scholarly journals Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers

2019 ◽  
Vol 54 ◽  
Author(s):  
Carlos Alberto Araújo Júnior ◽  
Pábulo Diogo de Souza ◽  
Adriana Leandra de Assis ◽  
Christian Dias Cabacinha ◽  
Helio Garcia Leite ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Quantile Regression ◽  
Site Index ◽  
Permanent Plots ◽  
Artificial Neural ◽  
The Stability

Abstract: The objective of this work was to compare methods of obtaining the site index for eucalyptus (Eucalyptus spp.) stands, as well as to evaluate their impact on the stability of this index in databases with and without outliers. Three methods were tested, using linear regression, quantile regression, and artificial neural network. Twenty-two permanent plots from a continuous forest inventory were used, measured in trees with ages from 23 to 83 months. The outliers were identified using a boxplot graphic. The artificial neural network showed better results than the linear and quantile regressions, both for dominant height and site index estimates. The stability obtained for the site index classification by the artificial neural network was also better than the one obtained by the other methods, regardless of the presence or the absence of outliers in the database. This shows that the artificial neural network is a solid modelling technique in the presence of outliers. When the cause of the presence of outliers in the database is not known, they can be kept in it if techniques as artificial neural networks or quantile regression are used.

scholarly journals Peramalan Penjualan Kendaraan Mobil Segmen B2B dengan Metode Regresi Linear Berganda, Jaringan Saraf Tiruan dan Jaringan Saraf Tiruan – Algoritma Genetika

2020 ◽  
Vol 3 (2) ◽  
pp. 83
Author(s):  
Muhammad Agung Nugraha ◽  
Farizal Farizal ◽  
Djoko Sihono Gabriel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Decision Making Unit ◽  
Car Sales ◽  
Artificial Neural

This study aims to create an effective forecasting model in predicting sales of car products in the B2B segment (Business to Business) to obtain estimates of product sales in the future. This research uses multiple linear regression and artificial neural networks that are optimized by genetic algorithms. Forecasting factors for car sales are generally issued by total national car sales, the Consumer Price Index, the Consumer Confidence Index, the Inflation Rate, Gross Domestic Product (GDP), and Fuel Oil Price. The author has also gotten the factors that play a role in the sale of B2B segment by diverting the survey to 106 DMU (Decision Making Unit) who decide to purchase cars in their company. Then we evaluate the results of the questionnaire in training data and simulations on the Artificial Neural Network. Optimized Artificial Neural Networks with Genetic Algorithms can improve B2B segment car sales' accuracy when comparing error values in the ordinary Artificial Neural Network and Multiple Linear Regression.

scholarly journals Prediction of sweet corn yield depending on cultivation technology parameters by using linear regression and artificial neural network methods

2018 ◽  
Vol 26 (1) ◽  
pp. 11-15 ◽  
Author(s):  
P. V. Lykhovyd
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Data Processing ◽  
Field Experiments ◽  
Sweet Corn ◽  
Corn Yield ◽  
Artificial Neural

Artificial neural networks and linear regression are widely used in particularly all branches of science for modeling and prediction. Linear regression is an old data processing tool, and artificial neural networks are a comparatively new one. The goal of the study was to determine whether artificial neural networks are more accurate than linear regression in sweet corn yield prediction. In the study we used a dataset obtained from field experiments on the technological improvement of sweet corn cultivation. The field experiments were conducted during the period from 2014 to 2016 on dark-chestnut soil under drip irrigated conditions in the Steppe Zone of Ukraine. We studied the impact of the moldboard plowing depths, mineral fertilizer application rates and plant densities on the crop yield. A significant impact of all the studied factors on the sweet corn productivity was proved by using the analysis of variance. The highest yield of sweet corn ears without husks (10.93 t ha–1) was under the moldboard plowing at the depth of 20–22 cm, mineral fertilizers application rate of N120P120, plant density of 65,000 plants ha–1. Data processing by using the linear regression and artificial neural network methods showed that the latter is a great deal better than linear regression in sweet corn yield prediction. Higher accuracy of the artificial neural network prediction was proved by the higher value of the coefficient of determination (R2) – 0.978, in comparison to 0.897 for the linear regression prediction model. We conclude that artificial neural networks are a much better data processing tool, especially, in the life sciences and for prediction of the non-linear natural processes and phenomena. The main disadvantage of the neural network models is their “black box” nature. However, linear regression will not lose its popularity among scientists in the nearest future. Linear regression is a much simpler data analysis tool, it is easier to perform the prediction, but it still provides a sufficiently high level of accuracy.

scholarly journals USE OF ARTIFICIAL NEURAL NETWORKS TO PREDICT TERRITORIAL ECONOMIC INDICATORS

2019 ◽  
Vol 4 (8) ◽  
pp. 143-146
Author(s):  
Gocha Ugulava
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Economic Indicators ◽  
Regional Economic ◽  
The Neural Network ◽  
Artificial Neural ◽  
Predicted Values

Modern economic science is unthinkable without predicting and planning the prospects for economic life development. There are many different mathematical and statistical tools in the arsenal of scientists as well as practitioners and economists today in purpose of forecasting. To date, one of the most prominent effective tools for data analytics is artificial neural networks. Artificial Neural Network - is a mathematical mod- el created in the likeness of a human neural network, and its software and hardware implementation. We carried out modeling and forecasting of regional economic indicators using the artificial neural network of the three-layer perceptron architecture. The network architecture and neuron settings were automatically formatted through the programming language R and its package - Neuralnet. During the forecasting phase, the data vectors were presented as data frame in five input parameters (DFI, FAI, EMP, BT, CPI), according to the neural network forecast of the regional gross domestic product (RGDP_NN) was calculated. All data are from the Imereti region and are taken from official GeoStat sources. Forecasting was done at the same time scale (2006-2017) to enable us to compare the predicted values with the actual ones to verify the level of fore- cast accuracy. We also tested the results of the neural network in another way - compared to the predicted values using multiple linear regression on the same data. The accuracy of the predicted values calculated by the neural network was quite high, which was not declining but slightly ahead of the accuracy coefficients of the predicted values obtained through linear regression. Also, the predictive values calculated by the neural network with high adequacy and accuracy were compared with actual, existing ones. Presented material shows that the use of artificial neural networks for the prediction of territorial economic indicators is reasonable and justified. Their role in analyzing and predicting indicators that are characterized by nonstationarity, dynamism, lack of a definite trend, periodicity, nonlinear structure is especially increased. It is therefore advisable to apply this method in regional economic studies, in predicting territorial development plans, strategies, targets and indicators.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

scholarly journals FEATURES OF DIAGNOSTIC ARTIFICIAL NEURAL NETWORKS FOR HYBRID EXPERT SYSTEMS

2019 ◽  
Vol 26 ◽  
pp. 36-46
Author(s):  
S. KONOVALOV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Expert Systems ◽  
Technical System ◽  
Bulletin Board ◽  
Confidence Factor ◽  
Complex Technical System ◽  
Artificial Neural

In the proposed article, various methods of constructing an artificial neural network as one of the components of a hybrid expert system for diagnosis were investigated. A review of foreign literature in recent years was conducted, where hybrid expert systems were considered as an integral part of complex technical systems in the field of security. The advantages and disadvantages of artificial neural networks are listed, and the main problems in creating hybrid expert systems for diagnostics are indicated, proving the relevance of further development of artificial neural networks for hybrid expert systems. The approaches to the analysis of natural language sentences, which are used for the work of hybrid expert systems with artificial neural networks, are considered. A bulletin board is shown, its structure and principle of operation are described. The structure of the bulletin board is divided into levels and sublevels. At sublevels, a confidence factor is applied. The dependence of the values of the confidence factor on the fulfillment of a particular condition is shown. The links between the levels and sublevels of the bulletin board are also described. As an artificial neural network architecture, the «key-threshold» model is used, the rule of neuron operation is shown. In addition, an artificial neural network has the property of training, based on the application of the penalty property, which is able to calculate depending on the accident situation. The behavior of a complex technical system, as well as its faulty states, are modeled using a model that describes the structure and behavior of a given system. To optimize the data of a complex technical system, an evolutionary algorithm is used to minimize the objective function. Solutions to the optimization problem consist of Pareto solution vectors. Optimization and training tasks are solved by using the Hopfield network. In general, a hybrid expert system is described using semantic networks, which consist of vertices and edges. The reference model of a complex technical system is stored in the knowledge base and updated during the acquisition of new knowledge. In an emergency, or about its premise, with the help of neural networks, a search is made for the cause and the control action necessary to eliminate the accident. The considered approaches, interacting with each other, can improve the operation of diagnostic artificial neural networks in the case of emergency management, showing more accurate data in a short time. In addition, the use of such a network for analyzing the state of health, as well as forecasting based on diagnostic data using the example of a complex technical system, is presented.

scholarly journals An artificial neural network identifies glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

2019 ◽  
Author(s):  
René Janßen ◽  
Jakob Zabel ◽  
Uwe von Lukas ◽  
Matthias Labrenz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Environmental Monitoring ◽  
Microbial Communities ◽  
List Type ◽  
Neural Net ◽  
Next Generation ◽  
Artificial Neural

AbstractArtificial neural networks can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network to support environmental monitoring efforts in case of a contamination event by detecting induced changes towards the microbial communities. The neural net was trained on taxonomic cluster count tables obtained via next-generation amplicon sequencing of water column samples originating from a lab microcosm incubation experiment conducted over 140 days to determine the effects of the herbicide glyphosate on succession within brackish-water microbial communities. Glyphosate-treated assemblages were classified correctly; a subsetting approach identified the clusters primarily responsible for this, permitting the reduction of input features. This study demonstrates the potential of artificial neural networks to predict indicator species in cases of glyphosate contamination. The results could empower the development of environmental monitoring strategies with applications limited to neither glyphosate nor amplicon sequence data.Highlight bullet pointsAn artificial neural net was able to identify glyphosate-affected microbial community assemblages based on next generation sequencing dataDecision-relevant taxonomic clusters can be identified by a stochastically subsetting approachJust a fraction of present clusters is needed for classificationFiltering of input data improves classification

scholarly journals A single layer artificial neural network type architecture with molecular engineered bacteria for reversible and irreversible computing

2021 ◽  
Author(s):  
Kathakali Sarkar ◽  
Deepro Bonnerjee ◽  
Rajkamal Srivastava ◽  
Sangram Bagh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Basic Concept ◽  
Single Layer ◽  
Artificial Neural ◽  
Engineered Bacteria ◽  
Network Type

Here, we adapted the basic concept of artificial neural networks (ANN) and experimentally demonstrate a broadly applicable single layer ANN type architecture with molecular engineered bacteria to perform complex irreversible...

Forecasting Stock Returns with Artificial Neural Networks

2011 ◽  
pp. 47-79 ◽  
Author(s):  
Suraphan Thawornwong ◽  
David Enke
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Stock Returns ◽  
Future Research ◽  
Network Architectures ◽  
Network Applications ◽  
Artificial Neural ◽  
Neural Network Applications

During the last few years there has been growing literature on applications of artificial neural networks to business and financial domains. In fact, a great deal of attention has been placed in the area of stock return forecasting. This is due to the fact that once artificial neural network applications are successful, monetary rewards will be substantial. Many studies have reported promising results in successfully applying various types of artificial neural network architectures for predicting stock returns. This chapter reviews and discusses various neural network research methodologies used in 45 journal articles that attempted to forecast stock returns. Modeling techniques and suggestions from the literature are also compiled and addressed. The results show that artificial neural networks are an emerging and promising computational technology that will continue to be a challenging tool for future research.

Artificial Neural Network for Solving the Inventory Control Problem in Fuzzy Environments

2021 ◽  
pp. 126-143
Author(s):  
Mostafijur Rahaman ◽  
Sankar Prasad Mondal ◽  
Shariful Alam
Keyword(s):  
Neural Network ◽  
Differential Equation ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Inventory Control ◽  
Solution Procedure ◽  
Control Problems ◽  
Fuzzy Environment ◽  
Artificial Neural

In this chapter, different inventory control problems are formulated in fuzzy environment and solved by artificial neural network. Due to present the non-linearity associated with the differential equation in fuzzy environment, the solution procedure may be very complicated. To avoid the situation, artificial neural networks play an important role. In this chapter, different inventory control problems are formulated in fuzzy environment and solved by artificial neural network. Due to present the non-linearity associated with the differential equation in fuzzy environment, the solution procedure may be very complicated. To avoid the situation, artificial neural networks play an important role.

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