Performance Analysis of Learning Rate Parameter on Prediction of Signal Power Loss for Network Optimization and Better Generalization

2021 ◽  
Author(s):  
Virginia C. Ebhota ◽  
Viranjay M. Srivastava
Keyword(s):  
Performance Analysis ◽  
Network Optimization ◽  
Power Loss ◽  
Learning Rate ◽  
Signal Power ◽  
Rate Parameter

scholarly journals Enhanced Error Reduction of Signal Power Loss During Electromagnetic Propagation: Architectural Composition and Learning Rate Selection

2021 ◽  
pp. 450-456
Author(s):  
Virginia C. Ebhota ◽  
◽  
Viranjay M. Srivastava
Keyword(s):  
Neural Network ◽  
Power Loss ◽  
Single Layer ◽  
Signal Propagation ◽  
Learning Rate ◽  
Signal Power ◽  
Multi Layer Perceptron ◽  
Coefficient Of Correlation ◽  
Network Training ◽  
Hidden Layer

This research work analyses the effect of the architectural composition of Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) combined with the effect of the learning rate for effective prediction of signal power loss during electromagnetic signal propagation. A single hidden layer and two hidden layers of MLP ANN have been considered. Different configurations of the neural network architecture ranging from 4 to 100 for both MLP networks have been analyzed. The required hidden layer neurons for optimal training of a single layer multi-layer network were 40 neurons with 0.99670 coefficient of correlation and 1.28020 standard deviations, while [68 72] trained two hidden layers multi-layer perceptron with 0.98880 coefficient of correlation and standard deviation of 1.42820. Different learning rates were also adopted for the network training. The results further validate better MLP neural network training for signal power loss prediction using single-layer perceptron network compared to two hidden layers perceptron network with the coefficient of correlation of 0.99670 for single-layer network and 0.9888 for two hidden layers network. Furthermore, the learning rate of 0.003 shows the best training capability with lower mean squared error and higher training regression compared to other values of learning rate used for both single layer and two hidden layers perceptron MLP networks.

scholarly journals PENENTUAN PARAMETER LEARNING RATE SELAMA PEMBELAJARAN JARINGAN SYARAF TIRUAN BACKPROPAGATION MENGGUNAKAN ALGORITMA GENETIKA

2020 ◽  
Vol 14 (2) ◽  
pp. 202-212
Author(s):  
Ade chandra Saputra
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Learning Rate ◽  
Local Minima ◽  
Rate Parameter ◽  
Neural Network Learning ◽  
Value Of Learning ◽  
Backpropagation Learning

One of the weakness in backpropagation Artificial neural network(ANN) is being stuck in local minima. Learning rate parameter is an important parameter in order to determine how fast the ANN Learning. This research is conducted to determine a method of finding the value of learning rate parameter using a genetic algorithm when neural network learning stops and the error value is not reached the stopping criteria or has not reached the convergence. Genetic algorithm is used to determine the value of learning rate used is based on the calculation of the fitness function with the input of the ANN weights, gradient error, and bias. The calculation of the fitness function will produce an error value of each learning rate which represents each candidate solutions or individual genetic algorithms. Each individual is determined by sum of squared error value. One with the smallest SSE is the best individual. The value of learning rate has chosen will be used to continue learning so that it can lower the value of the error or speed up the learning towards convergence. The final result of this study is to provide a new solution to resolve the problem in the backpropagation learning that often have problems in determining the learning parameters. These results indicate that the method of genetic algorithms can provide a solution for backpropagation learning in order to decrease the value of SSE when learning of ANN has been static in large error conditions, or stuck in local minima

MIMO Radar Target Detection Performance Analysis with Unknown Parameters

2011 ◽  
Vol 403-408 ◽  
pp. 182-186
Author(s):  
Wei Wei Liu ◽  
Ning Cao ◽  
Hao Lu ◽  
Ju Rong Hu
Keyword(s):  
Performance Analysis ◽  
Multiple Input Multiple Output ◽  
Likelihood Estimation ◽  
Mimo Radar ◽  
Low Rank ◽  
Radar Target ◽  
Signal Power ◽  
Unknown Parameters ◽  
Multiple Input ◽  
Input Multiple Output

Motivated by the development of Multiple-Input Multiple-Output (MIMO) communication, MIMO radar has drawn considerable attention. While, to design of MIMO radar detector, transmitting signal power and noise are usually assumed known in advance, but in practice we may need to estimate the transmitting signal power and noise first. In this paper, we introduce MIMO radar target performance analysis with unknown parameters. First transmitting signal energy is estimated by Maximum likelihood Estimation(MLE) when multipath satisfy special diversity condition and multipath has low rank. Then the detector in the Neyman-Pearson is developed and analyzed with estimated parameters. The simulation results show that the performance with unknown parameters is approximate to the detector with known parameters. The method proposed in this paper can be used to design the MIMO radar detectors with unknown parameters.

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