two hidden layer networks
Hidden-layer Ensemble Fusion of MLP Neural Networks for Pedestrian Detection
... for two main reasons: firstly, we are not relying solely on one MLP, and sec- ondly, we are able to, during fusion, correct mistakes of the individual MLPs at the hidden features level as mentioned ...
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Calculating the turbulent fluxes in the atmospheric surface layer with neural networks
... these two stations be- cause the MOST method performed best for these stations; therefore it is a strong challenge for the ANNs to produce equivalent ...the networks that perform best on the validation set ...
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Prediction of wheat production using artificial neural networks and investigating indirect factors affecting it: Case study in Canterbury province, New Zealand
... with two hidden layers was ...neural networks after the input layer, which operates on the inputs to achieve some subtasks of the task the network expects to ...
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Power Generation Forecasting of Dual-Axis Solar Tracked PV System Based on Averaging and Simple Weighting Ensemble Neural Networks
... with two hidden ...The hidden neuron number in the first layer is varied on each base forecaster depend on the number of input (such as 4 neurons when the input number is 4) while the ...
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A New Optimization Algorithm for Single Hidden Layer Feedforward Neural Networks
... Furthermore, the optimization algorithm CCA can be extended to FNN having two or more hidden layers. Nevertheless, it is an interesting problem and will need a lot of efforts in applying GA to this ...
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Predicting the Daily Efficiency of Tehran Stock Share Price by Using of Artificial Neural Networks, Cascade Forward
... neural networks, the neural network with the feed forward cascade architecture and five input parameters, two hidden layer and 6 hidden node (with a 5-6-1 architecture), transfer ...
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Development and Evaluation of A Comprehensive Greenhouse Climate Control System Using Artificial Neural Network
... neural networks with multiple delays in the input, two-layer neural network with a feedback from hidden layer and input delay and three-layer neural network with two ...
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A constraint based random search algorithm for optimizing neural network architectures and ensemble construction in detecting loss of coolant accidents in nuclear power plants
... 2-hidden layer, 3-hidden layer and 4-hiddden layer network architectures are created using a proposed constraint satisfaction ...2-hidden layer network, an optimised ...
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Guaranteeing generalisation in neural networks
... Firstly, networks with sigmoid units are able to realise more complex IO pictures than the same networks with threshold ...given hidden layer, but without the requirement for a further ...
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Optimizing the Multilayer Feed Forward Artificial Neural Networks Architecture and Training Parameters using Genetic Algorithm
... optimal hidden layer architectures, connectivity, and training parameters for ANN for predicting community acquired pneumonia among patients with respiratory ...input layer, one node in the output ...
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Neural Network Programming with Java
... Neural Networks Learn, and now, we are going to explore the features of this learning paradigm in a detailed ...neural networks) without any error measure, and this is the crucial feature that distinguishes ...
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Design A Bartlett Window Based Digital Filter by Using GRNN
... A filter is essentially a system or network that selectively changes the wave shape amplitude – frequency and or phase – frequency characteristics of a signal in a desired manner. Common filtering objectives are to ...
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Fet Small Signal Modelling Based on the Dst and Mel Frequency Cepstral Coefficients
... neural networks size and storage capacity, a reduction in the training time and a large immunity to measurement errors in the testing ...Although two MESFET models have been used for the validation of the ...
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Railway Traffic Accident Forecast Based on an Optimized Deep Auto-encoder
... neural networks are still being im- proved, they are suitable for various ...of hidden layers, the num- ber of neurons in each layer and learning rates were decided by subjective or multiple ...
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Novel weighting in single hidden layer feedforward neural networks for data classification
... function networks, first introduced in the neural network domain by Broomhead and Lowe [2], represent a specific class of SLFNs in which the linearly weighted structure of the networks allows for easy and ...
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Influence spread in two-layer interdependent networks: designed single-layer or random two-layer initial spreaders?
... interdependent networks (M-IDN) has been studied in the last few years; however, prior works mostly focused on the spread that is initiated in a single layer of an ...a two-layer ...
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Modeling MOOC Student Behavior With Two-Layer Hidden Markov Models
... How should we represent behavioral patterns, and what does it mean to understand changes in student behavior with respect to these patterns? These are still very open questions and are active areas of research (Kizilcec ...
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Recurrent Neural Network Learning of Phonological Regularities in Turkish
... The aim of the study was to look at the represen- tations developed within the hidden layer of the network in order to investigate the extent to which such networks can learn phonologica[r] ...
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Synchronizability of Two Layer Cluster Ring Networks
... real-world networks do not exist in ...such networks are widespread; for instance, in a social system, a set of individuals in- teract between each other in various modes of social interactions between the ...
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Personal verification based on multi spectral finger texture lighting images
... -Al-Nima et al. [20] proposed a method to extract the full regions of the FTs from the four fingers based on the traditional contour. This publication confirmed that increasing the collected FT patterns would increase ...
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