Neural networks (Neurobiology)
Biosignals learning and synthesis using deep neural networks
... This paper proposes the application of a deep neural networks (DNN) to accurately synthesize the morphologies of a biosignal. The hypothesis is that if the created mod- els are capable of generating clean ...
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A Survey on Object Classification using Convolutional Neural Networks
... The work of LeCun et. al. which has been mainly inspired by the work of Fukushima, has been the basis for the types of convolutional neural networks we see today. The is known as LeNet5. The main difference ...
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REFERENCE EVAPOTRANSPIRATION FORECASTING BY ARTIFICIAL NEURAL NETWORKS
... ABSTRACT : Evapotranspiration (ET) is the main component of water balance in agricultural systems and the most active variable of the hydrological cycle. In the literature, few studies have used the forecast the day ...
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Personalized bank campaign using artificial neural networks
... Nowadays, high market competition requires Banks to focus more at individual customers´ behaviors. Specifically, customers prefer a personal relationship with the finance institution and they want to receive exclusive ...
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Hyperparameters optimization on neural networks for bond trading
... Artificial Neural Networks have been recently spotlighted as de facto tools used for ...the Neural Networks hyperparameters, we have compared the results obtained by the manual, evolutionary ...
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Memristive Ag2S Synapses: Towards Artificial Neural Networks
... Many dierent learning laws have been proposed for edges [39]. Adjustable edge weights are the de ning characteristic of neural networks and are the origin of their broad adaptive functionality [12]. An ...
101
ARTIFICIAL NEURAL NETWORKS FOR PREDICTING ANIMAL THERMAL COMFORT
... the neural networks with the best indexes was assessed using the Kolmogorov- Smirnov, Lilliefors, and Shapiro-Wilk tests, and the adjustment between the observed and estimated values was confirmed using the ...
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Empirical evaluation of random wired neural networks
... We explore this question through the lens of randomly wired neural networks that are sampled from stochastic network generators, in which a human-designed random process defines generation. To reduce bias ...
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Applications of neural networks to control systems
... artificial neural networks, in a short number of pages, is not an easy ...of neural networks have been ...artificial neural networks, without entering into the detail of any ...
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Apnea Recognition with Wavelet Neural Networks
... that Neural Networks delivered better results than Logistic Discriminant ...with Neural Network with the same parameter set, but we speculate that one of the reasons may be the linear combination of ...
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Ica and neural networks for kannada signature identification
... Abstract — In biometrics, signature is one of the largest and oldest part used for endorsement. In this paper, approach based on Independent Component Analysis (ICA) and neural networks is proposed to study ...
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Neural Networks through Shared Maps in Mobile Devices
... YBRIFD intelligent systems have consistently shown benefits that outperform those of their individual components in many tasks, especially when used along neural computing [1]. In recent years, two main areas of ...
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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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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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Spatial predictive mapping using artificial neural networks
... Artificial neural networks are usually organized in layers. The network topology describes the number of layers, the number of neurons in layers and the way of their interconnection: Important parameters ...
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Fuzzy nonlinear regression using artificial neural networks
... Artificial Neural Networks, have been employed for predicting the Indian summer monsoon rainfall (ISMR) using lagged relationships between the ISMRI and various combinations of Niño indices, Shukla et al, ...
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Estimating soybean yields with artificial neural networks
... Artificial neural networks (ANNs) are widely applied in research due to their ability to model highly nonlinear systems in which the relationships between variables are unknown or very complex (Russell ...
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Direct adaptive control using feedforward neural networks
... current Neural Networks), one as an identifier and the other as the controller, and an approach based on adap- tive learning rates to control this ...the neural network for simultaneous control and ...
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Insights on neural networks
... In 1957, intrigued with the operation of the eye, Frank Rosenblatt, a psychologist at Cornell Aeronautical Laboratory, New York, began working on single-layer neural networks called the perceptrons 2 ...
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Generation of Lyapunov Functions by Neural Networks
... Stability of nonlinear dynamic systems plays an important role in systems theory and engineering. There are several approaches in the literature addressing this problem. The most useful and general approach for studying ...
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