The estimation theory and optimization algorithm for the number of hidden units in the higher-order feedforward neural network

Li, Jinyan; Chow, Tommy W. S.; Yu, Yang

doi:10.1109/icnn.1995.487330

Cited by 26 publications

(5 citation statements)

References 5 publications

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“…Keras is a high-level Application Programming Interface (API) with the model and layers as the main structure. The most time-consuming process is to determine the optimum architecture of the model to minimize the error (Li et al, 1995). XGBoost or Extreme Gradient Boosting is a widely used machine learning method in data science and machine learning competitions because it performs well in most data sets (Chen & Guestrin, 2016).…”

Section: Supervised Machine Learning Algorithmmentioning

confidence: 99%

Applying Artificial Neural Network and XGBoost to Improve Data Analytics in Oil and Gas Industry

Simanjuntak

Irawan

2021

IJE

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The application of machine learning and artificial intelligence is popular nowadays to improve data analytics in the oil and gas industry. A huge amount of data can be processed to gain insights about the subsurface conditions, even reducing time for manual review or interpretation. There are three cases to be discussed in this study that starts from porosity estimation of thin core image using Otsu's thresholding, estimation of oil production rate from sucker-rod pumping wells and sonic travel-time log generation. Two supervised learning algorithms are applied, XGBoost and Keras. These algorithms will capture all possible correlations between the input and output data. From data normalization, exploratory data analysis and model building, the workflow is built on Google Colab. The original dataset is split into training and testing. Tuning hyperparameters such as the number of hidden layers, neurons, activation function, optimizers and learning rates are captured to reduce the complexity of the model. The model is evaluated by error values and the coefficient of determination to estimate the model skill on unseen data.

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Section: Supervised Machine Learning Algorithmmentioning

confidence: 99%

Applying Artificial Neural Network and XGBoost to Improve Data Analytics in Oil and Gas Industry

Simanjuntak

Irawan

2021

IJE

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“…In addition neural network were tested by changing the number of hidden layer from zero to 3 layers and the R 2 are used to evaluate the NN. Jin-Yan, and Ying-Lin [18] improved the theory proposed by previous study [19]. The developed method was applied and tested to the problems of the prediction of time series and system identification by higher-order neural network.…”

Section: Previousworkmentioning

confidence: 99%

A Comparison between Seven Heuristic Methods to Estimate the Number of Hidden Layer Neurons in a Multi Layer Perceptron Neural Network

2021

IJATCSE

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Multilayer Perceptron Neural Network (MLPNNs) constructs of input, at least one hidden and output layer. Number of the neurons in the hidden layer affects the NNs performance. It also consider difficult task to overcome. This research, aims to exanimate the performance of seven heuristic methods that have been used to estimate the neurons numbers in the hidden layer. The effectiveness of these methods was verified using a six of benchmark datasets. The number of hidden layer neurons that selected by each heuristic method for every data set was used to train the MLP. The results demonstrate that the number of hidden neurons selected by each method provides different accuracy and stability compared with other methods. The number of neurons selected by Hush method for ine data set was 26 neurons. It’s achieved the best accuracy with 99.90%and lowest accuracy achieved by Sheela method with 67.51% using 4 neurons. Using 22 neurons with 97.97% accuracy Ke, J method received the best result for Ionosphere data set. While the lowest accuracy was 96.95% with 5 neurons achieved by Kayama method.For Iris data set with 8 neurons achieved 97.19 as best accuracy achieved by Hush method. For the same data set the lowest results were 92.33 % using 3 neurons obtained by using Kayama method. For WBC data set 96.40% the best accuracy achieved using Sheela and Kaastra methods using 4 and 7neurons, while Kanellopoulos method achieved the lowest accuracy 94.18% with 7neurons. For Glass dataset, 87.15% was the best obtained accuracy using 18 neurons Hush method and using Wang method 82.27 % with 6 neurons was the lowest accuracy. Finally for PID 75.31% accuracy achieved by Kayama method with 3 neurons, where Kanellopoulos method obtained 72.17% through using 24 neurons.

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“…Typically, these recommendations are applicable to the specific cases of a particular network topology. [75] = (√1 + 8 − 1)/2 Tamura and Tateishi method [76] = − 1 Fujita method [77] = log‖ ‖/ log Zhang et al Method [78] = 2 + 1 ⁄ Jinchuan and Xinzhe method [79] = ( + )/ Xu and Chen method [80] = ( / log ) .…”

Section: Neural Network Architecture For Anomaly Prediction 31 the Prmentioning

confidence: 99%

Predicting anomaly conditions of energy equipment using neural networks

Sverdlova

Zaporozhets

2021

E3S Web Conf.

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In modern conditions for complex thermal power facilities, the issue of developing methods for predicting equipment failures is especially relevant. Methods based on the intellectualization of diagnostic systems and allowing to obtain predictive models based on the use of both current data received in real time from measuring equipment and retrospective information are considered promising. Intellectualization of the system in terms of the ability to learn allows to quickly adjust the parameters of forecasting models under changing conditions of equipment operation, to determine new deadlines for scheduled repairs and minimize equipment downtime. A limitation of the use of methods is the incompleteness of failure statistics, ie when equipment failures are rare or non-existent. Such diagnostics of energy equipment, especially thermal power facilities, contributes to a more environmentally friendly production.

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The estimation theory and optimization algorithm for the number of hidden units in the higher-order feedforward neural network

Cited by 26 publications

References 5 publications

Applying Artificial Neural Network and XGBoost to Improve Data Analytics in Oil and Gas Industry

Applying Artificial Neural Network and XGBoost to Improve Data Analytics in Oil and Gas Industry

A Comparison between Seven Heuristic Methods to Estimate the Number of Hidden Layer Neurons in a Multi Layer Perceptron Neural Network

Predicting anomaly conditions of energy equipment using neural networks

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