Application of BP Neural Networks on Prediction of Operating Condition of Loom

Abstract

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.

Research on classification of apple level based on neural network

“…The neurons in the input layer receives external inputs; the neurons in the output layer carries out the distinction and the decision-making on the input information; the neurons in the middle hidden layer can be used to express or memory knowledge [7]. BP algorithm is as following [8]:…”

“…where Wij is weight between the hidden layers and input layers, i and i are the number of neuron of hidden layer and input layer respectively, 8 i is the bias of hidden layer 1548 neurons; P is the number of hidden layer neurons, in order to simulate the characteristics of the nonlinear of biological neurons, U i is used as independent variables of S-type function (Sigmoid function), Yi is the output of the hidden layer neurons, S-function is as following: 4) The desired output dt and the actual output of neural network Y t is compared, the error is calculated [9], mean square error is used as following m e=1/2�:cdt-yt)2 (5) if e is less than q (q i&=predetermined value), the algorithm is end, otherwise go to the steps (5), that is back-propagation.…”