Artificial neural network associated to UV/Vis spectroscopy for monitoring bioreactions in biopharmaceutical processes

Currently, mammalian cells are the most utilized hosts for biopharmaceutical production. The culture media for these cell lines include commonly in their composition a pH indicator. Spectroscopic techniques are used for biopharmaceutical process monitoring, among them, UV–Vis spectroscopy has found scarce applications. This work aimed to define artificial neural networks architecture and fit its parameters to predict some nutrients and metabolites, as well as viable cell concentration based on UV–Vis spectral data of mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Off-line spectra of supernatant samples taken from batches performed at different dissolved oxygen concentrations in two bioreactor configurations and with two pH control strategies were used to define two artificial neural networks. According to absolute errors, glutamine (0.13 ± 0.14 mM), glutamate (0.02 ± 0.02 mM), glucose (1.11 ± 1.70 mM), lactate (0.84 ± 0.68 mM) and viable cell concentrations (1.89 105 ± 1.90 105 cell/mL) were suitably predicted. The prediction error averages for monitored variables were lower than those previously reported using different spectroscopic techniques in combination with partial least squares or artificial neural network. The present work allows for UV–VIS sensor development, and decreases cost related to nutrients and metabolite quantifications.

Neural models for predicting hole diameters in drilling processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Neural models for predicting hole diameters in drilling processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Aplicação de redes NeuroFuzzy ao processamento de peças automotivas por meio de injeção de polímeros

The injection molding of automotive parts is a complex process due to the many non-linear and multivariable phenomena that occur simultaneously. Commercial software applications exist for modeling the parameters of polymer injection but can be prohibitively expensive. It is possible to identify these parameters analytically, but applying classical theories of transport phenomena requires accurate information about the injection machine, product geometry, and process parameters. However, neurofuzzy networks, which achieve a synergy by combining the learning capabilities of an artificial neural network with a fuzzy set's inference mechanism, have shown success in this field. The purpose of this paper was to use a multilayer perceptron artificial neural network and a radial basis function artificial neural network combined with fuzzy sets to produce an inference mechanism that could predict injection mold cycle times. The results confirmed neurofuzzy networks as an effective alternative to solving such problems.

Previsão de cargas elétricas através de uma Rede Neural Híbrida Back-ART Fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

Utilização de redes neurais artificiais no ajuste de controladores suplementares e dispositivo FACTS STATCOM para a melhoria da estabilidade a pequenas perturbações do sistema elétrico de potência

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aplicação da rede neural MLP (Multilayer Perceptron) em indústria de pisos e revestimentos do Pólo Cerâmico de Santa Gertrudes - SP

Pós-graduação em Geociências e Meio Ambiente - IGCE

An alternative approach to solve convergence problems in the backpropagation algorithm

The multilayer perceptron network has become one of the most used in the solution of a wide variety of problems. The training process is based on the supervised method where the inputs are presented to the neural network and the output is compared with a desired value. However, the algorithm presents convergence problems when the desired output of the network has small slope in the discrete time samples or the output is a quasi-constant value. The proposal of this paper is presenting an alternative approach to solve this convergence problem with a pre-conditioning method of the desired output data set before the training process and a post-conditioning when the generalization results are obtained. Simulations results are presented in order to validate the proposed approach.

Prediction of occurrences of diverse chemical classes in the Asteraceae through artificial neural networks

The training and the application of a neural network system for the prediction of occurrences of secondary metabolites belonging to diverse chemical classes in the Asteraceae is described. From a database containing about 604 genera and 28,000 occurrences of secondary metabolites in the plant family, information was collected encompassing nine chemical classes and their respective occurrences for training of a multi-layer net using the back-propagation algorithm. The net supplied as output the presence or absence of the chemical classes as well as the number of compounds isolated from each taxon. The results provided by the net from the presence or absence of a chemical class showed a 89% hit rate; by excluding triterpenes from the analysis, only 5% of the genera studied exhibited errors greater than 10%. Copyright (C) 2004 John Wiley Sons, Ltd.

Avaliação estatística de métodos de poda aplicados em neurônios intermediários da rede neural MLP

There are several papers on pruning methods in the artificial neural networks area. However, with rare exceptions, none of them presents an appropriate statistical evaluation of such methods. In this article, we proved statistically the ability of some methods to reduce the number of neurons of the hidden layer of a multilayer perceptron neural network (MLP), and to maintain the same landing of classification error of the initial net. They are evaluated seven pruning methods. The experimental investigation was accomplished on five groups of generated data and in two groups of real data. Three variables were accompanied in the study: apparent classification error rate in the test group (REA); number of hidden neurons, obtained after the application of the pruning method; and number of training/retraining epochs, to evaluate the computational effort. The non-parametric Friedman's test was used to do the statistical analysis.

New contributions to the determination of resonance frequencies of rectangular microstrip antennas by neural networks

This paper uses artificial neural networks (ANN) to compute the resonance frequencies of rectangular microstrip antennas (MSA), used in mobile communications. Perceptron Multi-layers (PML) networks were used, with the Quasi-Newton method proposed by Broyden, Fletcher, Goldfarb and Shanno (BFGS). Due to the nature of the problem, two hundred and fifty networks were trained, and the resonance frequency for each test antenna was calculated by statistical methods. The estimate resonance frequencies for six test antennas were compared with others results obtained by deterministic and ANN based empirical models from the literature, and presented a better agreement with the experimental values.

Utilização de redes neurais artificiais aplicadas na discriminação de padrões de doenças florestais

Pós-graduação em Ciência Florestal - FCA

An Intelligent System for Petroleum Well Drilling Cutting Analysis

Cutting analysis is a important and crucial task task to detect and prevent problems during the petroleum well drilling process. Several studies have been developed for drilling inspection, but none of them takes care about analysing the generated cutting at the vibrating shale shakers. Here we proposed a system to analyse the cutting's concentration at the vibrating shale shakers, which can indicate problems during the petroleum well drilling process, such that the collapse of the well borehole walls. Cutting's images are acquired and sent to the data analysis module, which has as the main goal to extract features and to classify frames according to one of three previously classes of cutting's volume. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and efficiency. We used the Optimum-Path Forest (OPF), Artificial Neural Network using Multi layer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC) for this task. The first one outperformed all the remaining classifiers. Recall that we are also the first to introduce the OPF classifier in this field of knowledge. Very good results show the robustness of the proposed system, which can be also integrated with other commonly system (Mud-Logging) in order to improve the last one's efficiency.

Multidimensional polynomial powers of sigmoid (PPS) Wavelet neural networks

Wavelet functions have been used as the activation function in feedforward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical backpropagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As an example of application for the method proposed, it is studied the exclusive-or (XOR) problem.

Detection and classification of voltage disturbances using a Fuzzy-ARTMAP-wavelet network

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)