hidden-layer weights
Benchmarking the selection of the hidden-layer weights in extreme learning machines
... whose hidden-layer weights are randomly selected, such as Extreme Learning Machines ...the hidden-layer weights as a subset of the data have shown superior performance than ...
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Biologically plausible deep learning – but how far can we go with shallow networks?
... den layer and a single readout layer. The hidden layer weights are either fixed (random or random Gabor filters) or trained with unsupervised methods (Principal/Independent Component ...
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Password Based a Generalize Robust Security System Design Using Neural Network
... trained weights, hidden layer weights and output layer weights, from neural ...If hidden layer weights transfer to user memory device and output layer ...
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Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality
... the hidden layer, with significant increase of computational ...distinct hidden layer weights for each word, but with greater costs in memory ...distinct hidden layer ...
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Predicting phishing websites based on self structuring neural network
... each layer should have its own rules; however, it was not clear if the rules were established based on human experience, which is one of the problems we aim to resolve in this article, or extracted in an automated ...
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System for automatic crate recognition
... Multi-Layer Perceptron neural network Multi-Layer Perceptron neural network (MLP) is confi gured by the number of input and output neurons, by the number of neurons in the hidden layer, number ...
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Calculating the turbulent fluxes in the atmospheric surface layer with neural networks
... data completely unknown to them; this simulates the sit- uations in which ANNs would be used in climate models (where grid points play the role of stations). To test this, we choose the NL-Cab station for validation and ...
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Approaches in RSA Cryptosystem Using Artificial Neural Network
... for the Levenberg-Marquardt Backpropagation that can be seen in Table 4,5,6 that can be seen that a 2 hidden layer performance in prediction is better that the one hidden layer network. ...
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Application of Artificial Neuron Network in Analysis of Railway Delays
... In selecting the operation in the genetic algorithm, the large probability must be chosen as the parent individual. This parent individual intersects with other individuals or possibly mutates according the probability ...
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Mental Workload Assessment using Rnn
... The traditional PSD features obtain poor accuracy (less than random probability) for the cross-task problem using a single-hidden-layer back propagation (BP) artificial neural network (ANN). ERS/ERD ...
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Attentive Convolution: Equipping CNNs with RNN style Attention Mechanisms
... the weights of the post-convolution pooling layer are determined by ...These weights come from the matching process between hidden states of two text ...
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Traffic flow Prediction with Big Data Using SAE’S Algorithm
... the layer below as the input of the current layer ...first layer is trained as an autoencoder, with the training set as ...first hidden layer, the output of the kth hidden ...
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The Principled Design of Large-Scale Recursive Neural Network Architectures–DAG-RNNs and the Protein Structure Prediction Problem
... and hidden node variables; (2) parameterization of the relationship between each variable and its parent variables by feedforward neural networks; and (3) application of weight-sharing within appropriate subsets ...
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The Impact of the Neural Network Structure by the Detection of Undesirable Network Packets
... is with 40 neurons in hidden layer. But this area is very complex and there is still a lot of work to do. This work represents just a part of the overall problem. In the next we can solve number of ...
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Radial basis function neural network for software engineering measures A survey
... Every neuron has direct connections to the neurons of succeeding layer. Neural networks units have a sigmoid function as an active function in most of the applications. Now a day’s most of the research works are ...
6
Vol 8, No 9 (2018)
... input layer, at least one hidden layer and output ...The hidden and output layer nodes adjust the weights value depending on the error in ...and weights are updated to ...
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3D Modeling of Virtualized Reality Objects using Neural Computing P. Sheepa, A. Charles
... architecture and internal connections; hence we must use a set of test data to perform visualization. In fact that MLFFNN is a classifier trained to learn a volumetric model, test dataset is enter to the neural network, ...
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Artificial Neural Networks Application in Prediction of Water Quality
... input layer, hidden layer and output layer. Each layer consists neurons, and each neuron is connected to the next layer through ...input layer will send its output as ...
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Intrusion detection for IoT based on improved genetic algorithm and deep belief network
... the hidden layer in order to obtain the best ...of hidden neurons in the neural networks to avoid overfitting in the function ...of hidden neurons in an MLP network by using coarse-to-fine ...
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Optimizing Number of Hidden Nodes for Artificial Neural Network using Competitive Learning Approach
... of hidden nodes. The random selection of number of hidden nodes leads towards the problem of Overfitting or ...of hidden layer. The proposed method calculates the number of hidden ...
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