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Chaos-Based Neural Network Optimization Approach

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Electronic Nose: Algorithmic Challenges

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Abstract

E-nose combined with a pattern recognition module can be used for estimating gases concentration. This chapter introduces the concentration estimations of indoor contaminants using chaos-based optimization artificial neural network integrated into our E-nose instrument. Back-propagation neural network (BPNN) has been the common pattern recognition algorithm for E-nose; however, it has local optimal flaw. This chapter presents a novel chaotic sequence optimization BPNN method. Experimental results demonstrate the superiority and efficiency of the portable E-nose instrument integrated into chaos-based artificial neural network optimization algorithms in real-time monitoring of air quality in dwellings.

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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

Authors
  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). Chaos-Based Neural Network Optimization Approach. In: Electronic Nose: Algorithmic Challenges. Springer, Singapore. https://doi.org/10.1007/978-981-13-2167-2_4

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  • DOI: https://doi.org/10.1007/978-981-13-2167-2_4

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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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