MPC is used to optimize control inputs by approximating a reference trajectory using dt, N and T variables in a finite(2-3 seconds) time-horizon. This reference trajectory should encompass very small values of dt which will be multiplied by N (should be a number that when multiplied by dt is 2-3Continue Reading
Start by building a very simple network: import numpy as np class NeuralNetwork: def __init__(self, x,y): self.input = x self.y = y self.Weights1 = np.random.randn(self.input.shape[1],5) self.Weights2 = np.random.randn(5,1) self.output = np.zeros(self.y.shape) def sigmoid_z(self,x): #create a sigmoid function z = 1/(1 + np.exp(-x)) return z def sigmoid_z_derivative(self,x): return self.sigmoid_z(x)*(1-self.sigmoid_z(x)) def forwardpropogation(self):Continue Reading