Random activation weight neural net (RAWN) for fast non-iterative training.
Random activation weight neural net (RAWN) for fast non-iterative training.
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DOI：
10.1016/0952-1976(94)00056-s
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发表时间：
1995-02
影响因子：
8
通讯作者：
H. T. Braake;G. V. Straten
中科院分区：
文献类型：
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作者：
H. T. Braake;G. V. Straten
一般的函数逼近可以通过仅由一个非线性神经元隐藏层组成的前馈神经网络来获得。本文所描述的创新之处在于不需要训练输入层和隐藏层之间的权重。通过将这些所谓的“激活权重”随机化，所产生的问题在参数上是线性的，并且可以很容易地通过标准的普通最小二乘法来解决。本文证明，通过使用这样的“随机激活权重网”（RAWNs），可以得到良好的映射，只要激活权重的选择，使回归矩阵是非奇异的。通过正则化和适当选择用于训练的激励信号可以实现进一步的改进。它被发现，（i）获得更小的误差相比，反向传播网络具有相同的自由度，（ii）的映射只稍微依赖于隐藏的权重的实际值，提供了网络有足够的神经元，和（iii）由于不需要迭代，计算速度是无与伦比的速度比反向传播快得多。这些特性使RAW Net特别适合于控制应用。静态和动态的各种例子，给出了该方法的可行性和优势。
General function approximation can be obtained by feed-forward neural nets consisting of just one hidden layer of non-linear neurons. The innovations described in this paper is that training of the weights between the input and hidden layers is not required. By taking these so-called “activation weights” as random, the resulting problem is linear in the parameters and can easily be solved by standard ordinary least-squares methods. This paper demonstrates that by using such “random activation weight nets” (RAWNs), excellent mappings can be obtained, provided that the activation weights are chosen so that the regression matrix is non-singular. Further improvements can be achieved by regularization, and by the proper choice of the excitation signal used for training. It is found that (i) much smaller errors are obtained as compared to backpropagation nets with the same degrees of freedom, (ii) the mapping depends only slightly upon the actual values of the hidden weights, provided the net has sufficient neurons, and (iii) since no iteration is needed, the speed of computation is incomparably much faster than with backpropagation. These properties make the RAW-Net particularly suitable for control applications. Various examples, both static and dynamic, are given to show the feasibility and advantages of the approach.