paper: Adaptable Deadband with Perceptrons

[E Dawg]

What is it?

A deadband that changes itself based on the user input using perceptrons. For example, if the driver is constantly providing maximum input, the deadband change to increase the threshold. If the driver is providing low input, the deadband will change to decrease the threshold.

What are perceptrons?

Perceptrons are a form of learning AI that mimics a human brain cell. It can take any number of inputs. It then modifies each input by an individual weight. This weight is what is modified as the perceptron learns. The weighted sum is then passed through an activation function to either 'fire' or not. The equation for changing the weight is as follows:

W(n) = W(n+1) + η(d(n)-y(n))*x(n)
Above W(n) is the old weights vector, W(n+1) is the new weights vector η is a user-defined constant called the teaching step, d(n) is the target vector, y(n) is the actual output of the network and x(n) is the corresponding input.

Like a neuron, perceptrons can also be linked together. This deadband only uses one perceptron, however.

You can download the source code above.