Modeling sonic logs in oil wells: a comparison of neural networks ensembles and kernel methods

Oil well logs are frequently used to determine the mineralogy and physical properties of potential reservoir rocks, and the nature of the fluids they contain. Recently we reported an exploratory use of neural network ensembles for modeling these records. We showed that ensembles are clearly superior to linear multivariate regression as modeling technique, revealing an underlying nonlinear functional dependency between the correlated variables. In this work we use kernel methods to develop nonlinear local models relating Sonic logs (transit time of compressional waves) with other commonly measured properties (Resistivity and Natural Formation Radioactivity Level or Gamma Ray log). The kernel considered is conceptually simple and numerically robust, and allows to obtain the same performance as neural networks ensembles on this task.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

The Kernel Adaline: A New Algorithm for Non-Linear Signal Processing and Regression

A certain class of non-linear algorithm for signal processing and machine learning is based on the same intrinsic principle. Some given samples (training points) are in the first stage mapped into a very high-dimensional LINEARISATION space (the feature space of pattern recognition theory) and then a linear algorithm performs its work in this space. The expensive expansion into the linearisation space can be performed efficiently using Mercer's kernel functions, as studied by Aizerman et al. in the 1960's. In this work a non-linear adaptation of the (so far linear) Adaline algorithm by Widrow and Hoff is proposed. The new algorithm combines the conceptual simplicity of a least mean square algorithm for linear regression but exhibits the power of a universal non-linear function to approximator. The kernel Adaline algorithm is introduced and the first experimental results are given

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

The 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. 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.

An Investigation into Neuromorphic ICs using Memristor-CMOS Hybrid Circuits

The memristance of a memristor depends on the amount of charge flowing through it and when current stops flowing through it, it remembers the state. Thus, memristors are extremely suited for implementation of memory units. Memristors find great application in neuromorphic circuits as it is possible to couple memory and processing, compared to traditional Von-Neumann digital architectures where memory and processing are separate. Neural networks have a layered structure where information passes from one layer to another and each of these layers have the possibility of a high degree of parallelism. CMOS-Memristor based neural network accelerators provide a method of speeding up neural networks by making use of this parallelism and analog computation. In this project we have conducted an initial investigation into the current state of the art implementation of memristor based programming circuits. Various memristor programming circuits and basic neuromorphic circuits have been simulated. The next phase of our project revolved around designing basic building blocks which can be used to design neural networks. A memristor bridge based synaptic weighting block, a operational transconductor based summing block were initially designed. We then designed activation function blocks which are used to introduce controlled non-linearity. Blocks for a basic rectified linear unit and a novel implementation for tan-hyperbolic function have been proposed. An artificial neural network has been designed using these blocks to validate and test their performance. We have also used these fundamental blocks to design basic layers of Convolutional Neural Networks. Convolutional Neural Networks are heavily used in image processing applications. The core convolutional block has been designed and it has been used as an image processing kernel to test its performance.Comment: Bachelor's thesi