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- neural net forecasting model 神经网络预测模型
- To make perdition in an effective way, power load forecasting model based on BP neural network is established. 在此基础上,为了对河北省南部电网月用电量进行有效的预测,建立了BP神经网络的负荷预测模型。
- Even though this picture is a simplification of the biological facts, it is sufficiently powerful to serve as a model for the neural net. 尽管这是个生物行为的简化描述。但同样可以充分有力地被看作是神经网络的模型。
- It also check the forecasting model very carefully. 并对预报模型进行了精度检验。
- Based on the artificial neural net and the genetic algorithm,the neural net model for diagnosing transformer faults is founded by DGA. 运用油中溶解气体分析法,在人工神经网络和遗传算法的基础上,建立了变压器故障诊断的神经网络模型。
- A RBF network forecasting model for wear rate is built with a workbox of artificial neural network.Results are shown as the following: 1. 针对二硅化钼材料的磨损，利用MATLAB神经网络工具箱，以温度、载荷、转速为影响因子，以磨损率为输出，建立了一个径向基神经网络模型。
- Aim at feature decision edge existing certain nonlinearity, the paper propse the model and method of cutting tool state recognition based on BP neural net. 针对特征判决边界存在一定的非线性特性，本文提出了基于BP神经网络的刀具状态识别模型和方法；
- In allusion to the non-linear and complex character in EDM,an effect forecast model is established based on artifical neural network. 针对电火花加工非线性及复杂性的特点,提出了基于遗传神经网络的电火花加工效果的预测模型。
- In this paper, a speaker identification system is proposed based on classify Fea-ture Sub-space Gaussian Mixture Model and Neural Net fusion (FS-GMM/NN) . 该文提出了一种基于分类高斯混合模型和神经网络融合(FS-GMM/NN)的说话人识别方法，通过对特征矢量进行聚类分析，将说话人的训练语音分成若干类。
- In order to reveal the evolutionary law of RCE time series process, a forecasting model with the wavelet transform and the BP neural network combined together is established. 为研究参考作物腾发量在时间尺度上的分布规律，提出了一种基于小波变换与人工神经网络相结合的参考作物腾发量预测模型。
- This would be accomplished by training a neural net to make two cuts. 把神经网络培训成能实现两个切割就可完成这种工作。
- Finally, the outputs of every classify GMM will be fused by Neural Net (NN). 并采用神经网络实现各个分类高斯混合模型输出的融合。
- The model not onlyovercomes the shortage that timing series forecasting model just can do linear forecast，but also averts the disfigurements oftradition neural networks. 该模型不仅克服了时间序列预测模型只能进行线性预测的不足，而且还避免了传统神经网络的固有缺陷。
- Based on the ELMAN neural network, and the method of relevant station history data, a flow difference value forecast model was built. 摘要利用ELMAN神经网络，采用相关站点历史数据逼近的方法，建立了流量差值预测模型；
- Neural nets are also supposed to learn. 神经网络也可假定为可以学习。
- After the thoroughly study of the BP neural network, gray systematic theory and SVM, aiming at their insufficiencies, new methods are raised and new forecasting model is created. 本论文通过对BP神经网络、灰色系统理论和支持向量机的深入研究，针对其中的不足，提出了新的解决方法，建立了新的预测模型。
- The results reveal that the combined forecasting model is more effective. 结果表明，此组合预测平均误差和预测平方根误差均较小。
- Research Areas: robotics, neural net, Gaussian process, scientific computing, and software/ hardware development. 主要研究领域为冗余机器人，递归神经网络，高斯过程，科学计算和软硬件开发。
- In the end time-variant weights combined forecasting model is discussed. 最后也探讨了变权系数的组合预测模型
- Similarly, in weight modification, a neural net can seek a weight distribution that minimizes error. 在权系数的调整中,神经网络将会找到一种将误差减少到最小的权系数的分配方式。
