Classical neural networks achieve only limited convergence in hard problems
such as XOR or parity when the number of hidden neurons is small. With the
motivation to improve the success rate of neural networks in these problems, we
propose a new neural network model inspired by existing neural network models
with so called product neurons and a learning rule derived from classical error
backpropagation, which elegantly solves the problem of mutually exclusive
situations. Unlike existing product neurons, which have weights that are preset
and not adaptable, our product layers of neurons also do learn. We tested the
model and compared its success rate to a classical multilayer perceptron in the
aforementioned problems as well as in other hard problems such as the two
spirals. Our results indicate that our model is clearly more successful than
the classical MLP and has the potential to be used in many tasks and
applications.

我们提出了一种灵感来自现有神经网络模型的新的神经网络模型，该模型具有所谓的乘积神经元和源自传统误差反向传播的学习规则，优雅地解决了互斥情况的问题。与现有的乘积神经元不同，我们的神经元层的乘积层也可以学习。我们对模型进行了测试，并将其成功率与经典多层感知机在上述问题以及其他难题（如两个螺旋）中进行了比较。结果表明我们的模型明显比经典的多层感知机更成功，并且具有在许多任务和应用中使用的潜力。