Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
A generalized supertwisting algorithm
The work proposes a generalized supertwisting algorithm (GSTA) and its constructive
design strategy. In contrast with the conventional STA, the most remarkable characteristic of …
design strategy. In contrast with the conventional STA, the most remarkable characteristic of …
Neural networks for portfolio analysis with cardinality constraints
X Cao, S Li - IEEE Transactions on Neural Networks and …, 2023 - ieeexplore.ieee.org
Portfolio analysis is a crucial subject within modern finance. However, the classical
Markowitz model, which was awarded the Nobel Prize in Economics in 1991, faces new …
Markowitz model, which was awarded the Nobel Prize in Economics in 1991, faces new …
Research on robot motion planning based on RRT algorithm with nonholonomic constraints
Y Gan, B Zhang, C Ke, X Zhu, W He, T Ihara - Neural Processing Letters, 2021 - Springer
Abstract A 1–0 Bg-RRT algorithm is proposed to reduce computational time and complexity,
even in complex environments. Different from Rapidly-exploring Random Tree (RRT) and …
even in complex environments. Different from Rapidly-exploring Random Tree (RRT) and …
A novel dynamic neural system for nonconvex portfolio optimization with cardinality restrictions
X Cao, S Li - IEEE Transactions on Systems, Man, and …, 2023 - ieeexplore.ieee.org
The Markowitz model, a portfolio analysis model that won the Nobel Prize, lays the
theoretical groundwork for modern finance. The transaction cost and the cardinality …
theoretical groundwork for modern finance. The transaction cost and the cardinality …
[HTML][HTML] A novel recurrent neural network based online portfolio analysis for high frequency trading
Abstract The Markowitz model, a Nobel Prize winning model for portfolio analysis, paves the
theoretical foundation in finance for modern investment. However, it remains a challenging …
theoretical foundation in finance for modern investment. However, it remains a challenging …
A strictly predefined-time convergent and noise-tolerant neural model for solving linear equations with robotic applications
Nowadays, there are time-critical applications involving linear equations, such as the fault
reconstruction problem, where hard response time constraints and robustness to external …
reconstruction problem, where hard response time constraints and robustness to external …