Development of Temperature-based Weather Forecasting Models Using Neural Networks and Fuzzy Logic

Al-Matarneh, L.; Sheta, Alaa; Bani‐Ahmad, Sulieman; Alshaer, Jawdat; Al‐Oqily, Ibrahim

doi:10.14257/ijmue.2014.9.12.31

Cited by 30 publications

(13 citation statements)

References 28 publications

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“…The artificial neural network (ANN) is a nonlinear model used in this research to model the SEMG signal with three loads 3 kg, 5 kg and 7 kg, since the SEMG signal is nonlinear. A multilayer perceptron (MLP) is a feed forward neural network consists of multiple layers with back-propagation algorithm technique to minimize the errors and supervised learning [19][20][21]. This research designed the ANN with MATLAB R2015a, and has chosen MLP with a Lavenberg-Marquardt training back-propagation algorithm, which can be used to enhance different area of research as in [22][23][24].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

et al. 2019

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Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

et al. 2019

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“…Pada beberapa penelitian sebelumnya, telah dilakukan penelitian tentang peramalan intensitas matahari dengan input suhu udara, kelembaban udara menggunakan Backpropagation (Rahmalia dan Herlambang, 2017), peramalan cuaca menggunakan Fuzzy Logic (Matarneh, 2014), peramalan menggunakan exponential smoothing (Rahmalia, 2018). Karena hasil peramalan bergantung pada nilai korelasi data, maka pada penelitian ini akan diteliti mengenai pengaruh korelasi data pada peramalan suhu udara (Han, 2012).…”

Section: Pendahuluanunclassified

Pengaruh Korelasi Data pada Peramalan Suhu Udara Menggunakan Backpropagation Neural Network

Rahmalia

Aini

2018

zeta

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“…It includes evolutionary learning and fuzzy rule learning algorithms based on different approaches (Alcala-Fdez et al, 2008). Al-Matarneh et al (2014) proposed automated weather forecasting model using temperature to predict the daily temperature using two techniques, artificial neural networks and fuzzy logic. They have developed a different weather forecasting models based on the two techniques over different regions.…”

Section: Introductionmentioning

confidence: 99%

Intelligent decision support system based on rough set and fuzzy logic approach for efficacious precipitation forecast

Sudha¹

2017

10.5267/j.dsl

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Weather forecasting is essential and demanding scientific task of meteorological services across the world. It is a complex procedure that includes many specific technological field of study. The prediction is intricate process in meteorology because all decisions are made within a facet of uncertainty associated with weather systems. This research finding introduces a novel rough fuzzy computing approach for a short term rainfall forecasts. The model consists of rough set based optimal weather parameter selection module and fuzzy rule based classification module. The proposed fuzzy decision support model is compared with benchmarked classification approaches. The fuzzy classification model used in fuzzy decision support system is trained and tested using the reduct sets generated using proposed maximum frequency weighted feature reduction technique. The optimal reduct set constituting the weather parameters; minimum temperature, relative humidity and solar radiation achieved better prediction accuracy than complete feature set and the reducts. Most of the classification models have shown better accuracy when trained using the selected subsets of the target input. Thorough evaluation of the proposed model has revealed that coupling fuzzy decision support system and rough based pre-processing techniques was a better approach than traditional techniques. The experimental results revealed the proposed rough fuzzy model as a better rainfall prediction approach for modeling short range rainfall forecast.Growing Science Ltd. All rights reserved. 7

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Development of Temperature-based Weather Forecasting Models Using Neural Networks and Fuzzy Logic

Cited by 30 publications

References 28 publications

Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

Pengaruh Korelasi Data pada Peramalan Suhu Udara Menggunakan Backpropagation Neural Network

Intelligent decision support system based on rough set and fuzzy logic approach for efficacious precipitation forecast

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