823 research outputs found
Neural networks in geophysical applications
Neural networks are increasingly popular in geophysics.
Because they are universal approximators, these
tools can approximate any continuous function with an
arbitrary precision. Hence, they may yield important
contributions to finding solutions to a variety of geophysical applications.
However, knowledge of many methods and techniques
recently developed to increase the performance
and to facilitate the use of neural networks does not seem
to be widespread in the geophysical community. Therefore,
the power of these tools has not yet been explored to
their full extent. In this paper, techniques are described
for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size
and architecture
Algumas aplicações da Inteligência Artificial em Biotecnologia
The present work is a revision about neural networks. Initially presents a little introduction to neural networks, fuzzy logic, a brief history, and the applications of Neural Networks on Biotechnology. The chosen sub-areas of the applications of Neural Networks on Biotechnology are, Solid-State Fermentation Optimization, DNA Sequencing, Molecular Sequencing Analysis, Quantitative Structure-Activity Relationship, Soft Sensing, Spectra Interpretation, Data Mining, each one use a special kind of neural network like feedforward, recurrent, siamese, art, among others. Applications of the Neural-Networks in spectra interpretation and Quantitative Structure-activity relationships, is a direct application to Chemistry and consequently also to Biochemistry and Biotechnology. Soft Sensing is a special example for applications on Biotechnology. It is a method to measure variables that normally can’t be directly measure. Solid state fermentation was optimized and presenting, as result, a strong increasing of production efficiency.O presente trabalho é uma revisão sobre redes neurais. Inicialmente apresenta uma breve introdução a redes neurais, lógica difusa, um breve histórico, e aplicações de Redes Neurais em Biotecnologia. As subáreas escolhidas para aplicação das redes neurais são, Otimização da Fermentação no Estado-Sólido, Sequenciamento de DNA, Análise Molecular Sequencial, Relação Quantitativa Strutura-Atividade, Sensores inteligentes, Interpretação de espectros, Mineração de Dados, sendo que cada um usa um tipo especial de rede neural, tais como feed forward, recorrente, siamesa, art, entre outros. Aplicações de Redes Neurais em interpretação de espectros e Relação Quantitativa Estrutura-Atividade, como uma aplicação direta à química e consequentemente também para a Bioquímica e Biotecnologia. Os sensores Inteligentes são um exemplo especial de aplicação em Biotecnologia. É um método de medir variáveis que normalmente não podem ser medidas de forma direta. Fermentações no Estado-sólido foram otimizadas e, apresentaram como resultado um forte aumento do rendimento na produção final
Non-Gaussian Hybrid Transfer Functions: Memorizing Mine Survivability Calculations
Hybrid algorithms and models have received significant interest in recent years and are increasingly used to solve real-world problems. Different from existing methods in radial basis transfer function construction, this study proposes a novel nonlinear-weight hybrid algorithm involving the non-Gaussian type radial basis transfer functions. The speed and simplicity of the non-Gaussian type with the accuracy and simplicity of radial basis function are used to produce fast and accurate on-the-fly model for survivability of emergency mine rescue operations, that is, the survivability under all conditions is precalculated and used to train the neural network. The proposed hybrid uses genetic algorithm as a learning method which performs parameter optimization within an integrated analytic framework, to improve network efficiency. Finally, the network parameters including mean iteration, standard variation, standard deviation, convergent time, and optimized error are evaluated using the mean squared error. The results demonstrate that the hybrid model is able to reduce the computation complexity, increase the robustness and optimize its parameters. This novel hybrid model shows outstanding performance and is competitive over other existing models
MLP-BASED SOURCE SEPARATION FOR MLP-LIKE NONLINEAR MIXTURES
In this paper, the nonlinear blind source separation problem is addressed by using a multilayer perceptron (MLP) as separating system, which is justified in the universal approximation property of MLP networks. An adaptive learning algorithm for a perceptron with two hidden-layers is presented. The algorithm minimizes the mutual information between the outputs of the MLP. The performance of the proposed method is illustrated by some experiments. 1. INTRODUCTION. Blind Source Separation (BSS) is a fundamental problem in signal processing. It consists of retrieving unobserved sources s1(t),..., sN (t), assumed to be statistically independent (which is phisically plausible when the source
A Comprehensive Study on Metaheuristic Techniques Using Genetic Approach
Most real-life optimization problems involve multiple objective functions. Finding a solution that satisfies the decision-maker is very difficult owing to conflict between the objectives. Furthermore, the solution depends on the decision-maker’s preference. Metaheuristic solution methods have become common tools to solve these problems. The task of obtaining solutions that take account of a decision-maker’s preference is at the forefront of current research. It is also possible to have multiple decision-makers with different preferences and with different decision-making powers. It may not be easy to express a preference using crisp numbers. In this study, the preferences of multiple decision-makers were simulated and a solution based on a genetic algorithm was developed to solve multi-objective optimization problems. The preferences were collected as fuzzy conditional trade-offs and they were updated while running the algorithm interactively with the decision-makers. The proposed method was tested using well-known benchmark problems. The solutions were found to converge around the Pareto front of the problems
Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural Networks
We apply a multilayer perceptron machine learning (ML) regression approach to
infer electromagnetic (EM) duct heights within the marine atmospheric boundary
layer (MABL) using sparsely sampled EM propagation data obtained within a
bistatic context. This paper explains the rationale behind the selection of the
ML network architecture, along with other model hyperparameters, in an effort
to demystify the process of arriving at a useful ML model. The resulting speed
of our ML predictions of EM duct heights, using sparse data measurements within
MABL, indicates the suitability of the proposed method for real-time
applications.Comment: 13 pages, 7 figure
Personalized Health Monitoring Using Evolvable Block-based Neural Networks
This dissertation presents personalized health monitoring using evolvable block-based neural networks. Personalized health monitoring plays an increasingly important role in modern society as the population enjoys longer life. Personalization in health monitoring considers physiological variations brought by temporal, personal or environmental differences, and demands solutions capable to reconfigure and adapt to specific requirements. Block-based neural networks (BbNNs) consist of 2-D arrays of modular basic blocks that can be easily implemented using reconfigurable digital hardware such as field programmable gate arrays (FPGAs) that allow on-line partial reorganization. The modular structure of BbNNs enables easy expansion in size by adding more blocks. A computationally efficient evolutionary algorithm is developed that simultaneously optimizes structure and weights of BbNNs. This evolutionary algorithm increases optimization speed by integrating a local search operator. An adaptive rate update scheme removing manual tuning of operator rates enhances the fitness trend compared to pre-determined fixed rates. A fitness scaling with generalized disruptive pressure reduces the possibility of premature convergence. The BbNN platform promises an evolvable solution that changes structures and parameters for personalized health monitoring. A BbNN evolved with the proposed evolutionary algorithm using the Hermite transform coefficients and a time interval between two neighboring R peaks of ECG signal, provides a patient-specific ECG heartbeat classification system. Experimental results using the MIT-BIH Arrhythmia database demonstrate a potential for significant performance enhancements over other major techniques
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