| 4 | 1/1 | 返回列表 |
| 查看: 464 | 回復: 3 | ||
jimyang2008木蟲 (正式寫手)
|
[求助]
幫忙查詢論文EI收錄情況 已有1人參與
|
|
各位大家好! 誰能幫忙查詢一下論文ei收錄情況,如果已經(jīng)ei收錄,煩請將收錄頁面以網(wǎng)頁截圖形式發(fā)送到郵箱: jimyang2008@163.com. 先謝謝大家了! 論文題目: multi hidden layer extreme learning machine optimised with batch intrinsic plasticity 期刊: international journal of computational science and engineering, vol. 18, no. 4, 2019 作者: shan pang, xinyi yang |

鐵桿木蟲 (知名作家)
|
Multi hidden layer extreme learning machine optimised with batch intrinsic plasticity Accession number: 20191706826940 Authors: Pang, Shan 1 ; Yang, Xinyi 2 Author affiliations : 1 College of Information and Electrical Engineering, Ludong University, Yantai; 264025, China 2 Department of Aircraft Engineering, Naval Aeronautical and Astronautical University, Yantai; 264001, China Corresponding author: Pang, Shan (pangshanpp@163.com) Source title: International Journal of Computational Science and Engineering Abbreviated source title: Int. J. Comput. Sci. Eng. Volume: 18 Issue: 4 Issue date: 2019 Publication Year: 2019 Pages: 375-382 Language: English ISSN: 17427185 E-ISSN: 17427193 Document type: Journal article (JA) Publisher: Inderscience Enterprises Ltd. Abstract: Extreme learning machine (ELM) is a novel learning algorithm where the training is restricted to the output weights to achieve a fast learning speed. However, ELM tends to require more neurons in the hidden layer and sometimes leads to ill-condition problem due to random selection of input weights and hidden biases. To address these problems, we propose a multi hidden layer extreme learning machine optimised with batch intrinsic plasticity (BIP) scheme. The proposed algorithm has a deep structure and thus learns features more efficiently. The combination of BIP scheme helps to achieve better generalisation ability. Comparisons with some state-of-the-art ELM algorithms on both regression and classification problems have verified the performance and effectiveness of our proposed algorithm. Copyright © 2019 Inderscience Enterprises Ltd. Number of references: 24 Main heading: Learning algorithms Controlled terms: Knowledge acquisition - Learning systems - Network layers - Neural networks Uncontrolled terms: Deep structure - Extreme learning machine - Generalisation - Hidden layers - Ill-conditions - Intrinsic plasticity - Random selection - State of the art Classification code: 723Computer Software, Data Handling and Applications - 723.4Artificial Intelligence DOI: 10.1504/IJCSE.2019.099075 |
鐵桿木蟲 (知名作家)
木蟲 (正式寫手)

| 4 | 1/1 | 返回列表 |
| 最具人氣熱帖推薦 [查看全部] | 作者 | 回/看 | 最后發(fā)表 | |
|---|---|---|---|---|
|
[考研] 一志愿北京理工大學本科211材料工程294求調(diào)劑 +6 | mikasa的圍巾 2026-03-28 | 6/300 |
|
|---|---|---|---|---|
|
[考研] 070305高分子化學與物理 304分求調(diào)劑 +7 | c297914 2026-03-28 | 7/350 |
|
|
[考研] 材料學碩333求調(diào)劑 +10 | 北道巷 2026-03-24 | 10/500 |
|
|
[考研] 求調(diào)劑 +7 | 爭取九點睡 2026-03-28 | 8/400 |
|
|
[考研] 343求調(diào)劑 +5 | 愛羈絆 2026-03-28 | 5/250 |
|
|
[考研] 071000生物學求調(diào)劑,初試成績343 +7 | 小小甜面團 2026-03-25 | 7/350 |
|
|
[考研] 275求調(diào)劑 +10 | Micky11223 2026-03-25 | 14/700 |
|
|
[考研] 283求調(diào)劑 +3 | A child 2026-03-28 | 3/150 |
|
|
[考研] 調(diào)劑 +3 | 好好讀書。 2026-03-28 | 3/150 |
|
|
[考研] 340求調(diào)劑 +5 | jhx777 2026-03-27 | 5/250 |
|
|
[考研] 07化學280分求調(diào)劑 +10 | 722865 2026-03-23 | 10/500 |
|
|
[考研] 085600,材料與化工321分調(diào)劑 +4 | 大饞小子 2026-03-27 | 6/300 |
|
|
[考研] 324求調(diào)劑 +5 | hanamiko 2026-03-26 | 5/250 |
|
|
[考研]
材料學碩,求調(diào)劑
6+5
|
糖葫蘆888ll 2026-03-22 | 10/500 |
|
|
[考研] 求調(diào)劑 一志愿 本科 北科大 化學 343 +6 | 13831862839 2026-03-24 | 7/350 |
|
|
[考研] 281求調(diào)劑 +6 | Koxui 2026-03-24 | 7/350 |
|
|
[考研] 303求調(diào)劑 +6 | 藍山月 2026-03-25 | 6/300 |
|
|
[考研] 材料專碩 335 分求調(diào)劑 +4 | 拒絕冷暴力 2026-03-25 | 4/200 |
|
|
[考研] 333求調(diào)劑 +3 | ALULU4408 2026-03-23 | 3/150 |
|
|
[考研] 求老師收我 +3 | zzh16938784 2026-03-23 | 3/150 |
|