简体   繁体   English

人工神经网络中的无监督学习

[英]Unsupervised learning in artificial neural networks

If I were to train an artificial neural network's weights using a genetic algorithm what type of learning would this be classed as? 如果我要使用遗传算法训练人工神经网络的权重,将其归类为哪种学习类型? I believe it's unsupervised but does it have a name? 我相信它是不受监督的,但是它有名字吗? It seems like it could fall under reinforcement learning but I'm not too sure. 看来它可能属于强化学习范畴,但我不太确定。

Genetic algorithm itself is an optimization algorithm rather than a learning algorithm. 遗传算法本身是一种优化算法,而不是一种学习算法。 And you probably don't want to ignore the performance of neural network and only consider the weight changes. 您可能不想忽略神经网络的性能,而只考虑权重的变化。 So what type of learning is a combination of neural network and genetic algorithm depends on the learning type of neural network. 因此,什么类型的学习是神经网络和遗传算法的结合,取决于神经网络的学习类型。 A neural network can be used for supervised learning, reinforcement learning, and even unsupervised learning. 神经网络可用于监督学习,强化学习,甚至是无监督学习。

It seems such a combination applies more in reinforcement, because genetic algorithm is slower than most backpropagation-based optimization algorithms with gradient information. 这样的组合似乎在增强方面更有用,因为遗传算法比大多数基于梯度传播的基于反向传播的优化算法要慢。 Updating the weights with genetic algorithm is also called neuroevolution. 使用遗传算法更新权重也称为神经进化。 This post cited many research on neuroevolution over traditional neural network in case you may be interested in. 这篇文章引用了许多有关传统神经网络上神经进化的研究,以备您感兴趣。

It is also possible to apply genetic algorithm to unsupervised neural network. 也可以将遗传算法应用于无监督的神经网络。 In Shibata et al's Nonlinear backlash compensation using recurrent neural network. Shibata等人的使用循环神经网络的非线性反冲补偿中。 Unsupervised learning by genetic algorithm , the authors applied the genetic algorithm to determine the weights of the recurrent neural networks, and the approach does not need the teaching signals. 通过遗传算法的无监督学习 ,作者将遗传算法应用于递归神经网络的权重确定,该方法不需要教学信号。

声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.

 
粤ICP备18138465号  © 2020-2024 STACKOOM.COM