论文标题
机械神经网络（MNN） - 多层感知器进行教育和动手实验的物理实施
The Mechanical Neural Network(MNN) -- A physical implementation of a multilayer perceptron for education and hands-on experimentation
论文作者
论文摘要
在本文中，引入了机械神经网络（MNN），这是具有Relu激活功能的多层感知器（MLP）的物理实现，两个输入神经元，四个隐藏的神经元和两个输出神经元。 MLP的这种物理模型用于教育中，以提供经验，并使学生体验更改网络参数对输出的效果。神经元是通过线连接的小木质杆。学生可以通过移动通过线程连接神经元的夹具来调整神经元之间的重量。 MNN可以对包括XOR在内的真实有价值的功能和逻辑运算符建模。
In this paper the Mechanical Neural Network(MNN) is introduced, a physical implementation of a multilayer perceptron(MLP) with ReLU activation functions, two input neurons, four hidden neurons and two output neurons. This physical model of a MLP is used in education to give a hands on experience and allow students to experience the effect of changing the parameters of the network on the output. Neurons are small wooden levers which are connected by threads. Students can adapt the weights between the neurons by moving the clamps connecting a neuron via a thread to the next. The MNN can model real valued functions and logical operators including XOR.
