Neural Network and machine learning Note--rosenblatt Perceptron

Rosenblatt Sensor

Perceptron is the simplest neural network model for classifying linear sub-modes (patterns located on both sides of the plane), essentially consisting of a neuron with adjustable synaptic weights and biases.

Rosenblatt proves that the perceptron algorithm is convergent when the pattern (vector) used to train the Perceptron is taken from two linearly-divided classes, and the decision surface is a hyper-plane between the two classes. The convergence of the algorithm is called the Perceptron convergence theorem.

Neural Network and machine learning Note--rosenblatt Perceptron