scholarly journals Artificial Neural Network Controller for a Modular Robot Using a Software Defined Radio Communication System

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1626
Author(s):  
Luis Fernando Pedraza ◽  
Henry Alberto Hernández ◽  
Cesar Augusto Hernández
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Software Defined Radio ◽  
Signal To Noise Ratio ◽  
Flexible Structures ◽  
Modular Robots ◽  
Modular Robot ◽  
Radio Communication ◽  
Neural Network Controller ◽  
Artificial Neural

Modular robots are flexible structures that offer versatility and configuration options for carrying out different types of movements; however, disconnection problems between the modules can lead to the loss of information, and, therefore, the proposed displacement objectives are not met. This work proposes the control of a chain-type modular robot using an artificial neural network (ANN) that enables the robot to go through different environments. The main contribution of this research is that it uses a software defined radio (SDR) system, where the Wi-Fi channel with the best signal-to-noise Ratio (SNR) is selected to send the information regarding the simulated movement parameters and obtained by the controller to the modular robot. This allows for faster communication with fewer errors. In case of a disconnection, these parameters are stored in the simulator, so they can be sent again, which increases the tolerance to communication failures. Additionally, the robot sends information about the average angular velocity, which is stored in the cloud. The errors in the ANN controller results, in terms of the traveled distance and time estimated by the simulator, are less than 6% of the real robot values.

An Improved DTC Strategy for a DFIG using an Artificial Neural Network Controller

2021 ◽  
Author(s):  
Ibrahim Yaichi ◽  
Abdelkader Harrouz ◽  
Ibrahim Boussaid ◽  
Abdelhafid Semmah ◽  
Patrice Wira ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Artificial Neural

Artificial neural network-based seismic detector

1995 ◽  
Vol 85 (1) ◽  
pp. 308-319 ◽  
Author(s):  
Jin Wang ◽  
Ta-Liang Teng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Input Data ◽  
Seismic Event ◽  
Signal To Noise Ratio ◽  
Type B ◽  
Earthquake Detection ◽  
Seismic Event Detection ◽  
Artificial Neural ◽  
Better Than

Abstract An artificial neural network-based pattern classification system is applied to seismic event detection. We have designed two types of Artificial Neural Detector (AND) for real-time earthquake detection. Type A artificial neural detector (AND-A) uses the recursive STA/LTA time series as input data, and type B (AND-B) uses moving window spectrograms as input data to detect earthquake signals. The two AND's are trained under supervised learning by using a set of seismic recordings, and then the trained AND's are applied to another set of recordings for testing. Results show that the accuracy of the artificial neural network-based seismic detectors is better than that of the conventional algorithms solely based on the STA/LTA threshold. This is especially true for signals with either low signal-to-noise ratio or spikelike noises.

Analysis of neural networks efficiency when accounting for weight coefficients jitter, leading to reduction in output signal/noise ratio

2021 ◽  
Author(s):  
S.V. Zimina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Output Signal ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Useful Signal ◽  
Artificial Neural ◽  
Noise Ratio ◽  
Weight Coefficients

Setting up artificial neural networks using iterative algorithms is accompanied by fluctuations in weight coefficients. When an artificial neural network solves the problem of allocating a useful signal against the background of interference, fluctuations in the weight vector lead to a deterioration of the useful signal allocated by the network and, in particular, losses in the output signal-to-noise ratio. The goal of the research is to perform a statistical analysis of an artificial neural network, that includes analysis of losses in the output signal-to-noise ratio associated with fluctuations in the weight coefficients of an artificial neural network. We considered artificial neural networks that are configured using discrete gradient, fast recurrent algorithms with restrictions, and the Hebb algorithm. It is shown that fluctuations lead to losses in the output signal/noise ratio, the level of which depends on the type of algorithm under consideration and the speed of setting up an artificial neural network. Taking into account the fluctuations of the weight vector in the analysis of the output signal-to-noise ratio allows us to correlate the permissible level of loss in the output signal-to-noise ratio and the speed of network configuration corresponding to this level when working with an artificial neural network.

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