基于AQPSO-LSTM-BN的APU故障诊断模型 辅助动力装置(Auxiliary Power Unit,APU)作为飞机的重要装置,不仅可以保证飞机安全启动,在飞机停在地面时,还为飞机供气、供电,保证客舱舒适性。因此,对飞机APU进行故障诊断研究显得尤为重要。 APU故障发生时,排故人员会结合故障发生的现场和自身的相关经验、故障手册的规定等对故障情...
LSTM Layer : 在定义LSTM层时,我们保持Batch First = True和隐藏单元的数量= 512。 1 # initializing the hidden state to 0 2 hidden=None 3 lstm = nn.LSTM(input_size=embedding_dim, hidden_size=512, num_layers=1, batch_first=True) 4 lstm_out, h = lstm(embeds_out, hidden) 5 print ('L...
从QAR数据库中整理出需要的APU故障数据,将其进行归一化处理并分为训练集和测试集两部分,建立CSV文档数据库;对量子粒子群进行改进,使用改进后的量子粒子群对长短期记忆网络的隐含层单元数目进行寻优;将优化参数后的长短期记忆网络与批规范化相结合搭建网络模型,并在网络最顶层加入Softmax模型,生成AQPSO-LSTM-BN故障...
This paper proposes a novel long-term forecasting method of FTS movement based on a modified adaptive LSTM model. The adaptive network mainly consists of two LSTM layers followed by a pair of batch normalization (BN) layers, a dropout layer and a binary classifier. In order to capture the ...
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我想定义一个多层LSTM_cell,需要对每层输出增加一个BN层和激活函数,代码如下: def get_lstm_cell(rnn_size,keep_prob): lstm_cell = tf.contrib.rnn.LSTMCell(rnn_size, initializer=tf.truncated_normal_initializer(stddev=0.1,seed=2)) lstm_cell = tf.layers.batch_normalization(lstm_cell,training=True)...
Movement forecasting of financial time series based on adaptive LSTM-BN networkZhen Fang a EnvelopeXu Ma a Person EnvelopeHuifeng Pan b EnvelopeGuangbing Yang c EnvelopeGonzalo R. Arce d e EnvelopeExpert Systems with Applications
https://github.com/karpathy/char-rnn/blob/master/model/LSTM.lua, and Brendan Shillingford. Usage: local rnn = LSTM(input_size, rnn_size, n, dropout, bn) ]]-- require 'nn' require 'nngraph' local function LSTM(input_size, rnn_size, n, dropout, bn) dropout = dropout or 0 -- the...
TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码,实战教程。 - add bn and lstm · infiniterror/TensorFlow-2.x-Tutorials@4757602
高频数据下股票波动率预测 ———基于Realized GARCH与LSTM的混合模型 股市是一把"双刃剑",既可加速我国社会经济的发展,提升我国的总体经济实力,又因具有强烈的波动效应而存在较高的金融风险.我国股市在2015年和2018年发生过两次重大的股... 张颖芝 - 浙江工商大学 被...