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Wyświetlanie 1-15 z 124
Tytuł:
A Numerical Algorithm for Filtering and State Observation
Autorzy:
Ibrir, S.
Powiązania:
https://bibliotekanauki.pl/articles/908263.pdf
Data publikacji:
1999
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
krzywa składana
różniczkowanie numeryczne
obserwator
spline functions
numerical differentiation
observers
smooth filters
Opis:
This paper deals with a numerical method for data fitting and estimation of continuous higher-order derivatives of a given signal from its non-exactsampled data. The proposed algorithm is a generalization of the algorithm proposed by Reinsch (1967). This algorithm is conceived as a key element in the structure of the numerical observer discussed in our recent papers. Satisfactory results are obtained which prove the efficiency of the proposed approach.
Źródło:
International Journal of Applied Mathematics and Computer Science; 1999, 9, 4; 855-869
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hybrid algorithm for solving inverse problems in elasticity
Autorzy:
Barabasz, B.
Gajda-Zagórska, E.
Migórski, S.
Paszyński, M.
Schaefer, R.
Smołka, M.
Powiązania:
https://bibliotekanauki.pl/articles/331427.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
inverse problem
hierarchic genetic strategy
hybrid optimization
automatic hp adaptive finite element method
zagadnienie odwrotne
strategia genetyczna
optymalizacja hybrydowa
metoda elementów skończonych
Opis:
The paper offers a new approach to handling difficult parametric inverse problems in elasticity and thermo-elasticity, formulated as global optimization ones. The proposed strategy is composed of two phases. In the first, global phase, the stochastic hp-HGS algorithm recognizes the basins of attraction of various objective minima. In the second phase, the local objective minimizers are closer approached by steepest descent processes executed singly in each basin of attraction. The proposed complex strategy is especially dedicated to ill-posed problems with multimodal objective functionals. The strategy offers comparatively low computational and memory costs resulting from a double-adaptive technique in both forward and inverse problem domains. We provide a result on the Lipschitz continuity of the objective functional composed of the elastic energy and the boundary displacement misfits with respect to the unknown constitutive parameters. It allows common scaling of the accuracy of solving forward and inverse problems, which is the core of the introduced double-adaptive technique. The capability of the proposed method of finding multiple solutions is illustrated by a computational example which consists in restoring all feasible Young modulus distributions minimizing an objective functional in a 3D domain of a photo polymer template obtained during step and flash imprint lithography.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 4; 865-886
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A genetic algorithm for the maximum 2-packing set problem
Autorzy:
Trejo-Sánchez, Joel Antonio
Fajardo-Delgado, Daniel
Gutierrez-Garcia, J. Octavio
Powiązania:
https://bibliotekanauki.pl/articles/330154.pdf
Data publikacji:
2020
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
maximum 2-packing set
genetic algorithms
graph algorithms
algorytm genetyczny
algorytm grafowy
Opis:
Given an undirected connected graph G = (V, E), a subset of vertices S is a maximum 2-packing set if the number of edges in the shortest path between any pair of vertices in S is at least 3 and S has the maximum cardinality. In this paper, we present a genetic algorithm for the maximum 2-packing set problem on arbitrary graphs, which is an NP-hard problem. To the best of our knowledge, this work is a pioneering effort to tackle this problem for arbitrary graphs. For comparison, we extended and outperformed a well-known genetic algorithm originally designed for the maximum independent set problem. We also compared our genetic algorithm with a polynomial-time one for the maximum 2-packing set problem on cactus graphs. Empirical results show that our genetic algorithm is capable of finding 2-packing sets with a cardinality relatively close (or equal) to that of the maximum 2-packing sets. Moreover, the cardinality of the 2-packing sets found by our genetic algorithm increases linearly with the number of vertices and with a larger population and a larger number of generations. Furthermore, we provide a theoretical proof demonstrating that our genetic algorithm increases the fitness for each candidate solution when certain conditions are met.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2020, 30, 1; 173-184
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach
Autorzy:
Yan, F.
Dridi, M.
El Moudni, A.
Powiązania:
https://bibliotekanauki.pl/articles/329874.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
autonomous vehicle
autonomous intersection management
genetic algorithm
dynamic programming
heuristics
pojazd autonomiczny
algorytm genetyczny
programowanie dynamiczne
Opis:
This paper addresses a vehicle sequencing problem for adjacent intersections under the framework of Autonomous Intersection Management (AIM). In the context of AIM, autonomous vehicles are considered to be independent individuals and the traffic control aims at deciding on an efficient vehicle passing sequence. Since there are considerable vehicle passing combinations, how to find an efficient vehicle passing sequence in a short time becomes a big challenge, especially for more than one intersection. In this paper, we present a technique for combining certain vehicles into some basic groups with reference to some properties discussed in our earlier works. A genetic algorithm based on these basic groups is designed to find an optimal or a near-optimal vehicle passing sequence for each intersection. Computational experiments verify that the proposed genetic algorithms can response quickly for several intersections. Simulations with continuous vehicles are carried out with application of the proposed algorithm or existing traffic control methods. The results show that the traffic condition can be significantly improved by our algorithm.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2013, 23, 1; 183-200
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A genetic algorithm based optimized convolutional neural network for face recognition
Autorzy:
Karlupia, Namrata
Mahajan, Palak
Abrol, Pawanesh
Lehana, Parveen K.
Powiązania:
https://bibliotekanauki.pl/articles/2201023.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
convolutional neural network
genetic algorithm
deep learning
evolutionary technique
sieć neuronowa konwolucyjna
algorytm genetyczny
uczenie głębokie
technika ewolucyjna
Opis:
Face recognition (FR) is one of the most active research areas in the field of computer vision. Convolutional neural networks (CNNs) have been extensively used in this field due to their good efficiency. Thus, it is important to find the best CNN parameters for its best performance. Hyperparameter optimization is one of the various techniques for increasing the performance of CNN models. Since manual tuning of hyperparameters is a tedious and time-consuming task, population based metaheuristic techniques can be used for the automatic hyperparameter optimization of CNNs. Automatic tuning of parameters reduces manual efforts and improves the efficiency of the CNN model. In the proposed work, genetic algorithm (GA) based hyperparameter optimization of CNNs is applied for face recognition. GAs are used for the optimization of various hyperparameters like filter size as well as the number of filters and of hidden layers. For analysis, a benchmark dataset for FR with ninety subjects is used. The experimental results indicate that the proposed GA-CNN model generates an improved model accuracy in comparison with existing CNN models. In each iteration, the GA minimizes the objective function by selecting the best combination set of CNN hyperparameters. An improved accuracy of 94.5% is obtained for FR.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 21--31
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Measuring and Maintaining Consistency: a Hybrid Ftf Algorithm
Autorzy:
Bunch, J. R.
Le Borne, R. C.
Proudler, I. K.
Powiązania:
https://bibliotekanauki.pl/articles/908062.pdf
Data publikacji:
2001
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
algorytmy
matematyka
FTF
numerical stability
consistency
Opis:
Due to the versatility as well as its ease of implementation, the Fast Transversal Filters algorithm is attractive for many adaptive filtering applications. However, it is not widely used because of its undesirable tendency to diverge when operating in finite precision arithmetic. To compensate, modifications to the algorithm have been introduced that are either occasional (performed when a predefined condition(s) is violated) or structured as part of the normal update iteration. However, in neither case is any confidence explicitly given that the computed parameters are in fact close to the desired ones. Here, we introduce a time invariant parameter that provides the user with more flexibility in establishing confidence in the consistency of the updated filter parameters. Additionally, we provide evidence through the introduction of a hybrid FTF algorithm that when sufficient time is given prior to catastrophic divergence, the update parameters of the FTF algorithm can be adjusted so that consistency can be acquired and maintained.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2001, 11, 5; 1203-1216
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A robust algorithm to solve the signal setting problem considering different traffic assignment approaches
Autorzy:
Adacher, L.
Gemma, A.
Powiązania:
https://bibliotekanauki.pl/articles/330229.pdf
Data publikacji:
2017
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
genetic algorithm
surrogate method
traffic signal synchronization
traffic assignment
simulation model
algorytm genetyczny
metoda zastępcza
synchronizacja sygnału ruchu
model symulacji
Opis:
In this paper we extend a stochastic discrete optimization algorithm so as to tackle the signal setting problem. Signalized junctions represent critical points of an urban transportation network, and the efficiency of their traffic signal setting influences the overall network performance. Since road congestion usually takes place at or close to junction areas, an improvement in signal settings contributes to improving travel times, drivers’ comfort, fuel consumption efficiency, pollution and safety. In a traffic network, the signal control strategy affects the travel time on the roads and influences drivers’ route choice behavior. The paper presents an algorithm for signal setting optimization of signalized junctions in a congested road network. The objective function used in this work is a weighted sum of delays caused by the signalized intersections. We propose an iterative procedure to solve the problem by alternately updating signal settings based on fixed flows and traffic assignment based on fixed signal settings. To show the robustness of our method, we consider two different assignment methods: one based on user equilibrium assignment, well established in the literature as well as in practice, and the other based on a platoon simulation model with vehicular flow propagation and spill-back. Our optimization algorithm is also compared with others well known in the literature for this problem. The surrogate method (SM), particle swarm optimization (PSO) and the genetic algorithm (GA) are compared for a combined problem of global optimization of signal settings and traffic assignment (GOSSTA). Numerical experiments on a real test network are reported.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2017, 27, 4; 815-826
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Using a vision cognitive algorithm to schedule virtual machines
Autorzy:
Zhao, J.
Mhedheb, Y.
Tao, J.
Jrad, F.
Liu, Q.
Streit, A.
Powiązania:
https://bibliotekanauki.pl/articles/330838.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
cloud computing
vision cognitive algorithm
VM scheduling
simulation
chmura obliczeniowa
algorytm poznawczy
szeregowanie
symulacja
Opis:
Scheduling virtual machines is a major research topic for cloud computing, because it directly influences the performance, the operation cost and the quality of services. A large cloud center is normally equipped with several hundred thousand physical machines. The mission of the scheduler is to select the best one to host a virtual machine. This is an NP-hard global optimization problem with grand challenges for researchers. This work studies the Virtual Machine (VM) scheduling problem on the cloud. Our primary concern with VM scheduling is the energy consumption, because the largest part of a cloud center operation cost goes to the kilowatts used. We designed a scheduling algorithm that allocates an incoming virtual machine instance on the host machine, which results in the lowest energy consumption of the entire system. More specifically, we developed a new algorithm, called vision cognition, to solve the global optimization problem. This algorithm is inspired by the observation of how human eyes see directly the smallest/largest item without comparing them pairwisely. We theoretically proved that the algorithm works correctly and converges fast. Practically, we validated the novel algorithm, together with the scheduling concept, using a simulation approach. The adopted cloud simulator models different cloud infrastructures with various properties and detailed runtime information that can usually not be acquired from real clouds. The experimental results demonstrate the benefit of our approach in terms of reducing the cloud center energy consumption.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 535-550
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
K3M: A universal algorithm for image skeletonization and a review of thinning techniques
Autorzy:
Saeed, K.
Tabędzki, M.
Rybnik, M.
Adamski, M.
Powiązania:
https://bibliotekanauki.pl/articles/907744.pdf
Data publikacji:
2010
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
szkieletyzacja
obraz cyfrowy
przetwarzanie obrazu
przetwarzanie równoległe
skeletonization
thinning
digital image processing
parallelization
iteration
thinning methodologies
sequential thinning
parallel thinning
Opis:
This paper aims at three aspects closely related to each other: first, it presents the state of the art in the area of thinning methodologies, by giving descriptions of general ideas of the most significant algorithms with a comparison between them. Secondly, it proposes a new thinning algorithm that presents interesting properties in terms of processing quality and algorithm clarity, enriched with examples. Thirdly, the work considers parallelization issues for intrinsically sequential algorithms of thinning. The main advantage of the suggested algorithm is its universality, which makes it useful and versatile for a variety of applications.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2010, 20, 2; 317-335
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Minimal Decision Rules Based on the Apriori Algorithm
Autorzy:
Fernandez, M. C.
Menasalvas, E.
Marban, O.
Pena, J. M.
Millan, S.
Powiązania:
https://bibliotekanauki.pl/articles/908364.pdf
Data publikacji:
2001
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
baza danych
algorytmy
rough sets
rough dependencies
association rules
a priori algorithm
minimal decision rules
Opis:
Based on rough set theory many algorithms for rules extraction from data have been proposed. Decision rules can be obtained directly from a database. Some condition values may be unnecessary in a decision rule produced directly from the database. Such values can then be eliminated to create a more comprehensible (minimal) rule. Most of the algorithms that have been proposed to calculate minimal rules are based on rough set theory or machine learning. In our approach, in a post-processing stage, we apply the Apriori algorithm to reduce the decision rules obtained through rough sets. The set of dependencies thus obtained will help us discover irrelevant attribute values.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2001, 11, 3; 691-704
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A factor graph based genetic algorithm
Autorzy:
Helmi, B. H.
Rahmani, A. T.
Pelikan, M.
Powiązania:
https://bibliotekanauki.pl/articles/330811.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
optimization problem
genetic algorithm
estimation
distribution algorithm
factor graph
matrix factorization
problem optymalizacji
algorytm genetyczny
algorytm estymacji rozkładu
faktoryzacja macierzy
Opis:
We propose a new linkage learning genetic algorithm called the Factor Graph based Genetic Algorithm (FGGA). In the FGGA, a factor graph is used to encode the underlying dependencies between variables of the problem. In order to learn the factor graph from a population of potential solutions, a symmetric non-negative matrix factorization is employed to factorize the matrix of pair-wise dependencies. To show the performance of the FGGA, encouraging experimental results on different separable problems are provided as support for the mathematical analysis of the approach. The experiments show that FGGA is capable of learning linkages and solving the optimization problems in polynomial time with a polynomial number of evaluations.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 621-633
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A generalization of the graph Laplacian with application to a distributed consensus algorithm
Autorzy:
Zhai, G.
Powiązania:
https://bibliotekanauki.pl/articles/331278.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
graph Laplacian
generalized graph Laplacian
adjacency weights
distributed consensus algorithm
cooperative control
Opis:
In order to describe the interconnection among agents with multi-dimensional states, we generalize the notion of a graph Laplacian by extending the adjacency weights (or weighted interconnection coefficients) from scalars to matrices. More precisely, we use positive definite matrices to denote full multi-dimensional interconnections, while using nonnegative definite matrices to denote partial multi-dimensional interconnections. We prove that the generalized graph Laplacian inherits the spectral properties of the graph Laplacian. As an application, we use the generalized graph Laplacian to establish a distributed consensus algorithm for agents described by multi-dimensional integrators.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2015, 25, 2; 353-360
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
GrDBSCAN: A granular density-based clustering algorithm
Autorzy:
Suchy, Dawid
Siminski, Krzysztof
Powiązania:
https://bibliotekanauki.pl/articles/15548018.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
granular computing
DBSCAN
clustering algorithm
GrDBSCAN
przetwarzanie ziarniste
algorytm grupowania
Opis:
Density-based spatial clustering of applications with noise (DBSCAN) is a commonly known and used algorithm for data clustering. It applies a density-based approach and can produce clusters of any shape. However, it has a drawback-its worst-case computational complexity is O(n2) with regard to the number of data items n. The paper presents GrDBSCAN: a granular modification of DBSCAN with reduced complexity. The proposed GrDBSCAN first granulates data into fuzzy granules and then runs density-based clustering on the resulting granules. The complexity of GrDBSCAN is linear with regard to the input data size and higher only for the number of granules. That number is, however, a parameter of the GrDBSCAN algorithm and is (significantly) lower than that of input data items. This results in shorter clustering time than in the case of DBSCAN. The paper is accompanied by numerical experiments. The implementation of GrDBSCAN is freely available from a public repository.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2023, 33, 2; 297--312
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A complete gradient clustering algorithm formed with kernel estimators
Autorzy:
Kulczycki, P.
Charytanowicz, M.
Powiązania:
https://bibliotekanauki.pl/articles/907781.pdf
Data publikacji:
2010
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
analiza danych
eksploracja danych
grupowanie
metoda statystyczna
estymacja jądrowa
obliczenia numeryczne
data analysis
data mining
clustering
gradient procedures
nonparametric statistical methods
kernel estimators
numerical calculations
Opis:
The aim of this paper is to provide a gradient clustering algorithm in its complete form, suitable for direct use without requiring a deeper statistical knowledge. The values of all parameters are effectively calculated using optimizing procedures. Moreover, an illustrative analysis of the meaning of particular parameters is shown, followed by the effects resulting from possible modifications with respect to their primarily assigned optimal values. The proposed algorithm does not demand strict assumptions regarding the desired number of clusters, which allows the obtained number to be better suited to a real data structure. Moreover, a feature specific to it is the possibility to influence the proportion between the number of clusters in areas where data elements are dense as opposed to their sparse regions. Finally, the algorithm-by the detection of one-element clusters-allows identifying atypical elements, which enables their elimination or possible designation to bigger clusters, thus increasing the homogeneity of the data set.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2010, 20, 1; 123-134
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Nonlinear system identification with a real-coded genetic algorithm (RCGA)
Autorzy:
Cherif, I.
Fnaiech, F.
Powiązania:
https://bibliotekanauki.pl/articles/329753.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
blind nonlinear identification
Volterra series
higher order cumulants
real-coded genetic algorithm
szereg Volterry
kumulanta wyższego rzędu
algorytm genetyczny kodowania rzeczywistego
Opis:
This paper is devoted to the blind identification problem of a special class of nonlinear systems, namely, Volterra models, using a real-coded genetic algorithm (RCGA). The model input is assumed to be a stationary Gaussian sequence or an independent identically distributed (i.i.d.) process. The order of the Volterra series is assumed to be known. The fitness function is defined as the difference between the calculated cumulant values and analytical equations in which the kernels and the input variances are considered. Simulation results and a comparative study for the proposed method and some existing techniques are given. They clearly show that the RCGA identification method performs better in terms of precision, time of convergence and simplicity of programming.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2015, 25, 4; 863-875
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 124

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