Recurrent Neural Network Topologies for Spectral State Estimation and Differentiation

2002 ◽  
Vol 4 (2) ◽  
pp. 75-89 ◽  
Author(s):  
Melik Dölen ◽  
Ekrem Kayikci ◽  
Robert D. Lorenz
Keyword(s):  
Neural Network ◽  
State Estimation ◽  
Recurrent Neural Network ◽  
Network Topologies ◽  
Spectral State

scholarly journals Finding gene network topologies for given biological function with recurrent neural network

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jingxiang Shen ◽  
Feng Liu ◽  
Yuhai Tu ◽  
Chao Tang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Topology ◽  
Biological Function ◽  
Biochemical Networks ◽  
Perturbative Method ◽  
Complex Functions ◽  
Network Topologies ◽  
And Function ◽  
The Hill

AbstractSearching for possible biochemical networks that perform a certain function is a challenge in systems biology. For simple functions and small networks, this can be achieved through an exhaustive search of the network topology space. However, it is difficult to scale this approach up to larger networks and more complex functions. Here we tackle this problem by training a recurrent neural network (RNN) to perform the desired function. By developing a systematic perturbative method to interrogate the successfully trained RNNs, we are able to distill the underlying regulatory network among the biological elements (genes, proteins, etc.). Furthermore, we show several cases where the regulation networks found by RNN can achieve the desired biological function when its edges are expressed by more realistic response functions, such as the Hill-function. This method can be used to link topology and function by helping uncover the regulation logic and network topology for complex tasks.

Vehicle State Estimation Based on Recurrent Neural Network

2021 ◽  
Author(s):  
Ge Dong ◽  
Guangxu Che ◽  
Mengjian Tian ◽  
Haiyan Zhao ◽  
Bingzhao Gao
Keyword(s):  
Neural Network ◽  
State Estimation ◽  
Recurrent Neural Network ◽  
Vehicle State Estimation

Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8927-8935
Author(s):  
Bing Zheng ◽  
Dawei Yun ◽  
Yan Liang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Behavior Pattern ◽  
Rapid Development ◽  
Video Data ◽  
Support Vector ◽  
Behavior Recognition ◽  
Learning Methods ◽  
The Impact ◽  
Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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