Zbigniew Suraj | University of Rzeszów (original) (raw)
Papers by Zbigniew Suraj
Lecture notes in networks and systems, 2024
Lecture notes in computer science, 2024
Fundamenta Informaticae, 2013
In the paper, a computer tool called ROSECON, used for modeling and analyzing systems of concurre... more In the paper, a computer tool called ROSECON, used for modeling and analyzing systems of concurrent processes, is described. A special attention is focused on synthesis and verification of concurrent systems specified by information systems. Two kinds of models, synchronous and asynchronous, are considered. In the first approach, all processes included in the modeled system are synchronized globally whereas in the second one, each process is synchronized individually. The presented tool allows generating automatically an appropriate model of a system of concurrent processes, in the form of colored Petri nets, from the specification given by an information system. Analysis of the model behaviors enables users to verify the correctness and/or optimality of the obtained models and to provide some modification procedures to get correct and/or more optimal solutions. Examples of selected well known problems in concurrency, in the paper, emphasize usefulness of the tool in the designing systems of concurrent processes.
The paper presents a new methodology for knowledge representation and reasoning based on paramete... more The paper presents a new methodology for knowledge representation and reasoning based on parameterised fuzzy Petri nets. Recently, this net model has been proposed as a natural extension of generalised fuzzy Petri nets. The new class extends the generalised fuzzy Petri nets by introducing two parameterised families of sums and products, which are supposed to provide the suitable t-norms and s-norms. The nature of the fuzzy reasoning realised by a given net model changes variously depending on tand/or s-norms to be used. However, it is very difficult to select a suitable tand/or s-norm function for actual applications. Therefore, we proposed to use in the net model parameterised families of sums and products, which nature change variously depending on the values of the parameters. Taking into account this aspect, we can say that the parameterised fuzzy Petri nets are more flexible than the classical fuzzy Petri nets, because they allow to define the parameterised input/output operators. Moreover, the choice of suitable operators for a given reasoning process and the speed of reasoning process are very important, especially in real-time decision support systems. Some advantages of the proposed methodology are shown in its application in train traffic control decision support.
Abstract Rough set theory is a new soft computing tool which deals with vagueness and uncertainty... more Abstract Rough set theory is a new soft computing tool which deals with vagueness and uncertainty. It has attracted the attention of researchers and practitioners worldwide, and has been successfully applied to many fields such as knowledge discovery, decision support, pattern recognition, and machine learning. Rough Computing: Theories, Technologies and Applications offers the most comprehensive coverage of key rough computing research, surveying a full range of topics from granular computing to pansystems theory. With its ...
IGI Global eBooks, May 24, 2011
Data mining and knowledge discovery are crucial and current research problems in the modern compu... more Data mining and knowledge discovery are crucial and current research problems in the modern computer sciences (Cios, Pedrycz, & Swiniarski, 1998). Discovering hidden relationships in data is a main goal of machine learning. In a lot of cases, data are generated by concurrent processes. Therefore, discovering concurrent system models is essential from the point of view of understanding the
2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021
In this paper, we present an approach to construct a concurrent algorithm that supports real-time... more In this paper, we present an approach to construct a concurrent algorithm that supports real-time decision making based on the knowledge extracted from empirical data. The data is represented by a decision table in the Pawlak sense, while the concurrent algorithm is represented as a weighted priority fuzzy Petri net. This idea overcomes the difficulties that arise when field experts are entrusted with determining the values of net parameters. In the proposed approach, we assume that the decision tables contain conditional attribute values that are obtained from measurements made by sensors in real time. The Petri net built within the presented conception allows for the fastest possible identification of objects in decision tables in order to make the right decision. The sensor output values are transmitted over the net at the maximum possible speed. We achieve this effect thanks to the appropriate implementation of all true and acceptable rules generated from a given decision table.
Springer eBooks, Aug 13, 2012
This paper introduces a measure defined in the context of rough sets. Rough set theory provides a... more This paper introduces a measure defined in the context of rough sets. Rough set theory provides a variety of set functions that can be studied relative to various measure spaces. In particular, the rough membership function is considered. The particular rough membership function given in this paper is a non-negative set function that is additive. It is an example of a rough measure. The idea of a rough integral is revisited in the context of the discrete Choquet integral that is defined relative to a rough measure. This rough integral computes a form of ordered, weighted ”average” of the values of a measurable function. Rough integrals are useful in culling from a collection of active sensors those sensors with the greatest relevance in a problem-solving effort such as classification of a ”perceived” phenomenon in the environment of an agent.
Summary. This chapter presents Petri net models for two forms of rough neural comput ing: traini... more Summary. This chapter presents Petri net models for two forms of rough neural comput ing: training set production and approximate reasoning schemes (AR schemes) defined in the context of parameterized approximation spaces. The focus of the first form of rough-neural computing is inductive learning and the production of training (optimal feature set selection), using knowledge reduction algorithms. This first form of neural computing can be important in designing neural networks defined in the context of parameterized approximation spaces. A high-level Petri net model of a neural network classifier with an optimal feature selection procedure in its front end is given. This model is followed by the development of a number of Petri net models of what are known as elementary approximation neurons (EA neurons). The design of an EA neuron includes an uncertainty function that constructs a granule appro ximation and a rough inclusion (threshold activation) function that measures the degree to which granule approximation is part of a target granule. The output of an EA neuron is an elementary granule. There are many forms of elementary granules (e.g., conjunction of de scriptors, rough inclusion function value). Each of the EA neurons considered in this chapter output a rough inclusion function value. An EA neuron can be designed so that it is trainable, that is, a feedback loop can be included in the design of the EA neuron so that one or more approximation space parameters can be tuned to improve the performance of the neuron. The design of three sample EA neurons is given. One of these neurons behaves like a high-pass filter.
Springer eBooks, Nov 3, 2011
... Wei-Zhi Wu, Yu-Fang Yang, and You-Hong Xu Neighborhood Rough Sets Based Matrix Approach for C... more ... Wei-Zhi Wu, Yu-Fang Yang, and You-Hong Xu Neighborhood Rough Sets Based Matrix Approach for Calculation of the Approximations ... Tonny Rutayisire, Yan Yang, Chao Lin, and Jinyuan Zhang A NIS-Apriori Based Rule Generator in Prolog and Its Functionality for Table Data ...
Springer eBooks, Sep 10, 2008
Fundamenta Informaticae, Feb 1, 2005
CS&P, 2015
In [14], we have presented a fuzzy forward reasoning methodology for rule-based systems using the... more In [14], we have presented a fuzzy forward reasoning methodology for rule-based systems using the functional representation of rules (fuzzy implications). In this paper, we extend methodology for selecting relevant fuzzy implications from [14] in backward reasoning. The proposed methodology takes full advantage of the functional representation of fuzzy implications and the algebraic properties of the family of all fuzzy implications. It allows to compare two fuzzy implications. If the truth value of the conclusion and the truth value of the implication are given, we can easily optimize the truth value of the implication premise. This methodology can be useful for the design of an inference engine based on the rule knowledge for a given rule-based system.
Springer eBooks, Aug 15, 2012
Lecture notes in networks and systems, 2024
Lecture notes in computer science, 2024
Fundamenta Informaticae, 2013
In the paper, a computer tool called ROSECON, used for modeling and analyzing systems of concurre... more In the paper, a computer tool called ROSECON, used for modeling and analyzing systems of concurrent processes, is described. A special attention is focused on synthesis and verification of concurrent systems specified by information systems. Two kinds of models, synchronous and asynchronous, are considered. In the first approach, all processes included in the modeled system are synchronized globally whereas in the second one, each process is synchronized individually. The presented tool allows generating automatically an appropriate model of a system of concurrent processes, in the form of colored Petri nets, from the specification given by an information system. Analysis of the model behaviors enables users to verify the correctness and/or optimality of the obtained models and to provide some modification procedures to get correct and/or more optimal solutions. Examples of selected well known problems in concurrency, in the paper, emphasize usefulness of the tool in the designing systems of concurrent processes.
The paper presents a new methodology for knowledge representation and reasoning based on paramete... more The paper presents a new methodology for knowledge representation and reasoning based on parameterised fuzzy Petri nets. Recently, this net model has been proposed as a natural extension of generalised fuzzy Petri nets. The new class extends the generalised fuzzy Petri nets by introducing two parameterised families of sums and products, which are supposed to provide the suitable t-norms and s-norms. The nature of the fuzzy reasoning realised by a given net model changes variously depending on tand/or s-norms to be used. However, it is very difficult to select a suitable tand/or s-norm function for actual applications. Therefore, we proposed to use in the net model parameterised families of sums and products, which nature change variously depending on the values of the parameters. Taking into account this aspect, we can say that the parameterised fuzzy Petri nets are more flexible than the classical fuzzy Petri nets, because they allow to define the parameterised input/output operators. Moreover, the choice of suitable operators for a given reasoning process and the speed of reasoning process are very important, especially in real-time decision support systems. Some advantages of the proposed methodology are shown in its application in train traffic control decision support.
Abstract Rough set theory is a new soft computing tool which deals with vagueness and uncertainty... more Abstract Rough set theory is a new soft computing tool which deals with vagueness and uncertainty. It has attracted the attention of researchers and practitioners worldwide, and has been successfully applied to many fields such as knowledge discovery, decision support, pattern recognition, and machine learning. Rough Computing: Theories, Technologies and Applications offers the most comprehensive coverage of key rough computing research, surveying a full range of topics from granular computing to pansystems theory. With its ...
IGI Global eBooks, May 24, 2011
Data mining and knowledge discovery are crucial and current research problems in the modern compu... more Data mining and knowledge discovery are crucial and current research problems in the modern computer sciences (Cios, Pedrycz, & Swiniarski, 1998). Discovering hidden relationships in data is a main goal of machine learning. In a lot of cases, data are generated by concurrent processes. Therefore, discovering concurrent system models is essential from the point of view of understanding the
2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021
In this paper, we present an approach to construct a concurrent algorithm that supports real-time... more In this paper, we present an approach to construct a concurrent algorithm that supports real-time decision making based on the knowledge extracted from empirical data. The data is represented by a decision table in the Pawlak sense, while the concurrent algorithm is represented as a weighted priority fuzzy Petri net. This idea overcomes the difficulties that arise when field experts are entrusted with determining the values of net parameters. In the proposed approach, we assume that the decision tables contain conditional attribute values that are obtained from measurements made by sensors in real time. The Petri net built within the presented conception allows for the fastest possible identification of objects in decision tables in order to make the right decision. The sensor output values are transmitted over the net at the maximum possible speed. We achieve this effect thanks to the appropriate implementation of all true and acceptable rules generated from a given decision table.
Springer eBooks, Aug 13, 2012
This paper introduces a measure defined in the context of rough sets. Rough set theory provides a... more This paper introduces a measure defined in the context of rough sets. Rough set theory provides a variety of set functions that can be studied relative to various measure spaces. In particular, the rough membership function is considered. The particular rough membership function given in this paper is a non-negative set function that is additive. It is an example of a rough measure. The idea of a rough integral is revisited in the context of the discrete Choquet integral that is defined relative to a rough measure. This rough integral computes a form of ordered, weighted ”average” of the values of a measurable function. Rough integrals are useful in culling from a collection of active sensors those sensors with the greatest relevance in a problem-solving effort such as classification of a ”perceived” phenomenon in the environment of an agent.
Summary. This chapter presents Petri net models for two forms of rough neural comput ing: traini... more Summary. This chapter presents Petri net models for two forms of rough neural comput ing: training set production and approximate reasoning schemes (AR schemes) defined in the context of parameterized approximation spaces. The focus of the first form of rough-neural computing is inductive learning and the production of training (optimal feature set selection), using knowledge reduction algorithms. This first form of neural computing can be important in designing neural networks defined in the context of parameterized approximation spaces. A high-level Petri net model of a neural network classifier with an optimal feature selection procedure in its front end is given. This model is followed by the development of a number of Petri net models of what are known as elementary approximation neurons (EA neurons). The design of an EA neuron includes an uncertainty function that constructs a granule appro ximation and a rough inclusion (threshold activation) function that measures the degree to which granule approximation is part of a target granule. The output of an EA neuron is an elementary granule. There are many forms of elementary granules (e.g., conjunction of de scriptors, rough inclusion function value). Each of the EA neurons considered in this chapter output a rough inclusion function value. An EA neuron can be designed so that it is trainable, that is, a feedback loop can be included in the design of the EA neuron so that one or more approximation space parameters can be tuned to improve the performance of the neuron. The design of three sample EA neurons is given. One of these neurons behaves like a high-pass filter.
Springer eBooks, Nov 3, 2011
... Wei-Zhi Wu, Yu-Fang Yang, and You-Hong Xu Neighborhood Rough Sets Based Matrix Approach for C... more ... Wei-Zhi Wu, Yu-Fang Yang, and You-Hong Xu Neighborhood Rough Sets Based Matrix Approach for Calculation of the Approximations ... Tonny Rutayisire, Yan Yang, Chao Lin, and Jinyuan Zhang A NIS-Apriori Based Rule Generator in Prolog and Its Functionality for Table Data ...
Springer eBooks, Sep 10, 2008
Fundamenta Informaticae, Feb 1, 2005
CS&P, 2015
In [14], we have presented a fuzzy forward reasoning methodology for rule-based systems using the... more In [14], we have presented a fuzzy forward reasoning methodology for rule-based systems using the functional representation of rules (fuzzy implications). In this paper, we extend methodology for selecting relevant fuzzy implications from [14] in backward reasoning. The proposed methodology takes full advantage of the functional representation of fuzzy implications and the algebraic properties of the family of all fuzzy implications. It allows to compare two fuzzy implications. If the truth value of the conclusion and the truth value of the implication are given, we can easily optimize the truth value of the implication premise. This methodology can be useful for the design of an inference engine based on the rule knowledge for a given rule-based system.
Springer eBooks, Aug 15, 2012