Importance of input data normalization for the application of neural networks to complex industrial problems

Solà, Josep; Sevilla, J.

doi:10.1109/23.589532

Cited by 686 publications

(312 citation statements)

References 5 publications

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“…We have found that input data normalization with certain criteria prior to a training process is crucial to obtaining good results as well as to significantly accelerate the calculations [31][32][33].…”

Section: Normalization Of the Training Samplesmentioning

confidence: 99%

An Artificial Nerve Network Realization in the Measurement of Material Permittivity

Chen¹,

Huang²,

Yang³

et al. 2011

PIER

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Abstract-Effective complex permittivity measurements of materials are important in microwave engineering and microwave chemistry. Artificial neural network (ANN) computational module has been used in microwave technology and becomes a useful tool recently. A neural network can be trained to learn the behavior of an effective permittivity of material under microwave irradiation in a test system, and it can provide a fast and accurate result for the permittivity measurement of material. Thus, an on-line measurement has been realized. This paper presents a simple and convenient reconstruction algorithm for determining the dielectric properties of materials. First, a measurement system is designed, and the reflection coefficient is calculated by employing full-wave simulations. Second, an artificial nerve network has been applied, and adequate simulated materials are utilized to train the networks. Last, the trained network is employed to reconstruct the effective permittivity of several organic solvents using the measured scattering parameters, and the reconstructed results for several organic solvents agree well with reference data and the relative errors between them are less than 5%.

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Section: Normalization Of the Training Samplesmentioning

confidence: 99%

An Artificial Nerve Network Realization in the Measurement of Material Permittivity

Chen¹,

Huang²,

Yang³

et al. 2011

PIER

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“…Noise and missing data would affect the performance of ANN models (Sola and Sevilla, 1997). Thus, before training and testing the ANN, it is important to perform data checking and cleansing to maximize the performance of ANN forecasting.…”

Section: Data Pre-processingmentioning

confidence: 99%

“…Moreover, according to (Chai et al, 2009), normalization speeds up the training process of the ANN and reduces the likelihood of the ANN getting stuck in local minima. Adequate data normalization before applying it into the ANN can reduce the estimation error generated by the ANN in a factor between 5 and 10 ( Sola and Sevilla, 1997). In this study, the input data was normalized so that the minimum and maximum values for each input row are between +1 and -1.…”

Section: Data Normalizationmentioning

confidence: 99%

Backpropagation Vs. Radial Basis Function Neural Model: Rainfall Intensity Classification For Flood Prediction Using Meteorology Data

Chai¹,

Wong²,

Goh³

2016

Journal of Computer Science

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Rainfall is one of the important weather variables that vary in space and time. High mean daily rainfall (>30 mm) has a high possibility of resulting in flood. Accurate prediction of this variable would save human lives and properties. Soft computing methods have been widely applied in this field. Among the various soft computing methods, Artificial Neural Network (ANN) is the most commonly used methodology. While numerous ANN algorithms were applied, the most commonly applied are the Backpropagation (BPN) and Radial Basis Function (RFN) models. However, there was no research conducted to verify which model among these two produces a superior result. Therefore, this study will fill this gap. In this study, using the meteorology data, the two ANN models were trained to classify the rainfall intensity based on four different classes: Light (<10 mm), moderate (11-30 mm), heavy (31-50 mm) and very heavy (>51 mm). The architecture of the neural networks models based on the different combination of inputs and number of hidden neurons to obtain the optimum classification were verified in this study. The influence of the number of training data on the classification results was also analyzed. Results obtained showed, in term of classification accuracy, BPN model performed better than the RFN model. However, in term of consistency, the RFN model outperformed BPN model.

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“…Data were normalized so that they were in the interval [−1, 1], and ensuring that all selected features represent the same dynamic range to achieve faster computation, which is by the way a desirable property when considering implementation issues [18] .…”

Section: ) Normalizationmentioning

confidence: 99%

Advanced methods of signal processing for Power Quality assessment.

Montero¹,

Agüera-Pérez²,

Rosa³

et al. 2024

RE&PQJ

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Abstract. The aim of this work is by using artificial neural networks (ANNs) compare six regression algorithms supported by 14 power-quality features, based on higher-order statistics (HOS). In addition, we have combined time and frequency domain estimators to deal with non-stationary measurement sequences; the final target is to implement the system in a smart grid to guarantee compatibility between all the equipment connected. The main results were based on spectral kurtosis measurements, which easily adapt to the impulsive nature of the power quality events. Through these results we have verified that the developed technique is capable of offering interesting results at classifying power quality (PQ) disturbance. We can conclude that using radial basis networks, generalized regression and multilayer perceptron, we have obtained the best results mainly due to the non-linear nature of data.

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Importance of input data normalization for the application of neural networks to complex industrial problems

Cited by 686 publications

References 5 publications

An Artificial Nerve Network Realization in the Measurement of Material Permittivity

An Artificial Nerve Network Realization in the Measurement of Material Permittivity

Backpropagation Vs. Radial Basis Function Neural Model: Rainfall Intensity Classification For Flood Prediction Using Meteorology Data

Advanced methods of signal processing for Power Quality assessment.

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