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PubMed2002

Integrating fuzzy logic, optimization, and GIS for ecological impact assessments.

Appraisal of ecological impacts has been problematic because of the behavior of ecological system and the responses of these systems to human intervention are far from fully understood. While it has been relatively easy to itemize the potential ecological impacts, it has been difficult to arrive at accurate predictions of how these impacts affect populations, communities, or ecosystems. Furthermore, the spatial heterogeneity of ecological systems has been overlooked because its examination is practically impossible through matrix techniques, the most commonly used impact assessment approach. Besides, the public has become increasingly aware of the importance of the EIA in decision-making and thus the interpretation of impact significance is complicated further by the different value judgments of stakeholders. Moreover, impact assessments are carried out with a minimum of data, high uncertainty, and poor conceptual understanding. Hence, the evaluation of ecological impacts entails the integration of subjective and often conflicting judgments from a variety of experts and stakeholders. The purpose of this paper is to present an environmental impact assessment approach based on the integration fuzzy logic, geographical information systems and optimization techniques. This approach enables environmental analysts to deal with the intrinsic imprecision and ambiguity associated with the judgments of experts and stakeholders, the description of ecological systems, and the prediction of ecological impacts. The application of this approach is illustrated through an example, which shows how consensus about impact mitigation can be attained within a conflict resolution framework.

Decision Making↗
PubMed2005

Fuzzy logic.

Explore the source record for details and available documents.

Decision Making↗
PubMed1979

A fuzzy logical model of letter identification.

Stimuli were generated by factorially varying two sets of features that distinguish between two letter patterns. Subjects rated the degree to which each stimulus was an instance of one letter rather than the alternative. The obtained ratings were relatively continuous and systematic functions of the feature manipulations. The results were well accounted for by a model in which (a) each feature has an associated fuzzy predicate that is used to independently evaluate the degree to which it is true that the feature is present in the stimulus; (b) the featural truth values are integrated according to fuzzy logical expressions that correspond directly to propositional descriptions of each letter pattern; and (c) the resulting goodness of match to the stimulus for each letter is compared to that of the alternatives to determine the final identification.

Discrimination Learning↗
PubMed2003

A fuzzy logic approach toward solving the analytic enigma of health system financing.

Improved health, equity, macroeconomic efficiency, efficient provision of care, and client satisfaction are the common goals of any health system. The relative significance of these goals varies, however, across nations, communities and with time. As for health care finance, the attainment of these goals under varying circumstances involves alternative policy options for each of the following elements: sources of finance, allocation of finance, payment to providers, and public-private mix. The intricate set of multiple goals, elements and policy options defies human reasoning, and, hence, hinders effective policymaking. Indeed, "health system finance" is not amenable to a clear set of structural relationships. Neither is there a universe that can be subject to statistical scrutiny: each health system is unique. "Fuzzy logic" models human reasoning by managing "expert knowledge" close to the way it is handled by human language. It is used here for guiding policy making by a systematic analysis of health system finance. Assuming equal welfare weights for alternative goals and mutually exclusive policy options under each health-financing element, the exploratory model we present here suggests that a German-type health system is best. Other solutions depend on the welfare weights for system goals and mixes of policy options.

Journal Article↗
PubMed2004

Heterogeneous fuzzy logic networks: fundamentals and development studies.

The recent trend in the development of neurofuzzy systems has profoundly emphasized the importance of synergy between the fundamentals of fuzzy sets and neural networks. The resulting frameworks of the neurofuzzy systems took advantage of an array of learning mechanisms primarily originating within the theory of neurocomputing and the use of fuzzy models (predominantly rule-based systems) being well established in the realm of fuzzy sets. Ideally, one can anticipate that neurofuzzy systems should fully exploit the linkages between these two technologies while strongly preserving their evident identities (plasticity or learning abilities to be shared by the transparency and full interpretability of the resulting neurofuzzy constructs). Interestingly, this synergy still becomes a target yet to be satisfied. This study is an attempt to address the fundamental interpretability challenge of neurofuzzy systems. Our underlying conjecture is that the transparency of any neurofuzzy system links directly with the logic fabric of the system so the logic fundamentals of the underlying architecture become of primordial relevance. Having this in mind the development of neurofuzzy models hinges on a collection of logic driven processing units named here fuzzy (logic) neurons. These are conceptually simple logic-oriented elements that come with a well-defined semantics and plasticity. Owing to their diversity, such neurons form essential building blocks of the networks. The study revisits the existing categories of logic neurons, provides with their taxonomy, helps understand their functional features and sheds light on their behavior when being treated as computational components of any neurofuzzy architecture. The two main categories of aggregative and reference neurons are deeply rooted in the fundamental operations encountered in the technology of fuzzy sets (including logic operations, linguistic modifiers, and logic reference operations). The developed heterogeneous networks come with a well-defined semantics and high interpretability (which directly translates into the rule-based representation of the networks). As the network takes advantage of various logic neurons, this imposes an immediate requirement of structural optimization, which in this study is addressed by utilizing various mechanisms of genetic optimization (genetic algorithms). We discuss the development of the networks, elaborate on the interpretation aspects and include a number of illustrative numeric examples.

Algorithms↗
PubMed1999

Incorporating qualitative knowledge in enzyme kinetic models using fuzzy logic.

Modeling of metabolic pathway dynamics requires detailed kinetic equations at the enzyme level. In particular, the kinetic equations must account for metabolite effectors that contribute significantly to the pathway regulation in vivo. Unfortunately, most kinetic rate laws available in the literature do not consider all the effectors simultaneously, and much kinetic information exists in a qualitative or semiquantitative form. In this article, we present a strategy to incorporate such information into the kinetic equation. This strategy uses fuzzy logic-based factors to modify algebraic rate laws that account for partial kinetic characteristics. The parameters introduced by the fuzzy factors are then optimized by use of a hybrid of simplex and genetic algorithms. The resulting model provides a flexible form that can simulate various kinetic behaviors. Such kinetic models are suitable for pathway modeling without complete enzyme mechanisms. Three enzymes in Escherichia coli central metabolism are used as examples: phosphoenolpyruvate carboxylase; phosphoenolpyruvate carboxykinase; and pyruvate kinase I. Results show that, with fuzzy logic-augmented models, the kinetic data can be much better described. In particular, complex behavior, such as allosteric inhibition, can be captured using fuzzy rules. The resulting models, even though they do not provide additional physical meaning in enzyme mechanisms, allow the model to incorporate semiquantitative information in metabolic pathway models.

Bacterial Proteins↗
PubMed2000

Improvement in the information content of the Glasgow Coma Scale for the prediction of full cognitive recovery after head injury using fuzzy logic.

BACKGROUND: The objective of this study was to modify the existing Glasgow Coma Scale (GCS) into a fuzzy GCS by using fuzzy information representation and fuzzy inferencing. The study compared the information content of the existing GCS with the new fuzzy GCS for prediction of full cognitive recovery in patients with head injury. METHODS: A record-based study was conducted at the Government Medical College and Hospital, a tertiary care facility in Nagpur, India. The study, which covered the period from January 1 to December 31, 1997, included 253 patients with head injuries. Opinions of 17 clinical experts who routinely deal with head injury cases were used for the construction of the fuzzy GCS. RESULTS: By using the max operator for summarization, eye, motor, and verbal stimuli were all significantly associated with the possibility of full cognitive recovery with the fuzzy GCS (P < .001). Nonspecificity of the classical GCS, the min-operated fuzzy GCS, and the max-operated fuzzy GCS was comparable. A reduction in Shannon entropy was maximum with the max-operated fuzzy GCS. Min-operated fuzzy GCS better predicted a lack of full cognitive recovery. CONCLUSIONS: Fuzzy GCS substantially improves the information content for prediction of the possibility of full cognitive recovery after head injury. Eye, motor, and verbal stimuli all uniquely and significantly contribute to prediction of this possibility. We recommend the use of fuzzy GCS for prediction of the possibility of full cognitive recovery in patients with head injuries.

Cognition↗
PubMed1999

Automatic detection of left ventricular contours from cardiac cine magnetic resonance imaging using fuzzy logic.

RATIONALE AND OBJECTIVES: Gated cardiac cine magnetic resonance imaging provides accurate dynamic data of the left ventricular function. However, the manual extraction of important physiologic parameters such as myocardium wall thickness and left ventricular volumes is invariably time consuming and subjective. To reduce the variability and time constraints inherent in observer contour tracing, the authors developed an automatic left ventricle contour-detection method. METHODS: The purpose was to apply fuzzy logic-based automatic contour detection to identification of endocardial and epicardial borders in short-axis magnetic resonance images. The automatic contouring was compared with manual tracing using the calculated ejection fraction as the comparison criterion. RESULTS: A good correlation was found between the two approaches (r2 = 0.98). CONCLUSIONS: The ejection fraction can be obtained using this automatic contouring method.

Adult↗
PubMed1998

Fuzzy logic and inflammatory protein variations.

This tutorial style paper has been written for physicians and biologists who are not necessarily familiar with fuzzy set theory and biomedical applications. The field is introduced in the framework of medical diagnosis problems and illustrated with an application to inflammatory protein variations. The model is of special interest in the processing of borderline cases, allowing a graded assignment of diagnoses to patients. Relationships between signs and diagnoses are interpreted as labels of fuzzy sets and it is shown how diagnoses can be derived from soft matching processes. In cases of poor diagnostic classification, appropriate weights are introduced, acting on characterizations of signs, in order to decrease their relative influence. As a consequence, when pattern matching is achieved, the final ranking of inflammatory syndromes assigned to a given patient might change to better fit the actual classification.

Acute-Phase Proteins↗