The application research of improving neural network algorithm in the grain monitoring

Wu Jianjun, B. Biao
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Abstract

In allusion to the insufficiencies such as knowledge acquirement, reasoning ability and self-learning ability, the paper applies neural network into expert system, combines with the system of measurement and control for grain storage, and puts forward an improved BP algorithm. This algorithm does not need the prior hypothesis model and has a good compatibility to the complete and noisy information; it also can solve the non-linear problem well. This new algorithm has coordinated contradictions between learning efficiency and convergence rate and improved skilled speed and convergence rate. From the results of experiment, we can see that the new algorithm has some advantages, such as quickly, validity and practicability.
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改进神经网络算法在粮食监测中的应用研究
针对专家系统在知识获取、推理能力、自学习能力等方面的不足,将神经网络应用到专家系统中,结合储粮测控系统,提出了一种改进的BP算法。该算法不需要先验假设模型,对完整信息和有噪声信息具有良好的兼容性;它还能很好地解决非线性问题。该算法协调了学习效率和收敛速度之间的矛盾,提高了熟练速度和收敛速度。实验结果表明,新算法具有快速、有效、实用等优点。
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