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Recurrent Neural Networks
This chapter is concerned with the recurrent neural networks which are advanced from the MLP. In the previous chapter, we studied the MLP as a...
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Modeling the Mechanical Response of Cement-Admixed Clay Under Different Stress Paths Using Recurrent Neural Networks
Cement–admixed clay (CAC) is a widely-used soil stabilization technique for enhancing the strength and stiffness of soft clay. However, the...
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Rapid training of quantum recurrent neural networks
Time series prediction is essential for human activities in diverse areas. A common approach to this task is to harness recurrent neural networks...
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Innovative Hybrid Approach for Enhanced Renewable Energy Generation Forecasting Using Recurrent Neural Networks and Generative Adversarial Networks
Renewable energy sources hold the key to a sustainable and green future, yet their inherent variability poses significant challenges for reliable...
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Input-to-state Practical Stability of Event-triggered Estimators for Discrete-time Recurrent Neural Networks With Unknown Time-delay
In this paper, event-triggered estimators are designed for discrete-time recurrent neural networks (RNNs) with unknown time-delay. Owing to the...
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Solar Flare Prediction with Recurrent Neural Networks
As the star closest to Earth, the Sun offers a wealth of information on its own composition and behavior, as well as a basis for the composition and...
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Recurrent neural networks for enhanced joint channel estimation and interference cancellation in FBMC and OFDM systems: unveiling the potential for 5G networks
FBMC is a pivotal system in 5G, serving as a cornerstone for efficient use of available bandwidth while simultaneously meeting stringent requirements...
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Construction Forecasting Using Recurrent Neural Networks
Despite all their advantages, univariate and multivariate time series models are linear statistical methods subject to significant limitations for...
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Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion
Neural networks need the right representations of input data to learn. Here we ask how gradient-based learning shapes a fundamental property of...
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Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings
Brain networks exist within the confines of resource limitations. As a result, a brain network must overcome the metabolic costs of growing and...
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Comparative Study of Pruning Techniques in Recurrent Neural Networks
In recent years, there has been a drastic development in the field of neural networks. They have evolved from simple feed-forward neural networks to...
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Copper price movement prediction using recurrent neural networks and ensemble averaging
The motivation for this paper is to investigate the use of three promising types of recurrent neural networks (RNNs), i.e., the long short-term...
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Recurrent neural networks (RNNs) learn the constitutive law of viscoelasticity
Recurrent neural networks (RNNs) have demonstrated very impressive performances in learning sequential data, such as in language translation and...
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Evaluation of Gated Recurrent Neural Networks for Embedded Systems Applications
Artificial Neural Networks (ANNs), based on the concept of neuron cells, are widely used nowadays for multiple applications. Recurrent Neural...
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Adaptive Learning-Based IoT Security Framework Using Recurrent Neural Networks
The rapid proliferation of the Internet of Things (IoT) has ushered in a new era of connectivity and automation across various industries. However,...
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A Comparative Review of Convolutional Neural Networks, Long Short-Term Memory, and Recurrent Neural Networks in Recommendation Systems
Deep learning (DL) computing has emerged as the Gold Standard in the machine learning (ML) community in recent years. There are numerous recommender...
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Reconciling Deep Learning and Control Theory: Recurrent Neural Networks for Indirect Data-Driven Control
This Brief aims to discuss the potential of Recurrent Neural Networks (RNNs) for indirect data-driven control. Indeed, while RNNs have long been...
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Progressive Convolutional Recurrent Neural Networks for Speech Enhancement
The progressive technique is a promising methodology to revise network implementations for speech enhancement purposes. Newer architectures such as...
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High-speed photonic neuromorphic computing using recurrent optical spectrum slicing neural networks
Neuromorphic computing using photonic hardware is a promising route towards ultrafast processing while maintaining low power consumption. Here we...
