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Results for “Fuzzy Logic”

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PubMed2006

Characterization of trabecular bone structure from high-resolution magnetic resonance images using fuzzy logic.

The purpose of this work was to apply fuzzy logic image processing techniques to characterize the trabecular bone structure with high-resolution magnetic resonance images. Fifteen ex vivo high-resolution magnetic resonance images of specimens of human radii at 1.5 T and 12 in vivo high-resolution magnetic resonance images of the calcanei of peri- and postmenopausal women at 3 T were obtained. Soft segmentation using fuzzy clustering was applied to MR data to obtain fuzzy bone volume fraction maps, which were then analyzed with three-dimensional (3D) fuzzy geometrical parameters and measures of fuzziness. Geometrical parameters included fuzzy perimeter and fuzzy compactness, while measures of fuzziness included linear index of fuzziness, quadratic index of fuzziness, logarithmic fuzzy entropy, and exponential fuzzy entropy. Fuzzy parameters were validated at 1.5 T with 3D structural parameters computed from microcomputed tomography images, which allow the observation of true trabecular bone structure and with apparent MR structural indexes at 1.5 T and 3 T. The validation was statistically performed with the Pearson correlation coefficient as well as with the Bland-Altman method. Bone volume fraction correlation values (r) were up to .99 (P<.001) with good agreements based on Bland-Altman analysis showing that fuzzy clustering is a valid technique to quantify this parameter. Measures of fuzziness also showed consistent correlations to trabecular number parameters (r>.85; P<.001) and good agreements based on Bland-Altman analysis, suggesting that the level of fuzziness in high-resolution magnetic resonance images could be related to the trabecular bone structure.

Aged↗
PubMedDate not supplied

The across-fiber pattern theory and fuzzy logic: a matter of taste.

This article discusses a Fuzzy Logic (FL)-based model of neural coding and integration, proposed to be a formal extension of the Across-Fiber Pattern (AFP) theory. FL integration is conceptually similar to Bayesian reasoning, thus providing close-to-optimal decisions, and is also robust in that it does not require complete information. As a formal extension of AFP theory, the FL model describes sensory integration given multiple sources of information. When applied to gustation, the FL model is suggested to describe integration of information at the level of real-time pattern of single neural responses, population coding, and taste perception, as well as to provide a suitable description of taste mixtures.

Animals↗
PubMed2004

Differential diagnosis of pleural effusions by fuzzy-logic-based analysis of cytokines.

Pleural effusions can be caused by highly different underlying diseases and are characterized by complex interactions of various local and circulating cells as well as numerous soluble parameters like interleukins (IL). Knowledge about this complex network could help to indicate underlying disease. Therefore, we have investigated immunoreactive concentrations of IL-4, IL-6, IL-11, IL-15, IL-17, IL-18, and tumor necrosis factor-alpha (TNF-alpha) in pleural effusions and peripheral blood from patients with tuberculosis, bronchial carcinoma and other carcinomas as well as congestive heart failure (CHF) and pneumonias. To determine the value of cytokine measurement for differential diagnosis, statistical and fuzzy-logic methods were applied. Quantitative analysis showed high concentrations of IL-6 and IL-11 only in pleural effusions. IL-15, IL-17, IL-18 and TNF-alpha could be detected also in blood plasma. Lowest amounts were detected in CHF indicating the non-inflammatory origin of effusions. Statistical analysis did not provide evidence for diagnostic relevance of singular cytokines. Fuzzy-logic analysis was able to assign patients to the correct diseases with 80% accuracy using IL-6 and IL-15 measurement. Our results confirm the pathogenetic role of these cytokines in pleural effusions. Fuzzy-logic-based procedures may help to characterize and distinguish effusions of unknown origin even in small patient groups.

Adult↗
PubMed2006

Fuzzy logic in medicine and bioinformatics.

The purpose of this paper is to present a general view of the current applications of fuzzy logic in medicine and bioinformatics. We particularly review the medical literature using fuzzy logic. We then recall the geometrical interpretation of fuzzy sets as points in a fuzzy hypercube and present two concrete illustrations in medicine (drug addictions) and in bioinformatics (comparison of genomes).

Journal Article↗
PubMed1997

Fuzzy logic for model adaptation of a pharmacokinetic-based closed loop delivery system for pancuronium.

In this paper, we investigate the ability of fuzzy to adapt the parameters of a pharmacokinetic and pharmacodynamic model-based controller for the delivery of the muscle relaxant pancuronium. The system uses the model to control the rate of drug delivery and uses feedback from a sensor which measures muscle relaxation level to adapt the model using fuzzy logic. The control strategy administers mini-bolus doses of pancuronium and modulates the magnitude and time interval between the bolus doses to maintain a patient's muscle relaxation within an allowable range specified by the user. Before each new dose is given, the fuzzy logic adaptation scheme uses the error between the predicted patient response and the measured response to adapt the model. The system was tested using computer simulation by varying the parameters of the model by 50% from their nominal values. It was also evaluated in a clinical trial of five patients undergoing surgical procedures lasting 5 h or longer.

Adult↗
PubMed1994

Relationship between a fuzzy logic and a steepest descent approach to optimize a feedforward artificial neural network configuration.

The neural network designer must take into consideration many factors when selecting an appropriate network configuration. The performance of a given network configuration is influenced by many different factors such as: accuracy, training time, sensitivity, and the number of neurons used in the implementation. Using a cost function based on the four criteria mentioned previously, the various network paradigms can be evaluated relative to one another. If the mathematical models of the evaluation criteria as functions of the network configuration are known, then traditional techniques (such as the steepest descent method) could be used to determine the optimal network configuration. The difficulty in selecting an appropriate network configuration is due to the difficulty involved in determining the mathematical models of the evaluation criteria. This difficulty can be avoided by using fuzzy logic techniques to perform the network optimization as opposed to the traditional techniques. Fuzzy logic avoids the need of a detailed mathematical description of the relationship between the network performance and the network configuration, by using heuristic reasoning and linguistic variables. A comparison will be made between the fuzzy logic approach and the steepest descent method for the optimization of the cost function. The fuzzy optimization procedure could be applied to other systems where there is a priori information about their characteristics.

Electronic Data Processing↗
PubMed2002

Comparison of conventional rule based flow control with control processes based on fuzzy logic in a combined sewer system.

While conventional rule based, real time flow control of sewer systems is in common use, control systems based on fuzzy logic have been used only rarely, but successfully. The intention of this study is to compare a conventional rule based control of a combined sewer system with a fuzzy logic control by using hydrodynamic simulation. The objective of both control strategies is to reduce the combined sewer overflow volume by an optimization of the utilized storage capacities of four combined sewer overflow tanks. The control systems affect the outflow of four combined sewer overflow tanks depending on the water levels inside the structures. Both systems use an identical rule base. The developed control systems are tested and optimized for a single storm event which affects heterogeneously hydraulic load conditions and local discharge. Finally the efficiencies of the two different control systems are compared for two more storm events. The results indicate that the conventional rule based control and the fuzzy control similarly reach the objective of the control strategy. In spite of the higher expense to design the fuzzy control system its use provides no advantages in this case.

Cities↗
PubMed1995

Fuzzy logic and medical device technology.

This article introduces the concept of fuzzy logic and discusses its application in analytical processes. Terms such as fuzzy sets and membership functions, which can be used to describe the process states and conditions of a system, are defined. An example is given of how this reasoning technique could be applied to an actual device.

Biomedical Engineering↗
PubMed2000

PID gain scheduling using fuzzy logic.

A simple, yet robust and stable alternative to proportional, integral, derivative (PID) gain scheduling is developed using fuzzy logic. This fuzzy gain scheduling allows simple online duplication of PID control and the online improvement of PID control performance. The method is demonstrated with a physical model where PID control performance is improved to levels comparable to model predictive control. The fuzzy formulation is uniquely characterized by; (i) one fuzzy input variable involving the PID manipulated variable, (ii) two parameters to be tuned, while previously tuned PID parameters are retained, and (iii) a gain scheduling differential equation which relates the fuzzy and conventional PID manipulated variables and enables fuzzy gain scheduling.

Journal Article↗
PubMed1992

Application of fuzzy logic to the Apgar scoring system.

Apgar fuzzy expert system (AFES) applied fuzzy logic to the Apgar Scoring System (APG). We prepared the AFESs for both an inexperienced obstetrician (resident A) and an experienced obstetrician (specialist B) separately. We then compared their evaluations of the same 80 neonates at 1 min after birth using both the AFES and the APG. Analysis of the relationship of the general evaluation by the APG (scores below 6 or over 7) and the AFES (bad or good) to the umbilical arterial blood gases (pH below or above 7.20) showed that the sensitivities were 0.36 for the APG evaluation by resident A, 0.50 for the APG evaluation by specialist B, 0.60 for the AFES rating by resident A, and 0.71 for the AFES rating by specialist B. The specificities were 0.94, 0.95, 0.97 and 0.97, respectively. These results demonstrated that the AFES was capable of reflecting the recognition of the examiner.

Apgar Score↗
PubMed2002

State detection and control of overloads in the anaerobic wastewater treatment using fuzzy logic.

The two-stage anaerobic wastewater pre-treatment was modelled and controlled. The biological state of the reactors could be predicted using a fuzzy logic system and based upon this, proper control actions were taken automatically in order to avoid an overload. The system was designed to handle very strong fluctuations in the concentration of the substrate and the volumetric loading rate. Hydrogen concentration together with methane concentration, gas production rate. pH and the filling level of the acidification buffer tank were used as input variables for the fuzzy logic system. The manipulated variables were the flow rate from the acidification buffer tank into the methane reactor, the temperature and pH of both reactors, the circulation rate of the fixed bed reactor, back flow from the methane reactor into the acidification, and the control of the feed into the acidification buffer tank. The developed control system was successfully tested on a fully automated lab scale two-stage anaerobic digester. Different types of wastewater from food processing industries were successfully applied. Even a restart of feeding with very high COD concentrations (100 gl(-1) after several days of stand by was handled successfully. Effluent concentrations could be kept low without using TOC, COD or equivalent measurements.

Bacteria, Anaerobic↗
PubMed1997

Automatic detection of cardiac contours on MR images using fuzzy logic and dynamic programming.

This paper deals with the use of fuzzy logic and dynamic programming in the detection of cardiac contours in MR Images. The definition of two parameters for each pixel allows the construction of the fuzzy set of the cardiac contour points. The first parameter takes into account the grey level, and the second the presence of an edge. A corresponding fuzzy matrix is derived from the initial image. Finally, a dynamic programming with graph searching is performed on this fuzzy matrix. The method has been tested on several MR images and the results of the contouring were validated by an expert in the domain. This preliminary work clearly demonstrates the interest of this method, although a formal evaluation has to be done.

Fuzzy Logic↗
PubMed1995

Symbiosis of nurse and machine through fuzzy logic: improved specificity of a neonatal pulse oximeter alarm.

This article describes the theoretical underpinnings that preceded the design of a new neonatal pulse oximeter alarm that differentiates true from false alarms based on an artificial intelligence theory called "fuzzy logic." The connection between the intuition and sense of advocacy gained through neonatal primary nursing and the application of fuzzy logic to solve the problem of false alarms--that is, the symbiosis between nurse and machine--are explained. Emphasis is placed throughout on the importance of involving nurses in the development of the technology for which they are responsible.

Decision Making↗
PubMed2004

Fuzzy logic automatic control of the Phoenix-7 total artificial heart.

Automatic physiological control of the pneumatic Phoenix-7 total artificial heart (TAH) occupies a pivotal position for clinical application of the device. We developed a fuzzy logic automatic control algorithm for the Phoenix-7. The object of the automatic control system is to regulate the cardiac output at a level desirable for the given preload. The system uses cardiac output-type fuzzy pressure control combined with expert knowledge, most of which is of the form "If condition, then action." As a demonstration of the utility of the control algorithm, the effects of inlet air pressures, aortic pressure, pulmonary artery pressure, and heart rate on the cardiac output were analyzed. In addition, an in vitro experiment was carried out that verified good performance of the control algorithm. This fuzzy logic control algorithm possesses the potential for totally automatic operation, eliminating any need for manual intervention under variable hemodynamic conditions.

Automation↗
PubMed1997

Using fuzzy logic to predict response to citalopram in alcohol dependence.

INTRODUCTION: The prediction of patient response to new pharmacotherapies for alcohol dependence has usually not been successful with standard statistical techniques. We hypothesized that fuzzy logic, a qualitative computational approach, could predict response to 40 mg/day citalopram and 40 mg/day citalopram with a brief psychosocial intervention in alcohol-dependent patients. METHODS: Two data sets were formed with patients from our studies who received 40 mg/day citalopram alone (n = 34) or 40 mg/day citalopram and a brief psychosocial intervention (n = 28). The output variable, "response," was the percentage decrease in alcohol intake from baseline. Input variables included age, gender, baseline alcohol intake, and levels of anxiety, depression, alcohol dependence, and alcohol-related problems. RESULTS: A fuzzy rulebase was created from the data of 26 randomly chosen patients who received 40 mg/day citalopram and was used to predict the responses of the remaining eight patients. Eight rules related response with depression, anxiety, alcohol dependence, alcohol-related problems, age, and baseline alcohol intake. The average magnitude of the error in the predictions (RMSE) was 2.6 with a bias (ME) of 0.6. Predicted and actual response correlated (r = 0.99; p < 0.001). A fuzzy rulebase was created from the data of 28 randomly chosen patients who received 40 mg/day citalopram and a brief psychosocial intervention and was used to predict the responses of the remaining five patients. Six rules related response with age, anxiety, depression, alcohol dependence, and baseline alcohol intake with good predictive performance (RMSE = 6.4; ME = -1.5; r = 0.96; p < 0.01). CONCLUSIONS: This study indicates that fuzzy logic modeling can predict response to pharmacotherapies for alcohol dependence.

Alcoholism↗
PubMed2006

HPV infection in relation to OSCC histological grading and TNM stage. Evaluation by traditional statistics and fuzzy logic model.

We aimed to evaluate if in oral squamous cell carcinoma (OSCC) there is a relationship between histological grading (HG), TNM clinical stage and HPV infection; and to study the performance of fuzzy logic compared to traditional statistics, in the analysis of HPV status and correlates of OSCC. In cross-sectional analysis, the study group comprised 63 patients (mean age 68.89 years (SD +/-11.78), range (32-93); males 28 (44.4%), females 35 (55.6%)) with OSCC histologically diagnosed. HPV-DNA was studied in exfoliated oral epithelial cells by nested PCR (MY09/MY11 and GP5+/GP6+ primers). Data were analysed in parallel by traditional statistics with multivariate analysis and a fuzzy logic (FL) technique (membership functions as input, the ANFIS methodology, and the Sugeno's model of first order). HPV infection was detected in 24/63 (38.1%) of OSCC, as being HPV+ve 14/36 (38.9%) in G1, 7/18 (38.9%) in G2, and 3/9 (33.3%) in G3; HPV+ve 8/33 (24.2%) in Stage I, 9/12 (75.0%) in Stage II, 6/11(54.5%) in Stage III, and 1/7 (14.3%) in Stage IV. In both methods of analysis, no significantly increased risk of HPV infection was found for any HG score; whereas, TNM stage II was significantly associated to HPV infection (p=0.004; OR=9.375 (95% CI=2.030:43.30); OR'=11.148 (95% CI=1.951:43.30)), and, in particular, to primary tumour size T2 (p=0.0036; OR=7.812 (95% CI=1.914:31.890); OR'=9.414 (95% CI=1.846:48.013)); FL (% of prevision: 79.8; Root Mean-Square Error (RMSE): 0.29). No association was found between HPV infection and any demographical variable. Our findings show an association between HPV infection with TNM (stage II-T2), but not with histological grading of OSCC. Also, FL seems to be an additional effective tool in analysing the relationship of HPV infection with correlates of OSCC.

Adult↗
PubMed2000

Decision trees and fuzzy logic: a comparison of models for the selection of measles vaccination strategies in Brazil.

In 1997, health authorities of the state of São Paulo, Brazil designed a vaccination campaign against measles based on a decision model that utilized fuzzy logic. The chosen mass vaccination strategy was implemented and changed the natural course of the epidemic in that state. We have built a model using a decision tree and compare it to the fuzzy logic model. Using essentially the same set of assumptions about this problem, we contrast the two approaches. The models identify the same strategy as being the best one, but exhibit differences in the ranking of the remaining strategies.

Brazil↗
PubMed1995

Arterial pressure control with isoflurane using fuzzy logic.

Arterial pressure is still one of the most important measures in estimating the required dose of inhaled anaesthetics. It is measured easily and reacts rapidly which makes it suitable as a variable for feedback control of depth of anaesthesia. Fuzzy logic, a novel approach to feedback control, was used to control arterial pressure in 10 patients during intraabdominal surgery by automatic adjustment of the concentration of isoflurane in fresh gas. During anaesthesia, fuzzy control periods of 45-min duration were alternated randomly with human control periods of equal duration. During the skin incision period (-3 to + 12 min) 48.2% of all fuzzy control pressure values were within +/- 10% of the desired mean arterial pressure compared with 40.4% of the human control values (P < 0.05). The corresponding values for the remainder of the operation were 78.3% and 83.2%, respectively. Thus fuzzy out-performed human control at skin incision, but was slightly inferior during the rest of the operation. We conclude that fuzzy logic is a promising new technique for control of isoflurane delivery during routine anaesthesia.

Adult↗