Automatic Modulation Recognition Based on a DCN-BiLSTM Network

Automatic modulation recognition (AMR) is a significant technology in noncooperative wireless communication systems. This paper proposes a deep complex network that cascades the bidirectional long short-term memory network (DCN-BiLSTM) for AMR. In view of the fact that the convolution operation of the traditional convolutional neural network (CNN) loses the partial phase information of the modulated signal, resulting in low recognition accuracy, we first apply a deep complex network (DCN) to extract the features of the modulated signal containing phase and amplitude information. Then, we cascade bidirectional long short-term memory (BiLSTM) layers to build a bidirectional long short-term memory model according to the extracted features. The BiLSTM layers can extract the contextual information of signals well and address the long-term dependence problems. Next, we feed the features into a fully connected layer. Finally, a softmax classifier is used to perform classification. Simulation experiments show that the performance of our proposed algorithm is better than that of other neural network recognition algorithms. When the signal-to-noise ratio (SNR) exceeds 4 dB, our model’s recognition rate for the 11 modulation signals can reach 90%.

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Estimation of municipal solid waste amount based on one-dimension convolutional neural network and long short-term memory with attention mechanism model: A case study of Shanghai

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.148088 ◽

2021 ◽

Vol 791 ◽

pp. 148088

Author(s):

Kunsen Lin ◽

Youcai Zhao ◽

Lu Tian ◽

Chunlong Zhao ◽

Meilan Zhang ◽

...

Keyword(s):

Neural Network ◽

Municipal Solid Waste ◽

Convolutional Neural Network ◽

Short Term Memory ◽

One Dimension ◽

Short Term ◽

Term Memory ◽

Mechanism Model ◽

Long Short Term Memory

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

Sustainability ◽

10.3390/su13010104 ◽

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.

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