AIMC Journal:
IEEE transactions on neural networks and learning systems

Showing 281 to 290 of 780 articles

Data-Driven Designs of Fault Detection Systems via Neural Network-Aided Learning.

IEEE transactions on neural networks and learning systems Oct 5, 2022
With the aid of neural networks, this article develops two data-driven designs of fault detection (FD) for dynamic systems. The first neural network is constructed for generating residual signals in the so-called finite impulse response (FIR) filter-...
Neural Networks, Computer Algorithms Computer Simulation
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Improved Results on Fixed-/Preassigned-Time Synchronization for Memristive Complex-Valued Neural Networks.

IEEE transactions on neural networks and learning systems Oct 5, 2022
This article concerns the problems of synchronization in a fixed time or prespecified time for memristive complex-valued neural networks (MCVNNs), in which the state variables, activation functions, rates of neuron self-inhibition, neural connection ...
Neural Networks, Computer Time Factors
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Degradation Alignment in Remaining Useful Life Prediction Using Deep Cycle-Consistent Learning.

IEEE transactions on neural networks and learning systems Oct 5, 2022
Due to the benefits of reduced maintenance cost and increased operational safety, effective prognostic methods have always been highly demanded in real industries. In the recent years, intelligent data-driven remaining useful life (RUL) prediction ap...
Neural Networks, Computer Algorithms
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Face Sketch Synthesis Using Regularized Broad Learning System.

IEEE transactions on neural networks and learning systems Oct 5, 2022
There are two main categories of face sketch synthesis: data- and model-driven. The data-driven method synthesizes sketches from training photograph-sketch patches at the cost of detail loss. The model-driven method can preserve more details, but the...
Humans Neural Networks, Computer Algorithms Bayes Theorem Face
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Neural Time-Aware Sequential Recommendation by Jointly Modeling Preference Dynamics and Explicit Feature Couplings.

IEEE transactions on neural networks and learning systems Oct 5, 2022
In recommendation, both stationary and dynamic user preferences on items are embedded in the interactions between users and items (e.g., rating or clicking) within their contexts. Sequential recommender systems (SRSs) need to jointly involve such con...
Neural Networks, Computer Learning
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Spike-Timing-Dependent Plasticity With Activation-Dependent Scaling for Receptive Fields Development.

IEEE transactions on neural networks and learning systems Oct 5, 2022
Spike-timing-dependent plasticity (STDP) is one of the most popular and deeply biologically motivated forms of unsupervised Hebbian-type learning. In this article, we propose a variant of STDP extended by an additional activation-dependent scale fact...
Neural Networks, Computer Learning Neuronal Plasticity Models, Neurological Neurons
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Fully Decoupled Neural Network Learning Using Delayed Gradients.

IEEE transactions on neural networks and learning systems Oct 5, 2022
Training neural networks with backpropagation (BP) requires a sequential passing of activations and gradients. This has been recognized as the lockings (i.e., the forward, backward, and update lockings) among modules (each module contains a stack of ...
Humans Neural Networks, Computer Machine Learning Fluorodeoxyglucose F18
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Synchronization of Chaotic Neural Networks: Average-Delay Impulsive Control.

IEEE transactions on neural networks and learning systems Oct 5, 2022
In the brief, delayed impulsive control is investigated for the synchronization of chaotic neural networks. In order to overcome the difficulty that the delays in impulsive control input can be flexible, we utilize the concept of average impulsive de...
Neural Networks, Computer Algorithms Time Factors
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EDropout: Energy-Based Dropout and Pruning of Deep Neural Networks.

IEEE transactions on neural networks and learning systems Oct 5, 2022
Dropout is a well-known regularization method by sampling a sub-network from a larger deep neural network and training different sub-networks on different subsets of the data. Inspired by the dropout concept, we propose EDropout as an energy-based fr...
Neural Networks, Computer Models, Statistical
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Finite-Time Synchronization of Markovian Coupled Neural Networks With Delays via Intermittent Quantized Control: Linear Programming Approach.

IEEE transactions on neural networks and learning systems Oct 5, 2022
This article is devoted to investigating finite-time synchronization (FTS) for coupled neural networks (CNNs) with time-varying delays and Markovian jumping topologies by using an intermittent quantized controller. Due to the intermittent property, i...
Neural Networks, Computer Algorithms Time Factors Programming, Linear
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