Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

93
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
93
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

83
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
83
Introduction to Learning01:18

Introduction to Learning

321
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
321
Classification of Systems-II01:31

Classification of Systems-II

133
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
133
Classification of Systems-I01:26

Classification of Systems-I

167
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
167
Associative Learning01:27

Associative Learning

275
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
275

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Variance-constrained multi-view ensemble broad network for imbalanced data.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Learning to Super-Resolve Face Images via Dual-Domain Multi-scale Feature Interaction.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Effectiveness of heterologous mRNA vaccine boosters during an Omicron wave of COVID-19: a cross-sectional study in Macao (China).

Journal of thoracic disease·2026
Same author

Fast BCIs: Leveraging Dual-Scale Time Windows with Test-Time Adaptation to Enhance Accuracy.

IEEE transactions on bio-medical engineering·2026
Same author

Riemannian Acceleration for Sparse PCA With Separable Structure and Second-Order Information Exploration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Hierarchical memory-based deep reinforcement learning in simulated survival environments.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026
Same journal

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

IEEE transactions on neural networks and learning systems·2026
Same journal

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: May 23, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K

基于分数特征优化的广泛学习系统.

Dan Zhang, Tong Zhang, C L Philip Chen

    IEEE transactions on neural networks and learning systems
    |March 7, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了BLS-FC,这是一种新方法,将广义学习系统 (BLS) 与微积分计算相结合,用于增强数据分类和回归. 这种新的方法提高了特征表示和稳定性,优于现有的方法.

    更多相关视频

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

    Published on: March 1, 2024

    616
    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

    Published on: June 30, 2020

    7.4K

    相关实验视频

    Last Updated: May 23, 2025

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    7.4K
    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

    Published on: March 1, 2024

    616
    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

    Published on: June 30, 2020

    7.4K

    科学领域:

    • 机器学习 机器学习
    • 数据科学数据科学数据科学
    • 信号处理 信号处理

    背景情况:

    • 广义学习系统 (BLS) 在像图像分类等任务的速度和准确性方面表现出色.
    • 目前的BLS方法依赖于线性特征和稀疏优化,缺乏稳定性.
    • 现有的BLS改进不包括分数计算.

    研究的目的:

    • 提出一种新的数据分类和回归方法,BLS-FC,将BLS与分数计算集成在一起.
    • 为了增强功能节点提取和优化在BLS使用分数计算属性.
    • 提高BLS的稳定性和特征表示能力.

    主要方法:

    • 在BLS特征节点提取 (BLS-Frft) 中集成的分数福里埃变换 (Frft).
    • 集成的微积分计算在稀疏表示增强功能优化 (BLS-FS).
    • 开发了一个分数顺序多尺度特征交互 (BLS-MF) 来稳定随机分数顺序子空间.

    主要成果:

    • 拟议的BLS-FC方法在各种分类和回归数据集中表现出卓越的性能.
    • BLS-Frft通过结合时间和频率域信息来丰富节点功能.
    • BLS-FS和BLS-MF通过分数微分记忆和多尺度交互增强了特征表示.

    结论:

    • 通过利用分数计算,BLS-FC在数据分类和回归方面取得了重大进展.
    • 整合Frft和微积分计算可以提高功能稳定性和表示性.
    • 拟议的方法在复杂的数据分析任务中显示出有希望的结果.