AIMC Topic: Neural Networks, Computer

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A Midbrain Inspired Recurrent Neural Network Model for Robust Change Detection.

The Journal of neuroscience : the official journal of the Society for Neuroscience
We present a biologically inspired recurrent neural network (RNN) that efficiently detects changes in natural images. The model features sparse, topographic connectivity (st-RNN), closely modeled on the circuit architecture of a "midbrain attention n...

Quantization-aware training for low precision photonic neural networks.

Neural networks : the official journal of the International Neural Network Society
Recent advances in Deep Learning (DL) fueled the interest in developing neuromorphic hardware accelerators that can improve the computational speed and energy efficiency of existing accelerators. Among the most promising research directions towards t...

Orbital Mixer: Using Atomic Orbital Features for Basis-Dependent Prediction of Molecular Wavefunctions.

Journal of chemical theory and computation
Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm of many previous studies focuses on generating predictions for only a f...

A Multimodal Data Processing System for LiDAR-Based Human Activity Recognition.

IEEE transactions on cybernetics
Increasingly, the task of detecting and recognizing the actions of a human has been delegated to some form of neural network processing camera or wearable sensor data. Due to the degree to which the camera can be affected by lighting and wearable sen...

A Novel Ground Metric for Optimal Transport-Based Chronological Age Estimation.

IEEE transactions on cybernetics
Label distribution learning (LDL) is the state-of-the-art approach to dealing with a number of real-world applications, such as chronological age estimation from a face image, where there is an inherent similarity among adjacent age labels. LDL takes...

Recursive Minimum-Variance Filter Design for State-Saturated Complex Networks With Uncertain Coupling Strengths Subject to Deception Attacks.

IEEE transactions on cybernetics
In this article, the recursive filtering problem is investigated for state-saturated complex networks (CNs) subject to uncertain coupling strengths (UCSs) and deception attacks. The measurement signals transmitted via the communication network may su...

Robust Sampled-Data Control for Switched Complex Dynamical Networks With Actuators Saturation.

IEEE transactions on cybernetics
In this article, an aperiodic sampled-data control problem is investigated for polytopic uncertain switched complex dynamical networks subject to actuator saturation. Due to the constraint on the upper bound of the sampling interval being no greater ...

Asymmetric Input-Output Constraint Control of a Flexible Variable-Length Rotary Crane Arm.

IEEE transactions on cybernetics
This article demonstrates the realization of angle tracking and deformation suppression by developing two boundary controllers for a flexible variable-length rotary crane arm with extraneous disturbances and asymmetric input-output constraints. The d...

Energy-Saving Robust Saturated Control for Active Suspension Systems via Employing Beneficial Nonlinearity and Disturbance.

IEEE transactions on cybernetics
This article proposes a novel control framework for active suspension systems by purposely employing beneficial nonlinearity and a useful disturbance effect for control performance enhancement. To this aim, a novel amplitude-limited PD-SMC control sc...

Optimal Bounded Ellipsoid Identification With Deterministic and Bounded Learning Gains: Design and Application to Euler-Lagrange Systems.

IEEE transactions on cybernetics
This article proposes an effective optimal bounded ellipsoid (OBE) identification algorithm for neural networks to reconstruct the dynamics of the uncertain Euler-Lagrange systems. To address the problem of unbounded growth or vanishing of the learni...