single hidden-layer neural networks
Agnostic learning and single hidden layer neural networks
... the neural networks (finding an LQO cover) and optimising over the discrete set of ...as single hidden layer neural networks with linear threshold hidden ...of ...
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Spoken language identification based on the enhanced self-adjusting extreme learning machine approach
... the single hidden layer feedforward networks (SLFNs), and has been demonstrated to have excellent learning accu- racy/speed in various ...(BP)-based neural networks (NNs), and ...
27
Novel weighting in single hidden layer feedforward neural networks for data classification
... the single hidden layer feedforward neural network (SLFN) using radial basis functions (RBFs) and sigmoid functions in the hidden ...input layer and the hidden ...
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Hidden-layer Ensemble Fusion of MLP Neural Networks for Pedestrian Detection
... For OursMLPEns, given a test feature vector x, there are 100 different matrix projections with each matrix hav- ing k rows and h columns (which is equal to the number of dimensions of the feature vector and the number of ...
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Guaranteeing generalisation in neural networks
... N hidden units in a single hidden layer, one input unit and one output ...1 hidden units are needed to realise their targets in the worst case, in which no two adjacent patterns has the ...
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Calculating the turbulent fluxes in the atmospheric surface layer with neural networks
... the networks that perform best on the validation set are summarised in Table 4, where we compare the ANNs according to the increasing complex- ity of their network ...simple networks (as defined in ...
15
Efficient Spam Detection using Single Hidden Layer Feed Forward Neural Network
... of neural networks is an iterative learning process in which records (rows) are presented to the network one at a time, and the weights associated with the input values are adjusted each ...of neural ...
7
Artificial Neural Networks for fMRI Data Analysis: A Survey
... feedforward neural network was the first and simplest type of artificial neural network ...the hidden nodes (if any) and to the output ...of neural network is a single-layer ...
8
A Comparative Study on Various Neural Network Algorithms
... Forward Neural Network is much slower than required and this has been a major drawback in past ...train neural networks are very slow, and 2) those slow gradient learning algorithms are used to tune ...
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Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality
... ral networks (RNN) usually involves aug- menting the size of the hidden layer, with significant increase of computational ...Recurrent neural tensor networks (RNTN) increase capacity ...
6
A New Optimization Algorithm for Single Hidden Layer Feedforward Neural Networks
... The organization of this paper is as follows. In Section 2, we present the methodology of optimization algorithm for the sin- gle hidden layer FNN. Let by this discussion, we show step by step how it can be ...
7
arxiv: v2 [cs.lg] 29 Jul 2020
... of neural networks with heteroscedastic loss ...20 single hidden layer networks with 4 hidden units, and use the mean value of the ensemble to make a ...
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Power Generation Forecasting of Dual-Axis Solar Tracked PV System Based on Averaging and Simple Weighting Ensemble Neural Networks
... ensemble neural network based forecaster of the power output of PV systems which will lead an efficient power ...perceptron neural networks with two hidden layers as base forecasters while the ...
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Investigation of Wireless Sensor Network Node Localization with Reduced Power Consumption
... WSN localization is an active area of research with several surveys [1] - [5], on this topic. In 2008, K. Benkic, et al.[2] indicated that for WSNs -indoor type, RSSI is a low accurate distance estimator. Many factors ...
9
Neural Network-Based High-Accuracy Motion Control of a Class of Torque-Controlled Motor Servo Systems with Input Saturation
... In this paper, considering the universal approximation ability of NN, a single hidden-layer NN based observer is designed to estimate the input saturation of the system, which is later compensated in ...
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An enhanced training algorithm for multilayer neural networks based on reference output of hidden layer
... output layer are fixed by the number of inputs and outputs of the system being ...the hidden layer are important design parameters: they not only deter- mine the structure of the network, but also ...
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Neural Network and Fuzzy Sets: An Effective and Optimize Alternative for Damage Assessment
... second layer is an axis with values between 0 and 1 ...the hidden layer and the form, resistance and construction values is ...output layer (in fact they are not the neurons, but the weights) ...
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The Impact of the Neural Network Structure by the Detection of Undesirable Network Packets
... ethernet networks and traditional technologies ...of neural networks we can reach a separate intelligent network security systems - intelligent firewall [6], which contains knowledge about the ...
5
Online Full Text
... applied neural network in the localized server ...applied neural networks. The architecture of neural networks’ model used an input layer, one and two hidden layers with n ...
6
Neural networks in geophysical applications
... The first network we used was a so-called 9-5-3 MLP net- work, i.e., nine input, five hidden, and three output nodes. The network was trained until convergence. The fact that this may have resulted in an overfit ...
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