The Mechanical Neural Network(MNN) -- A physical implementation of a multilayer perceptron for education and hands-on experimentation
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Implementing the ICE Estimator in Multilayer Perceptron Classifiers
This paper describes the techniques used to implement the ICE estimator for a multilayer perceptron model, and reviews the performance of the resulting models. The ICE estimator is implemented in the Apache Spark Multila…
Dissipative Latent Residual Physics-Informed Neural Networks for Modeling and Identification of Electromechanical Systems
Accurate dynamical modeling is essential for simulation and control of embodied systems, yet first-principles models of electromechanical systems often fail to capture complex dissipative effects such as joint friction, …
Comparison of EEG based epilepsy diagnosis using neural networks and wavelet transform
Epilepsy is one of the common neurological disorders characterized by recurrent and uncontrollable seizures, which seriously affect the life of patients. In many cases, electroencephalograms signal can provide important …
EEGElectroencephalogram (EEG)Study Design and Demystification of Physics Informed Neural Networks for Power Flow Simulation
In the context of the energy transition, with increasing integration of renewable sources and cross-border electricity exchanges, power grids are encountering greater uncertainty and operational risk. Maintaining grid st…
Autoencoders, Kernels, and Multilayer Perceptrons for Electron Micrograph Restoration and Compression
We present 14 autoencoders, 15 kernels and 14 multilayer perceptrons for electron micrograph restoration and compression. These have been trained for transmission electron microscopy (TEM), scanning transmission electron…
Denoising