<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></abstract>

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.

Approximation of phenol concentration using novel hybrid computational intelligence methods

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

International Journal of Applied Mathematics and Computer Science

This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These...

Approximation of phenol concentration using novel hybrid computational intelligence methods

Online veröffentlicht: 25. März 2014

Seitenbereich: 165 - 181

DOI: https://doi.org/10.2478/amcs-2014-0013

Schlüsselwörter
soft computing, neural networks, genetic algorithms, fuzzy systems, evolutionary-neural systems, pattern recognition, chemometrics.

This content is open access.

Approximation of phenol concentration using novel hybrid computational intelligence methods

Online veröffentlicht: 25. März 2014

Seitenbereich: 165 - 181

DOI: https://doi.org/10.2478/amcs-2014-0013

Schlüsselwörtersoft computing, neural networks, genetic algorithms, fuzzy systems, evolutionary-neural systems, pattern recognition, chemometrics.

This content is open access.

Schlüsselwörter
soft computing, neural networks, genetic algorithms, fuzzy systems, evolutionary-neural systems, pattern recognition, chemometrics.