Temat: series system - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Determining the weights of a Fourier series neural network on the basis of the multidimensional discrete Fourier transform
Autorzy:: Halawa, K.
Powiązania:: https://bibliotekanauki.pl/articles/907904.pdf
Data publikacji:: 2008
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: sieć neuronowa ortogonalna
szereg Fouriera
transformata Fouriera
aproksymacja
system nieliniowy
orthogonal neural networks
Fourier series
fast Fourier transform
approximation
nonlinear system
Opis:: This paper presents a method for training a Fourier series neural network on the basis of the multidimensional discrete Fourier transform. The proposed method is characterized by low computational complexity. The article shows how the method can be used for modelling dynamic systems.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2008, 18, 3; 369-375
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

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

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Skocz do pozycji: 3.

Tytuł:: Forecasting models for chaotic fractional-order oscillators using neural networks
Autorzy:: Bingi, Kishore
Prusty, B Rajanarayan
Powiązania:: https://bibliotekanauki.pl/articles/2055150.pdf
Data publikacji:: 2021
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: chaotic oscillators
data driven forecasting
fractional order system
model free analysis
neural network
time series prediction
oscylator chaotyczny
układ rzędu ułamkowego
sieć neuronowa
prognozowanie szeregów czasowych
Opis:: This paper proposes novel forecasting models for fractional-order chaotic oscillators, such as Duffing’s, Van der Pol’s, Tamaševičius’s and Chua’s, using feedforward neural networks. The models predict a change in the state values which bears a weighted relationship with the oscillator states. Such an arrangement is a suitable candidate model for out-of-sample forecasting of system states. The proposed neural network-assisted weighted model is applied to the above oscillators. The improved out-of-sample forecasting results of the proposed modeling strategy compared with the literature are comprehensively analyzed. The proposed models corresponding to the optimal weights result in the least mean square error (MSE) for all the system states. Further, the MSE for the proposed model is less in most of the oscillators compared with the one reported in the literature. The proposed prediction model’s out-of-sample forecasting plots show the best tracking ability to approximate future state values.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2021, 31, 3; 387--398
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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