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Heuristic and Bio-inspired Neural Network Model

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Electronic Nose: Algorithmic Challenges

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Abstract

E-nose technology for detecting indoor harmful gases and concentration estimation of harmful gases and estimating the concentration become feasible by using a multi-sensor system. The estimation accuracy in actual application is concerned too much by manufacturers and researchers. This chapter analyzes the application of different bio-inspired and heuristic techniques to improve the concentration estimation in experimental electronic nose application. In this chapter, seven different particle swarm optimization models are studied including six models used for numerical function optimization, and a novel hybrid model of particle swarm optimization and adaptive genetic algorithm, for optimizing back-propagation multilayer perceptron neural network. We present the performance of a particle swarm optimization technique, an adaptive genetic strategy, and a back-propagation artificial neural network approach to perform concentration estimation of chemical gases and improve the intelligence of an E-nose.

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Authors and Affiliations

  1. College of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China

    Lei Zhang

  2. College of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China

    Fengchun Tian

  3. School of Science and Engineering, Chinese University of Hong Kong (Shenzhen), Shenzhen, Guangdong, China

    David Zhang

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  1. Lei Zhang
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  2. Fengchun Tian
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  3. David Zhang
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Zhang, L., Tian, F., Zhang, D. (2018). Heuristic and Bio-inspired Neural Network Model. In: Electronic Nose: Algorithmic Challenges. Springer, Singapore. https://doi.org/10.1007/978-981-13-2167-2_3

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  • DOI: https://doi.org/10.1007/978-981-13-2167-2_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2166-5

  • Online ISBN: 978-981-13-2167-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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