The Influence of Input Data Standardization Method on Prediction Accuracy of Artificial Neural Networks

Anysz, Hubert; Zbiciak, Artur; Ibadov, Nabi

doi:10.1016/j.proeng.2016.08.081

Cited by 72 publications

(41 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This was achieved by dividing the value of the input by the maximum value of the input. This method is called "maximum linear standardization" [57]. The summary of this step is shown in Table 1.…”

Section: Methodsmentioning

confidence: 99%

A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games

Şahin

Erol

2017

MCA

View full text Add to dashboard Cite

Abstract:The main purpose of this study was to develop and apply a neural network (NN) approach and an adaptive neuro-fuzzy inference system (ANFIS) model for forecasting the attendance rates at soccer games. The models were designed based on the characteristics of the problem. Past real data was used. Training data was used for training the models, and the testing data was used for evaluating the performance of the forecasting models. The obtained forecasting results were compared to the actual data and to each other. To evaluate the performance of the models, two statistical indicators, Mean Absolute Deviation (MAD) and mean absolute percent error (MAPE), were used. Based on the results, the proposed neural network approach and the ANFIS model were shown to be effective in forecasting attendance at soccer games. The neural network approach performed better than the ANFIS model. The main contribution of this study is to introduce two effective techniques for estimating attendance at sports games. This is the first attempt to use an ANFIS model for that purpose.

show abstract

Section: Methodsmentioning

confidence: 99%

A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games

Şahin

Erol

2017

MCA

View full text Add to dashboard Cite

show abstract

“…In order to conduct statistical analysis on data of different dimensions or orders of magnitude, the Min-Max standardization was used in this paper, and the formula was expressed as follows [43][44][45]:…”

Section: Data Standardizationmentioning

confidence: 99%

The Effect of Climatic and Non-climatic Factors on Groundwater Levels in the Jinghuiqu Irrigation District of the Shaanxi Province, China

Zhao

Jia

Guan

et al. 2019

Water

View full text Add to dashboard Cite

A chronic decline of the groundwater levels has become one of the hot issues affecting groundwater resources management. The rising global temperature, the high frequency of extreme weather (higher temperature and stronger evaporation, heavy or less rainfall), and unreasonable management policies have become important driving factors, causing a dynamic change in groundwater levels, in many regions. This study aims to explore the impact of climate and non-climate factors on groundwater levels in the Jinghuiqu irrigation district. The climatic phases were defined by rainfall anomalies, and the Mann–Kendall trend test statistic (M–K test) and Sen’s slope method were used to statistically analyze the influence of temperature (1950–2017) and rainfall (1980–2017) on the groundwater level. The results showed that: (1) Dry, normal, and wet phases occurred alternately, including two normal, two wet, and one dry periods (Wet 1980–1984; Normal 1985–1996; Dry 1997–2002; Wet 2003–2011; Normal 2012–2017). (2) The groundwater levels in the dry phase, decreased significantly by 0.62 m/year (p < 0.05), and the groundwater levels in the wet phases did not have a complete recovery, due to the excessive extraction of groundwater. Meanwhile, extreme weather became an important signal to reflect the change of groundwater levels. (3) The groundwater levels decreased significantly in the west and northwest (p < 0.05), but not in the southeast, due to the regional difference of groundwater extraction, which is the primary factor resulting in a chronic decline of groundwater levels. (4) Besides human activities, temperature had a higher correlation with groundwater levels (p < 0.05), which indicated that the potential impact of climate change on groundwater levels should not be ignored while setting groundwater resource management policies for a sustainable cycle of atmosphere–land–water.

show abstract

“…It was checked empirically that for this case, i.e. predicting delays of completion dates, basing on the aforementioned inputs and [9], having 119 cases of input datasets, the following ANN gives the lowest MSE of predictions: ▶ 8 input neurons (two input parameters had to be represented by vectors [0, 1] or [1,0], so it has risen the number inputs from 6 to 8), ▶ one hidden layer with 14 neurons, ▶ single output neuron -the delay in completion date, ▶ activation function in the hidden layer -linear with limits <-1, 1> (called satlins in Matlab), ▶ activation function in the output layer -logistic (called logsig in Matlab), ▶ teaching algorithm -conjugate gradient backpropagation (the order trainscg in Matlab). It was set that first 79 cases create a teaching set of data.…”

Section: Ann Trained With the Set Of Real Numbers As An Output Reprementioning

confidence: 99%

Neuro-fuzzy predictions of construction site completion dates

Anysz

Ibadov

2017

View full text Add to dashboard Cite

Neuro-fuzzy predictions of construction site completion datesNeuro-rozmyte prognozowanie terminów zakończenia przedsięwzięć budowlanych AbstractThe results from two types of multi-layer perceptron artificial neural networks (Matlab R2015a was used) were compared. The first one, with only one neuron in an output layer having the value of delay in completion date of building site. The output layer of the second artificial neural network is created by three neurons. These three values represent the same delay, but in a form of three values of membership functions to fuzzy sets. In order to evaluate the accuracy of predictions, the mean squared error was used. It was necessary to find the best method of defuzzyfication predicted delays to compare the results from these two, aforementioned artificial neural networks. The level of prediction accuracy measured by mean squared error was discussed, too. Keywords: MLP artificial neural networks, fuzzy sets, delays in completion datesStreszczenie W artykule porównano wyniki prognoz uzyskane wielowarstwowymi sztucznymi sieciami neuronowymi (obliczenia wykonano pakietem Matlab R2015a). Pierwsza z nich miała jeden neuron w warstwie wyjścio-wej, któremu przypisano wartość opóźnienia w terminie zakończenia danej budowy. Warstwę wyjściową drugiej sieci stanowiły trzy neurony, zawierające wartości funkcji przynależności tego samego opóźnienia do trzech zborów rozmytych. Do porównania dokładności prognoz uzyskanych z dwóch ww. sztucznych sieci neuronowych zastosowano średni błąd kwadratowy. Wymagało to znalezienia najlepszej metody defuzzyfikacji prognoz otrzymanych w postaci liczb rozmytych. Dokładność prognoz została porównana i przedyskutowana.Słowa kluczowe: sztuczne sieci neuronowe MLP, zbiory rozmyte, opóźnienia terminu zakończenia budowy 52

show abstract

The Influence of Input Data Standardization Method on Prediction Accuracy of Artificial Neural Networks

Cited by 72 publications

References 3 publications

A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games

A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games

The Effect of Climatic and Non-climatic Factors on Groundwater Levels in the Jinghuiqu Irrigation District of the Shaanxi Province, China

Neuro-fuzzy predictions of construction site completion dates

Contact Info

Product

Resources

About