A hybrid approach of artificial neural network and multiple regression to forecast typhoon rainfall and groundwater-level change

2019 ◽  
Vol 64 (14) ◽  
pp. 1793-1802 ◽  
Author(s):  
Ping-Cheng Hsieh ◽  
Wei-An Tong ◽  
Yung-Chieh Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Groundwater Level ◽  
Hybrid Approach ◽  
Level Change ◽  
Typhoon Rainfall ◽  
Artificial Neural ◽  
Groundwater Level Change

scholarly journals Identification Accuracy of Additional Wave Resistance Through a Comparison of Multiple Regression and Artificial Neural Network Methods

2018 ◽  
Vol 1 (1) ◽  
pp. 197-204
Author(s):  
Tomasz Cepowski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Nonlinear Models ◽  
Linear Equations ◽  
Regression Method ◽  
Wave Resistance ◽  
Identification Accuracy ◽  
Linear Regression Method ◽  
Artificial Neural

Abstract The article presents the use of multiple regression method to identify added wave resistance. Added wave resistance was expressed in the form of a four-state nominal function of: “thrust”, “zero”, “minor” and “major” resistance values. Three regression models were developed for this purpose: a regression model with linear variables, nonlinear variables and a large number of nonlinear variables. The nonlinear models were developed using the author's algorithm based on heuristic techniques. The three models were compared with a model based on an artificial neural network. This study shows that non-linear equations developed through a multiple linear regression method using the author’s algorithm are relatively accurate, and in some respects, are more effective than artificial neural networks.

scholarly journals A Hybrid Approach to Gender Classification using Speech Signal

2019 ◽  
pp. 17-24
Author(s):  
M. Yasin Pir ◽  
Mohamad Idris Wani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Speech Signal ◽  
Hybrid Approach ◽  
Gender Classification ◽  
Power Spectral ◽  
Reconstructed Signal ◽  
Feed Forward Network ◽  
Artificial Neural

Speech forms a significant means of communication and the variation in pitch of a speech signal of a gender is commonly used to classify gender as male or female. In this study, we propose a system for gender classification from speech by combining hybrid model of 1-D Stationary Wavelet Transform (SWT) and artificial neural network. Features such as power spectral density, frequency, and amplitude of human voice samples were used to classify the gender. We use Daubechies wavelet transform at different levels for decomposition and reconstruction of the signal. The reconstructed signal is fed to artificial neural network using feed forward network for classification of gender. This study uses 400 voice samples of both the genders from Michigan University database which has been sampled at 16000 Hz. The experimental results show that the proposed method has more than 94% classification efficiency for both training and testing datasets.

scholarly journals Forecasting of Groundwater Level using Artificial Neural Network by incorporating river recharge and river bank infiltration

2017 ◽  
Vol 103 ◽  
pp. 04007 ◽  
Author(s):  
Mohd Khairul Nizar Shamsuddin ◽  
Faradiella Mohd Kusin ◽  
Wan Nor Azmin Sulaiman ◽  
Mohammad Firuz Ramli ◽  
Mohamad Faizal Tajul Baharuddin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Groundwater Level ◽  
River Bank ◽  
Artificial Neural ◽  
Bank Infiltration

Air carbon monoxide forecasting using an artificial neural network in comparison with multiple regression

2020 ◽  
Vol 6 (3) ◽  
pp. 1467-1475 ◽  
Author(s):  
Seyedeh Reyhaneh Shams ◽  
Ali Jahani ◽  
Mazaher Moeinaddini ◽  
Nematollah Khorasani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Carbon Monoxide ◽  
Multiple Regression ◽  
Artificial Neural

scholarly journals Comparison of Artificial Neural Network and Multiple Regression for Partial Discharge Sources Recognition

2017 ◽  
Author(s):  
Abdullahi Abubakar Masud ◽  
Firdaus Muhammad-Sukki ◽  
Ricardo Albarracin ◽  
Jorge Alfredo Ardila-Rev ◽  
Siti Hawa Abu-Bakar ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Partial Discharge ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document