IEEE transactions on neural networks and learning systems
Aug 31, 2022
Reservoir computing is a popular approach to design recurrent neural networks, due to its training simplicity and approximation performance. The recurrent part of these networks is not trained (e.g., via gradient descent), making them appealing for a...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
In this paper, an adaptive admittance control scheme is developed for robots to interact with time-varying environments. Admittance control is adopted to achieve a compliant physical robot-environment interaction, and the uncertain environment with t...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
Neural networks have been demonstrated to be trainable even with hundreds of layers, which exhibit remarkable improvement on expressive power and provide significant performance gains in a variety of tasks. However, the prohibitive computational cost...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
This article investigates the multistability and stabilization of fractional-order competitive neural networks (FOCNNs) with unbounded time-varying delays. By utilizing the monotone operator, several sufficient conditions of the coexistence of equili...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
This article investigates the synchronization of stochastic delayed neural networks under pinning impulsive control, where a small fraction of nodes are selected as the pinned nodes at each impulsive moment. By proposing a uniformly stable function a...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
The cerebellum plays a vital role in motor learning and control with supervised learning capability, while neuromorphic engineering devises diverse approaches to high-performance computation inspired by biological neural systems. This article present...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
In this article, we propose a novel model-parallel learning method, called local critic training, which trains neural networks using additional modules called local critic networks. The main network is divided into several layer groups, and each laye...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
Perturbation has a positive effect, as it contributes to the stability of neural systems through adaptation and robustness. For example, deep reinforcement learning generally engages in exploratory behavior by injecting noise into the action space an...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
Long short-term memory (LSTM) neural networks and attention mechanism have been widely used in sentiment representation learning and detection of texts. However, most of the existing deep learning models for text sentiment analysis ignore emotion's m...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
Enabling a neural network to sequentially learn multiple tasks is of great significance for expanding the applicability of neural networks in real-world applications. However, artificial neural networks face the well-known problem of catastrophic for...