Non-linear system modeling using LSTM neural networks

Jesús Gonzalez; Wen Yu

首页> 外文期刊>IFAC PapersOnLine >Non-linear system modeling using LSTM neural networks

【24h】

Non-linear system modeling using LSTM neural networks

机译：使用LSTM神经网络的非线性系统建模

获取原文

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

Long-Short Term Memory (LSTM) is a type of Recurrent Neural Networks (RNN). It takes sequences of information and uses recurrent mechanisms and gate techniques. LSTM has many advantages over other feedforward and recurrent NNs in modeling of time series, such as audio and video. However, in non-linear system modeling normal LSTM does not work well(Wang, 2017). In this paper, we combine LSTM with NN, and use the advantages. The novel neural model consists of hierarchical recurrent networks and one multilayer perceptron. We design a special learning algorithm which uses backpropagation, and backpropagation through time methods. We use two non-linear system examples to compare our neural modeling with other well known methods. The results show that for the simulation model (only the test input is used and the past test output is not used), the modified LSTM model proposed in this paper is much better than the other existing neural models.

机译：长期记忆（LSTM）是一种递归神经网络（RNN）。它采用信息序列，并使用递归机制和门技术。在时间序列建模（例如音频和视频）方面，LSTM与其他前馈和递归NN相比具有许多优势。但是，在非线性系统建模中，常规LSTM效果不佳（Wang，2017）。在本文中，我们将LSTM与NN相结合，并利用了这些优势。新的神经模型由分层递归网络和一个多层感知器组成。我们设计了一种特殊的学习算法，该算法使用反向传播，以及通过时间方法进行反向传播。我们使用两个非线性系统示例将我们的神经模型与其他众所周知的方法进行比较。结果表明，对于仿真模型（仅使用测试输入，不使用过去的测试输出），本文提出的改进的LSTM模型比其他现有的神经模型要好得多。

著录项

来源
《IFAC PapersOnLine》 |2018年第13期|共5页
作者
Jesús Gonzalez; Wen Yu;
展开▼
作者单位

展开▼
收录信息
原文格式 PDF
正文语种
中图分类自动化技术、计算机技术;
关键词
neural networksBlack-Box identificationLSTM;

机译：神经网络黑盒识别LSTM;

相似文献

外文文献
中文文献
专利

1. Non-linear system modeling using LSTM neural networks [J] . Jesús Gonzalez, Wen Yu IFAC PapersOnLine . 2018,第13期

机译：使用LSTM神经网络的非线性系统建模
2. Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX) [J] . Wunsch Andreas, Liesch Tanja, Broda Stefan Hydrology and Earth System Sciences Discussions . 2021,第3期

机译：与人工神经网络的地下水位预测：具有外源输入（NARX）的长短期记忆（LSTM），卷积神经网络（CNNS）和非线性自回归网络的比较
3. Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX) [J] . Wunsch Andreas, Liesch Tanja, Broda Stefan Hydrology and Earth System Sciences . 2021,第3期

机译：与人工神经网络的地下水位预测：具有外源输入（NARX）的长短期记忆（LSTM），卷积神经网络（CNNS）和非线性自回归网络的比较
4. Touch Wear: Context-Dependent and Self-Learning Personal Speech Assistant for Wearable Systems with Deep Neural Networks: Using Contextual LSTMs on Recurrent Neural Networks [C] . Joshua Ho, Chien-Min Wang International Conference on Smart Portable, Wearable, Implantable and Disability-oriented Devices and Systems . 2018

机译：触摸磨损：具有深度神经网络的可穿戴系统的上下文依赖和自学个人语音助手：在经常性神经网络上使用上下文LSTMS
5. Neural networks for control signal reconstruction in non-linear systems with an application to aircraft dynamics. [D] . Pispitsos, Stelios Pantelis. 1999

机译：用于非线性系统中控制信号重建的神经网络及其在飞机动力学中的应用。
6. Artificial Neural Networks Sequence-to-Sequence LSTMs and Exogenous Variables as Analytical Tools for NO [O] . Javier González-Enrique, Juan Jesús Ruiz-Aguilar, José Antonio Moscoso-López, 2021

机译：人工神经网络序列到序列LSTM以及外源变量作为否的分析工具
7. Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX) [O] . Andreas Wunsch, Tanja Liesch, Stefan Broda 2021

机译：人工神经网络的地下水位预测：具有外源输入（NARX）的长短期记忆（LSTM），卷积神经网络（CNNS）和非线性自回归网络的比较

Non-linear system modeling using LSTM neural networks

摘要

著录项

相似文献

相关主题

期刊订阅