Hashim Habiballa

The concept of LFLC 2000: its specificity, realization and power of applications

Linguistic Fuzzy Logic Controller (LFLC) 2000 is a complex tool for the design of linguistic descriptions and fuzzy control based on these descriptions. Unique methodology and theoretical results upon which is LFLC 2000 based are presented. Then, main purposes of it are sketched and some implementation aspects are discussed. Presentation of existing and perspective applications concludes the paper.

Recognition of damaged letters based on mathematical fuzzy logic analysis

This paper reports a real application whose task was to recognize characters printed on metal ingots. The problem is that surface of ingots is very uneven - ingots are hot or cold, cut by rough instrument, the printing machine can be worn down, etc. In this paper, we present two original recognition methods: the first one is based on application of mathematical fuzzy logic and the second one is based on representation of an image by a fuzzy-valued function. Results of these methods are compared with a simple neural network classifier and few other common methods.

Theoretical branches in teaching computer science

The present paper describes an educational experiment dealing with teaching the theory of formal languages and automata as well as their application concepts. It presents a practical application of an educational experiment and initial results based on comparative instruction of two samples of students (n = 56). The application concept should improve the efficiency of the teachers activity and professional education at the tertiary level. It could be used in disciplines having a strong logical structure and formalization. The concept is aimed at higher educational objectives such as understanding, application, analysis and synthesis. The students are presented with practical examples from their professional life—algorithmization, compilers etc. This educational method is especially recommended for teachers (in-service education) and practically oriented professionals (distance education). The results and conclusions are included in the teaching process at the Department of Computer Science University of Ostrava.

Parallelization of Fuzzy Logic Analysis for Pattern Recognition

Fuzzy logic analysis in pattern recognition is a unique method developed on Institute for Research and Applications of Fuzzy Modeling, University of Ostrava. Its efficiency was also proved on an industrial application for automated symbolic recognition of signatures on metal ingots. In this article we briefly recall main notions of Fuzzy Logic Analysis and we especially take into account parallel implementation of the algorithm.

Evolutionary search for automated generation of EQ-algebras

Searching for finite algebras requires enourmous load to human since the state-space is superexponential with respect to its size. We present a promising method for automated generation based on Genetic Algoritms. It enables to generate interesting EQ-algebras without manual selection and evaluation of required and optional properties. We present also software application for this task – EQCreator.

Predator-Prey Simulation's Parameters And Leverage Points.

For many natural systems there is an advantage in their simulating on a computer. In this article, a way how to determine the influence of the predator-prey simulation’s parameters on the model’s behaviour based on the leverage points is examined. An investigation whether it is possible to sort the parameters according to the importance and if it is possible to automate the process to a certain degree is performed.

Automated Design and Optimization of Specific Algebras by Genetic Algorithms

The need for special algebras is the common task for many research in mathematics and theoretical computer science. We present our research concerning automated generation of such algebras through evolutionary techniques. Our reserch concerning the usage genetic algorithms shows this task to be feasible and we demonstrate it on special algebras called EQ-algebras. We also present possible optimization of the process using an expert system.

ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster.

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

Modelling And Reasoning With Fuzzy Logic Redundant Knowledge Bases.

Fuzzy Predicate Logic with evaluated syntax together with resolution principle is presented. The paper focuses mainly on modelling and treating redudancy in knowledge bases. It presents original resolution rule together with algorithm DCF Detection of Consequent Formulas developed especially for fuzzy logic with evaluated syntax. REASONING IN FUZZY PREDICATE LOGIC WITH EVALUATED SYNTAX The problem of effective modelling and reasoning on knowledge bases rises especially when dealing with many-valued logics. We would like to recall previously devised notions and methods of fuzzy resolution principle and then show original efficient methods making such a reasoning tractable. Fuzzy Predicate Logic with Evaluated Syntax (FPL) [Novak, V., Perfilieva, I., Mockoř, J., 1999] is a well-studied and wide-used logic capable of expressing vagueness. It has a lot of applications based on robust theoretical background. It also requires an efficient formal proof theory. However the most widely applied resolution principle [Dukic, N., Avdagic, Z., 2005] brings syntactically several obstacles mainly arising from normal form transformation. There are also recent attempts of similarity based resolution [Mondal, B., Raha, S., 2012], but our approach is based on classical proof theory of FPL. FPL is associating with even harder problems when trying to use the resolution principle. Solutions to these obstacles based on the nonclausal resolution [Bachmair, L., Ganzinger, H., 1997] were already proposed in [Habiballa, H., 2006]. In this article it would be presented a natural integration of these two formal logical systems into fully functioning inference system with effective proof search strategies. It leads to the refutational resolution theorem prover for FPL (RRTPFPL). Another issue addressed in the paper concerns to the efficiency of presented inference strategies developed originally for the proving system. It is showed their perspectives in combination with standard proof-search strategies. The main problem for the fuzzy logic theorem proving lies in the large amount of possible proofs with different degrees and there is presented an algorithm (Detection of Consequent Formulas DCF) solving this problem. The algorithm is based on detection of such redundant formulas (proofs) with different degrees. The article presents the method which is the main point of the work on any automated prover. There is a lot of strategies which make proofs more efficient when we use refutational proving. We consider wellknown strategies orderings, filtration strategy, set of support etc. One of the most effective strategies is the elimination of consequent formulas. It means the check if a resolvent is not a logical consequence of a formula in set of axioms or a previous resolvent. If such a condition holds it is reasonable to not include the resolvent into the set of resolvents, because if the refutation can be deduced from it, then so it can be deduced from the original resolvent, which it implies of. Resolution and Fuzzy Predicate Logic The fuzzy predicate logic with evaluated syntax is a flexible and fully complete formalism, which will be used for the below presented extension [Novak, V., Perfilieva, I., Mockoř, J., 1999]. In order to use an efficient form of the resolution principle we have to extend the standard notion of a proof (provability value and degree) with the notion of refutational proof (refutation degree). Propositonal version of the fuzzy resolution principle has been already presented in [Habiballa, H., 2002]. We suppose that set of truth values is Lukasiewicz algebra. Therefore we assume standard notions of conjunction, disjunction etc. to be bound with Lukasiewicz operators. We will assume Lukasewicz algebra to be L L = 〈[0, 1],∧,∨,⊗,→, 0, 1〉 Proceedings 27th European Conference on Modelling and Simulation ©ECMS Webjorn Rekdalsbakken, Robin T. Bye, Houxiang Zhang (Editors) ISBN: 978-0-9564944-6-7 / ISBN: 978-0-9564944-7-4 (CD) where [0, 1] is the interval of reals between 0 and 1, which are the smallest and greatest elements respectively. Basic and additional operations are defined as

Resolution Principle and Fuzzy Logic

Fuzzy Predicate Logic with Evaluated Syntax (FPL) (Novak, V.) is a well-studied and wide-used logic capable of expressing vagueness. It has a lot of applications based on robust theoretical background. It also requires an efficient formal proof theory. However the most widely applied resolution principle (Dukic, N.) brings syntactically several obstacles mainly arising from normal form transformation. FPL is associating with even harder problems when trying to use the resolution principle. Solutions to these obstacles based on the non-clausal resolution (Bachmair, L.) were already proposed in (Habiballa, H.).