Rollover prediction and control in heavy vehicles via recurrent neural networks

2004 ◽  
Author(s):  
E.N. Sanchez ◽  
L.J. Ricalde ◽  
R. Langari ◽  
D. Shahmirzadi
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Heavy Vehicles ◽  
Prediction And Control ◽  
Rollover Prediction ◽  
And Control

Rollover Prediction and Control in Heavy Vehicles Via Recurrent High Order Neural Networks

2011 ◽  
Vol 17 (1) ◽  
pp. 95-107 ◽  
Author(s):  
Edgar N. Sanchez ◽  
Luis J. Ricalde ◽  
Reza Langari ◽  
Danial Shahmirzadi
Keyword(s):  
Neural Networks ◽  
High Order ◽  
Heavy Vehicles ◽  
Prediction And Control ◽  
Rollover Prediction ◽  
And Control

scholarly journals ELMAN-RECURRENT NEURAL NETWORK FOR LOAD SHEDDING OPTIMIZATION

SINERGI ◽  
2020 ◽  
Vol 24 (1) ◽  
pp. 29
Author(s):  
Widi Aribowo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Power System ◽  
Recurrent Neural Networks ◽  
Load Shedding ◽  
Feed Forward Neural Network ◽  
Linear Problems ◽  
Elman Recurrent Neural Network ◽  
And Control

Load shedding plays a key part in the avoidance of the power system outage. The frequency and voltage fluidity leads to the spread of a power system into sub-systems and leads to the outage as well as the severe breakdown of the system utility.  In recent years, Neural networks have been very victorious in several signal processing and control applications.  Recurrent Neural networks are capable of handling complex and non-linear problems. This paper provides an algorithm for load shedding using ELMAN Recurrent Neural Networks (RNN). Elman has proposed a partially RNN, where the feedforward connections are modifiable and the recurrent connections are fixed. The research is implemented in MATLAB and the performance is tested with a 6 bus system. The results are compared with the Genetic Algorithm (GA), Combining Genetic Algorithm with Feed Forward Neural Network (hybrid) and RNN. The proposed method is capable of assigning load releases needed and more efficient than other methods. 

Locally Recurrent Neural Networks and Their Applications

2010 ◽  
pp. 195-222
Author(s):  
Todor D. Ganchev
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Computational Models ◽  
Machine Learning Techniques ◽  
Spatial Correlations ◽  
Temporal Correlations ◽  
Learning Techniques ◽  
Locally Recurrent ◽  
The One ◽  
And Control

In this chapter we review various computational models of locally recurrent neurons and deliberate the architecture of some archetypal locally recurrent neural networks (LRNNs) that are based on them. Generalizations of these structures are discussed as well. Furthermore, we point at a number of realworld applications of LRNNs that have been reported in past and recent publications. These applications involve classification or prediction of temporal sequences, discovering and modeling of spatial and temporal correlations, process identification and control, etc. Validation experiments reported in these developments provide evidence that locally recurrent architectures are capable of identifying and exploiting temporal and spatial correlations (i.e., the context in which events occur), which is the main reason for their advantageous performance when compared with the one of their non-recurrent counterparts or other reasonable machine learning techniques.

NeSS: a Simulation Environment for Behavioral Design of Neural Networks for Prediction and Control

1999 ◽  
Vol 6 (3) ◽  
pp. 223-232
Author(s):  
Cesare Alippi ◽  
Fabio Casamatta ◽  
Leonardo Furlan ◽  
Andrea Pelagotti ◽  
Vincenzo Piuri
Keyword(s):  
Neural Networks ◽  
Simulation Environment ◽  
Behavioral Design ◽  
Prediction And Control ◽  
And Control
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