On the neural network approach for forecasting of nonstationary time series on the basis of the Hilbert-Huang transform

Kurbatskii, V. G.; Sidorov, Denis; Spiryaev, Vadim; Tomin, Nikita

doi:10.1134/s0005117911070083

Cited by 27 publications

(17 citation statements)

References 8 publications

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“…The spectrum of the basic models to form combined model is very wide; examples of such models are given in [34][35][36][37][38][39], including those developed by the authors of [40]. Combined models can be considered as probably the most effective models in predictions made by using a single method, i. e. without constructing any prognostic technology.…”

Section: Models and Methods Based On Markov Chainsmentioning

confidence: 99%

Formation of prognostic software support for strategic decision-making in an organization

Romanenkov

Vartanian

2016

EEJET

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FORMATION OF PROGNOSTIC SOFTWARE SUPPORT FOR STRATEGIC DECISION-MAKING IN AN ORGANIZATION Y u . R o m a n e n k o vPhD, Associate Professor* E-mail: KhAI.management@ukr.net V . V a r t a n i a n Doctor of Technical Sciences, Professor* E-mail: vartanyan_vm@ukr.net *Department of management M. E. Zhukovsky National Aerospace University «Kharkiv Aviation Institute» Chkalova str., 17, Kharkiv, Ukraine, 61070Проаналізовано фактори невизначеності у проце-сі прийняття стратегічних рішень і проведено порів-няльний аналіз традиційних статистичних моде-лей і методів прогнозування. Сформульовано основні завдання прогностичного забезпечення та обґрунтова-но необхідність розробки моделі прогностичного забез-печення підтримки прийняття стратегічних рішень для потреб організації. Запропоновано чотирирівневу модель системи із принципами її методичного насичен-ня, а також інструменти її налаштування Ключові слова: прогностичне забезпечення, під-тримка прийняття управлінських рішень, прогнозу-вання, комплексування прогнозних оцінок Проанализированы факторы неопределённости в процессе принятия стратегических решений и про-ведён сравнительный анализ традиционных ста-тистических моделей и методов прогнозирования. Сформулированы основные задачи прогностического обеспечения и обоснована необходимость разработки модели прогностического обеспечения поддержки при-нятия стратегических решений для нужд организа-ции. Предложена четырёхуровневая модель системы с принципами её методического насыщения, а также инструменты её настройки Ключевые слова: прогностическое обеспечение, поддержка принятия управленческих решений, про-гнозирование, комплексирование прогнозных оценок UDC 658.5:004.94

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Section: Models and Methods Based On Markov Chainsmentioning

confidence: 99%

Formation of prognostic software support for strategic decision-making in an organization

Romanenkov

Vartanian

2016

EEJET

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“…В [18] SVM комбинировалась с вейвлет-анализом. В [19] предложен двухэтапный адаптивный подход для прогнозирования ВР потребления электроэнергии. В этом подходе на первом этапе методом EMD (Empirical Mode Decomposition) производится декомпозиция прогнозируемого ВР на собственные модальные функции и применяется к ним преобразование Гильберта.…”

Section: анализ литературных данных и постановка проблемыunclassified

“…r , если используем модель(19) или(20); r , ε t , если используется модель(21) или (22) производится структурная и параметрическая идентификация моделей САРПСС (используя алгоритм раздела 4. 1.…”

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“Caterpillar”-SSA and Box-Jenkins hybrid models and methods for time series forecasting

Щелкалин

2014

EEJET

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“…The HHT is composed of empirical mode decomposition (EMD), proposed by Huang and Wu in 2008, and the Hilbert transform (HT) [15]. As the basis is adaptive, the HHT is not affected by the restrictions of previous approaches and becomes an attractive tool to find faults in diagnosis [16], speech recognition [17], signal denoising [18], pattern recognition [19], forecasting [20], and so on.After decomposing the original sound into a series of time-frequency characteristics, many researchers adopted some feature extractors to reduce the dimension and eliminate redundant information. The effectiveness of feature extracting is a key factor in the success of the recognition process.…”

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confidence: 99%

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confidence: 99%

Cutting Pattern Identification for Coal Mining Shearer through Sound Signals Based on a Convolutional Neural Network

Wang

Tan

et al. 2018

Symmetry

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Recently, sound-based diagnosis systems have been given much attention in many fields due to the advantages of their simple structure, non-touching measurement style, and low-power dissipation. In order to improve the efficiency of coal production and the safety of the coal mining process, accurate information is always essential. It is indicated that the sound signal produced during the cutting process of the coal mining shearer contains much cutting pattern identification information. In this paper, the original acoustic signal is first collected through an industrial microphone. To analyze the signal deeply, an adaptive Hilbert-Huang transform (HHT) was applied to decompose the sound to several intrinsic mode functions (IMFs) to subsequently acquire 1024 Hilbert marginal spectrum points. The 1024 time-frequency nodes were reorganized as a 32 × 32 feature map. Moreover, the LeNet-5 convolutional neural network (CNN), with three convolution layers and two sub-sampling layers, was used as the cutting pattern recognizer. A simulation example, with 10,000 training samples and 2000 testing samples, was conducted to prove the effectiveness of the proposed method. Finally, 1971 testing sound series were recognized accurately through the trained CNN and the proposed method achieved an identification rate of 98.55%. Therefore, it is widely applied in fault diagnosis [7,8], target detection [9], feature extraction [10], and so on.Unfortunately, the original cutting signal acquired from the coal mining field is always nonlinear, nonstationary, and discontinuous. It is an exceedingly difficult problem to extract key information from the signal. Thus, a powerful signal process method is one of the keys to settling the tough matter. However, typical sound signal analysis approaches such as short-time Fourier transform (STFT), wavelet transform (WT), and wavelet packet transform (WPT) are inappropriate to treat the cutting sound. Due to the characteristics of strong nonlinearity and nonstationarity, STFT is unable to play an effective role on the signal due to the Dirichlet condition and Heisenberg uncertainty principle [11,12]. Similarly, the WT and WPT do not work on interval cutting sound signal due to the fixed wavelet basis [13]. In 1998, an adaptive decomposing method, named the Hilbert-Huang transform (HHT), was established by National Aeronautics and Space Administration (NASA) [14]. The HHT is composed of empirical mode decomposition (EMD), proposed by Huang and Wu in 2008, and the Hilbert transform (HT) [15]. As the basis is adaptive, the HHT is not affected by the restrictions of previous approaches and becomes an attractive tool to find faults in diagnosis [16], speech recognition [17], signal denoising [18], pattern recognition [19], forecasting [20], and so on.After decomposing the original sound into a series of time-frequency characteristics, many researchers adopted some feature extractors to reduce the dimension and eliminate redundant information. The effectiveness of feature extracting is a key factor in t...

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On the neural network approach for forecasting of nonstationary time series on the basis of the Hilbert-Huang transform

Cited by 27 publications

References 8 publications

Formation of prognostic software support for strategic decision-making in an organization

Formation of prognostic software support for strategic decision-making in an organization

“Caterpillar”-SSA and Box-Jenkins hybrid models and methods for time series forecasting

Cutting Pattern Identification for Coal Mining Shearer through Sound Signals Based on a Convolutional Neural Network

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