neural networks
Identification of the non-linear systems using internal recurrent neural networks
... of neural networks took a distinct ampleness because of the following properties: distributed representation of information, capacity of generalization in case of uncontained situation in training data set, ...
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Estimating soybean yields with artificial neural networks
... ABSTRACT. The complexity of the statistical models used to estimate the productivity of many crops, including soybeans, restricts the use of this practice, but an alternative is the use of artificial neural ...
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Spatial predictive mapping using artificial neural networks
... Kallmeier, E., Roscher, M., Böhnke, R., Barth, A., Drebenstedt, C.: Modellierung und Bewertung der Stabilität von Tagebaukippen mit künstlichen neuronalen Netzen. 15. Geokinematischer Tag, TU Bergakademie Freiberg. 2014. ...
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LITHOFACIES RECOGNITION BASED ON FUZZY LOGIC AND NEURAL NETWORKS: A METHODOLOGICAL COMPARISON
... and neural networks methods are commonly applied in various areas of the petroleum ...backpropagation neural network models were produced using data from three key ...The neural ...
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Attribute-value inference using deep neural networks
... Supervised, semi-supervised and unsupervised machine learning are the meth- ods used to recognize and tag named entities, standing out for the huge adoption of supervised and semi-supervised methods where Hidden Markov ...
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Applications of neural networks to control systems
... by neural networks in trajectory ...Hopfield networks to solve the inverse kinematics ...a neural controller which learns hand-eye coordination from its own ...
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Generation of Lyapunov Functions by Neural Networks
... In this paper, we propose two straightforward approaches for constructing or approximating a Lyapunov function based on approximation theory and the features of artificial neural networks. Our approaches ...
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Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks
... has been used to estimate the risks of drought in the Tagus river basin. This is the longest river in the Iberian Peninsula. In the methodology applied, an essential phase is the generation of multiple future ...
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Estimating Bankruptcy Using Neural Networks Trained with Hidden Layer Learning Vector Quantization
... Artificial Neural Networks (ANN’s) are a set of algorithms inspired by the human brain’s distributed architectures and parallel processing capabilities. ANN’s are essentially multiple regression machines ...
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Flood routing modelling with Artificial Neural Networks
... Artificial Neural Networks are black box models, they only got a restricted extrapolation ...a neural network requires sets of input and output data covering the whole range of possible flood ...
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Apnea Recognition with Wavelet Neural Networks
... The first efforts on apnea pattern recognition were possible only after 2000, driven by the Chal- lenge from PhysioNet and Computers in Cardiology 2000, whose objective was to verify the possibility of using ECG in apnea ...
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Ica and neural networks for kannada signature identification
... the neural networks have been designed in such a way that mimic the way in which human brains ...regression neural network (GRNN), probabilistic neural network (PNN) architectures are ...
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Correction: Persistent Activity in Neural Networks with Dynamic Synapses.
... Correction: Persistent Activity in Neural Networks with Dynamic Synapses Omri Barak, Misha Tsodyks.. doi: 10.1371/journal.pcbi.0030035[r] ...
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Fuzzy nonlinear regression using artificial neural networks
... Artificial Neural networks are massively parallel, distributed processing systems representing a computational technology built on the analogy to the human information processing ...Artificial Neural ...
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CONSTRUCTIVE NEURAL NETWORKS: A REVIEW
... conventional neural networks, we have to define the architecture prior to training but in constructive neural networks the network architecture is constructed during the training ...
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Neural networks and its applications in multivariate calibration.
... NEURAL NETWORKS AND ITS APPLICATIONS IN MULTIVARIATE CALIBRATION. Neural Networks are a set of mathematical methods and computer programs designed to simulate the information process and the ...
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Neural Networks through Shared Maps in Mobile Devices
... We use Convolutional Neural Networks (CNN) [3] for the classification of image content. CNNs have become a general solution for image recognition with variable input data, as their results have outclassed ...
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Similarity-based Heterogeneous Neural Networks
... the networks highly interpretable. The resulting heterogeneous neural net- works are trained by means of a special-purpose ge- netic ...(neural networks, evo- lutionary algorithms and fuzzy ...
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CNNcon: improved protein contact maps prediction using cascaded neural networks.
... 3-layer neural networks trained with the same standard back- propagation algorithm ...sub- networks are the same and composed of 1747 input nodes, 5 hidden nodes and 1 output ...
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Ribosome binding site recognition using neural networks
... In the individual evaluation of answers of networks for each start codon, it was noted that there was a high level of generalization, identifying many possible RBS se- quences for a single gene. It was a predicted ...
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