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Wyszukujesz frazę "Particle Swarm Optimization" wg kryterium: Temat


Wyświetlanie 1-5 z 5
Tytuł:
Scheduling electric power generators using particle swarm optimization combined with the Lagrangian relaxation method
Autorzy:
Balci, H. H.
Valenzuela, J. F.
Powiązania:
https://bibliotekanauki.pl/articles/907641.pdf
Data publikacji:
2004
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
optymalizacja rojem cząstek
jednostka wytwórcza
relaksacja Lagrange'a
particle swarm optimization (PSO)
unit commitment
Lagrange relaxation
Opis:
This paper describes a procedure that uses particle swarm optimization (PSO) combined with the Lagrangian Relaxation (LR) framework to solve a power-generator scheduling problem known as the unit commitment problem (UCP). The UCP consists of determining the schedule and production amount of generating units within a power system subject to operating constraints. The LR framework is applied to relax coupling constraints of the optimization problem. Thus, the UCP is separated into independent optimization functions for each generating unit. Each of these sub-problems is solved using Dynamic Programming (DP). PSO is used to evolve the Lagrangian multipliers. PSO is a population based search technique, which belongs to the swarm intelligence paradigm that is motivated by the simulation of social behavior to manipulate individuals towards better solution areas. The performance of the PSO-LR procedure is compared with results of other algorithms in the literature used to solve the UCP. The comparison shows that the PSO-LR approach is efficient in terms of computational time while providing good solutions.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2004, 14, 3; 411-421
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A modified particle swarm optimization procedure for triggering fuzzy flip-flop neural networks
Autorzy:
Kowalski, Piotr A.
Słoczyński, Tomasz
Powiązania:
https://bibliotekanauki.pl/articles/2055168.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
fuzzy neural network
fuzzy flip-flop neuron
particle swarm optimization
training procedure
sieć neuronowa rozmyta
optymalizacja rojem cząstek
procedura szkoleniowa
Opis:
The aim of the presented study is to investigate the application of an optimization algorithm based on swarm intelligence to the configuration of a fuzzy flip-flop neural network. Research on solving this problem consists of the following stages. The first one is to analyze the impact of the basic internal parameters of the neural network and the particle swarm optimization (PSO) algorithm. Subsequently, some modifications to the PSO algorithm are investigated. Approximations of trigonometric functions are then adopted as the main task to be performed by the neural network. As a result of the numerical verification of the problem, a set of rules are developed that can be helpful in constructing a fuzzy flip-flop type neural network. The obtained results of the computations significantly simplify the structure of the neural network in relation to similar conditions known from the literature.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 577--586
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Intelligent financial time series forecasting: A complex neuro-fuzzy approach with multi-swarm intelligence
Autorzy:
Li, C.
Chiang, T. W.
Powiązania:
https://bibliotekanauki.pl/articles/331280.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
zbiór rozmyty
system neuronowo-rozmyty
optymalizacja rojem cząstek
szereg czasowy
complex fuzzy set
complex neuro fuzzy system
hierarchical multi swarm
particle swarm optimization (PSO)
recursive least squares estimator
time series forecasting
Opis:
Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial time series forecasting. The CNFS is an adaptive system, which is designed using Complex Fuzzy Sets (CFSs) whose membership functions are complex-valued and characterized within the unit disc of the complex plane. The application of CFSs to the CNFS can augment the adaptive capability of nonlinear functional mapping, which is valuable for nonlinear forecasting. Moreover, to optimize the CNFS for accurate forecasting, we devised a new hybrid learning method, called the HMSPSO-RLSE, which integrates in a hybrid way the so-called Hierarchical Multi-Swarm PSO (HMSPSO) and the well known Recursive Least Squares Estimator (RLSE). Three examples of financial time series are used to test the proposed approach, whose experimental results outperform those of other methods.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 787-800
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A novel fuzzy c-regression model algorithm using a new error measure and particle swarm optimization
Autorzy:
Soltani, M.
Chaari, A.
Ben Hmida, F.
Powiązania:
https://bibliotekanauki.pl/articles/330134.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
model rozmyty Takagi-Sugeno
algorytm grupowania
metoda najmniejszych kwadratów
optymalizacja rojem cząstek
Takagi-Sugeno fuzzy models
noise clustering algorithm
fuzzy c-regression model
orthogonal least squares
particle swarm optimization (PSO)
Opis:
This paper presents a new algorithm for fuzzy c-regression model clustering. The proposed methodology is based on adding a second regularization term in the objective function of a Fuzzy C-Regression Model (FCRM) clustering algorithm in order to take into account noisy data. In addition, a new error measure is used in the objective function of the FCRM algorithm, replacing the one used in this type of algorithm. Then, particle swarm optimization is employed to finally tune parameters of the obtained fuzzy model. The orthogonal least squares method is used to identify the unknown parameters of the local linear model. Finally, validation results of two examples are given to demonstrate the effectiveness and practicality of the proposed algorithm.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2012, 22, 3; 617-628
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Feature optimization using a two-tier hybrid optimizer in an Internet of Things network
Autorzy:
Agrawal, Akhileshwar Prasad
Singh, Nanhay
Powiązania:
https://bibliotekanauki.pl/articles/15548024.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
IoT
Internet of Things
anomaly mitigation
GWO
Gray Wolf Optimizer
feature optimization
PSO
particle swarm optimizer
Internet Rzeczy
optymalizacja funkcji
Opis:
The growing use of the Internet of Things (IoT) in smart applications necessitates improved security monitoring of IoT components. The security of such components is monitored using intrusion detection systems which run machine learning (ML) algorithms to classify access attempts as anomalous or normal. However, in this case, one of the issues is the large length of the data feature vector that any ML or deep learning technique implemented on resource-constrained intelligent nodes must handle. In this paper, the problem of selecting an optimal-feature set is investigated to reduce the curse of data dimensionality. A two-layered approach is proposed: the first tier makes use of a random forest while the second tier uses a hybrid of gray wolf optimizer (GWO) and the particle swarm optimizer (PSO) with the k-nearest neighbor as the wrapper method. Further, differential weight distribution is made to the local-best and global-best positions in the velocity equation of PSO. A new metric, i.e., the reduced feature to accuracy ratio (RFAR), is introduced for comparing various works. Three data sets, namely, NSLKDD, DS2OS and BoTIoT, are used to evaluate and validate the proposed work. Experiments demonstrate improvements in accuracy up to 99.44%, 99.44% and 99.98% with the length of the optimal-feature vector equal to 9, 4 and 8 for the NSLKDD, DS2OS and BoTIoT data sets, respectively. Furthermore, classification improves for many of the individual classes of attacks: denial-of-service (DoS) (99.75%) and normal (99.52%) for NSLKDD, malicious control (100%) and DoS (68.69%) for DS2OS, and theft (95.65%) for BoTIoT.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2023, 33, 2; 313--326
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-5 z 5

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