搜索：perceptron
找到约 16 项符合「perceptron」的查询结果
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人工智能/神经网络
Neural Network Perceptron use MFC
Neural Network Perceptron use MFC
https://www.eeworm.com/dl/650/251402.html
人工智能/神经网络
本程序实做MLP(Multi-layer perceptron)算法
本程序实做MLP(Multi-layer perceptron)算法，使用者可以自行设定训练数据集与测试数据集，将训练数据集加载，在2、3维下可以显示其分布状态，并分别设定键节值、学习率、迭代次数来训练其类神经网络，最后可观看辨识率与RMSE（Root Mean squared error）来判别训练是否可以停止。 ...
https://www.eeworm.com/dl/650/472030.html
人工智能/神经网络
A simple One layer perceptron classifier written in CSharp and using the AForge framework
A simple One layer perceptron classifier written in CSharp and using the AForge framework
https://www.eeworm.com/dl/665/302503.html
matlab例程
包含用lms、mse、perceptron准则函数的二类分类器
包含用lms、mse、perceptron准则函数的二类分类器
https://www.eeworm.com/dl/665/371501.html
matlab例程
％For the following 2-class problem determine the decision boundaries ％obtained by LMS and perceptro
％For the following 2-class problem determine the decision boundaries
％obtained by LMS and perceptron learning laws.
https://www.eeworm.com/dl/650/377948.html
人工智能/神经网络
感知器（Perceptron) 单层感知器神经元模型图,我们可以看出感知器的基本功能是将输入矢量转化成0或1的输出。这一功能可以通过在输入矢量空间里的作图来加以解释。
感知器（Perceptron)
单层感知器神经元模型图,我们可以看出感知器的基本功能是将输入矢量转化成0或1的输出。这一功能可以通过在输入矢量空间里的作图来加以解释。
https://www.eeworm.com/dl/650/363168.html
人工智能/神经网络
一个神经网络原型代码
一个神经网络原型代码，针对的例子为《Perceptron Learning》 (Russell & Norvig， 第742页)
https://www.eeworm.com/dl/650/165306.html
人工智能/神经网络
annie is an ANN, ie, Artificial Neural Network library developed in C++. It can be used to implement
annie is an ANN, ie, Artificial Neural Network library developed in C++. It can be used to implement various kinds of neural networks like Multi-Layer Perceptron, Radial basis function networks, Hopfield networks etc.
https://www.eeworm.com/dl/650/165315.html
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 generaliz ...