scholarly journals Estudio comparativo de los algoritmos backpropagation (bp) y multiple linear regression (mlr) a través del análisis estadístico de datos aplicado a redes neuronales artificiales

2020 ◽  
Vol 9 (3) ◽  
pp. 144-152
Author(s):  
Iván Mesias Hidalgo-Cajo ◽  
Saul Yasaca-Pucuna ◽  
Byron Geovanny Hidalgo-Cajo ◽  
Diego Patricio Hidalgo-Cajo ◽  
Nelly Baltazara Latorre-Benalcázar
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Sea Level ◽  
Total Error ◽  
Back Propagation ◽  
The Neural Network ◽  
Power Turbine ◽  
La Red ◽  
Network Type

El objetivo de la investigación es comparar el algoritmo Backpropagation desarrollado por el usuario bajo software libre Java y el algoritmo Multiple Linear Regression, dicha comparación demanda del análisis estadístico decriptivo basado en redes neuronales artificiales. Se utilizó específicamente dos modelos de algoritmos de predicción aplicados a 451 patrones o registros a procesar que están repartidos en las primeras 401 filas para entrenamiento de la red neuronal y los otros 50 registros para validación y prueba, conformado por  4 variables de entrada (Height above sea level, Fall, Net fall, Flux) y 1 variable a predecir (Power turbine), para las diferentes pruebas los parámetros de entrenamiento y selección con los mejores resultados son: Architecture of the neural network, Type of scaling of data, Initial range of weight and thresholds, Learning rate and Momentum, Batched / online, Number of training epochs. Entre los resultados de comparación de los algoritmos analizados se determinó que el error en mayor iteracciones es menor que son respuestas de los 50 patrones de prueba. En el algoritmo Multiple Linear Regression  la variable real es el valor de la variable a predecir, esta variable es la suministrada a predecir por el usuario y es el valor que se predijo de la red neuronal, la variable prediction es la diferencia que se hace de la resta de los anteriores errores y se lo realiza para calcular el error y el total error es el valor mínimo a obtener que representa el error calculado de todos los datos, es decir el porcentaje de error de la red neuronal de back-propagation. Entre más bajo es este porcentaje mejor será la red, porque menor será su porcentaje de error.

scholarly journals Study on process of chemical fiber filament automatic doffing system based on simulation platform and machine learning

2021 ◽  
Vol 16 ◽  
pp. 155892502110548
Author(s):  
Hongxin Zhu ◽  
Kun Zou ◽  
Wenlan Bao
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Production Line ◽  
Simulation Platform ◽  
Chemical Fiber ◽  
Production Lines ◽  
The Neural Network ◽  
Spss Software ◽  
The Relationship

In recent years, a large number of automatic equipment has been introduced into the chemical fiber filament doffing production line, but the related research on the fully automatic production line technology is not yet mature. At present, it is difficult to collect data due to test costs and confidentiality. This paper proposes to develop a simulation platform for a chemical fiber filament doffing production line, which enables us to effectively obtain data and quantitatively study the relationship between the number of manual interventions and other process parameters of the production line. Considering that the parameter research is a multi-factor problem, an orthogonal test was designed by using SPSS software and was carried out by using a simulation platform. The multiple linear regression (MLR) and the neural network optimized by genetic algorithm were adopted to fit the relationship between the number of manual interventions and other parameters of the production line. The SPSS software was applied to obtain the standardized coefficients of the multiple linear regression fitting and the neural network mean impact value (MIV) algorithm was applied to obtain the magnitude and direction of the impact of different parameters on the number of manual interventions. The above results provide important reference for the design of similar new production lines and for the improvement of old production lines.

scholarly journals Development of Models for Children—Pedestrian Crossing Speed at Signalized Crosswalks

2021 ◽  
Vol 13 (2) ◽  
pp. 777
Author(s):  
Irena Ištoka Otković ◽  
Aleksandra Deluka-Tibljaš ◽  
Sanja Šurdonja ◽  
Tiziana Campisi
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Multiple Linear Regression Model ◽  
Urban Environments ◽  
The Neural Network ◽  
The City ◽  
Traffic Behavior

Modeling the behavior of pedestrians is an important tool in the analysis of their behavior and consequently ensuring the safety of pedestrian traffic. Children pedestrians show specific traffic behavior which is related to cognitive development, and the parameters that affect their traffic behavior are very different. The aim of this paper is to develop a model of the children-pedestrian’s speed at a signalized pedestrian crosswalk. For the same set of data collected in the city of Osijek—Croatia, two models were developed based on neural network and multiple linear regression. In both cases the models are based on 300 data of measured children speed at signalized pedestrian crosswalks on primary city roads located near a primary school. As parameters, both models include the selected traffic infrastructure features and children’s characteristics and their movements. The models are validated on data collected on the same type of pedestrian crosswalks, using the same methodology in two other urban environments—the city of Rijeka, Croatia and Enna in Italy. It was shown that the neural network model, developed for Osijek, can be applied with sufficient reliability to the other two cities, while the multiple linear regression model is applicable with relatively satisfactory reliability only in Rijeka. A comparative analysis of the statistical indicators of reliability of these two models showed that better results are achieved by the neural network model.

scholarly journals Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dae-Hong Min ◽  
Hyung-Koo Yoon
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Linear Regression ◽  
High Reliability ◽  
Back Propagation ◽  
Measured Data ◽  
Landslide Susceptibility Mapping ◽  
Deterministic Models ◽  
Soil Thickness ◽  
The Neural Network

AbstractDeterministic models have been widely applied in landslide risk assessment (LRA), but they have limitations in obtaining various geotechnical and hydraulic properties. The objective of this study is to suggest a new deterministic method based on machine learning (ML) algorithms. Eight crucial variables of LRA are selected with reference to expert opinions, and the output value is set to the safety factor derived by Mohr–Coulomb failure theory in infinite slope. Linear regression and a neural network based on ML are applied to find the best model between independent and dependent variables. To increase the reliability of linear regression and the neural network, the results of back propagation, including gradient descent, Levenberg–Marquardt (LM), and Bayesian regularization (BR) methods, are compared. An 1800-item dataset is constructed through measured data and artificial data by using a geostatistical technique, which can provide the information of an unknown area based on measured data. The results of linear regression and the neural network show that the special LM and BR back propagation methods demonstrate a high determination of coefficient. The important variables are also investigated though random forest (RF) to overcome the number of various input variables. Only four variables—shear strength, soil thickness, elastic modulus, and fine content—demonstrate a high reliability for LRA. The results show that it is possible to perform LRA with ML, and four variables are enough when it is difficult to obtain various variables.

scholarly journals Machine Learning and Time Series Models for VNQ Market Predictions

2021 ◽  
pp. 29-44
Author(s):  
Yu-Min Lian ◽  
Chia-Hsuan Li ◽  
Yi-Hsuan Wei
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Real Estate ◽  
Multiple Linear Regression ◽  
Moving Average ◽  
Back Propagation ◽  
The United States ◽  
Back Propagation Neural Network ◽  
Autoregressive Integrated Moving Average ◽  
Input Variables

Abstract This study compares the price predictions of the Vanguard real estate exchange-traded fund (ETF) (VNQ) using the back propagation neural network (BPNN) and autoregressive integrated moving average (ARIMA) models. The input variables for BPNN include the past 3-day closing prices, daily trading volume, MA5, MA20, the S&P 500 index, the United States (US) dollar index, volatility index, 5-year treasury yields, and 10-year treasury yields. In addition, variable reduction is based on multiple linear regression (MLR). Mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to measure the prediction error between the actual closing price and the models’ forecasted price. The training set covers the period between January 1, 2015 and March 31, 2020, and the forecasting set covers the period from April 1, 2020 to June 30, 2020. The empirical results reveal that the BPNN model’s predictive ability is superior to the ARIMA model’s. The predictive accuracy of BPNN with one hidden layer is better than with two hidden layers. Our findings provide crucial market factors as input variables for BPNN that might inspire investors in VNQ markets. JEL classification numbers: C32, C45, C53, G17. Keywords: Vanguard real estate ETF (VNQ), Back propagation neural network (BPNN), Autoregressive integrated moving average (ARIMA), Multiple linear regression (MLR).

scholarly journals A Neural Network Nonlinear Multimodel Ensemble to Improve Precipitation Forecasts over Continental US

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Vladimir M. Krasnopolsky ◽  
Ying Lin
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Model Ensemble ◽  
Multimodel Ensemble ◽  
Learning From Data ◽  
Nonlinear Approach ◽  
The Neural Network ◽  
Model Ensembles ◽  
High Precipitation

A novel multimodel ensemble approach based on learning from data using the neural network (NN) technique is formulated and applied for improving 24-hour precipitation forecasts over the continental US. The developed nonlinear approach allowed us to account for nonlinear correlation between ensemble members and to produce “optimal” forecast represented by a nonlinear NN ensemble mean. The NN approach is compared with the conservative multi-model ensemble, with multiple linear regression ensemble approaches, and with results obtained by human forecasters. The NN multi-model ensemble improves upon conservative multi-model ensemble and multiple linear regression ensemble, it (1) significantly reduces high bias at low precipitation level, (2) significantly reduces low bias at high precipitation level, and (3) sharpens features making them closer to the observed ones. The NN multi-model ensemble performs at least as well as human forecasters supplied with the same information. The developed approach is a generic approach that can be applied to other multi-model ensemble fields as well as to single model ensembles.

Research on the Combination Model of Short-Term Traffic Flow Forecasting

2012 ◽  
Vol 178-181 ◽  
pp. 2668-2672
Author(s):  
Yuan Lin Liu ◽  
Wu Sheng Hu ◽  
Su Lan Li ◽  
Hong Wei Li
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Traffic Flow ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Model Error ◽  
Short Term ◽  
Combination Model ◽  
Input Layer

Short-term traffic flow is difficult to predict accurately and real-time, owing to the characteristics of very complexity, randomness, nonlinearity and uncertainty, etc.. In this paper, the method of combining multiple linear regression with back propagation (BP) neural network was proposed, using BP neural network to compensate the model error of multiple linear regression. The combination model and the corresponding algorithm program was made, and used to pedict the short-term traffic flow. Two different methods of selecting the input layer parameters were used and compared, while the new method has higher accuracy and stability.

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

scholarly journals Hysteresis Modeling and Compensation of Fast Steering Mirrors with Hysteresis Operator Based Back Propagation Neural Networks

Micromachines ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 732
Author(s):  
Kairui Cao ◽  
Guanglu Hao ◽  
Qingfeng Liu ◽  
Liying Tan ◽  
Jing Ma
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Hysteresis Model ◽  
Cooperative Movement ◽  
Hysteresis Operator ◽  
The Neural Network ◽  
Hysteresis Nonlinearity ◽  
Hysteresis Modeling ◽  
Hysteresis Characteristics ◽  
Inverse Hysteresis

Fast steering mirrors (FSMs), driven by piezoelectric ceramics, are usually used as actuators for high-precision beam control. A FSM generally contains four ceramics that are distributed in a crisscross pattern. The cooperative movement of the two ceramics along one radial direction generates the deflection of the FSM in the same orientation. Unlike the hysteresis nonlinearity of a single piezoelectric ceramic, which is symmetric or asymmetric, the FSM exhibits complex hysteresis characteristics. In this paper, a systematic way of modeling the hysteresis nonlinearity of FSMs is proposed using a Madelung’s rules based symmetric hysteresis operator with a cascaded neural network. The hysteresis operator provides a basic hysteresis motion for the FSM. The neural network modifies the basic hysteresis motion to accurately describe the hysteresis nonlinearity of FSMs. The wiping-out and congruency properties of the proposed method are also analyzed. Moreover, the inverse hysteresis model is constructed to reduce the hysteresis nonlinearity of FSMs. The effectiveness of the presented model is validated by experimental results.

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