| NFOA-BP融合算法及其在焚烧炉温度控制中的应用 |
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| 引用本文： | 卢保昆,云 涛,刘 航.NFOA-BP融合算法及其在焚烧炉温度控制中的应用[J].教育技术导刊,2020,19(1):185-189. |
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| 作者姓名： | 卢保昆 云 涛 刘 航 |
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| 作者单位： | 1. 上海理工大学 光电信息与计算机工程学院，上海 200093；2. 上海工业自动化仪表研究院，上海 200233 |
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| 摘 要： | 污泥焚烧炉温度控制过程中，由于投入污泥块热值不均以及外界环境干扰，传统的PID控制不能快速稳定地将炉温控制在所需范围内。为适应环境变化，实现更高效的炉温控制，提出一种基于NFOA-BP算法的污泥焚烧温度控制方法。该方法将改进型果蝇算法与BP神经网络结合，通过NFOA算法优化神经网络的初始权重和阈值，进而提高神经网络的全局搜索能力。将NFOA-BP算法应用于污泥焚烧炉温度控制系统，与传统PID温度控制系统进行仿真对比实验。结果表明该系统响应平稳、迅速，超调减小，正确率达到95%以上，比传统PID调节方法提高5%左右。
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| 关 键 词： | 果蝇优化算法 神经网络 PID温度控制器 污泥焚烧 |
| 收稿时间： | 2019-03-26 |
NFOA-BP Fusion Algorithm and Its Application in Temperature Control of Incinerator |
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| LU Bao-kun,YUN Tao,LIU Hang.NFOA-BP Fusion Algorithm and Its Application in Temperature Control of Incinerator[J].Introduction of Educational Technology,2020,19(1):185-189. |
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| Authors: | LU Bao-kun YUN Tao LIU Hang |
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| Institution: | 1. School of Optoelectronic Information and Computer Engineering， University of Shanghai for Science and Technology， Shanghai 200093， China； 2. Shanghai Institute of Industrial Automation Instrumentation， Shanghai 200233， China |
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| Abstract: | In the sludge incinerator temperature control system， the traditional PID control cannot quickly and stably control the furnace temperature within the required range due to the uneven heating value of the input sludge block and the interference of the external environment. In order to adapt to environmental changes and achieve more efficient furnace temperature control， a sludge incineration temperature control method based on NFOA-BP algorithm was proposed. This method combines improved Drosophila algorithm with BP neural network to optimize neural network by NFOA algorithm. The initial weights and thresholds， which in turn improve the global search capabilities of the neural network. Finally， the NFOA-BP algorithm is applied to the sludge incinerator temperature control system. Compared with the traditional PID temperature control system， the response time process of the proposed method is more stable， the overshoot is reduced， the response time is more rapid， and the correct rate is over 95%，about 5% higher than the traditional PID adjustment. |
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| Keywords: | drosophila optimization algorithm neural network PID temperature controller sludge incineration |
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