Feedforward Artificial Neural Network
Using artificial neural networks in estimating wood resistance
... the network training, in which the input and output variables were indicated for the ...a feedforward method and it uses the algorithm for unidirectional data flow without cycles (Haykin ...2001). ...
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Methodology to determine the topology, electrical parameters patterns, and training of an Artificial Neural Network for intelligent control.
... Resumen. El presente trabajo describe la metodología para determinar la topología y la implementación de una Red Neuronal Artificial tipo FeedForward, basada en reglas que se pueden utilizar como punto de ...
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TítuloHybrid modelling using neural network based prediction under Foundation Fieldbus
... as neural networks (NN) and genetic algorithms (GA) can remarkably enhance the regulatory and advanced control capabilities of many industrial processes [3, 8, ...of artificial neural networks (ANN) ...
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Analysis of volumetric swelling and shrinkage of heat treated woods: Experimental and artificial neural network modeling approach
... a neural network does not require a preliminary knowledge regarding the problem under ...the network is trained using the training ...established network (Hagan et ...
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A Wavelet neural network for detection of signals in communications
... wavelet network construction are ...wavelet neural networks implementation, when applied to the problem of function approximation, are reported in Section ...
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Neural visualization of network traffic data for intrusion detection
... or Neural Networks [15-20] among others) together with statistical [21] and signature verification [22] techniques have been applied mainly to perform a 2-class classification (normal/anomalous or ...
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Prediction of standard particleboard mechanical properties utilizing an artificial neural network and subsequent comparison with a multivariate regression model
... the network for the input values; and the hidden layer performs the operations designed to obtain an ...error. Neural networks are usually represented as [n 1 n 2,1 n 2,2 … n 2,m n 3 ], where n 1 is the ...
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Comparison between artificial neural network and multiple regression for the prediction of superficial roughness in dry turning
... and artificial neural network methods are techniques used in many industrial apli- ...the neural networks techniques are more accurate than the multiple regression techniques for this ...
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An artificial neural network model application for the estimation of thermal comfort conditions in mountainous regions, Greece
... neural network (ANN) model (Shank et al., 2008) is characterized by a high potential of complex, non-linear and time-varying input-output mapping (Dibike and Coulibaly, 2006). ANN models are based on the ...
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Adaptive Pointing Theory (APT) Artificial Neural Network
... The experi- mental results have proved that the performance of the face recognition system using blood perfusion data is signi¯cantly better than the temperature data.. Bhowmik et al.[r] ...
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Air pollution data classification by SOM Neural Network
... In the training (learning) phase, the SOM forms an elastic net that folds onto the "cloud" formed by the input data. Similar input vectors should be mapped close together on the nearby neurons, and group them ...
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TítuloShallow Recurrent Neural Network for Personality Recognition in Source Code
... To process these byte sequences we will use recurrent neu- ral networks, as they are a perfect fit for sequential data [5]. Thus, each byte from the input sequence is fed to the net- work at each time step through the ...
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Deep neural network and precision agriculture for grape yield estimation
... same network configuration shown in ...the network struggles in separating berries from the ...the network is able to better represent the density of clusters usually more present in the background ...
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Optimization of Convolutional Neural Network ensemble classifiers by Genetic Algorithms
... a network by training a network considering different parameters: the first phase tunes the base learning rate and the weight decay, while the second phase tunes the solver type and performs net surgery by ...
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TítuloA recurrent neural network for wastewater treatment plant effuents' prediction
... starts to drop and become worse than before. L2 consists in adding extra penalty to the weights optimization. As a consequence, it is harder for the network to match the training examples as for weights cannot ...
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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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A neural network clustering algorithm for the ATLAS silicon pixel detector
... the neural network clustering algorithm, particles are assumed to have been produced at the centre of the beam interaction region, which is estimated using the primary vertices of collisions from a ...
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ARTIFICIAL NEURAL NETWORK ALGORITHMS FOR ACTIVE NOISE CONTROL APPLICATIONS
... This paper shows the use of several methods commonly applied to training Artificial Neural Networks (ANN) in Active Noise Control (ANC) systems. Although ANN are usually focused on off-line training, ...
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Artificial analysis of molecular marker loci linked to tree resistance response by an artificial neural network
... Abstract: Citrus tristeza virus (CTV) is one of the most important pathogen affecting citrus and no CTV resistant scion varieties are available. Since Chandler pummelo was found to be CTV resistant, this variety is being ...
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TítuloNet Net AutoML selection of artificial neural network topology for brain connectome prediction
... descriptors from known ecosystems. The complex task of a priori selecting the best ANN topology for the BEN node connectivity prediction, Net-Net AutoML method was able to help us. Twelve types of classifiers have been ...
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