Many real natural or man-made dynamic systems can be better characterized using a fractional order dynamic model. Since in such a case the order assumes a non-integer value, it is of interest to consider the effect of...
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Many real natural or man-made dynamic systems can be better characterized using a fractional order dynamic model. Since in such a case the order assumes a non-integer value, it is of interest to consider the effect of small perturbation around the nominal value. It is of practical importance to analyze the influence of the order variations on the system behavior. This paper establishes an analytical method for the analysis of the sensitivity function of LTI fractional order dynamic systems with respect to the orders. The continuous dependent condition of the orders of fractional Green's function is derived, which is proved as a necessary and sufficient condition. The singularity of fractional Green's functions is discussed. Several examples are included for illustration.
This paper is concerned with the adjoint Jacobian motion planning algorithm for the Chaplygin sleigh. We introduce general idea about the algorithm and prove completeness of this algorithm for the Chaplygin sleigh. Fi...
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ISBN:
(纸本)9783902661555
This paper is concerned with the adjoint Jacobian motion planning algorithm for the Chaplygin sleigh. We introduce general idea about the algorithm and prove completeness of this algorithm for the Chaplygin sleigh. Finally we present the simulation result illustrating performance of this algorithm.
Two semiparametric algorithms to recover a nonlinear characteristic in a Hammerstein system are proposed. Both are obtained by incorporating a parametric component into the kernel nonparametric algorithm. For small nu...
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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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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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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.
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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