Neural network BP training algorithm based on gradient descend technique may lead to entrapment in local optimum so that the network inaccurately classifies input patterns.
基于梯度下降的神经网络训练算法易于陷入局部最小，从而使网络不能对输入模式进行准确分类。
Raw data preprocessing, the size of training data, as well as the size of input vectors have been studied separately, to find out the best parameter set of BP neural network prediction.
本文分别针对数据的预处理、训练数据集大小以及输入向量的大小分别进行了研究，以确定使用BP神经网络预测的一个最佳参数组合。
The training model of test simulation for car of inverted pendulum based on BP algorithm of artificial neural networks (ANN) is a BP network that has 4-input and 3-layer structure.
基于人工神经网络BP算法的倒立摆小车实验仿真训练模型，其倒立摆BP网络为4输入3层结构。
Neural network need not establish accurate mathematics model, it sums up the relation implicit in the systematic input output through studying input output training sample data.
神经网络不需要建立精确的数学模型，只是通过学习输入输出训练样本数据，就可归纳出隐含在系统输入输出中的关系；
Then, preprocessed data as input for genetic neural network of RBF accepted training. Finally, test data were sent to trained neural network to validate.
神经网络配合专家手工分类结果进行训练，训练好的神经网络再对测试数据进行分析。
Then, preprocessed data as input for genetic neural network of RBF accepted training. Finally, test data were sent to trained neural network to validate.
神经网络配合专家手工分类结果进行训练，训练好的神经网络再对测试数据进行分析。
应用推荐