Wszystkie pola: neural scheme - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimization
Autorzy:: Styrcz, A.
Mrozek, J.
Mazur, G.
Powiązania:: https://bibliotekanauki.pl/articles/907520.pdf
Data publikacji:: 2011
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: optymalizacja globalna
algorytm memetyczny
geometria cząsteczki
global optimization
memetic algorithm
molecular geometry
Opis:: A novel, neural network controlled, dynamic evolutionary algorithm is proposed for the purposes of molecular geometry optimization. The approach is tested for selected model molecules and some molecular systems of importance in biochemistry. The new algorithm is shown to compare favorably with the standard, statically parametrized memetic algorithm.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2011, 21, 3; 559-566
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:: Input constraints handling in an MPC/feedback linearization scheme
Autorzy:: Deng, J.
Becerra, V. M.
Stobart, R.
Powiązania:: https://bibliotekanauki.pl/articles/907653.pdf
Data publikacji:: 2009
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: sterowanie predykcyjne
sterowanie odwrotne
sieć neuronowa
system nieliniowy
predictive control
feedback linearization
neural network
nonlinear system
constraints
Opis:: The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear transformation introduced by feedback linearization. This paper introduces a technique for handling input constraints within a real timeMPC/FL scheme, where the plant model employed is a class of dynamic neural networks. The technique is based on a simple affine transformation of the feasible area. A simulated case study is presented to illustrate the use and benefits of the technique.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2009, 19, 2; 219-232
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:: Adaptive control scheme based on the least squares support vector machine network
Autorzy:: Mahmoud, T. K.
Powiązania:: https://bibliotekanauki.pl/articles/930155.pdf
Data publikacji:: 2011
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: modelowanie systemu
system nieliniowy
system sterowania
sieć neuronowa
maszyna wektorów wspierających
support vector machine (SVM)
neural network
nonlinear system modeling
nonlinear system control
pH control
Opis:: Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine network (MRWLS-SVM) as a predictor model, and the controller part that is developed to track a reference trajectory. By means of the Lyapunov stability criterion, stability analysis for the tracking errors is performed. Finally, simulation studies are performed to demonstrate the capability of the developed approach in controlling a pH process.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2011, 21, 4; 685-696
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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