online read us now
Paper details
Number 1 - March 2014
Volume 24 - 2014
Approximation of phenol concentration using novel hybrid computational intelligence methods
Paweł Pławiak, Ryszard Tadeusiewicz
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.
Keywords
soft computing, neural networks, genetic algorithms, fuzzy systems, evolutionary-neural systems, pattern recognition, chemometrics