In the paper a fast computational routines for identification algorithms for recovering nonlinearities in Hammerstein systems based on orthogonal series expansions of functions are proposed. It is ascertained that bot...
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ISBN:
(纸本)9789898111999
In the paper a fast computational routines for identification algorithms for recovering nonlinearities in Hammerstein systems based on orthogonal series expansions of functions are proposed. It is ascertained that both, convergence conditions and convergence rates of the computational algorithms are the same as their much less computationaly attractive 'theoretic' counterparts. The generic computational algorithm is derived and illustrated by three examples based on standard orthogonal series on interval, viz. Fourier, Legendre, and Haar systems. The exemplary algorithms are presented in a detailed, ready-to-implement, form and examined by means of computer simulations.
Problem of scheduling n preemptive jobs on m identical parallel processors is studied, in which for each job a distinct due window is given in advance and an integer release date is specified. If a job is completed wi...
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ISBN:
(纸本)9783902661555
Problem of scheduling n preemptive jobs on m identical parallel processors is studied, in which for each job a distinct due window is given in advance and an integer release date is specified. If a job is completed within its due window, then it incurs no penalty. Otherwise, it incurs a job-dependent earliness or tardiness cost. The objective is to find a job schedule such that a maximum of job-dependent costs associated with earliness, tardiness and a time a job is in process is minimized. It is proved that optimal solutions to this problem can be found by a solving a polynomial number of instances of classical maximum flow problem.
The paper deals with cyclic production system providing a mixture of various products, each of which is manufactured by unique sequence of operations on machines called job. The aim is to find the cyclic schedule of m...
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This paper presents a novel method of modeling spatial communication activity in wireless sensor network (WSN). We define native aspects of communication in WSN. Focusing on local/global activity dilemma, cooperation,...
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ISBN:
(纸本)9781629934747
This paper presents a novel method of modeling spatial communication activity in wireless sensor network (WSN). We define native aspects of communication in WSN. Focusing on local/global activity dilemma, cooperation, interference, network topology, and optimization aspects. A neighborhood abstraction is defined and we involve three binary relations: subordination, tolerance and collision to describe the cooperation in WSN. Using digital terrain model tools we model communication activity aspects as surfaces, stretched over WSN network. A network topology features are modeled using bare drainage surface. It is a component of a topographic map, which gives a direction towards the base station, determined by a slope of the modeled surface. Modeling node's instant energy level, we construct another surface represents node's instant level of consumed energy. Finally, we construct a drainage surface spread over each node neighborhood as superposition of bare drainage surface, energy consumed and relational surfaces.
The experiments aimed to compare data driven models for the valuation of residential premises were conducted using KEEL (Knowledge Extraction based on Evolutionary Learning) system. Twelve different regression algorit...
The experiments aimed to compare data driven models for the valuation of residential premises were conducted using KEEL (Knowledge Extraction based on Evolutionary Learning) system. Twelve different regression algorithms were applied to an actual data set derived from the cadastral system and the registry of real estate transactions. The 10-fold cross validation and statistical tests were applied. The lowest values of MSE provided models constructed and optimized by means of support vector machine, artificial neural network, decision trees for regression and quadratic regression, however differences between them were not statistically significant. Worse performance revealed algorithms employing evolutionary fuzzy rule learning. The experiments confirmed the usefulness of KEEL as a powerful tool with its numerous evolutionary algorithms together with classical learning approaches to carry out laborious investigation on a practical problem in a relatively short time.
Recognizing bacterial promoters is an important step towards understanding gene regulation. In this paper, we address the problem of predicting the location of promoters and their transcription start sites (TSSs) in E...
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The paper introduces speed boosting extension to a novel induction of fuzzy rules from raw data using Artificial Immune System methods. An improved approach uses a efficient initial population generation method. The s...
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Methods of designing of Totally Self Checking Sequential Machines are presented in this paper. The main problem in TSC sequential machines (TSC SM) designing is synthesis TSC functional excitation circuit. Formal cond...
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The aim of this paper is to show, using chosen example, the possibility to analyze a set of feasible solutions for a certain dual-criteria, complex decision making problem. The analyzed problem concerns, in particular...
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The paper presents an approach to processing of measurement data obtained from ultrasonic system. The approach makes possible to simplify computing of object location. The important advantage of the proposed method is...
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ISBN:
(纸本)9783902661555
The paper presents an approach to processing of measurement data obtained from ultrasonic system. The approach makes possible to simplify computing of object location. The important advantage of the proposed method is that it eliminates operations on float point numbers. Thus an algorithm based on this approach can be implemented using a simple microcontroller.
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