FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting

Alomar, Miquel L.; Canals, Vincent; Pérez-Mora, Nicolás; Martínez-Moll, Víctor; Rosselló, Josep L.

doi:10.1155/2016/3917892

Cited by 48 publications

(22 citation statements)

References 36 publications

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“…There are some works that have been reported the implementation of reservoir computing systems in the past decades, using field programmable gate arrays (FPGAs) and photonic systems. [85][86][87][88] Because of the inherent nonlinearity and short-term memory features, memristors are identified as good candidates for the reservoir computing system in the pattern classification and signal processing. [89][90][91][92] Here, we introduce two reservoir computing systems based on volatile memristors for pattern recognition.…”

Section: Volatile Memristor As Artificial Synapsementioning

confidence: 99%

Recent Advances of Volatile Memristors: Devices, Mechanisms, and Applications

Wang

Yang

Mao

et al. 2020

Advanced Intelligent Systems

136

113

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Due to the rapid development of artificial intelligence (AI) and internet of things (IoTs), neuromorphic computing and hardware security are becoming more and more important. The volatile memristors, which feature spontaneous decay of device conductance, own the distinct combination of high similarity to the biological neurons and synapses and unique physical mechanisms. They are excellent candidates for mimicking the synaptic functions and ideal randomness source of the entropy for hardware‐based security. Herein, the recent advances of volatile memristors in devices, mechanisms, and application aspects are summarized. First, a brief introduction is presented to describe the switching type, materials, and temporal response of volatile memristors. Second, the volatile switching mechanisms are discussed and grouped into ion effects, thermal effects, and electrical effects. Third, attention is focused on the applications of volatile memristors for access devices, neuromorphic computing (artificial neurons and synapses), and hardware security (true random number generators and physical unclonable functions). Finally, major challenges and future outlook of volatile memristors for neuromorphic computing and hardware security are discussed.

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Section: Volatile Memristor As Artificial Synapsementioning

confidence: 99%

Recent Advances of Volatile Memristors: Devices, Mechanisms, and Applications

Wang

Yang

Mao

et al. 2020

Advanced Intelligent Systems

136

113

View full text Add to dashboard Cite

show abstract

“…In order to operate the power plant efficiently, it is required to forecast accurately the solar generation from the field. To achieve such estimation for ST generation, it is needed to forecast the solar irradiation and take into consideration the aging of the solar collectors . The forecast information can be used on a power plant simulator aiming to improve generation strategies, reduce generation expenses, and maximize revenues in generation.…”

Section: District Coolingmentioning

confidence: 99%

Solar district heating and cooling: A review

et al. 2017

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Summary Both district heating and solar collector systems have been known and implemented for many years. However, the combination of the two, with solar collectors supplying heat to the district heating network, is relatively new, and no comprehensive review of scientific publications on this topic could be found. Thus, this paper summarizes the literature available on solar district heating and presents the state of the art and real experiences in this field. Given the lack of a generally accepted convention on the classification of solar district heating systems, this paper distinguishes centralized and decentralized solar district heating as well as block heating. For the different technologies, the paper describes commonly adopted control strategies, system configurations, types of installation, and integration. Real‐world examples are also given to provide a more detailed insight into how solar thermal technology can be integrated with district heating. Solar thermal technology combined with thermally driven chillers to provide cooling for cooling networks is also included in this paper. In order for a technology to spread successfully, not only technical but also economic issues need to be tackled. Hence, the paper identifies and describes different types of ownership and financing schemes currently used in this field.

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“…The idea of Apriori algorithm in association analysis was used to mine sequence rules. Three algorithms, AprioriAll, AprioriSome, and DynamicSome, were proposed [31][32][33]. An algorithm for finding numerical association rules from multiple synchronized data streams is proposed [34].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence algorithm for optimal time series data model

Wang

2024

IEEE Access

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In order to solve the limitation of a large number of literatures on the study of modeling, simulation and prediction of time series data, there is no model selection, and a certain model is directly used for analysis. For three types of artificial intelligence models often applied to time series analysis: hidden Markov Carrier model, artificial neural network model and autoregressive moving average model are used to study model selection based on simulation comparison method. The study of nonlinear integration methods, using intelligent system methods to learn the weighting mode, has made the model's generalization ability and the degree is of fit to the sample data have been significantly improved. At the same time, numerical simulations are performed on various models, and the characteristics of the time series generated by various models are investigated. Based on the characteristics, the theory and algorithm of model selection are proposed. The model selection theory and algorithm in this paper is used for empirical analysis. For the artificial intelligence models commonly used in time series analysis such as autoregressive moving average model, artificial neural network model, hidden Markov model, etc., when selecting the research model, the method of simulation comparison can be used. The experimental results show that the time series data generated by various models have different mathematical and physical characteristics, which provide a basis for model selection. At the same time, the selection theory is practical. The model selected by the theory has a good fit and prediction effect. The generation of different models has different mathematical characteristics of time series data, which also provides a basis for selecting models.

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FPGA-Based Stochastic Echo State Networks for Time-Series Forecasting

Cited by 48 publications

References 36 publications

Recent Advances of Volatile Memristors: Devices, Mechanisms, and Applications

Recent Advances of Volatile Memristors: Devices, Mechanisms, and Applications

Solar district heating and cooling: A review

Artificial intelligence algorithm for optimal time series data model

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