Capabilities of a four-layered feedforward neural network: four layers versus three

Tamura, Shinichi; Tateishi, Machiko

doi:10.1109/72.557662

Cited by 377 publications

(151 citation statements)

References 8 publications

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“…The principle which distinguishes ELM from the traditional neural network methodology is that all the parameters of the feed-forward networks (input weights and hidden layer biases) are not required to be tuned in the former. The studies of Tamura and Tateishi (1997) and Huang (2003) showed in their work that the SLFNs with randomly chosen input weights efficiently learn distinct training examples with minimum error. After randomly choosing input weights and the hidden layer biases, SLFNs can be simply considered as a linear system.…”

Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Deo

Şahin

2015

Atmospheric Research

268

102

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The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed datadriven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties.

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Section: Extreme Learning Machine (Elm)mentioning

confidence: 99%

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

Deo

Şahin

2015

Atmospheric Research

268

102

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“…Although one hidden layer is adequate to enable NN to approximate any given function, some researchers argued that NN with more than one hidden layer might require fewer hidden neurons to approximate the same function. It was theoretically shown in [26] that, given a desired degree of interpolation accuracy, NNs with two hidden layers require considerably fewer hidden neurons compared to NNs with one hidden layer. From a more practical perspective it has been shown in [27], through extensive experiments, that single-hidden-layer NNs are superior to networks with more than one hidden layer with the same level of complexity mainly due to the fact that the latter are more prone to fall into local minima.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks for Muscle Forces Prediction in Cycling

et al. 2014

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This paper documents the research towards the development of a system based on Artificial Neural Networks to predict muscle force patterns of an athlete during cycling. Two independent inverse problems must be solved for the force estimation: evaluation of the kinematic model and evaluation of the forces distribution along the limb. By solving repeatedly the two inverse problems for different subjects and conditions, a training pattern for an Artificial Neural Network was created. Then, the trained network was validated against an independent validation set, and compared to evaluate agreement between the two alternative approaches using Bland-Altman method. The obtained neural network for the different test patterns yields a normalized error well below 1% and the Bland-Altman plot shows a considerable correlation between the two methods. The new approach proposed herein allows a direct and fast computation for the inverse dynamics of a cyclist, opening the possibility of integrating such algorithm in a real time environment such as an embedded application.

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“…Class I refers to the NN that has more input than output variables, while Class II refers to NNs with an equal number of inputs and outputs and Class III refers to the NN which has more output than input variables. For Class I NNs (more inputs than outputs), one hidden layer is enough in most cases and, according to Tamura and Tateishi (1997), if N-1 neurons are used in the hidden layer (where N is the number of inputs), the NN will give an exact prediction. This recommendation works well when the system has a small number of inputs and the correlation between the data points (inputs and outputs) is not very complex; otherwise their recommendation will not always work and we recommend the use of 8 to 20 neurons in the hidden layer for better precision and shorter training time.…”

Section: Nn Topologymentioning

confidence: 99%