Analysis of neural networks and analysis of feature selection with genetic algorithm to discriminate among pollutant gas

Aleixandre, M.; Sayago, I.; Horrillo, M.C.; Fernández, Ma Jesús; Arés, L.; García, María L.; Santos, J.P.; Gutiérrez, J.

doi:10.1016/j.snb.2004.04.044

Cited by 48 publications

(21 citation statements)

References 5 publications

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“…Hence, the sensor count to be used for information extraction should be reduced to a reasonable number in a systematic way. Selection is an important task and requires expertise in both sensor development and sensor data processing area [15][16][17]. Our sensor selection criteria can be listed as follows: (1) sensitivity, (2) linearity, (3) redundancy, and (4) noise factor.…”

Section: Pre-processing the Sensor Datamentioning

confidence: 99%

Predicting gas concentrations of ternary gas mixtures for a predefined 3D sample space

Mumyakmaz

Özmen

Ebeoğlu

et al. 2008

Sensors and Actuators B: Chemical

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Section: Pre-processing the Sensor Datamentioning

confidence: 99%

Predicting gas concentrations of ternary gas mixtures for a predefined 3D sample space

Mumyakmaz

Özmen

Ebeoğlu

et al. 2008

Sensors and Actuators B: Chemical

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“…In [19], improvements in sensor selectivity and accuracy were achieved using a combination of a sensor array with a neural network. The signals from multisensors were evaluated using principal components analysis (PCA) [20,21] and artificial neural networks (ANNs), while the selection of possible features using a genetic algorithm (GA) facilitated classification in [22]. Environmental applications such as pollution monitoring and air quality control [23–25] have steadily attracted considerable attention because of the growing need for environmental protection in recent years.…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition for Selective Odor Detection with Gas Sensor Arrays

Kim

Lee

Kim

et al. 2012

Sensors

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This paper presents a new pattern recognition approach for enhancing the selectivity of gas sensor arrays for clustering intelligent odor detection. The aim of this approach was to accurately classify an odor using pattern recognition in order to enhance the selectivity of gas sensor arrays. This was achieved using an odor monitoring system with a newly developed neural-genetic classification algorithm (NGCA). The system shows the enhancement in the sensitivity of the detected gas. Experiments showed that the proposed NGCA delivered better performance than the previous genetic algorithm (GA) and artificial neural networks (ANN) methods. We also used PCA for data visualization. Our proposed system can enhance the reproducibility, reliability, and selectivity of odor sensor output, so it is expected to be applicable to diverse environmental problems including air pollution, and monitor the air quality of clean-air required buildings such as a kindergartens and hospitals.

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“…1,2 As a result, feature selection methods have become relevant techniques for effective pattern recognition, 3,4 exploratory data analysis, 5 and data mining. 6,7 With reference to the specific aim of this paper, in recent years, automated feature selection has become an essential building block of monitoring and "on condition" fault diagnosis in complex engineering systems, such as the nuclear power plants.…”

Section: Introductionmentioning

confidence: 99%

Application of a niched Pareto genetic algorithm for selecting features for nuclear transients classification

Baraldi

Pedroni

Zio

2009

Int. J. Intell. Syst.

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Feature selection for transient classification is the problem of choosing among several monitored parameters (i.e., the features) to be used for efficiently recognizing the developing transient patterns. It is a critical issue for the application of "on condition" diagnostic techniques in complex systems, such as the nuclear power plants, where hundreds of parameters are measured. Indeed, irrelevant and noisy features have been shown to unnecessarily increase the complexity of the classification problem and degrade the diagnostic performance. In this paper, the problem of selecting the features to be used for efficient transient classification is tackled by means of multiobjective genetic algorithms. The approach leads to the identification of a family of equivalently optimal subsets of features, in the Pareto sense. However, difficulties in the convergence of the standard Pareto-based multiobjective genetic algorithm search in large feature spaces may arise in terms of representativeness of the identified Pareto front whose elements may turn out to be unevenly distributed in the objective functions space, thus not providing a full picture of the potential Pareto-optimal solutions. To overcome this problem, a niched Pareto genetic algorithm is embraced in this work. The performance of the feature subsets examined during the search is evaluated in terms of two optimization objectives: the classification accuracy of a Fuzzy K-Nearest Neighbors classifier and the number of features in the subsets. During the genetic search, the algorithm applies a controlled "niching pressure" to spread out the population in the search space so that convergence is shared on different niches of the Pareto front, which is thus evenly covered. The method is tested on a diagnostic problem characterized by a very large number of process features available for the classification of simulated transients in the feedwater system of a boiling water reactor. The dynamics of the transient signals is captured by wavelet decomposition, which actually increases the complexity of the search for the optimal feature subsets by triplicating the number of features to be considered. C 2008 Wiley Periodicals, Inc.

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Analysis of neural networks and analysis of feature selection with genetic algorithm to discriminate among pollutant gas

Cited by 48 publications

References 5 publications

Predicting gas concentrations of ternary gas mixtures for a predefined 3D sample space

Predicting gas concentrations of ternary gas mixtures for a predefined 3D sample space

Pattern Recognition for Selective Odor Detection with Gas Sensor Arrays

Application of a niched Pareto genetic algorithm for selecting features for nuclear transients classification

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