INTRODUCTION
Pattern recognition is one of principle problems in computer science. Many issues such as controlling, making decisions or predictions are related to it. It also has the main position in robotics. Therefore, this branch of computer science has been developing for a long time both in theoretical and implementation aspects. In a lot of cases pattern recognition can be a difficult problem and consequently the only method commonly used to sort out this issue does not exist. Presently, a wide range of methods based on various elements of mathematics, for instance calculus of probability or approximation theory, is applied. However, a universal recognition method does not exists - a given one can be effective for a specific sort of tasks and can fail for others. This is the reason why new methods are created and the existing ones developed. For example, syntactic methods are supported with probabilistic mechanisms and methods, which are combination of different basic methods such as neural-fuzzy ones, are created or hybrid expert systems are built. This paper concerns recognition curves in relation to their structural features. The considered problem is situated in a group of problems where pattern representation is a sequence of primitives being elements of a context language. For this group of languages, admittedly, automata which analyze these languages exist but their complexity is non-polinominal and consequently their usefulness in practical applications is limited. Moreover, an algorithm of grammar inference does not exist, consequently a method of automatic creation of tables controlling parsers (conversion functions in automata) does not exist, which in a practical nontrivial application disqualifies these languages. So, for structural patterns whose representations belong to context languages syntactic methods allowing to analyze them do not exist. Therefore, an application of nonsyntactic methods to structural features analyzing seems to be valuable. The aim of this paper is to propose a new methodology of curves recognition in relation to their structural features taking advantage of fuzzy methods statistically aided. The possibility of a neural implementation of a recognition system based on the proposed methodology is tested. In the second chapter of this paper, the methodology of a decision function construction in an axiomatic recognition of patterns is presented. In the third chapter the proposed methodology is applied to classification curves describing relative changes in the cardiac rhythm between different people with and without a cognitive load, respectively. The curves were obtained in the Department of Psychophysiology of the Jagiellonian University. The experiment is described in details in [14], [15], [27]. The fourth chapter contains the description of a neural network computing the value of membership functions for each class.

INTRODUCTION
Pattern recognition is one of principle problems in computer science. Many issues such as controlling, making decisions or predictions are related to it. It also has the main position in robotics. Therefore, this branch of computer science has been developing for a long time both in theoretical and implementation aspects. In a lot of cases pattern recognition can be a difficult problem and consequently the only method commonly used to sort out this issue does not exist. Presently, a wide range of methods based on various elements of mathematics, for instance calculus of probability or approximation theory, is applied. However, a universal recognition method does not exists - a given one can be effective for a specific sort of tasks and can fail for others. This is the reason why new methods are created and the existing ones developed. For example, syntactic methods are supported with probabilistic mechanisms and methods, which are combination of different basic methods such as neural-fuzzy ones, are created or hybrid expert systems are built. This paper concerns recognition curves in relation to their structural features. The considered problem is situated in a group of problems where pattern representation is a sequence of primitives being elements of a context language. For this group of languages, admittedly, automata which analyze these languages exist but their complexity is non-polinominal and consequently their usefulness in practical applications is limited. Moreover, an algorithm of grammar inference does not exist, consequently a method of automatic creation of tables controlling parsers (conversion functions in automata) does not exist, which in a practical nontrivial application disqualifies these languages. So, for structural patterns whose representations belong to context languages syntactic methods allowing to analyze them do not exist. Therefore, an application of nonsyntactic methods to structural features analyzing seems to be valuable. The aim of this paper is to propose a new methodology of curves recognition in relation to their structural features taking advantage of fuzzy methods statistically aided. The possibility of a neural implementation of a recognition system based on the proposed methodology is tested. In the second chapter of this paper, the methodology of a decision function construction in an axiomatic recognition of patterns is presented. In the third chapter the proposed methodology is applied to classification curves describing relative changes in the cardiac rhythm between different people with and without a cognitive load, respectively. The curves were obtained in the Department of Psychophysiology of the Jagiellonian University. The experiment is described in details in [14], [15], [27]. The fourth chapter contains the description of a neural network computing the value of membership functions for each class.