- Tytuł:
- Approximation of phenol concentration using novel hybrid computational intelligence methods
- Autorzy:
-
Pławiak, P.
Tadeusiewicz, R. - Powiązania:
- https://bibliotekanauki.pl/articles/907935.pdf
- Data publikacji:
- 2014
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
soft computing
neural network
genetic algorithm
fuzzy system
evolutionary neural system
pattern recognition
chemometrics
przetwarzanie miękkie
sieć neuronowa
algorytm genetyczny
system rozmyty
rozpoznawanie obrazu
chemometria - Opis:
- This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed systems are a combination of data preprocessing methods, genetic algorithms and the Levenberg–Marquardt (LM) algorithm used for learning feed forward and recurrent neural networks. The initial weights and biases of neural networks chosen by the use of a genetic algorithm are then tuned with an LM algorithm. The evaluation is made on the basis of accuracy and complexity criteria. The main advantage of proposed systems is the elimination of random selection of the network weights and biases, resulting in increased efficiency of the systems.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2014, 24, 1; 165-181
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł