膨化食品质量的智能控制方法

Intelligent Control Methods for Extrudate Food Quality

质量控制一直是制约我国食品生产发展的主要问题之一，文章研究了用神经网络模式识别器来识别统计质量控制中的典型模式。进行了计算机模拟和实验。本研究展示了在食品质量控制过程中，应用计算机视觉系统和神经网络技术的潜在优势。性能评价的结果说明，误差逆向传播神经网络算法是一种行之有效的质量模式识别的工具，这项智能模式识别技术可以成功地应用在食品加工过程中。

Quality control is one of the bottle neck problems hindering the development of food production. In this paper, a neural network pattern recognizer was used to recognize the typical patterns in statistical quality control(SQC). Experiments and computer simulations were conducted to evaluate the performance of the pattern recognizer. This research demonstrates the potential and procedure of applying computer vision and neural network pattern recognition techniques to food quality control. Performance evaluation indicates that back propagation neural network algorithm is effective for pattern recognition and can be applied to extrusion process successfully.