找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2017; 26th International C Alessandra Lintas,Stefano Rovetta,Alessandro E.P. Confe

[復(fù)制鏈接]
樓主: Spouse
31#
發(fā)表于 2025-3-26 21:45:40 | 只看該作者
32#
發(fā)表于 2025-3-27 02:37:12 | 只看該作者
Towards Grasping with Spiking Neural Networks for Anthropomorphic Robot Handsdify them during execution based on the shape and the intended interaction with objects. We present a hierarchical spiking neural network with a biologically inspired architecture for representing different grasp motions. We demonstrate the ability of our network to learn from human demonstration us
33#
發(fā)表于 2025-3-27 09:11:01 | 只看該作者
34#
發(fā)表于 2025-3-27 13:16:37 | 只看該作者
35#
發(fā)表于 2025-3-27 15:27:34 | 只看該作者
36#
發(fā)表于 2025-3-27 20:40:11 | 只看該作者
Sensorimotor Prediction with Neural Networks on Continuous Spacestelligence argue that sensorimotor prediction is a fundamental building block of cognition. In this paper, we learn the sensorimotor prediction on data captured by a mobile robot equipped with distance sensors. We show that Neural Networks can learn the sensorimotor regularities and perform sensorim
37#
發(fā)表于 2025-3-28 01:12:13 | 只看該作者
Classifying Bio-Inspired Model of Point-Light Human Motion Using Echo State Networksan action descriptor. The Echo State Network (ESN) which also has a biological plausibility is chosen for classification. We demonstrate the efficiency and robustness of applying the proposed feature extraction technique with ESN by constraining the test data based on arbitrary untrained viewpoints,
38#
發(fā)表于 2025-3-28 03:00:18 | 只看該作者
A Prediction and Learning Based Approach to Network Selection in Dynamic Environmentsemented in static network environments while cannot handle unpredictable dynamics in practice. In this paper, we propose a prediction and learning based approach, which considers both the fluctuation of radio resource and the variation of user demand. The network selection scenario is modeled as a m
39#
發(fā)表于 2025-3-28 06:29:15 | 只看該作者
40#
發(fā)表于 2025-3-28 11:22:06 | 只看該作者
Learning Distance-Behavioural Preferences Using a Single Sensor in a Spiking Neural?Networkle non-calibrated sensor in combination with neural elements could provide flexibility through learning, to effectively cope with changing environments. The objective of this study was to design an adaptive system with the potential capability of learning behavioural preferences in relation to disti
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-12 18:28
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
青冈县| 金昌市| 深圳市| 高台县| 孟村| 伊春市| 肥西县| 大新县| 安远县| 西吉县| 搜索| 明星| 嵊州市| 大厂| 镇远县| 新营市| 曲阳县| 望奎县| 新晃| 鹿邑县| 来安县| 马龙县| 东山县| 双桥区| 卫辉市| 石阡县| 鄄城县| 合肥市| 和龙市| 繁昌县| 漳州市| 会东县| 兴宁市| 元阳县| 凭祥市| 马边| 长岭县| 唐海县| 林周县| 望谟县| 阳信县|