Titlebook: Artificial Intelligence and Soft Computing; 20th International C Leszek Rutkowski,Rafa? Scherer,Jacek M. Zurada Conference proceedings 2021

Hazardous

書(shū)目名稱Artificial Intelligence and Soft Computing影響因子(影響力)




書(shū)目名稱Artificial Intelligence and Soft Computing影響因子(影響力)學(xué)科排名




書(shū)目名稱Artificial Intelligence and Soft Computing網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Artificial Intelligence and Soft Computing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Artificial Intelligence and Soft Computing被引頻次




書(shū)目名稱Artificial Intelligence and Soft Computing被引頻次學(xué)科排名




書(shū)目名稱Artificial Intelligence and Soft Computing年度引用




書(shū)目名稱Artificial Intelligence and Soft Computing年度引用學(xué)科排名




書(shū)目名稱Artificial Intelligence and Soft Computing讀者反饋




書(shū)目名稱Artificial Intelligence and Soft Computing讀者反饋學(xué)科排名

Foam-Cells

Modification of Learning Feedforward Neural Networks with the BP Methodethod converges relatively slowly. In this paper a new approach to the backpropagation algorithm is presented. The proposed solution speeds up the BP method by using vector calculations. This modification of the BP algorithm was tested on a few standard examples. The obtained performance of both methods was compared.

發(fā)酵劑

https://doi.org/10.1007/978-3-030-87986-0artificial intelligence; classification; computer networks; computer science; computer systems; computer

Mettle

懶洋洋

Spirometry

Consensus

Durchleuchtung und Subtraktionsangiographieethod converges relatively slowly. In this paper a new approach to the backpropagation algorithm is presented. The proposed solution speeds up the BP method by using vector calculations. This modification of the BP algorithm was tested on a few standard examples. The obtained performance of both methods was compared.

組成

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162313.jpg

小口啜飲

Empirische und theoretische Ausgangspunkte,a (e.g.: investors required return, risk tolerance, goals, and time frame). The objective of this research is to present a two phase deep learning module to csonstruct a financial stocks portfolio that can be used repeatedly to select the most promising stocks and adjust stocks allocations (namely q

neolith

Methodologie und Design des Gesamtprojekts,ology of the Factor Augmented Artificial Neural Network Model is applied to improve the predictive capacity of liquidity models compared to traditional econometric methodologies. This hybrid methodology based on dynamic factor models and neural networks is compared with Deep Learning methodologies s