Approximation of phenol concentration using novel hybrid computational intelligence methods

• Autorzy: Pławiak P. , Tadeusiewicz R.

Identyfikatory

DOI

10.2478/amcs-2014-0013

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

soft computing; neural network; genetic algorithm; fuzzy system; evolutionary neural system; pattern recognition; chemometrics

PL

przetwarzanie miękkie; sieć neuronowa; algorytm genetyczny; system rozmyty; rozpoznawanie obrazu; chemometria

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2014
• Tom: Vol. 24, no. 1
• Strony: 165--181
• Opis fizyczny: Bibliogr. 35 poz., rys., tab., wykr.

Twórcy

autor

Pławiak P.

• Institute of Telecomputing, Cracow University of Technology, ul. Warszawska 24, F-5, 31-155 Cracow, Poland; Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Cracow, Poland

autor

Tadeusiewicz R.

• Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Cracow, Poland

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