Results 61 to 70 of about 2,128 (206)
Efficient Moth-Flame-Based Neuroevolution Models
, 2019 This chapter proposes a new efficient moth-flame-embedded multilayer perceptrons (MLP) neuroevolution model to deal with classification problems. Moth-flame optimizer (MFO) is one of the effective swarm-based metaheuristic methods inspired by the natural
Seyed Mohammad Jafar Jalali +11 more
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Sensors, 2018 Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc.
Alejandro Baldominos +2 more
doaj +1 more source
Neuroevolutionary Control for Autonomous Soaring
Aerospace, 2021 The energy efficiency and flight endurance of small unmanned aerial vehicles (SUAVs) can be improved through the implementation of autonomous soaring strategies.
Eric J. Kim, Ruben E. Perez
doaj +1 more source
Advanced Intelligent Systems, EarlyView.Neuro‐evolution can boost machine‐learning optimization of chiral metasurfaces. By integrating the NEAT algorithm into a deep‐learning framework, we enable the efficient design of visible‐spectrum chiroptical responses. NEAT autonomously evolves neural‐network architectures and weights, reducing manual tuning.
Davide Filippozzi, Arash Rahimi‐Iman
wiley +1 more source
Electron, EarlyView.The underlying interface physics and engineering strategies across key POI material systems, including LiNbO3/Si, LiNbO3/SiC, LiNbO3/diamond, and AlN/diamond, were reviewed. The key engineering strategies such as atomic‐scale bonding optimization, nanostructuring, ultrathin phonon‐bridge interlayers, and ferroelectric domain wall engineering for ...
Yunjia Bao +6 more
wiley +1 more source
On‐Chip Learning With Crossbar Arrays for Adaptive Edge Intelligence
Advanced Computing, Volume 1, Issue 1, December 2026.Memristor crossbar arrays implemented on chip combines memory, computation, and learning. They allow for adapting the neural weights using the incoming sensor data. This architecture reduces data movement leading to low‐latency, energy‐efficient intelligence in edge while being reliability‐aware co‐design across devices, circuits, and algorithms that ...
Alex James
wiley +1 more source
Application of Neuroevolution in Autonomous Cars [PDF]
, 2021 With the onset of Electric vehicles, and them becoming more and more popular, autonomous cars are the future in the travel/driving experience. The barrier to reaching level 5 autonomy is the difficulty in the collection of data that incorporates good driving habits and the lack thereof.
G. Sainath +3 more
openaire +2 more sources
Advanced Science, Volume 13, Issue 47, 24 August 2026.This review critically examines thermal transport and radiative properties of ultra‐high temperature ceramics for hypersonic flight, advanced nuclear systems, and next‐generation energy conversion devices. It explores phonon–photon–electron interactions, microstructural engineering, thermoelectric conversion, and machine learning‐accelerated multiscale
Zhipeng Pei +8 more
wiley +1 more source
CGNEP‐MB‐pol: A Single‐Site Coarse‐Grained Machine Learning Potential for Water
Materials Genome Engineering Advances, Volume 4, Issue 3, August 2026.ABSTRACT Coarse‐grained (CG) molecular dynamics (MD) can greatly extend the accessible time and length scales for water, provided that the reduced model captures key structural, dynamical, and thermodynamic properties. Here, we introduce a CG machine learning potential (MLP) for water, named CGNEP‐MB‐pol, which integrates a one‐molecule to one‐bead ...
Ke Xu +7 more
wiley +1 more source
Neuroevolution trajectory networks : illuminating the evolution of artificial neural networks [PDF]
, 2023 Neuroevolution is the discipline whereby ANNs are automatically generated using EC. This field began with the evolution of dense (shallow) neural networks for reinforcement learning task; neurocontrollers capable of evolving specific behaviours as ...
Sarti, Stefano
core