找回密碼
 To register

QQ登錄

只需一步,快速開(kāi)始

掃一掃,訪(fǎng)問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: Neural Networks and Sea Time Series; Reconstruction and E Brunello Tirozzi,Silvia Puca,Stefano Corsini Book 2006 Birkh?user Boston 2006 Exc

[復(fù)制鏈接]
樓主: 和善
11#
發(fā)表于 2025-3-23 10:20:02 | 只看該作者
Basic Notions on Waves and Tides,efinitions of the quantities describing waves and a description of the current instruments and methodologies for their measurement. We describe the network of buoys used for attaining the significant wave height (SWH) time series analyzed in this book. We use a similar approach for tides: some of th
12#
發(fā)表于 2025-3-23 16:53:04 | 只看該作者
The Wave Amplitude Model, we will compare the results of neural network (NN) reconstruction with those of the wave amplitude model (WAM) model. This comparison is done to check the order of magnitude of the significant wave height (SWH) reconstructed by means of the NN. Moreover, an understanding of this chapter is useful t
13#
發(fā)表于 2025-3-23 21:30:02 | 只看該作者
14#
發(fā)表于 2025-3-23 23:29:11 | 只看該作者
Approximation Theory,of the sigmoidal function corresponding to an NN can approximate any function is a simple consequence of the Stone-Weierstrass theorem and so such an approach is a convincing one. Furthermore, in the case of approximation theory the synaptic weights are given by some a priori estimates and in many c
15#
發(fā)表于 2025-3-24 04:26:02 | 只看該作者
16#
發(fā)表于 2025-3-24 09:32:13 | 只看該作者
Application of ANN to Sea Time Series,ight (SWH) measurements. As specified in Chapter 2, SL is the height of the tide, and SWH is the significant wave height. The phenomenologies of the two time series are different and each has its own problems.
17#
發(fā)表于 2025-3-24 13:05:12 | 只看該作者
Application of Approximation Theory and ARIMA Models,m. Many algorithms, unlike ANN and simply NN, have been used for solving analogous problems. We selected two algorithms: the approximation operators which are a different version of ANN, already studied and explained in detail in Chapter 5, and the classical autoregressive integrated moving average
18#
發(fā)表于 2025-3-24 18:15:04 | 只看該作者
19#
發(fā)表于 2025-3-24 22:48:12 | 只看該作者
20#
發(fā)表于 2025-3-24 23:34:37 | 只看該作者
Conclusions,r developing the analysis. The choice among the different algorithms has not been simple; we think that we have solved it in the optimal way, according to our taste and interests. The first principle used for collecting the various chapters has been to bring together all the theoretical and experime
 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(ài)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, 2025-10-9 12:27
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
钦州市| 蒙阴县| 无极县| 朝阳县| 忻州市| 中西区| 咸丰县| 江山市| 五河县| 平定县| 湾仔区| 北流市| 新民市| 桂平市| 香格里拉县| 牡丹江市| 吉首市| 辛集市| 肥城市| 千阳县| 乌拉特中旗| 阳新县| 陇南市| 抚顺县| 鹰潭市| 谷城县| 庄浪县| 鲜城| 稷山县| 霸州市| 乌拉特前旗| 台东县| 望江县| 沧州市| 宜黄县| 松阳县| 孙吴县| 永定县| 临颍县| 珲春市| 东乌珠穆沁旗|