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针对农业温室环境的精确建模和控制问题,提出了一种基于模糊神经网络的智能控制方案。首先,在考虑室内外环境因素下,构建一个有效的温室环境数学模型,获得通风量、喷雾量和加热量的微分表达式;然后,利用一种自适应模糊神经推理系统(ANFIS),以温度和湿度差作为输入,通过神经网络自学习和模糊推理获得控制输出;最后,通过遗传算法优化控制器的输出比例因子,提高控制响应速度和稳定性。实验结果表明:该方案能够快速且稳定地追踪环境设置值,具有很好的控制效果。
For the accurate modeling and control of agricultural greenhouse environment, an intelligent control scheme based on fuzzy neural network is proposed. Firstly, an effective mathematical model of greenhouse environment was constructed considering the indoor and outdoor environment factors, and the differential expressions of ventilation, spray volume and heating amount were obtained. Then, with an adaptive fuzzy neuro-inference system (ANFIS) And humidity difference as input, through the neural network self-learning and fuzzy reasoning to obtain the control output; Finally, the genetic algorithm to optimize the controller output scaling factor to improve the control response speed and stability. The experimental results show that this scheme can track environment settings quickly and stably and has good control effect.