Rainfall Forecasting Using Backpropagation Neural Network

2017 ◽  
pp. 277-288 ◽  
Author(s):  
Sudipta K. Mishra ◽  
Naresh Sharma
Keyword(s):  
Neural Network ◽  
Backpropagation Neural Network ◽  
Rainfall Forecasting

Min-Max Backpropagation Neural Network to Forecast e-Journal Visitors

2021 ◽  
Author(s):  
Aji Prasetya Wibawa ◽  
Zahra Nabila Izdihar ◽  
Agung Bella Putra Utama ◽  
Leonel Hernandez ◽  
Haviluddin
Keyword(s):  
Neural Network ◽  
Backpropagation Neural Network

scholarly journals Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2998
Author(s):  
Xinyong Zhang ◽  
Liwei Sun
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Bayesian Regularization ◽  
Backpropagation Neural Network ◽  
Swarm Optimization ◽  
Modal Frequency ◽  
Supporting Structure ◽  
First Order ◽  
Machine Structure

Fit of the highly nonlinear functional relationship between input variables and output response is important and challenging for the optical machine structure optimization design process. The backpropagation neural network method based on particle swarm optimization and Bayesian regularization algorithms (called BMPB) is proposed to solve this problem. A prediction model of the mass and first-order modal frequency of the supporting structure is developed using the supporting structure as an example. The first-order modal frequency is used as the constraint condition to optimize the lightweight design of the supporting structure’s mass. Results show that the prediction model has more than 99% accuracy in predicting the mass and the first-order modal frequency of the supporting structure, and converges quickly in the supporting structure’s mass-optimization process. The supporting structure results demonstrate the advantages of the method proposed in the article in terms of high accuracy and efficiency. The study in this paper provides an effective method for the optimized design of optical machine structures.

scholarly journals A Novel Neuron in Kernel Domain

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zahra Khandan ◽  
Hadi Sadoghi Yazdi
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Mean Square ◽  
Least Mean Square ◽  
Backpropagation Neural Network ◽  
Adaptive Parameters ◽  
Kernel Parameter ◽  
Online Learning Algorithms ◽  
Adaptive Kernel ◽  
Kernel Least Mean Square

Kernel-based neural network (KNN) is proposed as a neuron that is applicable in online learning with adaptive parameters. This neuron with adaptive kernel parameter can classify data accurately instead of using a multilayer error backpropagation neural network. The proposed method, whose heart is kernel least-mean-square, can reduce memory requirement with sparsification technique, and the kernel can adaptively spread. Our experiments will reveal that this method is much faster and more accurate than previous online learning algorithms.

scholarly journals Volatility Degree Forecasting of Stock Market by Stochastic Time Strength Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Haiyan Mo ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Price ◽  
Strength Function ◽  
Global Market ◽  
Chinese Stock Market ◽  
Backpropagation Neural Network ◽  
Time Strength ◽  
Market Indexes ◽  
Stochastic Time

In view of the applications of artificial neural networks in economic and financial forecasting, a stochastic time strength function is introduced in the backpropagation neural network model to predict the fluctuations of stock price changes. In this model, stochastic time strength function gives a weight for each historical datum and makes the model have the effect of random movement, and then we investigate and forecast the behavior of volatility degrees of returns for the Chinese stock market indexes and some global market indexes. The empirical research is performed in testing the prediction effect of SSE, SZSE, HSI, DJIA, IXIC, and S&P 500 with different selected volatility degrees in the established model.

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