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PubMed2000

Fuzzy signal detection theory: basic postulates and formulas for analyzing human and machine performance.

Signal detection theory (SDT) assumes a division of objective truths or "states of the world" into the nonoverlapping categories of signal and noise. The definition of a signal in many real settings, however, varies with context and over time. In the terminology of fuzzy logic, a real-world signal has a value that falls in a range between unequivocal presence and unequivocal absence. The definition of a response can also be nonbinary. Accordingly the methods of fuzzy logic can be combined with SDT, yielding fuzzy SDT. We describe the basic postulates of fuzzy SDT and provide formulas for fuzzy analysis of detection performance, based on four steps: (a) selection of mapping functions for signal and response; (b) use of mixed-implication functions to assign degrees of membership in hits, false alarms, misses, and correct rejections; (c) computation of fuzzy hit, false alarm, miss, and correct rejection rates; and (d) computation of fuzzy sensitivity and bias measures. Fuzzy SDT can considerably extend the range and utility of SDT by handling the contextual and temporal variability of most real-world signals. Actual or potential applications of fuzzy SDT include evaluation of the performance of human, machine, and human-machine detectors in real systems.

Fuzzy Logic↗
PubMed2004

A complex systems model approach to quantified mineral resource appraisal.

For federal and state land management agencies, mineral resource appraisal has evolved from value-based to outcome-based procedures wherein the consequences of resource development are compared with those of other management options. Complex systems modeling is proposed as a general framework in which to build models that can evaluate outcomes. Three frequently used methods of mineral resource appraisal (subjective probabilistic estimates, weights of evidence modeling, and fuzzy logic modeling) are discussed to obtain insight into methods of incorporating complexity into mineral resource appraisal models. Fuzzy logic and weights of evidence are most easily utilized in complex systems models. A fundamental product of new appraisals is the production of reusable, accessible databases and methodologies so that appraisals can easily be repeated with new or refined data. The data are representations of complex systems and must be so regarded if all of their information content is to be utilized. The proposed generalized model framework is applicable to mineral assessment and other geoscience problems. We begin with a (fuzzy) cognitive map using (+1,0,-1) values for the links and evaluate the map for various scenarios to obtain a ranking of the importance of various links. Fieldwork and modeling studies identify important links and help identify unanticipated links. Next, the links are given membership functions in accordance with the data. Finally, processes are associated with the links; ideally, the controlling physical and chemical events and equations are found for each link. After calibration and testing, this complex systems model is used for predictions under various scenarios.

Conservation of Natural Resources↗
PubMed1995

Computational intelligence for laboratory information systems.

Non-linear models, such as given by neural networks and fuzzy logic, have established a good reputation for medical data analysis as computational and logical counterparts to statistical methods. Whereas multilayer perceptrons perform well with large data sets, a combination of neural learning together with fuzzy logical network interpretations provides a network reduction well suited for smaller data sets. The aim of this paper is to present an approach to neural fuzzy systems data analysis and knowledge acquisition in laboratory information systems. We also describe a software system, DiagaiD, which provides an analysis and development workbench involving laboratory data.

Clinical Laboratory Information Systems↗
PubMed2006

Volatile biomarkers of pulmonary tuberculosis in the breath.

Pulmonary tuberculosis may alter volatile organic compounds (VOCs) in breath because Mycobacteria and oxidative stress resulting from Mycobacterial infection both generate distinctive VOCs. The objective of this study was to determine if breath VOCs contain biomarkers of active pulmonary tuberculosis. Head space VOCs from cultured Mycobacterium tuberculosis were captured on sorbent traps and assayed by gas chromatography/mass spectroscopy (GC/MS). One hundred and thirty different VOCs were consistently detected. The most abundant were naphthalene, 1-methyl-, 3-heptanone, methylcyclododecane, heptane, 2,2,4,6,6-pentamethyl-, benzene, 1-methyl-4-(1-methylethyl)-, and cyclohexane, 1,4-dimethyl-. Breath VOCs were assayed by GC/MS in 42 patients hospitalized for suspicion of pulmonary tuberculosis and in 59 healthy controls. Sputum cultures were positive for Mycobacteria in 23/42 and negative in19/42 patients. Breath markers of oxidative stress were increased in all hospitalized patients (p<0.04). Pattern recognition analysis and fuzzy logic analysis of breath VOCs independently distinguished healthy controls from hospitalized patients with 100% sensitivity and 100% specificity. Fuzzy logic analysis identified patients with positive sputum cultures with 100% sensitivity and 100% specificity (95.7% sensitivity and 78.9% specificity on leave-one-out cross-validation); breath VOC markers were similar to those observed in vitro, including naphthalene, 1-methyl- and cyclohexane, 1,4-dimethyl-. Pattern recognition analysis identified patients with positive sputum cultures with 82.6% sensitivity (19/23) and 100% specificity (18/18), employing 12 principal components from 134 breath VOCs. We conclude that volatile biomarkers in breath were sensitive and specific for pulmonary tuberculosis: the breath test distinguished between "sick versus well" i.e. between normal controls and patients hospitalized for suspicion of pulmonary tuberculosis, and between infected versus non-infected patients i.e. between those whose sputum cultures were positive or negative for Mycobacteria.

Adult↗
PubMed2004

Trajectory priming with dynamic fuzzy networks in nonlinear optimal control.

Fuzzy logic systems have been recognized as a robust and attractive alternative to some classical control methods. The application of classical fuzzy logic (FL) technology to dynamic system control has been constrained by the nondynamic nature of popular FL architectures. Many difficulties include large rule bases (i.e., curse of dimensionality), long training times, etc. These problems can be overcome with a dynamic fuzzy network (DFN), a network with unconstrained connectivity and dynamic fuzzy processing units called "feurons." In this study, DFN as an optimal control trajectory priming system is considered as a nonlinear optimization with dynamic equality constraints. The overall algorithm operates as an autotrainer for DFN (a self-learning structure) and generates optimal feed-forward control trajectories in a significantly smaller number of iterations. For this, DFN encapsulates and generalizes the optimal control trajectories. By the algorithm, the time-varying optimal feedback gains are also generated along the trajectory as byproducts. This structure assists the speeding up of trajectory calculations for intelligent nonlinear optimal control. For this purpose, the direct-descent-curvature algorithm is used with some modifications [called modified-descend-controller (MDC) algorithm] for the nonlinear optimal control computations. The algorithm has numerically generated robust solutions with respect to conjugate points. The minimization of an integral quadratic cost functional subject to dynamic equality constraints (which is DFN) is considered for trajectory obtained by MDC tracking applications. The adjoint theory (whose computational complexity is significantly less than direct method) has been used in the training of DFN, which is as a quasilinear dynamic system. The updating of weights (identification of DFN parameters) are based on Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. Simulation results are given for controlling a difficult nonlinear second-order system using fully connected three-feuron DFN.

Fuzzy Logic↗
PubMed2004

Simulating flocks on the wing: the fuzzy approach.

Traditionally the systematic study of animal behaviour using simulations requires the construction of a suitable mathematical model. The construction of such models in most cases requires advanced mathematical skills and exact knowledge of the studied animal's behaviour. Exact knowledge is rarely available. Usually it is available in the form of the observer's linguistic explanations and descriptions of the perceived behaviour. Mathematical models thus require a transition from the linguistic description to a mathematical formula that is seldom straightforward. The substantial increase of the processing power of personal computers has had as a result a notable progress in the field of fuzzy logic. In this paper we present a novel approach to the construction of artificial animals (animats) that is based on fuzzy logic. Our leading hypothesis is, that by omitting the transition from linguistic descriptions to mathematical formulas, ethologists would gain a tool for testing the existing or forming new hypotheses about 'why' and 'how' animals behave the way they do.

Animals↗
PubMed2004

Evolutionary fuzzy modeling human diagnostic decisions.

Fuzzy CoCo is a methodology, combining fuzzy logic and evolutionary computation, for constructing systems able to accurately predict the outcome of a human decision-making process, while providing an understandable explanation of the underlying reasoning. Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (accuracy) and linguistic representation (interpretability). However, fuzzy modeling--meaning the construction of fuzzy systems--is an arduous task, demanding the identification of many parameters. To solve it, we use evolutionary computation techniques (specifically cooperative coevolution), which are widely used to search for adequate solutions in complex spaces. We have successfully applied the algorithm to model the decision processes involved in two breast cancer diagnostic problems, the WBCD problem and the Catalonia mammography interpretation problem, obtaining systems both of high performance and high interpretability. For the Catalonia problem, an evolved system was embedded within a Web-based tool-called COBRA-for aiding radiologists in mammography interpretation.

Breast Neoplasms↗
PubMed1999

Quantifying stenosis in renal arteriograms: a fuzzy syntactic analysis.

The introduction of fuzzy logic improves a system for the automatic quantification of renal artery lesions seen in digital subtraction angiograms. A two-step approach has been followed. An earlier system based on non-fuzzy syntactic analysis provided a clear symbolic description of the stenotic lesions. Although this system worked correctly, it did not take into account the variability and uncertainty inherent to image processing and to knowledge on the reference diameter. This system has been improved by the introduction of fuzzy logic in the representation of the reference diameter. It provides a description of the stenosis in terms of fuzzy quantities. To illustrate the benefits of the fuzzy approach, the results of the two systems have been compared by plotting the differences of an index of variability. It appears that the differences are statistically different when using a two-tailed paired t-test (t = 2.37; p = 0.025). The result shows that the fuzzy approach is better than a non-fuzzy approach in the sense that the index of variability is reduced significantly.

Angiography, Digital Subtraction↗
PubMed1997

Perception of visible speech: influence of spatial quantization.

Visible speech reading was studied to determine which features are functional and to test several models of pattern recognition. Nine test syllables differing in their initial consonant were presented in intact form or under various levels of spatial quantization. Performance decreased in increasing quantization but remained relatively good at moderate levels of degradation. Different models were tested against the confusion matrices. Six features were identified as functional in distinguishing among the nine consonant--vowel syllables. These features were used as sources of information in a fuzzy-logical model of perception and an additive model. The fuzzy-logical model provided a significantly better description of the confusion matrices, showing that speech reading is analogous to other domains of pattern recognition such as face recognition and facial-affect perception.

Computer Graphics↗
PubMed2002

Fuzzy assessment of land suitability for scientific research reserves.

Evaluating the characteristics of a set of sites as potential scientific research reserves is an example of land suitability assessment. Suitability in this case is based upon multiple criteria, many of which can be linguistically imprecise and often incompatible. Fuzzy logic is a useful method for characterizing imprecise suitability criteria and for combining criteria into an overall suitability rating. The Ecosystem Management Decision Support software combined a fuzzy logic knowledge base we developed to represent the assessment problem with a GIS database providing site-specific data for the assessment. Assessment of sites as a potential natural reserve for the new University of California campus at Merced demonstrates the benefits of fuzzy suitability assessment. The study was conducted in three stages of successively smaller assessment regions with increasingly fine spatial resolution and specificity of criteria. Several sites were identified that best satisfy the suitability criteria for a reserve to represent vernal pool habitat.

Conservation of Natural Resources↗
PubMed1996

A fuzzy adaptive learning control network with on-line structure and parameter learning.

This paper addresses a general connectionist model, called Fuzzy Adaptive Learning Control Network (FALCON), for the realization of a fuzzy logic control system. An on-line supervised structure/parameter learning algorithm is proposed for constructing the FALCON dynamically. It combines the backpropagation learning scheme for parameter learning and the fuzzy ART algorithm for structure learning. The supervised learning algorithm has some important features. First of all, it partitions the input state space and output control space using irregular fuzzy hyperboxes according to the distribution of training data. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into "grids". As the number of input/output variables increase, the number of partitioned grids will grow combinatorially. To avoid the problem of combinatorial growing of partitioned grids in some complex systems, the proposed learning algorithm partitions the input/output spaces in a flexible way based on the distribution of training data. Second, the proposed learning algorithm can create and train the FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. The users thus need not give it any a priori knowledge or even any initial information on these. In some real-time applications, exact training data may be expensive or even impossible to obtain. To solve this problem, a Reinforcement Fuzzy Adaptive Learning Control Network (RFALCON) is further proposed. The proposed RFALCON is constructed by integrating two FALCONs, one FALCON as a critic network, and the other as an action network. By combining temporal difference techniques, stochastic exploration, and a proposed on-line supervised structure/parameter learning algorithm, a reinforcement structure/parameter learning algorithm is proposed, which can construct a RFALCON dynamically through a reward/penalty signal. The ball and beam balancing system is presented to illustrate the performance and applicability of the proposed models and learning algorithms.

Algorithms↗
PubMed2005

Small, fuzzy and interpretable gene expression based classifiers.

MOTIVATION: Interpretation of classification models derived from gene-expression data is usually not simple, yet it is an important aspect in the analytical process. We investigate the performance of small rule-based classifiers based on fuzzy logic in five datasets that are different in size, laboratory origin and biomedical domain. RESULTS: The classifiers resulted in rules that can be readily examined by biomedical researchers. The fuzzy-logic-based classifiers compare favorably with logistic regression in all datasets. AVAILABILITY: Prototype available upon request.

Algorithms↗
PubMed1997

[Electromyograms in erectile dysfunction and computer-assisted interpretation].

The electromyogram of the corpora cavernosa (CC-EMG) provides information on the autonomic innervation and/or smooth musculature. The interpretation of the CC-EMG, usually by evaluating signal patterns of higher activity, is time-consuming. To improve this situation, a computer-aided diagnosis employing a Microsoft Windows user interface was developed. The computer-aided interpretation is based on digital measurement data obtained using a 170.6 Hz sampling frequency and quantization of 10 V/12 bits of the signal. The first task of the program is to extract signal patterns of higher activity from stored data. To describe these patterns in mathematical terms, the features "relative time position", "relative reproducibility", "portion of normal phases", and "portion of whip phases" are calculated. Characteristic signal forms (normal and whip phases) are identified by means of syntactic pattern recognition. Using fuzzy logic, the features are used to effect pattern evaluation. To summarize the evaluated patterns, the variable "global normality" has been established, and linked to the "global synchronicity" at a second fuzzy logic level, to produce the final diagnosis. Finally, 30 records were evaluated independently by a team of experts and by the computer program. In relation to four established diagnostic classes, a correspondence of 70% was found. Furthermore, the accuracy achieved in each of the individual classes was better than 50%. Discrimination between normal and abnormal evaluation, which is of particular interest, reached 80%.

Autonomic Nervous System↗
PubMed1995

Prototype theory and compositionality.

Osherson and Smith (1981, Cognition, 11, 237-262) discuss a number of problems which arise for a prototype-based account of the meanings of simple and complex concepts. Assuming that concept combination in such a theory is to be analyzed in terms of fuzzy logic, they show that some complex concepts inevitably get assigned the wrong meanings. In the present paper we argue that many of the problems O&S discovered are due to difficulties that are intrinsic to fuzzy set theory, and that most of them disappear when fuzzy logic is replaced by supervaluation theory. However, even after this replacement one of O&S's central problems remains: the theory still predicts that the degree to which an object is an instance of, say, "stripped apple" must be less than or equal to both the degree to which it is an instance of "striped" and the degree to which it is an instance of "apple", but this constraint conflicts with and the degree to which it is an instance of "apple", but this constraint conflicts with O&S's experimental results. The second part of the paper explores ways of solving this and related problems. This leads us to suggest a number of distinctions and principles concerning how prototypicality and other mechanisms interact and which seem important for semantics generally. Prominent among these are (i) the distinction between on the one hand the logical and semantic properties of concepts and on the other the linguistic that between concepts for which the extension is determined by their prototype and concepts for which extension and prototypicality are independent.

Attention↗
PubMed2006

Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction.

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are demonstrated to be competent when applied individually to a variety of problems. Recently, there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have been evolved. In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for epileptic seizure detection. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Some conclusions concerning the impacts of features on the detection of epileptic seizures were obtained through analysis of the ANFIS. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANN model in terms of training performances and classification accuracies. The results confirmed that the proposed ANFIS model has some potential in epileptic seizure detection. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.

Electroencephalography↗
PubMed2006

How artificial intelligence tools can be used to assess individual patient risk in cardiovascular disease: problems with the current methods.

BACKGROUND: In recent years a number of algorithms for cardiovascular risk assessment has been proposed to the medical community. These algorithms consider a number of variables and express their results as the percentage risk of developing a major fatal or non-fatal cardiovascular event in the following 10 to 20 years DISCUSSION: The author has identified three major pitfalls of these algorithms, linked to the limitation of the classical statistical approach in dealing with this kind of non linear and complex information. The pitfalls are the inability to capture the disease complexity, the inability to capture process dynamics, and the wide confidence interval of individual risk assessment. Artificial Intelligence tools can provide potential advantage in trying to overcome these limitations. The theoretical background and some application examples related to artificial neural networks and fuzzy logic have been reviewed and discussed. SUMMARY: The use of predictive algorithms to assess individual absolute risk of cardiovascular future events is currently hampered by methodological and mathematical flaws. The use of newer approaches, such as fuzzy logic and artificial neural networks, linked to artificial intelligence, seems to better address both the challenge of increasing complexity resulting from a correlation between predisposing factors, data on the occurrence of cardiovascular events, and the prediction of future events on an individual level.

Artificial Intelligence↗
PubMed1999

Memories in context.

Context-dependent associative memories are models that allow the retrieval of different vectorial responses given a same vectorial stimulus, depending on the context presented to the memory. The contextualization is obtained by doing the Kronecker product between two vectorial entries to the associative memory: the key stimulus and the context. These memories are able to display a wide variety of behaviors that range from all the basic operations of the logical calculus (including fuzzy logics) to the selective extraction of features from complex vectorial patterns. In the present contribution, we show that a context-dependent memory matrix stores a large amount of possible virtual associative memories, that awaken in the presence of a context. We show how the vectorial context allows a memory matrix to be representable in terms of its singular-value decomposition. We describe a neural interpretation of the model in which the Kronecker product is performed on the same neurons that sustain the memory. We explored, with numerical experiments, the reliability of chains of contextualized associations. In some cases, random disconnection produces the emergence of oscillatory behaviors of the system. Our results show that associative chains retain their performances for relatively large dimensions. Finally, we analyze the properties of some modules of context-dependent autoassociative memories inserted in recursive nets: the perceptual autoorganization in the presence of ambiguous inputs (e.g. the disambiguation of the Necker's cube figure), the construction of intersection filters, and the feature extraction capabilities.

Memory↗
PubMedDate not supplied

Neuro-fuzzy closed-loop control of depth of anaesthesia.

The utility of the auditory evoked potential (AEP) is under investigation as a feedback signal for the automatic closed-loop control of general anaesthesia using neural networks and fuzzy logic. The AEP is a signal derived from the electroencephalogram (EEG) in response to auditory stimulation, which may be useful as an index of the 'depth' of anaesthesia. A simple back-propagation neural network can learn the AEP and provides a satisfactory input to a fuzzy logic infusion controller for the administration of anaesthetic drugs, but the problem remains that of reliable signal acquisition.

Anesthesia, Closed-Circuit↗