加权网络是复杂网络研究的一个重要分支,连边权重的异质性有助于刻画复杂系统的各种特性.但长期以来,由于加权网络上各种统计量的定义不统一、物理意义不明确,很多学者直接抛弃交互作用的强度,使用门限值将加权网络变为二值无权网络后再进行研究.本文综述了加权网络上常用的统计量,并简要介绍了这些统计量在实际复杂系统分析中的应用.本研究有助于相关研究人员明确各种加权网络统计量的物理意义,使用加权网络对复杂系统进行分析和刻画.同时,理解各种常用统计量的内在联系和应用背景是构造更有效加权网络统计量的基础.最后介绍了各种权重网络的随机化置乱方法,为分析和理解实际加权网络统计量的绝对值提供了参考和比较.
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复变函数与应用 第九版 英文版 作者: [美]詹姆斯·沃德·布朗 等 (中文版 当当上有卖)
2021-09-24 11:33:38 13.09MB 复变函数
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Complex Variables and Applications
2021-09-24 11:30:36 4.33MB Complex Variables
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第三版 作者 JERROLD E. MARSDEN 复变函数分析 经典教材
2021-09-14 10:51:08 35.28MB complex analysis
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Do not use this for your term test, please. Solution to Complex Variables and Applications, 8th Edition by Brown, James; Churchill, Ruel Publisher: McGraw-Hill Higher Education Copyright Year: 2009 Course: Complex Analysis Pages: 176
2021-09-10 16:28:40 4.78MB complex function answers
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Elias M. Stein, Rami Shakarchi
2021-09-10 09:49:31 2.86MB Complex Analysis
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This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas. Table of Contents Chapter 1 Introduction Chapter 2 Complex Networks Chapter 3 Machine Learning Chapter 4 Network Construction Techniques Chapter 5 Network-Based Supervised Le
2021-09-08 13:25:24 8.46MB Complex Networks Machine Learning
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Complex_Convolutional_Neural_Network_Architecture 该存储库进一步体现了我对一些著名的复杂卷积神经网络架构的实现。 这些模型是使用Tensorflow的Keras功能API从零开始开发的,这是一种创建比tf.keras.Sequential API更灵活的模型的方法。 功能性API可以处理具有非线性拓扑的模型,具有共享层的模型以及具有多个输入或输出的模型。 这种架构使神经网络可以学习深度模式(使用深度路径)和简单规则(通过短路径)。 开发型号清单 从分支悬空模型到深度卷积和点卷积的模型已经进行了实验。 我还实现了U-net,这是专门用于生物医学图像分割的独特体系结构。 最后,我制作了一个自定义的复杂模型,并在上进行了训练。 AlexNet-AlexNet是卷积神经网络的名称,它对机器学习领域产生了重大影响,特别是在将深度学习应用于机器视觉
2021-09-03 16:41:50 707KB keras resnet unet alexnet-model
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很基础的介绍复信号处理的论文,通信与信号处理的很适用。
2021-08-31 11:17:45 515KB Complex Signal Processing
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复信号和实信号频谱对比,适合信号处理初学者
2021-08-30 14:02:08 4KB 频谱 复信号
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