In order to forecast quickly the operating condition of the loom, optimize the parameters of loom production, so that the production efficiency of loom will be improved. This paper studies the prediction of the operating condition of the loom based on the neural networks. The neural networks technology is applied to forecast the operating condition of the loom production, establishes corresponding prediction model of loom production. With the help of neural networks samples are trained and checked, then are applied to forecast the operating condition of the loom production, the results are compared with the Bayesian theorem. The study indicates that network model based on the neural networks has reliability and high accuracy.
How to quickly and accurately identify the quality of grain seed, grain type and other basic indicators and improve the efficiency of breeding quality, wheat production and quality in the work of selection or breeding have become a serious problem. The project developed the necessary software and hardware detection device platform, carried out research on the segmentation and counting algorithms of particle images for the system, and designed and implemented image processing techniques based on particle quality grain seed of the determination system. The result of the research has broad application prospects.
For the shortage of artificial methods in classification of apple level, a method of classification of apple level based on BP artificial neural networks is presented. Artificial neural network provides technical means for research on apple classification, because it can provide nonlinear mapping of the input vector and output vector with arbitrary dimension, and also can reach any nonlinear continuous system. The BP neural network model is established for the classification by taking feature parameters as input vectors and apple level as output vectors. The results has shown that the classification precision of model is very high, and which has good application in realizing automatic identification of apple level.
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