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In a fuzzy set, the probability that a result for a patient is increased (or decreased) is equal to the probability of incidence of reference values equal and lower (or higher) than the evaluated result. Application of fuzzy logic to computer analysis of haematological data enables more appropriate interpretation of continuous pathological changes.
This paper proposes a fuzzy logic based procedure able to control the behavior of a patient's blood pressure during a dialysis session, allowing him to reach the planned dry weight. A proportional integrative discrete-time fuzzy control is used to subject the controlled variables (blood pressure and blood volume) to the reference values. Two different tables that refer to pressure and volume errors and rates are consulted and the proposed control actions are taken to obtain the final value (net ultrafiltration rate). A saturation-threshold function of volemia acts on the second control variable, Na concentration in the dialysate. The adaptive control system was simulated on an IBM PC, and rules and terms were expressed by linguistic judgments such as: IF "situation," THEN "action." A pre processor converts the rules into the numerical values in the tables. The obtained simulation results are satisfactory, and the introduction of Na control allows achievement of the target dry weight of the patient with a stable blood pressure.
BACKGROUND: Recent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are needed to interpret the resulting large and complex data sets. Rationally designed perturbations (e.g., gene knock-outs) can be used to iteratively refine hypothetical models, suggesting an approach for high-throughput biological system analysis. We introduce an approach to gene network modeling based on a scalable linear variant of fuzzy logic: a framework with greater resolution than Boolean logic models, but which, while still semi-quantitative, does not require the precise parameter measurement needed for chemical kinetics-based modeling. RESULTS: We demonstrated our approach with exhaustive search for fuzzy gene interaction models that best fit transcription measurements by microarray of twelve selected genes regulating the yeast cell cycle. Applying an efficient, universally applicable data normalization and fuzzification scheme, the search converged to a small number of models that individually predict experimental data within an error tolerance. Because only gene transcription levels are used to develop the models, they include both direct and indirect regulation of genes. CONCLUSION: Biological relationships in the best-fitting fuzzy gene network models successfully recover direct and indirect interactions predicted from previous knowledge to result in transcriptional correlation. Fuzzy models fit on one yeast cell cycle data set robustly predict another experimental data set for the same system. Linear fuzzy gene networks and exhaustive rule search are the first steps towards a framework for an integrated modeling and experiment approach to high-throughput "reverse engineering" of complex biological systems.
In this paper we propose a decision support system that can provide information on the environmental impact of anthropic activities by examining their effects on groundwater quality. We use the combined value of both intrinsic vulnerability of a specific local aquifer, obtained by implementing a parametric managerial model (SINTACS), and a degree of hazard value, which takes into account specific human activities. Incomplete information is notoriously common in environmental planning. To overcome this deficiency we apply an algorithmic and a qualitative approach, based on expert judgment incorporated into the system's knowledge base. The decision support system takes into account the uncertainty of the environmental domain by using fuzzy logic and evaluates the reliability of the results according to information availability.
The paper proposes a fuzzy logic based procedure able to control as far as possible the behaviour of the blood pressure of a patient during a dialysis session, allowing him to reach the foreseen dry weight. A PI discrete-time fuzzy control is used in order to compare the controlled variables concerning the (blood pressure and blood volume) to the reference values. Two different reference tables, concerning the pressure and volume errors and rates are introduced, then the proposed control actions are mixed in order to obtain the final value (net ultrafiltration rate). A smooth function of volemia acts on the second control variable, Na concentration in the dialysate. The adaptive control system was simulated on an IBM-PC, rules and terms were expressed by linguistic judgements like: IF "situation", THEN "action". A pre-processor converts the rules into the numerical values of the reference tables. The obtained simulation results are satisfactory, the introduction of the Na control allows reaching the target dry weight of the patient with a stable blood pressure.
This paper presents the KASIMIR research project for the management of decision protocols in oncology. A decision protocol is a kind of decision tree implemented in an object-based representation formalism. A reasoner based on such a formalism and on hierarchical classification is coupled with a knowledge editor. This association provides an assistance for editing and maintenance of protocols, enabling the detection of errors and the comparison between versions of the protocol. In this way, a management of protocols takes fully advantage of the underlying knowledge representation and reasoning tools. This straightforward use of the protocol may be insufficient in some situations. Then, the protocol may have to be adapted for these situations. A study of protocol adaptation is presented. In particular a reasoner based on a combination of hierarchical classification and fuzzy logic is introduced.
This paper presents part of our research work concerned with the realisation of an Intelligent Vehicle and the technologies required for its routing, navigation, and control. An automated driver prototype has been developed using a self-organising fuzzy rule-based system (POPFNN-CRI(S)) to model and subsequently emulate human driving expertise. The ability of fuzzy logic to represent vague information using linguistic variables makes it a powerful tool to develop rule-based control systems when an exact working model is not available, as is the case of any vehicle-driving task. Designing a fuzzy system, however, is a complex endeavour, due to the need to define the variables and their associated fuzzy sets, and determine a suitable rule base. Many efforts have thus been devoted to automating this process, yielding the development of learning and optimisation techniques. One of them is the family of POP-FNNs, or Pseudo-Outer Product Fuzzy Neural Networks (TVR, AARS(S), AARS(NS), CRI, Yager). These generic self-organising neural networks developed at the Intelligent Systems Laboratory (ISL/NTU) are based on formal fuzzy mathematical theory and are able to objectively extract a fuzzy rule base from training data. In this application, a driving simulator has been developed, that integrates a detailed model of the car dynamics, complete with engine characteristics and environmental parameters, and an OpenGL-based 3D-simulation interface coupled with driving wheel and accelerator/ brake pedals. The simulator has been used on various road scenarios to record from a human pilot driving data consisting of steering and speed control actions associated to road features. Specifically, the POPFNN-CRI(S) system is used to cluster the data and extract a fuzzy rule base modelling the human driving behaviour. Finally, the effectiveness of the generated rule base has been validated using the simulator in autopilot mode.
This paper presents a fuzzy neural network for learning the knowledge of a fuzzy logic rule-based system. The network contains five layers: an Input Layer, Membership-function Layer, AND Layer, OR Layer, and Defuzzification Layer. We propose a backpropagation-like learning algorithm to train this neural network to acquire the fuzzy rules and to fine-tune the knowledge on the parameters of AND and OR nodes. Compared with methods other than the gradient descent search, the proposed learning process acquires more precise knowledge. In addition, the functions of the AND and OR nodes in the network are formulated with the minimum or maximum operations, respectively. Therefore, the adjustments of the learnable weights (parameters) can be focused on the dominant terms related to the (minimum/maximum) operations. The convergence time for the proposed learning algorithm is much faster than that for conventional backpropagation algorithms. In summary, the learnable weights (parameters) of the network are adjusted very quickly to obtain precise knowledge. Simulation results show that in learning the truck backer-upper problem, our network completes the training procedure in only several dozen epochs with an error rate of less than 1%.
An Adaptive Fuzzy Partition (AFP) algorithm, derived from Fuzzy Logic concepts, was used to classify an anticancer data set, including about 1300 compounds subdivided into eight mechanisms of action. AFP classification builds relationships between molecular descriptors and bio-activities by dynamically dividing the descriptor hyperspace into a set of fuzzy subspaces. These subspaces are described by simple linguistic rules, from which scores ranging between 0 and 1 can be derived. The latter values define, for each compound, the degrees of membership of the different mechanisms analyzed. A particular attention was devoted to develop structure-activity relations that have a real utility. Then, well-defined and widely accepted protocols were used to validate the models by defining their robustness and prediction ability. More particularly, after selecting the most relevant descriptors with help of a genetic algorithm, a training set of 640 compounds was isolated by a rational procedure based on Self-Organizing Maps. The related AFP model was then validated with help of a validation set and, above all, of cross-validation and Y-randomization procedures. Good validation scores of about 80% were obtained, underlining the robustness of the model. Moreover, the prediction ability was evaluated with 374 test compounds that had not been used to establish the model and 77% of them were predicted correctly.
In 1987, the American Rheumatism Association issued a set of criteria for the classification of rheumatoid arthritis (RA) to provide a uniform definition of RA patients. Fuzzy set theory and fuzzy logic were used to transform this set of criteria into a diagnostic tool that offers diagnoses at different levels of confidence: a definite level, which was consistent with the original criteria definition, as well as several possible and superdefinite levels. Two fuzzy models and a reference model which provided results at a definite level only were applied to 292 clinical cases from a hospital for rheumatic diseases. At the definite level, all models yielded a sensitivity rate of 72.6% and a specificity rate of 87.0%. Sensitivity and specificity rates at the possible levels ranged from 73.3% to 85.6% and from 83.6% to 87.0%. At the superdefinite levels, sensitivity rates ranged from 39.0% to 63.7% and specificity rates from 90.4% to 95.2%. Fuzzy techniques were helpful to add flexibility to preexisting diagnostic criteria in order to obtain diagnoses at the desired level of confidence.
AIM: The aim of this paper was to explore the concept of expertise in nursing from the perspective of how it relates to current driving forces in health care in which it discusses the potential barriers to acceptance of nursing expertise in a climate in which quantification of value and cost containment run high on agendas. BACKGROUND: Expert nursing practice can be argued to be central to high quality, holistic, individualized patient care. However, changes in government policy which have led to the inception of comprehensive guidelines or protocols of care are in danger of relegating the 'expert nurse' to being an icon of the past. Indeed, it could be argued that expert nurses are an expensive commodity within the nursing workforce. Consequently, with this change to the use of clinical guidelines, it calls into question how expert nursing practice will develop within this framework of care. METHOD: The article critically reviews the evidence related to the role of the Expert Nurse in an attempt to identify the key concepts and ideas, and how the inception of care protocols has implications for their role. CONCLUSION: Nursing expertise which focuses on the provision of individualized, holistic care and is based largely on intuitive decision making cannot, should not be reduced to being articulated in positivist terms. However, the dominant power and decision-making focus in health care means that nurses must be confident in articulating the value of a concept which may be outside the scope of knowledge of those with whom they are debating. RELEVANCE TO CLINICAL PRACTICE: The principles of abduction or fuzzy logic may be useful in assisting nurses to explain in terms which others can comprehend, the value of nursing expertise.
In the recent past, many efforts have been carried out in order to evaluate the feasibility of implementing closed-loop controlled neuroprostheses based on the processing of sensory electroneurographic (ENG) signals. The success of these techniques mostly relies on the development of processing algorithms capable of extracting the necessary kinematic information from these signals. Soft-computing algorithms can be very useful when dealing with the complexity of the neuromuscular system because of their generalization ability and model-free structure. In this paper, these techniques were used to extract angular position information from the ENG signals recorded from muscle afferents in animal model using cuff electrodes. Specifically, a genetic algorithm-based dynamic nonsingleton fuzzy logic system (named GA-DNSFLS) was developed and tested on different types of angular trajectories (characterized by small or large angular excursions). In particular, two different Takagi-Sugeno-Kang (TSK)-like structures were used in the consequent part of the neuro-fuzzy model in order to verify which one could improve the generalization abilities (intrasubject and intersubject). The results showed that the GA-DNSFLS was able to reconstruct the trajectories giving interesting results in terms of correlation between the actual and the predicted trajectories for small excursion movements during intrasubject and intersubject tests. Particularly, one of the TSK models showed better results in terms of intersubject generalization. The simulations conducted with the large excursion movements led in some cases to interesting results but further experiments are necessary in order to analyze this point more in deep.
We present a Web-based application that uses whole-protein masses determined by mass spectrometry to identify putative co- and posttranslational proteolytic cleavages and chemical modifications. The protein cleavage and modification engine (PROCLAME) requires as input an intact mass measurement and a precursor identification based on peptide mass fingerprinting or tandem mass spectrometry. This approach predicts mass-modifying events using a depth-first tree search, bounded by a set of rules controlled by a custom-built fuzzy logic engine, to explore a large number of possible combinations of modifications accounting for the experimental mass. Candidates are saved during a search if they are within a user-specified instrument mass accuracy; the total number of possible candidates searched is based on a specified fuzzy cutoff score. Candidates are scored and ranked using a simple probabilistic model. There is generally not enough information in an intact mass measurement to determine a single unique protein characterization; however, the program provides utility by expediting the identification of sets of putative events consistent with the mass data and ranking them for further investigation. This approach uses a simple, intuitive rule base and lends itself to discovery of unannotated posttranslational events. We have assessed the program with both in silico-generated test data and with published data from an analysis of large ribosomal subunit proteins, both from the yeast S. cerevisiae. Results indicate a high degree of sensitivity and specificity in characterizing proteins whose masses resulted from reasonable proteolysis and covalent modification scenarios. The application is available on the web at http://proclame.unc.edu.
This paper outlines some of the theoretical and practical developments being made in neurofuzzy systems. As the name suggests, neurofuzzy networks were developed by fusing the ideas that originated in the fields of neural and fuzzy systems. A neurofuzzy network attempts to combine the transparent, linguistic, symbolic representation associated with fuzzy logic with the architecture and learning rules commonly used in neural networks. These hybrid structures have both a qualitative and a quantitative interpretation and can overcome some of the difficulties associated with solely neural algorithms which can usually be regarded as black box mappings, and with fuzzy systems where few modelling and learning theories existed. Both B-spline and Gaussian Radial Basis Function networks can be regarded as neurofuzzy systems and soft inductive learning algorithms can be used to extract unknown, qualitative information about the relationships contained in the training data. In a similar manner, qualitative rules or information about the network's structure can be used to initialise the system. These areas, coupled with the extensive work being carried out on theoretically analysing their modelling, convergence and stability properties means that this research topic is highly applicable in "intelligent" modelling and control problems. Apart from outlining this work, the paper also discusses a wide variety of open research questions and suggests areas where new efforts may be fruitfully applied.
Current copiousness of genomic information stored in biological databases [Mar Albà, M., Lee, M., Pearl, D., Shepherd, F.M.G., Martin, A.J., Orengo, N., Kellam, C.A., 2001. P. VIDA: a virus database system for the organisation of virus genome open reading frames. Nuleic Acids Res. 133-136] makes ultimately feasible the proposal for an application of knowledge management aimed to discover general rules in subcellular phenomena. The goal of this work is primarily to discover relationships between genes by microarray analysis. The tools exploited come from clustering techniques and are mainly based on Knowledge Discovery in Databases (KDD) concepts [Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., 1996. From data mining to knowledge discovery in databases. AI Magazine 17(3), 37-54]. Starting from a data set, each element can be represented by a characteristic matrix, which sums up all data attributes. In this case data mining is oriented to perform a Pattern Recognition of related sequences, hidden in databases [Hand, D.J., Nicholas, A., 2005. Heard finding groups in gene expression data. J. Biomed. Biotechnol. 215-225]. Following a bottom up approach, the next refinement is to compare retrieved data to gather similar features, by dedicated clustering algorithms [Kaufman, L., Rousseeuw, P.J., 1990. Finding groups in data. An Introduction to Cluster Analysis. John Wiley & Sons, New York; Forman, G., Zhang, B., 2000. Distributed Data clustering can be efficient and exact HP. Laboratories Palo Alto HPL-2000, p. 158], driven by fuzzy logic, allowing us to perceive by intuition a common denominator for various genomic families and to anticipate likely future developments.
An experiment was conducted to determine how orthographic and conceptual information are integrated during incidental and intentional retrieval. Subjects studied word lists with either a shallow (counting vowels) or deep (rating pleasantness) processing task, then received either an implicit or explicit word fragment completion (WFC) test. At test, word fragments contained 0, 1, 2, or 4 letters, and were accompanied by 0, 1, 2, or 3 semantically related words. On both the implicit and explicit tests, performance improved with increases in the numbers of letters and words. When semantic cues were presented with the word fragments, the implicit test became more conceptually drive. Still, conceptual processing had a larger effect in intentional than in incidental retrieval. The Fuzzy Logical Model of Perception (FLMP) provided a good description of how orthographic, semantic, and episodic information were combined during retrieval.
A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.
The anthropogenic activity on the surface can modify and introduce new mechanisms of recharging the groundwater system, modifying the tax, the frequency and the quality of recharge of underground waters. The understanding of these mechanisms and the correct evaluation of such modifications are fundamental in determining the vulnerability of groundwater contamination. The groundwater flow of the South Paraíba Compartment, in the region of São José dos Campos, São Paulo, is directly related to structural features of the Taubaté Basin and, therefore, the analysis of its behaviour enhances the understanding of tectonic structure. The methodology adopted for this work consists in pre-processing and processing of the satellite images, visual interpretation of HSI products, field work and data integration. The derivation of the main structural features was based on visual analysis of the texture elements of drainage, and the relief in sedimentary and crystalline rocks. Statistical analysis of the feature densities and the metric-geometric relations between the analysed elements have been conducted. The crystalline rocks, on which the sediments were laying, conditions and controls the structural arrangement of sedimentary formations. The formation of the South Paraíba Grabén is associated with Cenozoic distensive movement which reactivated old features of crust weakness and generated previous cycles with normal characteristics. The environmental analysis is based on the integration of the existing methodology to characterise vulnerability of an universal pollutant and density fracture zone. The digital integration was processed using GIS (Geographic Information System) to delineate five defined vulnerability classes. The hydrogeological settings were analysed in each thematic map and, using fuzzy logic, an index for each different vulnerability class was compiled. Evidence maps could be combined in a series of steps using map algebra.