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人工智能/神经网络  MASON代表多主体邻里或网络仿真(Multi-Agent Simulator of Neighborhoods or Networks)。它是乔治梅森大学用Java开发的离散事件多主体仿真核心库

MASON代表多主体邻里或网络仿真(Multi-Agent Simulator of Neighborhoods or Networks)。它是乔治梅森大学用Java开发的离散事件多主体仿真核心库，具有快速、灵活和便携的特点。它本身支持轻量级的模拟需求，自含模型可以嵌入到其他Java应用当中，还可以选择2D和3D图形显示。 ...

https://www.eeworm.com/dl/650/147972.html

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人工智能/神经网络  Back propagation neural networks and its Application: Time-Series Forecasting Prediction of the Annu

Back propagation neural networks and its Application: Time-Series Forecasting Prediction of the Annual Number of Sunspots

https://www.eeworm.com/dl/650/152223.html

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人工智能/神经网络  高级人工神经网络<Advanced Algorithms For Neural Networks>的随书程序

高级人工神经网络<Advanced Algorithms For Neural Networks>的随书程序

https://www.eeworm.com/dl/650/153445.html

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教育系统应用  Research on some key technologies of QoS provisioning for mobile AD-HOC networks

Research on some key technologies of QoS provisioning for mobile AD-HOC networks

https://www.eeworm.com/dl/621/155284.html

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人工智能/神经网络  Face Recognition Demo using Neural Networks

Face Recognition Demo using Neural Networks

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人工智能/神经网络  人工神经网络和人工智能Delphi版语言的源程序（Neural Networks and AI Delphi Sources）。

人工神经网络和人工智能Delphi版语言的源程序（Neural Networks and AI Delphi Sources）。

https://www.eeworm.com/dl/650/159598.html

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matlab例程  IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs)

IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs)

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人工智能/神经网络  Single-layer neural networks can be trained using various learning algorithms. The best-known algori

Single-layer neural networks can be trained using various learning algorithms. The best-known algorithms are the Adaline, Perceptron and Backpropagation algorithms for supervised learning. The first two are specific to single-layer neural networks while the third can be generalized to multi-layer pe ...

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人工智能/神经网络  Simon Haykin的《Neural NetWorks》例子原码

Simon Haykin的《Neural NetWorks》例子原码，相当经典。相信很有用，特别SVM PCA等

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文章/文档  RTK Networks based on Geo++&reg GNSMART &#8722 Concepts, Implementation, Results

RTK Networks based on Geo++&reg GNSMART &#8722 Concepts, Implementation, Results

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