Neural networks (Computing)
A neural networks benchmark for image classification
... A neural network is a computing system inspired by the biological neural networks that constitute animal ...Artificial Neural Networks (ANN) are composed of neurons and ...
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Acoustic Fingerprint Recognition Using Artificial Neural Networks
... Artificial Neural Networks (ANN) for acoustic fingerprints recognition, applied to the identification of marine ...of neural networks is ...
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Time series modeling and synchronization using neural networks
... [5], neural networks [6], delay reconstruction space [7], wavelets [8], functional networks [9], ...artificial Neural Networks (NNs) have been successfully applied in many practical ...
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Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks
... artificial neural networks for their iden- tification as a red blood cell is ...of neural networks (MLP) as a standard classification technique with (MLP) is compared with new proposals ...
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A parallel approach for backpropagation learning of neural networks
... backpropagation neural networks using a pattern partitioning scheme with a set-training ...small neural net selected for the testing case a substantial acceleration ranging from 4 to more than ten ...
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A Brief Introduction to Neural Networks
... of neural networks, it would be use- ful to briefly discuss the biology of neu- ral networks and the cognition of living organisms – the reader may skip the fol- lowing chapter without missing any ...
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Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.
... x (i) d = x (i) d + v d (i) (7) Regarding the PSO algorithm, different variants have been developed. Most of them aimed at speeding up the convergence of it. In addition to the unconstrained optimization problem in ...
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Pattern recognition in medical images using neural networks
... sense, neural networks are extremely useful, since not only are they capable of recognizing patterns with the aid of the expert, but also of generalizing the information contained in the input data, thus ...
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Data Mining with Enhanced Neural Networks-CMMSE
... If there are not hidden layers then the degree of the polynomial is two, which is a quadratic polynomial in the output of the network. The feature of being able to increase the degree of the polynomial output, adding ...
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Artificial neural networks applied to forecasting time series
... cial neural networks (ANN) have aroused great interest in fi elds as diverse as biology, psychology, medicine, economics, mathematics, statistics and computer ...
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Distant galaxies analysis with deep neural networks
... • Design and training of 4 different neural networks • Prediction of Redshift, Stellarity, Stellar Mass and Spectral Type of galaxies • Data from Alhambra Survey • Comparison of Predicte[r] ...
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Content Based Image Retrieval by Convolutional Neural Networks
... Self-Organizing Neural Networks for Information Tech- nologies; and TIC-657, project name Self-organizing systems and robust estima- tors for video ...
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Using Neural Networks to Simulate the Alzheimer's Disease
... artificial neural networks that implement Grossberg’s presynaptic learning rule, we simulate the possible effects of calcium dysregulation in the neuron’s activation function, to represent the most accepted ...
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Potential of neural networks for structural damage localization
... In recent years, several authors (e.g., [1-3]) have concluded that structural damage detection is a problem of pattern recognition, in which a classification is made as function of physical properties of a system. Within ...
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Deep Neural Networks to find genetics signatures
... “Deep Neural Networks to find genetics signatures” wich wants prove the viability of use Deep Neural Networks (DNN) to identify relationships between genes and clinical symptoms and create new ...
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Detecting Simulated Attacks in Computer Networks Using Resilient Propagation Artificial Neural Networks
... using neural networks have been deemed a promising solution to detect such ...that neural networks have some advantages such as learning from training and being able to categorize ...applying ...
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Using artificial neural networks in estimating wood resistance
... considerably favorable energetic characteristics. Some elementary features of wood are directly related to their properties. Among these are the wood density and its response to the propagation of acoustic waves by ...
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Glucose-Insulin regulator for type 1 diabetes using high order neural networks
... artificial neural networks (ANN) is ...order neural network in order to identify and control a nonlinear dynamical system which represents the pancreas’ beta-cells behavior of a virtual ...nonlinear ...
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Facial action unit detection with convolutional neural networks
... Convolutional Neural Networks (CNNs), the family of techniques that has revolutionized visual recognition in the last years, to the study of this ...
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TítuloDeep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications
... Feedforward Neural Networks (DFNN), Deep Belief Networks (DBN), Deep AutoEncoder Networks, Deep Boltzmann Machines (DBM), Deep Convolutional Neural Networks (DCNN) and Deep ...
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