Application of Extreme Learning Machine-Autoencoder to Medium Term Electricity Price Forecasting

Najafi, Arsalan; Homaee, Omid; Jasiński, Michał; Golshan, Mahdi; Leonowicz, Zbigniew

doi:10.1109/eeeic/icpseurope54979.2022.9854736

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…The ANFIS is a union of neural networks and fuzzy logic to overcome individual weaknesses and provides more attractive features [4–9]. To improve the ANFIS design for huge amounts of parameters, optimization techniques such as GA are suitable [9–11].…”

Section: Related Workmentioning

confidence: 99%

“…In reference [2], the ANFIS structure has noise reduction, image normalization, classification, and model training [12]. To detect brain tumours, feature extraction, and morphological operations were used with the ANFIS classifier [4–8]. Artificial neural networks have various applications [5] and are also used for tumour detection [6], while fuzzy c‐means and fuzzy k‐means have been combined with morphological operations to segment the abnormal parts of the brain MRIs [7–13].…”

Section: Related Workmentioning

confidence: 99%

“…To detect brain tumours, feature extraction, and morphological operations were used with the ANFIS classifier [4–8]. Artificial neural networks have various applications [5] and are also used for tumour detection [6], while fuzzy c‐means and fuzzy k‐means have been combined with morphological operations to segment the abnormal parts of the brain MRIs [7–13]. An ANFIS non‐dominated sorting genetic algorithm‐II (NSGA‐II) was presented as a powerful computation method [14].…”

Section: Related Workmentioning

confidence: 99%

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An adaptive neuro‐fuzzy inference system optimized by genetic algorithm for brain tumour detection in magnetic resonance images

Ghahramani,

Shiri

2024

IET Image Processing

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An adaptive neuro‐fuzzy inference system is presented which is optimized by a genetic algorithm to classify normal and abnormal brain tumours. The classifier is fast and simple, named genetic algorithm‐adaptive neuro‐fuzzy inference system, and the determined learning rules minimize its error and improve its accuracy. The presented system follows five steps including preprocessing, morphological operation, feature extraction, feature selection, and classification. Morphological operators segment the abnormal regions and calculate the tumour area. The statistical features and the grey‐level co‐occurrence matrix are employed for feature extraction. Magnetic resonance images are considered and 12 statistical features are extracted, then the genetic algorithm‐based selection technique helps to select features and reduce the extracted features and improves the accuracy and decision time. So, the high dimensionality and the computational complexity of the adaptive neuro‐fuzzy inference system are reduced, and the classifier decides more efficiently. The input data are the figshare brain tumour dataset with 670 abnormal and 670 normal magnetic resonance images, and the classifier requires 10.788 s for classification. The efficient performance of the genetic algorithm‐adaptive neuro‐fuzzy inference system is confirmed by the accuracy of 99.85%, sensitivity of 99.7%, specificity of 100%, precision of 100%, and mean square error of 0.0027.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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An adaptive neuro‐fuzzy inference system optimized by genetic algorithm for brain tumour detection in magnetic resonance images

Ghahramani,

Shiri

2024

IET Image Processing

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“…ELM was initially suggested for single-hidden layer feedforward neural networks (SLFNs). It is used in many applications, such as electricity price forecasting (Najafi et al 2022;Parhizkari et al 2020). The functional relationship between fuzzy logic systems (FLSs) and ANNs with one hidden layer allows for FLSs such as T1FLS, T2FLS, IT2FLS, and neuro-fuzzy systems to be interpreted as a particular type of SLFNs, which can be trained using ELM learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the above, several derivations of ELM have been suggested. Some of them include a fast and accurate online sequential ELM (OS-ELM) (Liang et al 2006) and online sequential fuzzy ELM (OS-FELM) (Rong et al 2009), an ensemble of OS-ELM (EOS-ELM) with a forgetting mechanism (Jianwei Zhao et al 2012), weighted ELM for imbalance learning (Zong et al 2013), hierarchical extreme learning machine (Han et al 2014), ELM with two hidden neurons (Qu et al 2016), Rough-ELM with uncertainty measures (Feng et al 2019), a chaotic ensemble of online recurrent ELM (ORELM) (Luhang Liu et al 2020), and a new structure of deep autoencoder based on ELM (Najafi et al 2022;Tissera and McDonnell 2016).…”

Section: Introductionmentioning

confidence: 99%

Brain-inspired emotional learning algorithm enhanced with type-one and interval type-two fuzzy extreme learning machine in noisy data

Golshan,

Teshnehlab,

Sharifi

2023

Preprint

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One of the advanced machine learning branches is emotional learning based on brain emotional learning (BEL), which has been widely used for almost three decades. BEL mimics the emotional learning mechanism in the mammalian brain, which has the superior features of fast learning, quick reacting, and low computational complexity. The original BEL model inspired by the limbic system is composed of two neural network components, namely the amygdala (AMYG) and orbitofrontal cortex (ORBI), which interact with each other. Using a fuzzy extreme learning machine (FELM), a brain-inspired emotional learning model was developed in this study to predict noisy, chaotic time series. The exchange of information between AMYG and ORBI is facilitated by an online sequential type-one FELM with interactive recurrent memory (OIRMS-T1FELM). The CenteroMedial (CM) section of AMYG uses type-one FELM (T1FELM) and interval type-two FELM (IT2FELM). Hence, the proposed model is named BEL-OIRMS-T1/IT2 FELM. The impact of noise on time series targets was determined by altering the various standard deviation of Gaussian noise levels (Sig). The experimental results show that the interaction between AMYG and ORBI leads to the reduction of RMSE in their outputs. Also, when IT2FELM is used instead of T1FELM in the CM of AMYG, the RMSE and MAPE of the final prediction output are lower, and the R is higher. In this state, by increasing the noise level, the proposed method with MF=2 performs better than other states.

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Temporal Convolutional Network Ensemble for Day-Ahead and Week-Ahead Electricity Price Forecasting

Lee,

Mandal

2023

2023 IEEE Industry Applications Society Annual Meeting (IAS)

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Application of Extreme Learning Machine-Autoencoder to Medium Term Electricity Price Forecasting

Cited by 6 publications

References 15 publications

An adaptive neuro‐fuzzy inference system optimized by genetic algorithm for brain tumour detection in magnetic resonance images

An adaptive neuro‐fuzzy inference system optimized by genetic algorithm for brain tumour detection in magnetic resonance images

Brain-inspired emotional learning algorithm enhanced with type-one and interval type-two fuzzy extreme learning machine in noisy data

Temporal Convolutional Network Ensemble for Day-Ahead and Week-Ahead Electricity Price Forecasting

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