Grammatical Inference deals with the problem of learning structural models, such as grammars, from different sort of data patterns, such as artificial languages, natural languages, biosequences, speech and so on. This...
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Grammatical Inference deals with the problem of learning structural models, such as grammars, from different sort of data patterns, such as artificial languages, natural languages, biosequences, speech and so on. This article describes a new grammatical inference tool, Grammar-based Classifier System (GCS) dedicated to learn grammar from data. GCS is a new model of Learning Classifier Systems in which the population of classifiers has a form of a context-free grammar rule set in a Chomsky Normal Form. GCS has been proposed to address both regular language induction and the natural language grammar induction as well as learning formal grammar for DNA sequence. In all cases near-optimal solutions or better than reported in the literature were obtained.
Abstract The paper presents the concept of genetic algorithm based optimization for the EMG pattern recognition system controlling the hand prosthesis. The recognition of EMG signals for determining the hand movements...
Abstract The paper presents the concept of genetic algorithm based optimization for the EMG pattern recognition system controlling the hand prosthesis. The recognition of EMG signals for determining the hand movements is made by a linear neural network discriminating between five predefined grasps. The input feature vector for the classification was established using AR model, and its coefficients became the features. The genetic algorithms are used to optimize the number of elements in the input feature vector and simultaneously maintain the recognition efficiency at the same level. Experimental results show a good efficiency in the optimization method maintaining the performance of the recognition system for the studied grasp movements’ repertoire.
The self-adaptive model of a XCS-based ensemble machine solving data-mining tasks has been presented. The results of experiments have shown the ability of the architecture to adapt the parameters of single XCS: the mu...
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Learning Classifier Systems (LCSs) have gained increasing interest in the genetic and evolutionary computation literature. Many real-world problems are not conveniently expressed using the ternary representation typic...
A new sampling scheme for 2D images is proposed, which is dedicated for fast processing and storing long sequences of industrial images. It was proved that the spectrum of non-bandlimited images can be approximated up...
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A new sampling scheme for 2D images is proposed, which is dedicated for fast processing and storing long sequences of industrial images. It was proved that the spectrum of non-bandlimited images can be approximated up to a desired accuracy when number of samples grows. A simple reconstruction scheme is also proposed. copyright by EURASIP.
The paper presents a method of analyzing functionality aspects of complex information systems. The system analysis is based on integrating different computer system models, the networked systems and applications model...
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This work is devoted to some theoretical aspects of events monitoring in Complex Information Systems. Authors depict some aspects of nowadays monitoring systems that make them ineffective. Next, research areas are pre...
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In this paper, we present a distributed market-based algorithm called S+T, which solves the multi-robot task allocation (MRTA) problem in applications that require the cooperation among the robots to accomplish all th...
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In times of known hacking techniques as well as malicious software ubiquity, mostly commercialized information systems are seriously threaten. For this reason nowadays network security and dependable operation has bec...
In times of known hacking techniques as well as malicious software ubiquity, mostly commercialized information systems are seriously threaten. For this reason nowadays network security and dependable operation has become a major priority for design and implementation. Looking for powerful means network engineers started to often use computer simulation technique as a way for better understanding and development of complex computer systems. Therefore, plenitude of specialized development initiatives appeared in last few years. Currently network simulators are tools useful not only for creating new and better protocols, but also become money savers. By extending their functionalities e.g. post-processing capabilities for evaluating security and dependability metrics they turn out to be a fundamental state of the art network management platforms. This paper presents the information system, proposes metrics definitions, and describes the procedure of their evaluation using a simulator and a post-processing module.
In this work we deal with a group of heterogeneous robots that cooperatively accomplish a task in a common area. For such a system, we develop a distributed control mechanism that ensures the correct coordination of t...
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In this work we deal with a group of heterogeneous robots that cooperatively accomplish a task in a common area. For such a system, we develop a distributed control mechanism that ensures the correct coordination of their concurrent motion in the shared workspace. The concept assumes an additional level of robot motion control, that enforces temporary velocity reductions in order to avoid collisions as well as deadlocks and livelocks among the robots. The event-driven mechanism underlying the developed model ensures a robust coordination of mutually asynchronous robot controllers, and the mathematical character of the employed abstraction formally guarantees the control correctness.
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