Description: VC++下的不规则三角网生成算法,这是不规则三角网的生长算法,通过建立TIN类,在TIN类内实现一些方法.-VC under the TIN generation algorithm, which is triangular irregular network growth algorithm, through the establishment of TIN category, the category TIN achieve some ways. Platform: |
Size: 2025 |
Author:feng |
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Description: VC++下的不规则三角网生成算法,这是不规则三角网的生长算法,通过建立TIN类,在TIN类内实现一些方法.-VC under the TIN generation algorithm, which is triangular irregular network growth algorithm, through the establishment of TIN category, the category TIN achieve some ways. Platform: |
Size: 2048 |
Author:feng |
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Description: 基于Matlab的BA无标度网络拓扑生成算法，BA模型有两个重要特性：增长特性和优先连接特性。-Matlab based on the BA scale-free network topology generation algorithm, BA model has two important characteristics: growth characteristics and properties of priority connections. Platform: |
Size: 1024 |
Author:xiaoli |
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Description: 不规则三角网的生长算法实现
较为实用的生长算法
操作简单
-Triangulated irregular network growth algorithm for a more practical realization of the growth algorithm is simple Platform: |
Size: 67584 |
Author:都海伦 |
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Description: 基于PageRank算法的网络增长模型的度分布图像，正常运行-PageRank algorithm based on degree distribution network growth model image, normal operation Platform: |
Size: 4096 |
Author:yk |
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Description: 经过新息准则处理过的核自适应滤波算法KLMS.该算法可以降低klms的网络增长问题。-After the new interest guidelines processed nuclear adaptive filtering algorithm KLMS. Klms this algorithm can reduce the problem of network growth. Platform: |
Size: 1024 |
Author:王仁 |
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Description: 供做算法研究人员参考，三相光伏逆变并网的仿真，计算晶粒的生长，入门级别程序。- Algorithm for researchers to do reference, Three-phase photovoltaic inverter and network simulation, Calculation of growth, entry-level program grain. Platform: |
Size: 5120 |
Author:wadgqped |
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Description: 基于Matlab的BA无标度网络拓扑生成算法，BA模型有两个重要特性：增长特性和优先连接特性(BA based scale-free network topology generation algorithm based on Matlab. There are two important characteristics of BA model: growth characteristics and priority connection characteristics.) Platform: |
Size: 1024 |
Author:小黑小白 |
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Description: Artificial intelligence (AI) is concerned with building systems that simulate intelligent
behavior. It encompasses a wide range of approaches, including those based on logic,
search, and probabilistic reasoning. Machine learning is a subset of AI that learns to
make decisions by fitting mathematical models to observed data. This area has seen
explosive growth and is now (incorrectly) almost synonymous with the term AI.
A deep neural network is one type of machine learning model, and when this model is
fitted to data, this is referred to as deep learning. At the time of writing, deep networks
are the most powerful and practical machine learning models and are often encountered
in day-to-day life. It is commonplace to translate text from another language using a
natural language processing algorithm, to search the internet for images of a particular
object using a computer vision system, or to converse with a digital assistant via a speech
recognition interface. All of these applications are powered by deep learning.
As the title suggests, this book aims to help a reader new to this field understand
the principles behind deep learning. The book is neither terribly theoretical (there are
no proofs) nor extremely practical (there is almost no code). The goal is to explain the
underlying ideas; after consuming this volume, the reader will be able to apply deep
learning to novel situations where there is no existing recipe for success.
Machine learning methods can coarsely be divided into three areas: supervised, unsupervised, and reinforcement learning. At the time of writing, the cutting-edge methods
in all three areas rely on deep learning (figure 1.1). This introductory chapter describes
these three areas at a high level, and this taxonomy is also loosely reflected in the book’s
organization. Platform: |
Size: 11646296 |
Author:ihaveap1 |
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Search - network growth algorithm