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PubMed1992

[The use of multivariate mathematical methods in medical diagnostic systems--a model for the evaluation of cytological smears].

The methods of the multivariate mathematics have been applied in several studies to increase the diagnostic reliability of medical decision support system. In the recent years some new algorithms for decision support (fuzzy logic) and for pattern recognition (neural nets), both specified by nonlinearity, were developed. This paper provides results for the application of this methods in the area of quantitative cytology and the comparison with the traditional classifiers. 21 normal, 15 dysplastic, 23 malignant, Feulgen stained gastric imprint smears were analysed on a Leitz Miamed DNA equipment. The determination of mean DNA content, the 2c deviation index (2cDI), 5c Exceeding rate (RcER), G1,S,G2 phase fraction ratios, cell nucleus area, form factor was performed. The discriminant analysis classified correctly the 95.6% of malignant cases, 86.7% of dysplasias, and 80.7% normal cases. Our diagnostic system using fuzzy logic made the diagnostic borders fine tuneable, and reliable. The back propagation neural net could classify all three diagnostic groups above 95% correctly. The application of nonlinear computational methods made the diagnostic system more reliable. The application of these algorithms are encouraged.

Algorithms↗
PubMed2002

Development of a constant surface pressure penetration langmuir balance based on axisymmetric drop shape analysis.

A new constant pressure pendant-drop penetration surface balance has been developed combining a pendant-drop surface balance, a rapid-subphase-exchange technique, and a fuzzy logic control algorithm. Beside the determination of insoluble monolayer compression-expansion isotherms, it allows performance of noninvasive kinetic studies of the adsorption of surfactants added to the new subphase onto the free surface and of the adsorption/penetration/reaction of the former onto/into/with surface layers, respectively. The interfacial pressure pi is a fundamental parameter in these studies: by working at constant pi one controls the height of the energy barrier to adsorption/penetration and can select different regimes and steps of the adsorption/penetration process. In our device a solution drop is formed at the tip of a coaxial double capillary, connected to a double microinjector. Drop profiles are extracted from digital drop micrographs and fitted to the equation of capillarity, yielding pi, the drop volume V, and the interfacial area A. pi is varied changing V (and hence A) with the microinjector. Control is based on a case-adaptable modulated fuzzy-logic PID algorithm able to maintain constant pi (or A) under a wide range of experimental conditions. The drop subphase liquid can be exchanged quantitatively by the coaxial capillaries. The adsorption/penetration/reaction kinetics at constant pi are then studied monitoring A(t), i.e., determining the relative area change necessary at each instant to compensate the pressure variation due to the interaction of the surfactant in the subsurface with the surface layer. A fully Windows-integrated program manages the whole setup. Examples of experimental protein adsorption and monolayer penetration kinetics are presented.

Journal Article↗
PubMed2006

OR/AND neurons and the development of interpretable logic models.

In this paper, we are concerned with the concept of fuzzy logic networks and logic-based data analysis realized within this framework. The networks under discussion are homogeneous architectures comprising of OR/AND neurons originally introduced by Hirota and Pedrycz. Being treated here as generic processing units, OR/AND neurons are neurofuzzy constructs that exhibit well-defined logic characteristics and are endowed with a high level of parametric flexibility and come with significant interpretation abilities. The composite logic nature of the logic neurons becomes instrumental in covering a broad spectrum of logic dependencies whose character spread in-between between those being captured by plain and and or logic descriptors (connectives). From the functional standpoint, the developed network realizes a logic approximation of multidimensional mappings between unit hypercubes, that is transformations from [0, 1]n to [0, 1]m. The way in which the structure of the network has been formed is highly modular and becomes reflective of a general concept of decomposition of logic expressions and Boolean functions (as being commonly encountered in two-valued logic). In essence, given a collection of input variables, selected is their subset and transformed into new composite variable, which in turn is used in the consecutive module of the network. These intermediate synthetic variables are the result of the successive problem (mapping) decomposition. The development of the network is realized through genetic optimization. This helps address important issues of structural optimization (where we are concerned with a selection of a subset of variables and their allocation within the network) and reaching a global minimum when carrying out an extensive parametric optimization (adjustments of the connections of the neurons). The paper offers a comprehensive and user-interactive design procedure including a simple pruning mechanism whose intention is to enhance the interpretability of the network while reducing its size. The experimental studies comprise of three parts. First, we demonstrate the performance of the network on Boolean data (that leads to some useful comparative observations considering a wealth of optimization tools available in two-valued logic and digital systems). Second, we discuss synthetic multivalued data that helps focus on the approximation abilities of the network. Finally, show the generation of logic expressions describing selected data sets coming from the machine learning repository.

Algorithms↗
PubMed1997

Fuzzy sets and fuzzy decision making in nutrition.

OBJECTIVE: This paper demonstrates that a nutrient intake can be described in a differentiated way and can be evaluated by employing fuzzy decision making. It also examines whether fuzzy decision making can simplify nutrition education by small individual improvements in food selection behaviour. RESULTS: The recommendations for nutrient intakes are presented as fuzzy sets, so that the intake of each nutrient can be evaluated by an objective fuzzy value. The evaluation of the harmonic minimum allows, for the first time, that the fuzzy value of an individual nutrient can be stated as a total value. On the basis of individual nutrition assessment, fuzzy logic in connection with fuzzy decision making, allows optimization of meals considering individual food preferences. This makes it possible in nutrition counselling to improve the nutrient intake markedly with relative small changes in food choice. CONCLUSION: Fuzzy decision making can simplify and optimize nutrition education.

Adult↗
PubMed2004

Overview of commonly used bioinformatics methods and their applications.

Bioinformatics, in its broad sense, involves application of computer processes to solve biological problems. A wide range of computational tools are needed to effectively and efficiently process large amounts of data being generated as a result of recent technological innovations in biology and medicine. A number of computational tools have been developed or adapted to deal with the experimental riches of complex and multivariate data and transition from data collection to information or knowledge. These include a wide variety of clustering and classification algorithms, including self-organized maps (SOM), artificial neural networks (ANN), support vector machines (SVM), fuzzy logic, and even hyphenated techniques as neuro-fuzzy networks. These bioinformatics tools are being evaluated and applied in various medical areas including early detection, risk assessment, classification, and prognosis of cancer. The goal of these efforts is to develop and identify bioinformatics methods with optimal sensitivity, specificity, and predictive capabilities.

Computational Biology↗
PubMed2004

Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN).

Type-2 fuzzy logic system (FLS) cascaded with neural network, type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of a type-2 fuzzy linguistic process as the antecedent part, and the two-layer interval neural network as the consequent part. A general T2FNN is computational-intensive due to the complexity of type 2 to type 1 reduction. Therefore, the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process (back propagation). It can also be shown both learning rates cannot be both negative. Further, due to variation of the initial MF parameters, i.e., the spread level of uncertain means or deviations of interval Gaussian MFs, the performance of back propagation training process may be affected. To achieve better total performance, a genetic algorithm (GA) is designed to search optimal spread rate for uncertain means and optimal learning for the antecedent part. Several examples are fully illustrated. Excellent results are obtained for the truck backing-up control and the identification of nonlinear system, which yield more improved performance than those using type-1 FNN.

Algorithms↗
PubMed1999

Growing of a Fuzzy Recurrent Artificial Neural Network (FRANN) for pattern classification.

This paper describes a method for growing a recurrent neural network of fuzzy threshold units for the classification of feature vectors. Fuzzy networks seem natural for performing classification, since classification is concerned with set membership and objects generally belonging to sets of various degrees. A fuzzy unit in the architecture proposed here determines the degree to which the input vector lies in the fuzzy set associated with the fuzzy unit. This is in contrast to perceptrons that determine the correlation between input vector and a weighting vector. The resulting membership value, in the case of the fuzzy unit, is compared with a threshold, which is interpreted as a membership value. Training of a fuzzy unit is based on an algorithm for linear inequalities similar to Ho-Kashyap recording. These fuzzy threshold units are fully connected in a recurrent network. The network grows as it is trained. The advantages of the network and its training method are: (1) Allowing the network to grow to the required size which is generally much smaller than the size of the network which would be obtained otherwise, implying better generalization, smaller storage requirements and fewer calculations during classification; (2) The training time is extremely short; (3) Recurrent networks such as this one are generally readily implemented in hardware; (4) Classification accuracy obtained on several standard data sets is better than that obtained by the majority of other standard methods; and (5) The use of fuzzy logic is very intuitive since class membership is generally fuzzy.

Algorithms↗
PubMed2005

Aggregation of fuzzy views of a large number of stakeholders for multi-objective flood management decision-making.

Flood management decision-making problems are often associated with multiple objectives and multiple stakeholders. The challenge remains on how to obtain the diversified opinions of a large number of stakeholders where uncertainty plays a major role. In response to this challenge, a methodology has been proposed to capture the views of multiple stakeholders using fuzzy set theory and fuzzy logic. Three possible forms of individual stakeholders' input, scale (crisp), linguistic (fuzzy), and conditional (fuzzy), are analyzed to obtain the aggregated input by using Fuzzy Expected Value. The methodology is applied to flood management in the Red River Basin, Manitoba, Canada. The outputs derived from the three different input types show good correlation. Thus, the proposed methodology shows alternative ways of soliciting the opinion of a large number of stakeholders and aggregating those opinions for use in multi-objective decision making.

Community Participation↗
PubMed2004

The study of control methods for the robotic testing system for human musculoskeletal joints.

Biomechanical testing of human musculoskeletal joints not only requires qualified testing machines and devices, but also needs an excellent control method to obtain better experimental results. In this paper, we take the human functional spinal unit (FSU) as an example to study how to improve the performance of the robotic testing system. First, the mechanical characteristics of the FSU are described and the simplified model (a rigid body-spring system) for the specimen is given. Because the location of the center of rotation (COR) of the specimen affects the performance of the system, so a comprehensive analysis on the location of the COR is carried out. Furthermore, the performance of the robotic testing system can be also improved through the improvement of the control methods. Two control methods have been proposed, one is the improved hybrid control and the other is fuzzy logic control (FLC).

Fuzzy Logic↗
PubMed2006

A sound and complete fuzzy temporal constraint logic.

In this work, we define an extended fuzzy temporal constraint logic (EFTCL) based on possibilistic logic. EFTCL allows us to handle fuzzy temporal constraints between temporal variables and, therefore, enables us to express interrelated events through fuzzy temporal constraints. EFTCL is compatible with a theoretical temporal reasoning model: the fuzzy temporal constraint networks (FTCN). The syntax, the semantics and the deduction and refutation theorems for EFTCL are similar to those defined for the sound and noncomplete fuzzy temporal constraint logic (FTCL). In this paper, a resolution principle for performing inferences which take these constraints into account is proposed for EFTCL. Moreover, we prove the soundness and the completeness of the refutation by resolution in EFTCL.

Algorithms↗
PubMed2005

Fuzzy control of nitrogen removal in predenitrification process using ORP.

In order to meet increasingly stringent discharge standards, new applications and control strategies for the sustainable removal of nitrogen from wastewater have to be implemented. In the past years, numerous studies have been carried out dealing with the application of fuzzy logic to improve the control of the activated sludge process. In this paper, fuzzy control strategies of predenitrification systems are presented that could lead to better effluent quality and, in parallel, to a reduction of chemicals consumption. Extensive experimental investigations on lab scale plant studies have shown that there was excellent correlation between nitrate concentration and ORP value at the end of the anoxic zone. Results indicated that ORP could be used as an on-line fuzzy control parameter of nitrate recirculation and external carbon addition. The optimal value of ORP to control nitrate recirculation and external carbon addition was - 86 +/- 2 mV and - 90 +/- 2 mV, respectively. The results obtained with real wastewater also showed the good performance and stability of the fuzzy controllers independently from external disturbances. The integrated control structure of nitrate recirculation and external carbon addition in the predenitrification system is also presented.

Bioreactors↗
PubMed2006

Neuro-fuzzy system for prostate cancer diagnosis.

OBJECTIVES: To develop a neuro-fuzzy system to predict the presence of prostate cancer. Neuro-fuzzy systems harness the power of two paradigms: fuzzy logic and artificial neural networks. We compared the predictive accuracy of our neuro-fuzzy system with that obtained by total prostate-specific antigen (tPSA) and percent free PSA (%fPSA). METHODS: The data from 1030 men (both outpatients and hospitalized patients) were used. All men had a tPSA level of less than 20 ng/mL. Of the 1030 men, 195 (18.9%) had prostate cancer. A neuro-fuzzy system was developed using the coactive neuro-fuzzy inference system model. RESULTS: The mean area under the receiver operating characteristic curve for the neuro-fuzzy system output was 0.799 +/- 0.029 (95% confidence interval 0.760 to 0.835), for tPSA, it was 0.724 +/- 0.032 (95% confidence interval 0.681 to 0.765), and for %fPSA, 0.766 +/- 0.024 (95% confidence interval 0.725 to 0.804). Furthermore, pairwise comparison of the area under the curves evidenced differences among %fPSA, tPSA, and neuro-fuzzy system's output (tPSA versus neuro-fuzzy system's output, P = 0.008; %fPSA versus neuro-fuzzy system's output, P = 0.032). The comparison at 95% sensitivity showed that the neuro-fuzzy system had the best specificity (31.9%). CONCLUSIONS: This study presented a neuro-fuzzy system based on both serum data (tPSA and %fPSA) and clinical data (age) to enhance the performance of tPSA to discriminate prostate cancer. The predictive accuracy of the neuro-fuzzy system was superior to that of tPSA and %fPSA.

Aged↗
PubMed2006

FCMAC-Yager: a novel Yager-inference-scheme-based fuzzy CMAC.

The cerebellum is a brain region important for a number of motor and cognitive functions. It is able to generate error correction signals to drive learning and for the acquisition of memory skills. The cerebellar model articulation controller (CMAC) is a neural network inspired by the neurophysiologic theory of the cerebellum and is recognized for its localized generalization and rapid algorithmic computation capabilities. The main deficiencies in the basic CMAC structure are: (1) it is difficult to interpret the internal operations of the CMAC network and (2) the resolution (quantization) problem arising from the partitioning of the input training space. These limitations lead to the synthesis of a fuzzy quantization technique and the mapping of a fuzzy inference scheme onto the CMAC structure. The discrete incremental clustering (DIC) technique is employed to alleviate the quantization problem in the CMAC structure, resulting in the fuzzy CMAC (FCMAC) network. The Yager inference scheme (Yager), which possesses firm fuzzy logic foundation and maps closely to the logical implication operations in the classical (binary) logic framework, is subsequently mapped onto the FCMAC structure. This results in a novel fuzzy neural architecture known as thefuzzy cerebellar model articulation controller-Yager (FCMAC-Yager) system. The proposed FCMAC-Yager network exhibits learning and memory capabilities of the cerebellum through the CMAC structure while emulating the human way of reasoning through the Yager. The new FCMAC-Yager network employs a two-phase training algorithm consisting of structural learning based on the DIC technique and parameter learning using hebbian learning (associative long-term potentiation). The proposed FCMAC-Yager architecture is evaluated using an extensive suite of real-life applications such as highway traffic-trend modeling and prediction and performing as an early warning system for bank failure classification and medical diagnosis of breast cancer. The experimental results are encouraging.

Algorithms↗
PubMed2006

Orthodontic treatment planning software.

A new software is described that can receive patient data in both graphic and numeric forms and then propose a treatment plan for nonsurgical orthodontic patients. The concepts of fuzzy logic enable the software to work with nominal parameters; the human brain is naturally accustomed to fuzzy variables. The computer program can propose treatment for some special cases, such as incomplete dentition.

Computer Simulation↗
PubMed2004

Fuzzy utility and equilibria.

A decision maker is frequently confronted with fuzzy constraints, fuzzy utility maximization, and fuzziness about the state of competitors. In this paper we present a framework for fuzzy decision-making, using techniques from fuzzy logic, game theory, and micro-economics. In the first part, we study the rationality of fuzzy choice. We introduce fuzzy constraints, and show that this can easily be combined with maximizing a fuzzy utility. The second part of the paper analyzes games with uncertainty about the state of the competitors. We implement fuzzy Cournot adjustment, define equilibria, and study their stability. Finally, we show how a play progresses where the players have uncertainty about the state of the other players, and about their utility. For a likely procedure of utility maximization, the equilibria are the same as for the game without utility maximization.

Algorithms↗
PubMedDate not supplied

Qualitative models and fuzzy systems: an integrated approach for learning from data.

This paper presents a method for the identification of the dynamics of non-linear systems by learning from data. The key idea which underlies our approach consists of the integration of qualitative modeling techniques with fuzzy logic systems. The resulting hybrid method exploits the a priori structural knowledge on the system to initialize a fuzzy inference procedure which determines, from the available experimental data, a functional approximation of the system dynamics that can be used as a reasonable predictor of the patient's future state. The major advantage which results from such an integrated framework lies in a significant improvement of both efficiency and robustness of identification methods based on fuzzy models which learn an input output relation from data. As a benchmark of our method, we have considered the problem of identifying the response to the insulin therapy from insulin-dependent diabetic patients: the results obtained are presented and discussed in the paper.

Algorithms↗
PubMed2002

Optimizing high strength acetic acid bioprocess by cognitive methods in an unsteady state cultivation.

Methods of adapting micro-organisms to an inhibiting factor in an active industrial bioprocess were examined with an acetic acid fermentation as model. With the aim of automatic control, a fuzzy-logic system was developed on the basis of the collected knowledge of skilled vinegar brewers. In a first step, this fuzzy system was to assess the actual adaptation degree of the bacteria on the basis of data from robust and reasonably priced sensors. From this information an appropriate setpoint value for the inhibiting factor 'final acid concentration' was derived for each batch cycle. As a result a further acid tolerance was found after several batch cycles. This adaptation effect should be used to increase the product concentration to more than 20 g per 100 ml acetic acid with a high productivity. The stepwise adapted culture was productive over the aimed acetic acid concentration, a 10% improvement of the product formation rate could be found compared with the status before conditioning. High product concentration and increased productivity finally result in shorter cycle times, less transport and storage volumes, an improved utilization of energy and material resources, and, last but not least, they are an essential steps towards the fulfillment of economical and ecological demands.

Acetic Acid↗
PubMed2000

Hybrid systems for virtual screening: interest of fuzzy clustering applied to olfaction.

Kohonen neural networks, also known as Self Organizing Map (SOM), offer a useful 2D representation of the compound distribution inside a large chemical database. This distribution results from the compound organization in a molecular diversity hyperspace derived from a large set of molecular descriptors. Fuzzy techniques based on the "concept of partial truth" reveal to be also a valuable tool for the direct exploitation of chemical databases or SOM. In such cases a fuzzy clustering algorithm is used. In this paper, a complete hybrid system, combining SOM and fuzzy clustering, is applied. As example, a series of olfactory compounds was selected. The complexity of such information is that a same compound may exhibit different odors. It is shown how fuzzy logic helps to have a better understanding of the organization of the compounds. These hybrid systems, using simultaneously SOM and fuzzy clustering, are foreseen as powerful tools for "virtual pre-screening".

Fuzzy Logic↗