Convergence rate of the gradient descent method with dilatation of the space

Cybernetics ◽  
1973 ◽  
Vol 6 (2) ◽  
pp. 102-108 ◽  
Author(s):  
N. Z. Shor
Keyword(s):  
Convergence Rate ◽  
Gradient Descent ◽  
Descent Method ◽  
Gradient Descent Method

Constant Modulus Algorithm Based on Different Error Functions Weighted by Variable Coefficient

2011 ◽  
Vol 66-68 ◽  
pp. 1579-1585
Author(s):  
Qiao Xi Zhou ◽  
Ye Cai Guo
Keyword(s):  
Theoretical Analysis ◽  
Convergence Rate ◽  
Variable Coefficient ◽  
Gradient Descent ◽  
Error Function ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Constant Modulus ◽  
Constant Modulus Algorithm ◽  
Error Functions

To improve equalization performance of the constant modulus algorithm (CMA), we study that error functions have an influence on the performance of the algorithm in this paper. Aiming at the character of different error functions, a new style of error function weighted by a variable coefficient is proposed. And a new CMA based on the new error function (VCMA) is proposed too. Because of variable-coefficient adjustability, the value of this new error function can become larger at the beginning of iteration and smaller at the end of iteration in the new algorithm. From gradient descent method, VCMA can have faster convergence rate and lower residual error than the CMA. Both theoretical analysis and experimental results have shown the effectiveness of the proposed algorithm.

scholarly journals Predicting the Number of COVID-19 Sufferers in Malang City Using the Backpropagation Neural Network with the Fletcher–Reeves Method

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Syaiful Anam ◽  
Mochamad Hakim Akbar Assidiq Maulana ◽  
Noor Hidayat ◽  
Indah Yanti ◽  
Zuraidah Fitriah ◽  
...  
Keyword(s):  
Neural Network ◽  
Convergence Rate ◽  
Gradient Descent ◽  
Infected Individual ◽  
Prediction Method ◽  
Optimization Method ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Backpropagation Neural Network ◽  
Congenital Diseases

COVID-19 is a type of an infectious disease that is caused by the new coronavirus. The spread of COVID-19 needs to be suppressed because COVID-19 can cause death, especially for sufferers with congenital diseases and a weak immune system. COVID-19 spreads through direct contact, wherein the infected individual spreads the COVID-19 virus through cough, sneeze, or close contacts. Predicting the number of COVID-19 sufferers becomes an important task in the effort to curb the spread of COVID-19. Artificial neural network (ANN) is the prediction method that delivers effective results in doing this job. Backpropagation, a type of ANN algorithm, offers predictive problem solving with good performance. However, its performance depends on the optimization method applied during the training process. In general, the optimization method in ANN is the gradient descent method, which is known to have a slow convergence rate. Meanwhile, the Fletcher–Reeves method has a faster convergence rate than the gradient descent method. Based on this hypothesis, this paper proposes a prediction model for the number of COVID-19 sufferers in Malang using the Backpropagation neural network with the Fletcher–Reeves method. The experimental results show that the Backpropagation neural network with the Fletcher–Reeves method has a better performance than the Backpropagation neural network with the gradient descent method. This is shown by the Means Square Error (MSE) resulting from the proposed method which is smaller than the MSE resulting from the Backpropagation neural network with the gradient descent method.

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