Approximation of phenol concentration using novel hybrid computational intelligence methods

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.

[1]  Dong-Ling Tong,et al.  Hybrid genetic algorithm-neural network: Feature extraction for unpreprocessed microarray data , 2011, Artif. Intell. Medicine.

[2]  César Hervás-Martínez,et al.  A two-stage algorithm in evolutionary product unit neural networks for classification , 2011, Expert Syst. Appl..

[3]  Ryszard Tadeusiewicz,et al.  Place and role of intelligent systems in computer science , 2010 .

[4]  Satoshi Nakata,et al.  Non-linear dynamic responses of a semiconductor gas sensor — Competition effect on the sensor responses to gaseous mixtures , 2001 .

[5]  Ryszard Tadeusiewicz,et al.  New trends in neurocybernetics , 2010 .

[6]  Lei Zhang,et al.  Gases concentration estimation using heuristics and bio-inspired optimization models for experimental chemical electronic nose , 2011 .

[7]  K. Ihokura,et al.  The Stannic Oxide Gas SensorPrinciples and Applications , 1994 .

[8]  Wojciech Maziarz,et al.  Dynamic response of a semiconductor gas sensor analysed with the help of fuzzy logic , 2003 .

[9]  Beatrice Lazzerini,et al.  Learning concurrently partition granularities and rule bases of Mamdani fuzzy systems in a multi-objective evolutionary framework , 2009, Int. J. Approx. Reason..

[10]  Cheng-Hung Chen,et al.  Nonlinear system control using self-evolving neural fuzzy inference networks with reinforcement evolutionary learning , 2011, Appl. Soft Comput..

[11]  Jacek M. Leski,et al.  Fuzzy and Neuro-Fuzzy Intelligent Systems , 2000, Studies in Fuzziness and Soft Computing.

[12]  C Cevoli,et al.  Classification of Pecorino cheeses using electronic nose combined with artificial neural network and comparison with GC-MS analysis of volatile compounds. , 2011, Food chemistry.

[13]  Amadou Dicko,et al.  Aging fingerprint characterization of beer using electronic nose , 2011 .

[14]  Min-Yuan Cheng,et al.  Evolutionary fuzzy hybrid neural network for project cash flow control , 2010, Eng. Appl. Artif. Intell..

[15]  Panos M. Pardalos,et al.  hGA: Hybrid genetic algorithm in fuzzy rule-based classification systems for high-dimensional problems , 2012, Appl. Soft Comput..

[16]  B. Snopok,et al.  Multisensor systems for chemical analysis: state-of-the-art in Electronic Nose technology and new trends in machine olfaction , 2002 .

[17]  Amine Bermak,et al.  FPGA implementation of a neural network classifier for gas sensor array applications , 2009, 2009 6th International Multi-Conference on Systems, Signals and Devices.

[18]  Shih-Hung Yang,et al.  An evolutionary constructive and pruning algorithm for artificial neural networks and its prediction applications , 2012, Neurocomputing.

[19]  S. M. Yang,et al.  A two-stage algorithm integrating genetic algorithm and modified Newton method for neural network training in engineering systems , 2011, Expert Syst. Appl..

[20]  Leszek Rutkowski,et al.  Computational intelligence - methods and techniques , 2008 .

[21]  José María Font,et al.  Evolutionary construction and adaptation of intelligent systems , 2010, Expert Syst. Appl..

[22]  Mengjie Zhang,et al.  Encoding subcomponents in cooperative co-evolutionary recurrent neural networks , 2011, Neurocomputing.

[23]  Jun Wang,et al.  Quality grade identification of green tea using the eigenvalues of PCA based on the E-nose signals , 2009 .

[24]  W. Maziarz,et al.  Gas sensors in a dynamic operation mode , 2008 .

[25]  Habibollah Haron,et al.  Accumulated risk of body postures in assembly line balancing problem and modeling through a multi-criteria fuzzy-genetic algorithm , 2012, Comput. Ind. Eng..

[26]  Afshin Fassihi,et al.  Application of an expert system based on Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System (GA-ANFIS) in QSAR of cathepsin K inhibitors , 2012, Expert Syst. Appl..

[27]  Krzysztof Patan,et al.  Optimal training strategies for locally recurrent neural networks , 2011 .

[28]  Tohru Nomura,et al.  BATTERY OPERATED SEMICONDUCTOR CO SENSOR USING PULSE HEATING METHOD , 1998 .

[29]  Lakhmi C. Jain,et al.  An introduction to intelligent systems , 1997 .

[30]  Anne-Claude Romain,et al.  Use of a simple tin oxide sensor array to identify five malodours collected in the field , 2000 .

[31]  Zbislaw Tabor,et al.  Surrogate data: A novel approach to object detection , 2010, Int. J. Appl. Math. Comput. Sci..

[32]  Abdelhakim Artiba,et al.  Artificial Intelligence Methods , 1998 .

[33]  Zbislaw Tabor.  Statistical Estimation of the Dynamics of Watershed Dams , 2009, Int. J. Appl. Math. Comput. Sci..