China
Africa
Explore chapters and articles related to this topic
Deep Learning
Published in Peter Wlodarczak, Machine Learning and its Applications, 2019
A recurrent neural network is trained using backpropagation and gradient descent, just like a feedforward network. However, because of the loops, the backpropagation mechanism does not work in the same way like for a feedforward network. In a feedforward network, the algorithm moves backwards from the final error through the layers and adjusts the weights up or down, whichever reduces the error. To train a recurrent network, an extention of backpropagation called backpropagation through time, or BPTT is used. Backpropagation through time is a gradient-based method for training recurrent networks. In backpropagation through time, time is defined by the ordered series of calculations moving from one time step to the other. In essence, the structure of the recurrent neural network is unfolded. A copy of the neurons that contain loops is created and the cyclic graphs of the recurrent neural network are transformed into acyclic graphs, turning the recurrent neural network into a feedforward network. Every copy shares the same parameters. A recurrent network and the unfolded network are shown in Figure 8.6.
Applications of IoT through Machines' Integration to Provide Smart Environment
Published in Nishu Gupta, Srinivas Kiran Gottapu, Rakesh Nayak, Anil Kumar Gupta, Mohammad Derawi, Jayden Khakurel, Human-Machine Interaction and IoT Applications for a Smarter World, 2023
Manikandan Jagarajan, Ramkumar Jayaraman, Amrita Laskar
For modeling time series tasks, feed-forward neural network is not convenient. Recurrent neural network (RNN) was designed to solve those kinds of tasks. The RNN receives both the current and previous input samples as input. RNN's output at time step “t” is dependent on RNN's output at time step “t−1.” Because of this, every neuron's output is given as input to the next following step. As a consequence, each RNN's neuron has an internal memory that stores data from the previous input. Backpropagation through time (BPTT), a version of the backpropagation algorithm, is used to train the network. RNN architecture is shown in Figure 3.5.
Solar Power Forecasting
Published in Bhavnesh Kumar, Bhanu Pratap, Vivek Shrivastava, Artificial Intelligence for Solar Photovoltaic Systems, 2023
Agrim Khurana, Ankit Dabas, Vaibhav Dhand, Rahul Kumar, Bhavnesh Kumar, Arjun Tyagi
The long-short term memory (LSTM) model is a special Recurrent Neural Networks (RNN) model, which is proposed to solve the problem of gradient dispersion of RNN models. In traditional RNN, the training algorithm uses BPTT (backpropagation through time). For a long time, the residuals that need to be transmitted will decrease exponentially, causing the network weights to update slowly and fail to reflect the long-term memory effect of the RNN. Therefore, a storage unit is required to store the memory, so the LSTM model is proposed. LSTM network is a special network structure with three “gates,” which are “forget gate,” “input gate,” and “output gate” in this order.
A Comprehensive Survey on GNSS Interferences and the Application of Neural Networks for Anti-jamming
Published in IETE Journal of Research, 2021
Kambham Jacob Silva Lorraine, Madhu Ramarakula
Gradient-based methods: Gradient descent (GD), also known as steepest descent, is one of the most prevalent and most commonly used gradient-based methods for training ANN. To minimize the error, the weights are changed in proportion to the derivative of the error function with respect to that particular weight. Backpropagation through time (BPTT) is the extension of GD through time and it is mostly used to train RNN. It is one of the most extensively used and computationally efficient algorithms. However, when the errors are being back-propagated through time, the gradient can exponentially decay thereby causing a vanishing gradient problem while sometimes the gradient can increase thereby causing exploding gradient problem. Also, it gets converged untimely to local optima and similarly, it cannot converge quickly. A comparison of GD methods has been provided in the literature [58]. Variants of the gradient descent algorithms have been discussed by Ruder [59].
Deep learning approach on tabular data to predict early-onset neonatal sepsis
Published in Journal of Information and Telecommunication, 2021
Redwan Hasif Alvi, Md. Habibur Rahman, Adib Al Shaeed Khan, Rashedur M. Rahman
When we train the recurrent neural network, we teach it to assign a suitable weight to each input feature. Afterwards, the RNN is able to determine which information is required to be sent back to the feedback loop with respect to gradient descent. This is known as Backpropagation Through Time (BPTT) as it creates a form ‘short-term memory’ for the recurrent neural net to refer back to.
Interactive natural language acquisition in a multi-modal recurrent neural architecture
Published in Connection Science, 2018
Stefan Heinrich, Stefan Wermter
During learning the MTRNN can be trained with sequences, and self-organises the weights and also the internal state values of the Csc units. The overall method can be a variant of backpropagation through time (BPTT), sped up with appropriate measures based on the task characteristics.