Técnicas de modelagem florestal empregadas na estimativa volumétrica de eucalipto

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Florestal, 2016.O objetivo do presente trabalho foi estimar a produção volumétrica de um hibrido clonal de Eucalyptus grandis x urophylla por meio de modelos volumétricos tradicionais e com a utilização de RNA. Redes neurais artificiais do tipo MultilayerPerceptron com algoritmo backpropagation e função de ativação logística usando o diâmetro e a altura total da árvore como variáveis preditoras conseguem estimar de maneira precisa o volume de um hibrido de Eucalyptus grandis x urophylla com 23 meses de idade. O modelo de Schumacher e Hall foi o que melhor se ajustou para o presente trabalho na estimativa do volume, porém as redes neurais artificiais foram melhores do que os modelos convencionais.The objective of this study was to estimate the volume production of a hybrid clone of Eucalyptus grandis x urophylla through traditional volumetric models and the use of RNA. Artificial neural networks MultilayerPerceptron type with backpropagation algorithm and activation function logistics using the diameter and the total tree height as predictor variables can estimate accurately the volume of a hybrid of Eucalyptus grandis x urophylla with 23 months old. The model of Schumacher and Hall was the best fit for this study to estimate the volume, but the artificial neural networks were better than conventional models

Optimization the initial weights of artificial neural networks via genetic algorithm applied to hip bone fracture prediction

This paper aims to find the optimal set of initial weights to enhance the accuracy of artificial neural networks (ANNs) by using genetic algorithms (GA). The sample in this study included 228 patients with first low-trauma hip fracture and 215 patients without hip fracture, both of them were interviewed with 78 questions. We used logistic regression to select 5 important factors (i.e., bone mineral density, experience of fracture, average hand grip strength, intake of coffee, and peak expiratory flow rate) for building artificial neural networks to predict the probabilities of hip fractures. Three-layer (one hidden layer) ANNs models with back-propagation training algorithms were adopted. The purpose in this paper is to find the optimal initial weights of neural networks via genetic algorithm to improve the predictability. Area under the ROC curve (AUC) was used to assess the performance of neural networks. The study results showed the genetic algorithm obtained an AUC of 0.858±0.00493 on modeling data and 0.802 ± 0.03318 on testing data. They were slightly better than the results of our previous study (0.868±0.00387 and 0.796±0.02559, resp.). Thus, the preliminary study for only using simple GA has been proved to be effective for improving the accuracy of artificial neural networks.This research was supported by the National Science Council (NSC) of Taiwan (Grant no. NSC98-2915-I-155-005), the Department of Education grant of Excellent Teaching Program of Yuan Ze University (Grant no. 217517) and the Center for Dynamical Biomarkers and Translational Medicine supported by National Science Council (Grant no. NSC 100- 2911-I-008-001)

Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions

A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. The goal of this work is to discover interpretable local Hebbian learning rules that can provide autonomous global learning. To achieve this, we use a discrete representation to encode the learning rules in a finite search space. These rules are then used to perform synaptic changes, based on the local interactions of the neurons. We employ genetic algorithms to optimize these rules to allow learning on two separate tasks (a foraging and a prey-predator scenario) in online lifetime learning settings. The resulting evolved rules converged into a set of well-defined interpretable types, that are thoroughly discussed. Notably, the performance of these rules, while adapting the ANNs during the learning tasks, is comparable to that of offline learning methods such as hill climbing.Comment: Evolutionary Computation Journa