Abstract: In intensive aquaculture, feeding level is directly related to production efficiency and breeding cost. How to achieve precise feeding is the key issue of aquaculture. In this paper, a prediction method of feeding quantity based on adaptive network fuzzy inference system was proposed. Taking the tilapia as  research object, 2 factors of water temperature and average weight of fish were selected as input variables, and an adaptive network fuzzy inference system prediction model was established. The temperature sensor and weight scale were used to record the water temperature and the average weight of the fish, respectively. The experiment was carried out for two months. Results showed that the root mean square error (RMSE), the mean absolute error (MAE) and mean absolute percentage error (MAPE) from the ANFIS feeding prediction models were 1.18, 0.74 and 0.003 1, respectively, which were far less than the index values of the fuzzy reasoning feeding amount prediction model. The validity and rationality of the feeding forecasting model were proved. It was clear that the ANFIS network forecasting ability was better than the fuzzy reasoning forecasting model. This model not only saved labor costs, but also provided technical support and theoretical support for scientific and reasonable feeding.

Key words: ANFIS, precise feeding, water temperature, weight, aquaculture

摘要： 在集约化水产养殖中，鱼类的投喂水平直接关系到养殖效率和生产成本。针对当前水产养殖中存在的投喂量不合理、饲料浪费严重的问题，以实现投喂量的精准预测为目的，提出了一种基于自适应模糊神经网络的鱼类投喂量预测方法。该方法以罗非鱼为研究对象，选择水温和鱼的平均体重2个因素作为输入变量，利用混合学习方法，通过训练和学习获得最优模糊规则库，基于自适应神经网络模糊推理系统（adaptive network fuzzy inference system,ANFIS）建立投喂量预测模型，取得了较好的预测效果。基于ANFIS的投喂量预测模型的均方根误差、平均绝对误差和平均绝对百分比误差分别为1.18、0.74和0003 1，均远远小于原始模糊推理投喂量预测模型的指标值，其网络预测能力优于原始模糊推理预测模型。因此，该模型不仅可以在无监督条件下对鱼进行科学投喂，节省人力成本，而且能为合理的投喂提供技术支撑和理论支持。

关键词: ANFIS, 精准投喂, 水温, 体重, 水产养殖