Predicting Movement of Homeless Young Adults: Artificial Neural Networks and Generalized Linear Models

2018 ◽  
Vol 9 (1) ◽  
pp. 89-106
Author(s):  
Edward Helderop ◽  
Kristin M. Ferguson ◽  
Tony H. Grubesic ◽  
Kimberly Bender
Keyword(s):  
Neural Networks ◽  
Young Adults ◽  
Artificial Neural Networks ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Homeless Young Adults ◽  
Artificial Neural

scholarly journals MODELAGEM DA ALTURA E DO INCREMENTO EM ÁREA TRANSVERSAL DE LOURO PARDO

Nativa ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. 191
Author(s):  
Ana Claudia da Silveira ◽  
Luis Paulo Baldissera Schorr ◽  
Elisabete Vuaden ◽  
Jéssica Talheimer Aguiar ◽  
Tarik Cuchi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Generalized Linear Models ◽  
Multilayer Perceptron ◽  
Linear Models ◽  
Annual Increment ◽  
Graphic Analysis ◽  
Artificial Neural ◽  
Pasture Area ◽  
Logarithmic Functions

O estudo teve como objetivo verificar a melhor técnica para a modelagem da altura e do incremento periódico anual em área transversal para Cordia trichotoma (Vell.) Arrab. ex Steud. Para isso, foram identificados e mensurados os diâmetros à altura do peito e as alturas totais de 35 indivíduos localizados em área de preservação permanente e de pastagem, com aproximadamente 4 ha, no município de Salto do Lontra, estado do Paraná. Posteriormente, foi realizada a análise de tronco pelo método não destrutivo verificando o incremento dos últimos 5 anos. Para a estimativa da altura e do incremento periódico anual em área transversal utilizou-se a técnica dos Modelos Lineares Generalizados (MLG) nas distribuições Gamma, Normal e Poisson nas funções de ligação identidade e logarítmica e Redes Neurais Artificiais (RNA) do tipo Multilayer Perceptron. Para comparação e escolha da melhor técnica, utilizou-se a correlação entre os valores observados e estimados, a raiz quadrada do erro médio e a análise gráfica dos resíduos. Os resultados mostraram que dentre os modelos de MLG, a distribuição Gamma função logarítmica foi indicada para modelagem da altura, ao passo que a distribuição Gamma função identidade foi a recomendada para a modelagem do incremento periódico em área transversal. Quando comparadas as duas técnicas evidenciou-se melhores resultados com a utilização das RNAs, as quais estimaram as variáveis estudadas com maior precisão.Palavra-chave: Cordia trichotoma, modelos lineares generalizados, redes neurais artificiais. MODELING HEIGHT AND TRANVERSAL AREA INCREMENT OF LOURO PARDO ABSTRACT:The present study aimed to verify the best technique for modeling height and annual periodic increment in transversal area for Cordia trichotoma (Vell.) Arrab. Ex Steud. For this purpose, we identify and measured the diameter at breast height and the total height of 35 individuals of this species which located in a permanent preservation and pasture area with approximately 4 hectares, in the municipality of Salto do Lontra, Paraná State, Brazil. Subsequently, the trunk analysis was performed by the non-destructive method, verifying the increment of the last 5 years. For the estimation of height and periodic annual increment in the transversal area, the Generalized Linear Models (MLG) technique was used in the Gamma, Normal and Poisson distributions in the identity and logarithmic functions and Artificial Neural Networks (RNA) of the Multilayer Perceptron type. For comparison and choice of the best technique, the correlation between the observed and estimated values, the square root of the mean error and the graphic analysis of the residues were used. The results showed that among the MLG models, the Gamma distribution with the logarithmic function was indicated for modeling height, whereas the Gamma with identity function was recommended for modeling periodic increment in transversal area. When we compared the two techniques, better results were obtained with the use of RNAs, which estimated the variables studied with greater accuracy.Keywords: Cordia trichotoma, generalized linear models, artificial neural networks. DOI:

scholarly journals Forecasting Albanian Time Series with Linear and Nonlinear Univariate Models

2021 ◽  
Vol 10 (5) ◽  
pp. 293
Author(s):  
Blerina Vika ◽  
Ilir Vika
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Linear Models ◽  
Smooth Transition ◽  
Parameter Method ◽  
Economic Time Series ◽  
Nonlinear Methods ◽  
Linear And Nonlinear ◽  
Artificial Neural

Albanian economic time series show irregular patterns since the 1990s that may affect economic analyses with linear methods. The purpose of this study is to assess the ability of nonlinear methods in producing forecasts that could improve upon univariate linear models. The latter are represented by the classic autoregressive (AR) technique, which is regularly used as a benchmark in forecasting. The nonlinear family is represented by two methods, i) the logistic smooth transition autoregressive (LSTAR) model as a special form of the time-varying parameter method, and ii) the nonparametric artificial neural networks (ANN) that mimic the brain’s problem solving process. Our analysis focuses on four basic economic indicators – the CPI prices, GDP, the T-bill interest rate and the lek exchange rate – that are commonly used in various macroeconomic models. Comparing the forecast ability of the models in 1, 4 and 8 quarters ahead, we find that nonlinear methods rank on the top for more than 75 percent of the out-of-sample forecasts, led by the feed-forward artificial neural networks. Although the loss differential between linear and nonlinear model forecasts is often found not statistically significant by the Diebold-Mariano test, our results suggest that it can be worth trying various alternatives beyond the linear estimation framework.   Received: 19 June 2021 / Accepted: 25 August 2021 / Published: 5 September 2021

scholarly journals Subjective Air Traffic Complexity Estimation Using Artificial Neural Networks

2019 ◽  
Vol 31 (4) ◽  
pp. 377-386 ◽  
Author(s):  
Petar Andraši ◽  
Tomislav Radišić ◽  
Doris Novak ◽  
Biljana Juričić
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Models ◽  
Air Traffic ◽  
Traffic Situation ◽  
Network Configuration ◽  
Air Traffic Controllers ◽  
Complexity Estimation ◽  
Artificial Neural

Air traffic complexity is usually defined as difficulty of monitoring and managing a specific air traffic situation. Since it is a psychological construct, best measure of complexity is that given by air traffic controllers. However, there is a need to make a method for complexity estimation which can be used without constant controller input. So far, mostly linear models were used. Here, the possibility of using artificial neural networks for complexity estimation is explored. Genetic algorithm has been used to search for the best artificial neural network configuration. The conclusion is that the artificial neural networks perform as well as linear models and that the remaining error in complexity estimation can only be explained as inter-rater or intra-rater unreliability. One advantage of artificial neural networks in comparison to linear models is that the data do not have to be filtered based on the concept of operations (conventional vs. trajectory-based).

scholarly journals ASSESSING ALTERNATIVES TO ESTIMATE THE STEM VOLUME OF A SEASONAL SEMI-DECIDUOUS FOREST

FLORESTA ◽  
2017 ◽  
Vol 47 (4) ◽  
pp. 375
Author(s):  
Jadson Coelho De Abreu ◽  
Carlos Pedro Boechat Soares ◽  
Helio Garcia Leite
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Machines ◽  
Linear Models ◽  
Deciduous Forest ◽  
Stem Volume ◽  
Categorical Variables ◽  
Support Vector ◽  
Vector Machines ◽  
Artificial Neural

AbstractThe objective of this study was to evaluate the use of linear and hybrid linear models, artificial neural networks (ANN) and support vector machine (SVM) in the estimation of the stem volume in a Seasonal Semi-deciduous Forest. Cubing data of 99 sample-trees of 15 species were used for this purpose. After analysis, we verified that the inclusion of the species as random effect did not contribute to increase the accuracy of the estimates in the structure of a hybrid model. Artificial neural networks and support vector machines, including species as input categorical variables, were the best alternatives to estimate the stem volume of trees of the Seasonal Semi-deciduous Forest.Keywords: Stem volume; artificial neural networks; support vector machines; hybrid linear models; uneven-aged forest. ResumoAvaliando alternativas para estimar o volume do fuste de uma Floresta Estacional Semidecidual. O objetivo desse estudo foi   avaliar o uso de modelos lineares e lineares mistos, redes neurais   artificiais (RNA) e máquina de vetor de suporte (MVS) na estimação dos   volumes dos fustes de árvores em uma Floresta Estacional Semidecidual. Dados de cubagem de 99 árvores-amostra   de 15 espécies foram utilizados para esta finalidade. Após análises, verificou-se que   a inclusão das espécies como efeito aleatório não contribuiu para aumentar a   exatidão das estimativas na estrutura de um modelo misto. As redes neurais artificiais e   as máquinas de vetores de suporte, incluindo as espécies como variáveis   categóricas de entrada, foram as melhores alternativas para estimar o volume   dos fustes das árvores da Floresta Estacional Semidecidual.Palavras-chaves: Volume do   fuste; redes neurais artificiais; máquinas de vetor de suporte; modelos   lineares mistos; floresta inequiânea. 

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