fuzzy logic approach can be used to formalize terms in the American College of Radiology (ACR) Breast Imaging Lexicon. In current practice, radiologists make a relatively subjective determination for many terms from the lexicon related to breast cancer diagnosis. Lobulation and microlobulation of nodules are two important features in the ACR lexicon. We offer an approach for formalizing the distinction of these features and also formalize the description of intermediate cases between lobulated and microlobulated masses. In this paper it is shown that fuzzy logic can be an effective tool in dealing with this kind of problem. The proposed formalization creates a basis for the next three steps (i) extended verification with blinded comparison studies. (ii) the automatic extraction of the related primitives from the image, and (iii) the detection of lobulated and microlobulated masses based on these primitives.

Breast Neoplasms↗
PubMed1999

Improving decisionmaking processes with the fuzzy logic approach in the epidemiology of sleep disorders.

Epidemiological studies can provide information not only on specific diagnostic entities but also on their underlying symptomatic constellations. For this purpose, an expert system was developed for the assessment of sleep disorders and endowed with the fuzzy logic capabilities necessary to determine the degree to which a given symptom corresponds to a specific diagnosis. Uncertainty is inherent in fields such as sleep medicine and psychiatry, and becomes evident in clinical practice at the stages of data collection and diagnostic formulation, when the clinician must determine whether a symptom is present and must choose from several diagnostic possibilities. The process involves a considerable degree of subjectivity on the part of the patient in trying to describe his or her symptoms, and of the clinician whose final diagnosis will depend on his or her clinical experience and interpretation of what is normal and what is pathological. Inferential models of the probabilistic or fuzzy logic type take into account such uncertainty. The Sleep-Eval system has been used in epidemiological and clinical studies involving 34,044 interviews collected by close to 300 interviewers. The diagnostic potential of these models is illustrated using data collected in an epidemiological study of the noninstitutionalized general population of Italy and underlines the advantages and limits of the binary, bayesian, and fuzzy logic methods and analyses.

Diagnosis, Computer-Assisted↗
PubMed2005

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.

Animals↗
PubMedDate not supplied

A self-tuning effect of membership functions in a fuzzy-logic-based cardiac pacing system.

This paper describes a self-tuning method of membership functions in a fuzzy-logic-based cardiac pacing system and validates its feasibility in a double sensor system which has minute ventilation and oxygen saturation level as its guides for the rate regulation. Though the agreement between the pacing rates (fuzzy rates) calculated with three linguistic variables for each parameter and the target rates were not satisfactory, it was improved significantly by tuning the membership functions. Almost the same evaluated values with those obtained by using six linguistic variables for each parameter were obtained. Time required for the self-tuning process was about 40 s (386CPU, 20 MHz) which was fast enough for the system. The smaller number of linguistic labels results in a smaller number of rules, which is beneficial in implantable cardiac pacemakers with limited memory capacity. A fuzzy-logic-based cardiac pacing system is promising for the realization of custom-made cardiac pacemakers.

Exercise Test↗
PubMed2002

Automatic determination of aortic compliance with cine-magnetic resonance imaging: an application of fuzzy logic theory.

RATIONALE AND OBJECTIVES: Aortic compliance is defined as the relative change in aortic cross-sectional area divided by the change in arterial pressure. Magnetic resonance imaging (MRI) is a useful imaging modality for the noninvasive evaluation of aortic compliance. However, manual tracing of the aortic contour is subject to important interobserver variations. To estimate the aortic compliance from cine-MRI, a method based on fuzzy logic theory was elaborated. MATERIALS AND METHODS: Seven healthy volunteers and eight patients with Marfan syndrome were examined using an ECG gated cine-MRI sequence. The aorta was imaged in the transverse plane at the level of the pulmonary trunk. A method based on fuzzy logic was developed to automatically detect the aortic contour. RESULTS: Through our robust automatic contouring method, the calculation of aortic cross-sectional areas allows an estimation of the aortic compliance. CONCLUSION: The aortic compliance can be obtained from a fuzzy logic based automatic contouring method, thereby avoiding the important interobserver variation often associated with manual tracing.

Adolescent↗
PubMed2001

Hybrid fuzzy logic committee neural networks for recognition of swallow acceleration signals.

Biological signals are complex and often require intelligent systems for recognition of characteristic signals. In order to improve the reliability of the recognition or automated diagnostic systems, hybrid fuzzy logic committee neural networks were developed and the system was used for recognition of swallow acceleration signals from artifacts. Two sets of fuzzy logic-committee networks (FCN) each consisting of seven member networks were developed, trained and evaluated. The FCN-I was used to recognize dysphagic swallow from artifacts, and the second committee FCN-II was used to recognize normal swallow from artifacts. Several networks were trained and the best seven were recruited into each committee. Acceleration signals from the throat were bandpass filtered, and several parameters were extracted and fed to the fuzzy logic block of either FCN-I or FCN-II. The fuzzified membership values were fed to the committee of neural networks which provided the signal classification. A majority opinion of the member networks was used to arrive at the final decision. Evaluation results revealed that FCN correctly identified 16 out of 16 artifacts and 31 out of 33 dysphagic swallows. In two cases, the decision was ambiguous due to the lack of a majority opinion. FCN-II correctly identified 24 out of 24 normal swallows, and 28 out of 29 artifacts. In one case, the decision was ambiguous due to the lack of a majority opinion. The present hybrid intelligent system consisting of fuzzy logic and committee networks provides a reliable tool for recognition and classification of acceleration signals due to swallowing.

Acceleration↗
PubMed2006

Application of fuzzy-logic models for metabolic control analysis.

A priori information or valuable qualitative knowledge can be incorporated explicitly to describe enzyme kinetics making use of fuzzy-logic models. Although restricted to linear relationships, it is shown that fuzzy-logic augmented models are not only able to capture non-linear features of enzyme kinetics but also allow the proper mathematical treatment of metabolic control analysis. The explicit incorporation of valuable qualitative knowledge is crucial, particularly when handling data estimated from in vivo kinetics studies, since this experimental information is scarce and usually contains measurement errors. Therefore, data-driven techniques, such as the one presented in this work, form a serious alternative to established kinetics approaches.

Animals↗
PubMed1997

Automation of matrix-assisted laser desorption/ionization mass spectrometry using fuzzy logic feedback control.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry is a sensitive and versatile method for biomolecular analysis which has potential for high-throughput screening in many applications. To obtain mass spectra of optimal quality, however, laser fluence is continuously adjusted during data acquisition to be close to the threshold level of ion production, requiring a skilled operator and several minutes of acquisition time per sample. Using real-time fuzzy logic control of the laser fluence, we here demonstrate that the acquisition of MALDI spectra can be automated without reduction of data quality. The control algorithm evaluates signal intensity and mass resolution of the base peak. It then regulates the laser fluence to keep the ion signal intensity within the dynamic range of the data acquisition hardware while maintaining high mass resolution. This fuzzy logic control system allows unattended data acquisition using either static ion extraction or delayed ion extraction MALDI. Even for difficult samples such as femtomole-level peptide mixtures, no significant reduction in data quality is observed, as compared to manually obtained spectra. Automated analysis of 78 chromatographic fractions with high mass accuracy demonstrates the utility of the method. The control algorithm has been combined with other software modules to completely automate database identification of proteins by their peptide mass maps. The success of fuzzy logic in MALDI automation suggests wider uses of this technique in mass spectrometry.

Automation↗