摘要
为了实现温室控制,针对温室环境的多输入、多输出、非线性和难以建立数学模型等特点,提出一种基于BP神经网络的专家系统并用于温室控制。该方法将传感器采集的温度、湿度等信息输入到神经网络专家系统,在获得决策结果的同时通知控制部分执行相应的决策。这种方法不仅解决了传统专家系统知识获取的瓶颈问题、推理能力差、智能化低的缺点,而且克服了神经网络不具有解释功能的问题。总之,成功实现了神经网络和专家系统功能上的互补,较好的用于温室控制。
In order to implement greenhouse control, in accordance with greenhouse environment, which has multipleinput, multiple-output, non-linear, and difficult to establish mathematical models, an expert system based on Back- Propagation neural network has been proposed and used which achieve from sensors are input in the expert system. in greenhouse control. Data as temperature or humidity When we receive the decision, the system also informs the control block to react. The method not only solves the drawbacks of conventional expert system such as knowledge acquiring bottleneck, poor capability in reasoning and lower intelligent level, but also overcomes shortcomings that the neural network doesn＇t have interpreting function. In conclusion, the method which succeeds integrating merits of neural network and expert system can be used in greenhouse control.
出处
《成都信息工程学院学报》
2010年第3期260-263,共4页
Journal of Chengdu University of Information Technology
关键词
BP神经网络
专家系统
温室控制
传感器
决策
Back-Propagation neural-network
expert system
greenhouse control
sensor
decision
