The ŁII and ŁII1/2 logics were introduced by Godo, Esteva and Montagna in [4] and further developed in my work [2]. These types of logic unite many other known propositional and predicate logics, including the three mainly investigated ones (Godel, Product and Łukasiewicz logic).
The aim of this paper is to show a tight connection between the ŁII logic and the product involutive logic. This logic was introduced by Esteva, Godo, Hájek and Navara in their paper [3].
We will see that all the connectives of the ŁII logic are definable from the connectives of this logic. In addition we show that the ŁII logic is an schernatic extension of this logic by a single axiom. We also make some simplification of the axiomatic system of this logic.
Patients suffering from Parkinson's disease must periodically undergo a series of tests, usually performed at medical facilities, to diagnose the current state of the disease. Parkinson's disease progression assessment is an important set of procedures that supports the clinical diagnosis. A common part of the diagnostic train is analysis of speech signal to identify the disease-specific communication issues. This contribution describes two types of computational models that map speech signal measurements to clinical outputs. Speech signal samples were acquired through measurements from patients suffering from Parkinson's disease. In addition to direct mapping, the developed systems must be able of generalization so that correct clinical scale values can be predicted from future, previously unseen speech signals. Computational methods considered in this paper are artificial neural networks, particularly feedforward networks with several variants of backpropagation learning algorithm, and adaptive network-based fuzzy inference system (ANFIS). In order to speed up the learning process, some of the algorithms were parallelized. Resulting diagnostic system could be implemented in an embedded form to support individual assessment of Parkinson's disease progression from patients' homes.
This paper deals with the changeover from the decision tree (bivalent logic) approach to the fuzzy logic approach to highway traffic control, particularly to variable speed limit displays. The usage of existing knowledge from decision tree control is one of the most suitable methods for identification of the new fuzzy model. However, such method introduces several difficulties. These difficulties are described and possible measures are proposed. Several fuzzy logic algorithms were developed and tested by a~microsimulation model. The results are presented and the finest algorithm is recommended for testing on the Prague City Ring Road in real conditions. This paper provides a~guidance for researchers and practitioners dealing with similar problem formulation.
Combining classifiers, so-called Multiple Classifier Systems (MCSs), gained a lot of interest has recent years. Researchers, developed a large variety of methods in order to exploit strengths of individual classifiers. In this paper, we address the problem of how to implement a multi-class classifier by an ensemble of one-class classifiers. To improve performance of a compound classifier, different individual classifiers (which may, e.g., differ in complexity, type, training algorithm or other) can be combined and that could increase its both performance, and robustness. The model of one-class classifiers can only recognize one of the classes, therefore, it is quite difficult to produce MCSs on the basis of one-class classifiers. Thus, we introduce a new scheme for decision-making in MCSs through a fuzzy inference system. Specifically, we address two important open problems in the context: model selection and combiner training. Classifiers' outputs as supports for given classes are combined by means of a fuzzy engine. Thus, we are interested in such individual classifiers which can return support for given classes. There are no other restrictions on the used classifiers. The proposed model has been evaluated by computer experiments on several benchmark datasets in the Matlab environment. Their results prove that fuzzy combination of binary classifiers may be a valuable classifier itself. Additionally, there are indicated both some application areas of the models, and new research frontiers to be examined.
In this paper, we extend the fuzzy type tlieory (FTT) by the description operator whose interpretation is a function which assigns a fuzzy set from Ma and element from Ma and is thus similar to the defuzzification operation introduced in the fuzzy set theory. The full fuzzy type theory is obtained when extending the FTT by the description operator together with a proper axiom. Some basic properties of the description operator have been proved as well as the cornpleteness of the full FTT.
The molten reactor core-concrete interaction, which describes the effect of molten reactor spread on the concrete oor of the reactor cavity, is a very complex process to simulate and predict, but the knowledge of this process is of major importance for planning the emergency counteractions for severe accidents with respect to the Stress Tests requirements after the Fukushima-Daiichi accident. The key issue is to predict the rate and most probable focusation of the melt-through process which is affected by the concrete composition, especially by the aggregate type. A limited number of small-scale experiments have been conducted over the past years along with accompanying numerical models which focused mainly on the siliceous type of aggregate. It is common for the concrete structures that the limestone type or the mixture of these two types of aggregate are used as well. Then, the objective of this paper is to extend the knowledge gained from the experiments with the siliceous aggregate to the concrete structures which are made of limestone aggregate or their combination, such as limestone sand and siliceous gravel. The proposed one-dimensional model of the melt-through process is based on the fuzzy-logic interpretation of the thermodynamic trends which reflect the aggregate type. This approach allows estimating the asymptotic cases in terms of the melt-through depth in the concrete oor over time with respect to the aggregate type, which may help to decide the rather expensive further experimental efforts.
This paper deals with many valued case of modus ponens. Cases with implicative and with clausal rules are studied. Many valued modus ponens via discrete connectives is studied with implicative rules as well as with clausal rules. Some properties of discrete modus ponens operator are given.
This article introduces a floppy logic – a new method of work with fuzzy sets. This theory is a nice connection between the logic, the probability theory and the fuzzy sets. The floppy logic has several advantages compared to the fuzzy logic: All propositions, which are equivalent in the bivalent logic, are equivalent in the floppy logic too. Logical operations are modeled unambiguously, not by using many alternative t-norms and t-conorms. In floppy logic, we can use the whole apparatus of Kolmogorov’s probability theory. This theory allows to work consistently with systems that are described by fuzzy sets, probability distributions and accurate values simultaneously.
This article provides a simple and practical tutorial on how to use floppy logic. The floppy logic is a method suitable for systems control and description. It preserves the simplicity of the fuzzy logic and the accuracy of the probability theory. The floppy logic allows to work consistently and simultaneously with data in the form of exact numbers, probability distributions and fuzzy sets.
One of the most difficult tasks in field of the operative hydrology is the prediction of occurrence and course of the flash floods. It is difficult to predict torrential rainfalls because their character (great intensity, short duration, small affected area). Usage of the nowcasting methods (weather forecast with two hour validity) holds hope. The torrential rainfall prediction should be followed by suitable hydrological model able to estimate at least the resultant peak outflow. The hydrological models construction interferes with high measure of uncertainty, inherent in the rainfall prediction, rainfall-runoff process and its simulation. The way how to eliminate influence of uncertainty is to use the fuzzy logic and other artificial intelligence methods. The fuzzy model was compiled through the Fuzzy Logic Toolbox in the developmental environment of MATLAB. A model was calibrated with the help of genetic algorithm, neural networks and different optimization methods. and V příspěvku jsou prezentovány výsledky experimentálního výzkumu pohybu rotující kulovité částice ve vodě. Kulovitá částice vyrobená z gumy o hustotě blízké hustotě vody byla uvedena do pohybu v šikmé štěrbině, kde získala rotační i translační rychlost v osové rovině štěrbiny. Trajektorie částic ve vodě byly snímány standardní video kamerou a byl vyhodnocen vliv dvou bezrozměrných parametrů (Reynoldsova čísla a rotačního Reynoldsova čísla) na pohyb částice. Z experimentálních údajů byly určeny hodnoty odporového koeficientu a odporového momentu částice a tyto hodnoty byly porovnány s výsledky numerické simulace pohybu částice. Byly vyhodnoceny vztahy vhodné pro využití při numerickém modelování a popisující vzájemné závislosti výše uvedených veličin a vzájemný vliv translačního a rotačního pohybu částice.