Neural Networks

This program makes the simulation of a neural network Perceptron and Adaline
The Perceptron is a type of artificial neural network developed in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt. It can be seen as the simplest type of neural feedforward network: a linear classifier.

ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early single-layer artificial neural network and the name of the physical device that implemented this network. The network uses memistors. It was developed by Professor Bernard Widrow and his graduate student Ted Hoff at Stanford University in 1960. It is based on the McCulloch–Pitts neuron. It consists of a weight, a bias and a summation function.

This program simulates the logic gates OR, AND, NAND, NOR, and XOR. Notice in the simulation that the XOR problem is not solved. This makes the simulation program of the neural network Perceptron and Adaline
The Perceptron is a type of artificial neural network developed in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt. It can be seen the the simplest type of neural feedforward network: a linear classifier.

ADALINE (Adaptive Linear Neuron or later Adaptive Linear Element) is an early artificial neural single-layer network and the name of the device physical que Implemented this network. The network uses memistors. It was developed by professor Bernard Widrow and his graduate student Ted Hoff at Stanford University in 1960. It is based on the McCulloch-Pitts neuron. It Consists of the weight, the bias and the summation function.

This program simulates the logic gates OR, AND, NAND, NOR, and XOR. Notice in the simulation que the XOR problem is not solved.