ANN与模糊算法在毛精纺织机效率预报中的对比

Comparing artificial neural network and fuzzy theory in efficiency forecast of worsted loom

介绍了人工神经网络技术和模糊算法在毛精纺面料织造预报过程中的应用,建立人工神经网络BP质量预报模型和模糊算法中模糊评判模型,利用这2种预报技术分别给出了织机效率预报模型,并比较2种预报模型对毛精纺织造质量预报的性能优劣。通过对人工神经网络技术和模糊算法的理论比较及其预报结果的对比分析,得出在毛精纺织造质量的预报中,人工神经网络BP质量预报模型优于模糊算法中的模糊评判模型,2种预报技术在解决非线性问题方面具有各自的适应性。

The artificial neural network technology and fuzzy theory used to forecast the weaving process of worsted fabric were introduced. The main jobs for setting up two kinds of forecast models, i.e., BP artificial neural network and fuzzy evaluation models for weaving process were put forward. And the two different models to predict the loom efficiency were given. The aim was to compare the characteristics of these two models for forecasting weaving quality of wool worsted. The results showed that the character of the neural network forecast model was better than that of the fuzzy evaluation forecast model for the weaving process of wool worsted. And the two theories had their own adaptability for solving the nonlinear problems.