Results 101 to 110 of about 92,621 (309)
Public feedback - but personal feedforward?
Journal of Learning Development in Higher Education, 2012 People can learn by considering and understanding examples. With assistance, students should see and appreciate in examples strengths on which they can build, and weaknesses which they should minimise. So feedback and feedforward to students can usefully
John Cowan, YiChing Jean Chiu
doaj +1 more source
Advanced Science, EarlyView.This Perspective charts the modeling landscape for soft manipulators, spanning sparse system identification, neural dynamics, physics‐informed models and hybrid intelligence. We examine how physics, data, sensing, adaptation and control can assume complementary roles within this landscape, and provide a framework for selecting modeling approaches ...
Wenbo Hui +4 more
wiley +1 more source
Design of an Actuator Controller Based on Frequency Feedforward Parameter Compensation
IEEE AccessThe actuator controller is the key component of the active vibration control system. With the rapid development of microelectronics and power electronics in recent years, the control type actuator controller technology based on high-frequency pulse width
Zhijun Jiang +5 more
doaj +1 more source
, 2015 Objeto de aprendizaje: Presentación interactivaEn este recurso educativo se introduce un aprendizaje a estas dos herramientas utilizadas en procesos de coaching individual y de grupo, además de una comparación en el uso de las mismas y sus aportes en el ...
Moreno Torres, Luz Danny
core
Multivariable fuzzy feedforward compensation [PDF]
, 2001 This work deals with some practical aspects of fuzzy PID controllers regarding multivariable non-linear feedforward compensation. This contribution concerns to the task of compensation for multivariable disturbances on the controlled variable.
Ferreiro García, Ramón +5 more
core +1 more source
Device Stochasticity Directly Implements Bayesian Sampling in Spiking Neurons
Advanced Science, EarlyView.Brain‐inspired stochastic spiking turns device noise into Bayesian uncertainty for robust, low‐power neuromorphic computing. ABSTRACT Device stochasticity is considered a non‐ideality in neuromorphic hardware, although stochastic neural activity supports probabilistic computation in biological systems.
Huannan Zheng, Jingli Liu, Kezhou Yang
wiley +1 more source
Hepatology, EarlyView., 2022 KAT7‐acetylated and cytoplasm‐translocated G‐protein GαS enhances IL‐6 effect and drives HCC progenitor cell progression. Abstract Background and Aims Hepatocarcinogenesis goes through HCC progenitor cells (HcPCs) to fully established HCC, and the mechanisms driving the development of HcPCs are still largely unknown.
Ye Zhou +15 more
wiley +1 more source
Fast batch processing of semidefinite programs with feedforward neural networks
, 2021 Fast semidefinite programming with feedforward neural ...
Brunner, Nicolas +4 more
core +3 more sources
Feedforward neural networks with constrained weights [PDF]
The conventional multilayer feedforward network having continuous-weights is expensive to implement in digital hardware. Two new types of networks are proposed which lend themselves to cost-effective implementations in hardware and have a fast forward ...
Khan, Altaf Hamid
core
Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference
Advanced Electronic Materials, EarlyView.This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho +6 more
wiley +1 more source