Titlebook: Electronic Nose: Algorithmic Challenges; Lei Zhang,Fengchun Tian,David Zhang Book 2018 Springer Nature Singapore Pte Ltd. 2018 Electronic

Indicative

簡略

Domain Correction-Based Adaptive Extreme Learning Machineansfer learning, especially from the perspective of domain correction and decision making, to realize the knowledge transfer for interference suppression and drift compensation. Experiments on a background interference dataset and a public benchmark sensor drift dataset via E-nose verify the effectiveness of the proposed DC-AELM method.

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貪心

The Geography of Design in the Cityntal results demonstrate that the HSVM model outperforms other classifiers in general. Also, HSVM classifier preliminarily shows its superiority in solution to discrimination in various electronic nose applications.

伸展

Costs of Energy and Drying Equipment,n; second, in the process of establishing classifiers ensemble, a new fusion approach conducting an effective base classifier weighted method is proposed. Experimental results show that the average classification accuracy of the proposed method has been significantly improved over base classifiers and majority voting-based fusion strategy.

個阿姨勾引你

Paul R. Ferguson,Glenys J. Fergusonansfer learning, especially from the perspective of domain correction and decision making, to realize the knowledge transfer for interference suppression and drift compensation. Experiments on a background interference dataset and a public benchmark sensor drift dataset via E-nose verify the effectiveness of the proposed DC-AELM method.

ALT

Book 2018aling to readers from the fields of artificial intelligence, computer science, electrical engineering, electronics, and instrumentation science, it addresses three main areas: First, readers will learn how to apply machine learning, pattern recognition and signal processing algorithms to real percep

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Work Autonomy and Product Innovation,work. 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.

彩色的蠟筆

Pfadwechsel — schwierig aber notwendigmonstrates the obvious chaotic behavior through the Lyapunov exponents. Results demonstrate that the proposed model can make long-term and accurate prediction of time series chemical sensor baseline and drift.

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