Comparison of neural network algorithms based on gas qualitative analysis

Yu Mingyan, Shi Yunbo, Z. Wenjie, Feng Qiaohua, Wang Xuan, S. Li-ning
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引用次数: 1

Abstract

For the problem of gas qualitatively identify in the field of gas detection, this paper is based on the multi-sensor and pattern recognition of neural network, the uniform change voltage of the sensor output is simulated by the gradient descent algorithm, the additional momentum algorithm and the LM algorithm of neural network, then compare the three simulation results of the three algorithms, the result proves that the LM algorithm is the optimal algorithm of the data simulation in this paper, in the range of allowable error, completed the gas qualitative identification.
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基于气体定性分析的神经网络算法比较
针对气体检测领域中的气体定性识别问题,本文基于神经网络的多传感器和模式识别,采用梯度下降算法、附加动量算法和神经网络的LM算法对传感器输出电压的均匀变化进行了模拟,然后对比了三种算法的三种仿真结果,结果证明LM算法是本文数据仿真的最优算法。在允许误差范围内,完成了气体定性鉴定。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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