Neural network inverted spring pendulum
Condition
The segment of the values of the coefficient of elasticity k = [0.1; 0.2 + (# div 3) / 10].
The segment of the values of the initial velocity of the weight v = [0.1; 1 - [# div 6) / 10]
Number of observation points P = 5 + (# mod 3).
Observation points - x_1, x_2, ... x_i ..., x_P. x_i = x (t0 + (i-1) * dt). For dt = # / 1000. t0 = [0; T], T is the period of oscillations.
The values of physical quantities are given in the basic units of the SI system.
Assume that div - integer division (rounding down), mod - the remainder of the integer division, # - option number.
The size of the input of the neural network - P (observation point). The size of the output of the neural network - 1 (coefficient of elasticity). The number of sampling elements - N = 125000 = 50 different values of k * 50 different values of v * 50 different values of t0 (values are selected evenly on given segments).
Environment
- R version 4.0.3 (Bunny-Wunnies Freak Out)
- C# 9.0 with .NET 5.0
Components
- DataGenerator using for generate data with params by neural network
- data.txt is generated file with data that normalized (0, 1)
- NeuralNetworkInvertedPendulum solution is neural network that using data.txt
- Neural.workspace.RData is exported memory workspace from R with trained neural network
About
Neural network inverted spring pendulum developing under the MIT license.