Self-organized Short-Term Memory Mechanism in Spiking Neural Network

2011 ◽  
pp. 120-129 ◽  
Author(s):  
Mikhail Kiselev
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Spiking Neural Network ◽  
Short Term ◽  
Term Memory ◽  
Memory Mechanism ◽  
Self Organized

scholarly journals Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

2020 ◽  
Vol 13 (1) ◽  
pp. 104
Author(s):  
Dana-Mihaela Petroșanu ◽  
Alexandru Pîrjan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Short Term Memory ◽  
Electricity Consumption ◽  
Short Term ◽  
Term Memory ◽  
Storage Room ◽  
Long Short Term Memory ◽  
Commercial Center ◽  
Business And Management

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.

An Approach for Rainfall Prediction Using Long Short Term Memory Neural Network

2020 ◽  
Author(s):  
Anjali Samad ◽  
Bhagyanidhi ◽  
Vaibhav Gautam ◽  
Piyush Jain ◽  
Sangeeta ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Rainfall Prediction ◽  
Long Short Term Memory

scholarly journals Automatic respiratory event scoring in obstructive sleep apnea using a long short-term memory neural network

2021 ◽  
pp. 1-1
Author(s):  
Sami Nikkonen ◽  
Henri Korkalainen ◽  
Akseli Leino ◽  
Sami Myllymaa ◽  
Brett Duce ◽  
...  
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Obstructive Sleep ◽  
Respiratory Event ◽  
Long Short Term Memory

An improved SPEI drought forecasting approach using the long short-term memory neural network

2021 ◽  
Vol 283 ◽  
pp. 111979
Author(s):  
Abhirup Dikshit ◽  
Biswajeet Pradhan ◽  
Alfredo Huete
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Drought Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory
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