【作者】 杨皓翔； 禹素萍； 许武军； 范红；

【Author】 Yang Haoxiang;Yu Suping;Xu Wujun;Fan Hong;College of Information Science and Technology, Donghua University;

【机构】 东华大学信息科学与技术学院；

【摘要】 智能观赏鱼缸逐渐进入大众视野,而温度控制是智能鱼缸中极为重要的一部分,观赏鱼对于水温的依赖性较大,因此,为了提高智能观赏鱼缸在水温控制方面的精确性,本文以智能观赏鱼缸水温为研究对象,提出一种基于BP神经网络的温度控制策略。该方法是在传统PID调节方法的基础上,利用BP神经网络进行调整,可以通过对系统性能的学习来实现具有最佳组合的PID控制,自适应调整系统中的关键参数,并使用测试法建立相应的水温数学模型。在实验中可以清楚看到BP神经网络的优势,同时采用Matlab进行仿真,对比验证了传统PID与BP-PID在水温控制方面的精确性。结果表明,BP神经网络对于智能鱼缸水温控制有着更好的效果。更多还原

【Abstract】 Smart ornamental fish tanks gradually enter the public eye and temperature control is a very important part of the intelligent fish tank.Ornamental fish’s dependence on water temperature is relatively large.Therefore, in order to improve the intelligent ornamental fish tank accuracy in terms of water temperature control, this paper takes the water temperature of the intelligent ornamental fish tank as the research object and puts forward a temperature control strategy based on BP neural network.This method is based on the traditional PID regulation method, using the BP neural network to adjust, which can achieve the PID control with the best combination by learning of the system performance and adaptively adjust the key parameters in the system.Meanwhile,l use the test method to establish the corresponding mathematical model of water temperature.The advantages of BP neural network can be clearly seen in the experiment, and Matlab simulation is used to compare verify the accuracy of traditional PID and BP-PID in water temperature control.The result shows that the BP neural network has a better effect on the water temperature control of the intelligent fish tank.更多还原