Discrete Wavelet Transform and Radial Basis Neural Network for Semiconductor Wet-Etching Fabrication Flow-Rate Analysis

2012 ◽  
Vol 61 (4) ◽  
pp. 865-875 ◽  
Author(s):  
Wen-Ren Yang
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Flow Rate ◽  
Wet Etching ◽  
Discrete Wavelet ◽  
Radial Basis Neural Network ◽  
Rate Analysis ◽  
Radial Basis

Maximal Overlap Discrete Wavelet Transform, Graph Theory And Backpropagation Neural Network In Stock Market Forecasting

2018 ◽  
Vol 5 (1) ◽  
pp. 41-46
Author(s):  
Rosalina Rosalina ◽  
Hendra Jayanto
Keyword(s):  
Neural Network ◽  
Graph Theory ◽  
Wavelet Transform ◽  
Stock Market ◽  
Discrete Wavelet Transform ◽  
Stock Price ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Stock Market Forecasting ◽  
Artificial Neural

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

scholarly journals PERAMALAN DATA PENUMPANG KERETA API DENGAN MENGGUNAKAN MAXIMAL OVERLAP DISCRETE WAVELET TRANSFORM- RECURRENT NEURAL NETWORK (MODWT-RNN)

2019 ◽  
Vol 12 (2) ◽  
pp. 164
Author(s):  
Mira Andriyani ◽  
Subanar Subanar
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Recurrent Neural Network ◽  
Public Transportation ◽  
Accurate Result ◽  
Nonlinear Models ◽  
Forecasting Model ◽  
Discrete Wavelet ◽  
The Public

The train is one of the public transportation that is very popular because it is affordable and free of congestion. There is often a buildup of train passengers at the station so that it sometimes causes an accumulation of passengers at the station and makes the situation at the station to be not conducive. In order to avoid a buildup of passengers, forecasting the number of passengers can be done. Forecasting is determined based on data in previous times. Data of train passengers in Java (excluding Jabodetabek) forms a non-stationary and contains nonlinear relationships between the lags. One of the nonlinear models that can be used is Recurrent Neural Network (RNN). Before RNN modeling, Maximal Overlap Wavelet Transform (MODWT) was used to make data more stationary. Forecasting model of train passengers in Java excluding Jabodetabek, Indonesia using MODWT-RNN results forecasting with RMSE is 252.85, while RMSE of SARIMA and RNN are 434.97 and 320.48. These results indicate that the MODWT-RNN model gives a more accurate result than SARIMA and RNN.

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