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After studying the disadvantage of BP neural network which has low convergent speed and trap into local minima easily, an idea of designing a new hybrid neural network model. By using Artificial Bee Colony Algorithm (ABC) to expand the updated space of weight and using the fitness functions to decide the better weight. On the basis, make the acquired better value as the weight of BP neural network...
In order to overcome the disadvantages such as low calculation precision and convergence rate of traditional BP neural network algorithm, a kind of nonlinear optimization method-BFGS method for unconstrained extreme problem is introduced into BP neural network algorithm, and a BFGS-BP neural network model is developed, which is applied well in structure deformation monitoring data processing and forecasting...
This article is to report a study based on fabric physical properties measured on the KES system. Grey incidence (GI) analysis, as a mathematic method that ranks the sequence of importance of lots of variables in complicated factors has been applied, In order to select the efficient input variables of ANN (artificial neural network) during the prediction of wearing comfort performance. A series of...
In order to improve the performance of switched reluctance driving system, it is necessary to build an accurate switched reluctance motor (SRM) model. In this paper, a nonlinear flux-linkage model and a torque model of SRM are presented by using the measured accurate flux-linkage data, torque data and nonlinear mapping ability of BP neural network, which is based on fast self-configuring algorithm...
The production period of the crystalline aluminium chloride is considerably long. However, the offline assay of AlCl3??6H2O content has large time delay. Thus soft sensor modeling is needed to analyze its content, and estimate the value to improve the product quality. The conventional back-propagation (BP) neural network training is easily trapped to the local minimum, To overcome this embarrassment,...
In allusion to the problems that the conventional wavelet neural network has disadvantages of training slowly, convergence to the local minimum easily and poor approximation performance, two aspects including initial parameters selection and network training methods were selected to be optimized after analyzing its approximation performance. A kind of self-adaptive method to get the number of hidden...
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