FeedForward
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Related Articles from SNS
Unlocking feedforward capabilities in Model Predictive Control algorithms to deal with measurable disturbances
Announce Type: new Abstract: Disturbance rejection is a central objective in process control, particularly when measurable disturbances can be exploited through feedforward action. Although Model Predictive Control (MPC) naturally incorporates disturbance models and prediction capabilities, standard formulations cannot achieve complete disturbance rejection since the cost function penalises control effort.
FPGA Based Feedforward System for Photonic Quantum Computing Applications
Announce Type: cross Abstract: Field-programmable gate arrays provide a high-performance solution for real-time signal processing in emerging quantum and photonic technologies. We present an FPGA-based fast feedforward system, that incorporates a high quantum efficiency fully fibre based homodyne detector, to enable low-latency signal processing critical for continuous variables (CV) measurement-based quantum information processing (MB-QIP) protocols. CV MB-QIP typically relies on adaptive...
Recursive Learning of Feedforward and Compliance Compensation Parameters for Precision Motion Systems
arXiv:2606.03533v1 Announce Type: new Abstract: To meet the stringent requirements of future motion systems exhibiting time-varying and/or position-dependent behavior, online data must be leveraged to improve control performance. This paper presents a recursive algorithm for simultaneous learning of feedforward and compliance compensation parameters. A multivariate regression formulation is proposed that jointly estimates friction, mass, jerk, and compliance compensation parameters while...
Inheritance Between Feedforward and Convolutional Networks via Model Projection
arXiv:2602.06245v2 Announce Type: replace-cross Abstract: Neural-network techniques are often transferred across architecture families by analogy, but such transfer is valid only when the assumptions required by a technique are preserved. We introduce this idea as inheritance between model classes. Using a unified node-level framework with tensor-valued activations, we prove that generalized feedforward networks (GFFNs) form a strict subset of generalized convolutional networks (GCNNs), so...
The Topological Trouble With Transformers
Announce Type: replace Abstract: Transformers encode structure in sequences via an expanding contextual history. However, their purely feedforward architecture fundamentally limits dynamic state tracking. State tracking -- the iterative updating of latent variables reflecting an evolving environment -- involves inherently sequential dependencies that feedforward networks struggle to maintain.
A Self-Priming Neural Chain Links Sequential Behaviors Across Timescales
Behavioral sequences are essential for survival, yet the neural mechanisms that link one action to the next remain incompletely understood. In classical chain models, sequential behaviors arise through feedforward propagation of activity across distinct neuronal populations or network modules. Here, we identify a distinct form of neural chain mechanism in which neurons active during a first behavior modulate themselves into a persistent state of elevated tonic firing that subsequently drives...
Intrinsic Population Dynamics are a Neuronal Substrate for Visual Attention
Perception results from a dynamic interplay between the feedforward processing of sensory stimuli and intrinsic neural activity, which is often dismissed as noise. To tailor perceptual processes to the organism's current needs on a continuous, moment-to-moment basis, intrinsic dynamics - rather than just being noise - have been suggested to reflect prior expectations, task demands, and attentional focus. Here, we identify a novel signature of attentive state in which intrinsic, collective...
Branch-Aware Quantum Constant Propagation for Dynamic Quantum Circuits
arXiv:2606.02018v1 Announce Type: cross Abstract: Compile-time optimization is important for improving the efficiency and reliability of quantum circuits on current noisy hardware. While many existing methods simplify circuits using structural patterns or quantum-state information, most of them target only unitary circuits and do not support dynamic circuits with mid-circuit measurements and classical feedforward. In this work, we present Branch-Aware Quantum Constant Propagation (BQCP), a...
Accelerating Divisible Load Processing Through Machine Learning: A Practical Framework for Large-Scale Workloads
Announce Type: replace Abstract: In this paper, we introduce the first machine learning framework for predicting optimal processing times in Single-Level Tree Network (SLTN) architectures for the Divisible Load Theory (DLT) paradigm. Using a feedforward neural network(FNN) with 16 engineered features, we train a model on 100,000 synthetically generated configurations to predict optimal processing times without explicit formulation of DLT equations. The model achieves 97-99% accuracy...
SurGe: Improved Surface Geometry in Point Maps
arXiv:2605.31577v1 Announce Type: new Abstract: Recent feedforward 3D reconstruction methods predict point maps and estimate global 3D geometry remarkably well. However, their predictions still exhibit inaccurate local surface geometry, which is clearly visible qualitatively but only weakly reflected in common metrics. To make these errors more explicit in evaluation, we introduce a point map normal metric that evaluates the local surface orientation induced by neighboring 3D predictions.