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一个C#下面的图像处理算法以及人工智能算法的类库。人工智能的算法包括神经网络算法,遗传算法,机器学习算法等。使用C#写人工智能算法的开发者可以直接调用这个类库,不用自己实现最底层的算法了。我曾经使用过其中的遗传算法类库,功能十分强大,推荐使用。
-English Descr iption: AForge.NET Framework is a C# framework designed for developers and researchers in the fields of Comput
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C#开发的计算机视觉及人工智能领域的一个功能非常强大的类库,包括图像处理库、计算机视觉库、机器学习库、视频处理库等。-AForge.NET Framework is a C# framework designed for developers and researchers in the fields of Computer Vision and Artificial Intelligence- image processing, computer vision, neural networks
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这个程序主要提高了神经网络,遗传算法,聚类等多种人工智能算法,采样c++编程,,很精简-This process mainly to improve the neural networks, genetic algorithms, clustering and many other artificial intelligence algorithms, sampling c++ Programmed, it is streamlined
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人工智能方面,这是一个简单的神经网络算法,进行sin(x)的拟合,vc中编译通过,require.h是thingking in c++中的一个很有用的头文件,asser的升级版-Artificial intelligence, this is a simple neural networks algorithm for sin (x) of the fitting, vc compiled by, require.h is thingking in c++ in a very useful he
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AForge.NET 是一个专门为开发者和研究者基于C#框架设计的,他包括计算机视觉与人工智能,图像处理,神经网络,遗传算法,机器学习,机器人等领域。-The article describes an open source C# framework for researchers in the areas of Computer Vision and Artificial Intelligence- image processing, neural networks, genetic alg
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AForge.NET 是一个专门为开发者和研究者基于C#框架设计的,他包括计算机视觉与人工智能,图像处理,神经网络,遗传算法,机器学习,机器人等领域。-AForge.NET is a C# framework designed for developers and researchers in the fields of Computer Vision and Artificial Intelligence- image processing, neural networks, genetic
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Training Artificial Neural Network.
XOR Problem.
Summation Units, Log-Sigmoid Neurons with Biases.
Input Layer: 2, Hidden Layer: 2, Output Layer: 1 neurons.
Returns mean square error between desired and actual outputs.
Reference Pape
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C codes and simulation examples for the ANFIS by Jang (Absolutely original version )-This attached file contains C code and simulation examples for the ANFIS (Adaptive-Network-based Fuzzy Inference System) architecture proposed in [1,2].
Related
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Training Artificial Neural Network. XOR Problem. Summation Units, Log-Sigmoid Neurons with Biases. Input Layer: 2, Hidden Layer: 2, Output Layer: 1 neurons. Returns mean square error between desired and actual outputs. Reference Papers: D. Karaboga,
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Training Artificial Neural Network. XOR Problem. Summation Units, Log-Sigmoid Neurons with Biases. Input Layer: 2, Hidden Layer: 2, Output Layer: 1 neurons. Returns mean square error between desired and actual outputs. Reference Papers: D. Karaboga,
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识别手写数字的人工神经网络的发展神经网络概念,如神经元,层,重量和反向传播等概念及c++实现在文中给出-This article chronicles the development of an artificial neural network designed to recognize handwritten digits. Although some theory of neural networks is given here, it would be better if you alr
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In this paper, the expert system is introduced in
order to detect and classify commonly power quality
disturbances. This system is using learning vector quantization
artificial neural networks. Clustering method named fuzzy
c-mean is also
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AForge.NET是一个专门为开发者和研究者基于C#框架设计的,他包括计算机视觉与人工智能,图像处理,神经网络,遗传算法,机器学习,模糊系统,机器人控制等领域。-AForge.NET is a specialized developer and researcher based C# framework designed, he included computer vision and artificial intelligence, image processing, neural netw
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DeepLearnToolbox_CNN_lzbV2.0
深度学习,卷积神经网络,Matlab工具箱
参考文献:
[1] Notes on Convolutional Neural Networks. Jake Bouvrie. 2006
[2] Gradient-Based Learning Applied to Document Recognition. Yann LeCun. 1998
[3] https://github.com/rasmusberg
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CNN - 主程序
参考文献:
[1] Notes on Convolutional Neural Networks. Jake Bouvrie. 2006
[2] Gradient-Based Learning Applied to Document Recognition. Yann LeCun. 1998
[3] https://github.com/rasmusbergpalm/DeepLearnToolbox
作者:陆振波
电子
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Fast Artificial Neural Network Library是一个免费开源的神经网络库,它用C实现了支持全连接及稀连接的多层人工神经网络,并且支持定点和浮点数的跨平台运行。库中包含有易于处理训练数据的框架。-Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural network
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一个C#下面的图像处理算法以及人工智能算法的类库。人工智能的算法包括神经网络算法,遗传算法,机器学习算法等。使用C#写人工智能算法的开发者可以直接调用这个类库,不用自己实现最底层的算法了。我曾经使用过其中的遗传算法类库,功能十分强大,推荐使用。(English Descr iption: AForge.NET Framework is a C# framework designed for developers and researchers in the fields of Computer
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为了提升配网供电可靠性的预测精度!提出了基于主成分分析和粒子群优化极限学习机的配网供电可靠
性预测模型$ 从多方面分析影响供电可靠性的指标!利用主成分分析得到综合变量!实现对数据的降维$ 在此基
础上!构建人工神经网络并利用粒子群算法优化极限学习机的输入权值和阈值!完成对训练供电可靠性预测模型
的训练$ 以某大型电网的 ?L 个供电局样本 !% 种影响供电可靠性因素为例进行仿真分析!并将 E S R C E FQ C 4 G D算
法与 ! 种回归拟合算法对比!验证了该方法的有效性(It i
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