Development of a hybrid neural network model to predict feeding method in fed-batch cultivation for enhanced recombinant streptokinase productivity in Escherichia coli

2011 ◽  
Vol 87 (2) ◽  
pp. 280-285 ◽  
Author(s):  
Sundaresan Geethalakshmi ◽  
Sekar Narendran ◽  
Natarajan Pappa ◽  
Subramanian Ramalingam
Keyword(s):  
Neural Network ◽  
Escherichia Coli ◽  
Network Model ◽  
Neural Network Model ◽  
Batch Cultivation ◽  
Fed Batch ◽  
Hybrid Neural Network ◽  
Feeding Method ◽  
Fed Batch Cultivation

scholarly journals PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6512
Author(s):  
Mario Tovar ◽  
Miguel Robles ◽  
Felipe Rashid
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Network Model ◽  
Neural Network Model ◽  
Short Term Memory ◽  
Lasso Regression ◽  
Neural Network Models ◽  
Model Case ◽  
Hybrid Neural Network ◽  
Photovoltaic Power

Due to the intermittent nature of solar energy, accurate photovoltaic power predictions are very important for energy integration into existing energy systems. The evolution of deep learning has also opened the possibility to apply neural network models to predict time series, achieving excellent results. In this paper, a five layer CNN-LSTM model is proposed for photovoltaic power predictions using real data from a location in Temixco, Morelos in Mexico. In the proposed hybrid model, the convolutional layer acts like a filter, extracting local features of the data; then the temporal features are extracted by the long short-term memory network. Finally, the performance of the hybrid model with five layers is compared with a single model (a single LSTM), a CNN-LSTM hybrid model with two layers and two well known popular benchmarks. The results also shows that the hybrid neural network model has better prediction effect than the two layer hybrid model, the single prediction model, the Lasso regression or the Ridge regression.

scholarly journals Physical Hybrid Neural Network Model to Forecast Typhoon Floods

Water ◽  
2018 ◽  
Vol 10 (5) ◽  
pp. 632 ◽  
Author(s):  
You-Da Jhong ◽  
Chang-Shian Chen ◽  
Hsin-Ping Lin ◽  
Shien-Tsung Chen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Hybrid Neural Network
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