Resilient Backpropagation

Resilient Backpropagation (Rprop) is a training algorithm for neural networks that focuses on efficiently adjusting the weights (connections) between neurons. Unlike traditional methods that consider the size of errors, Rprop only looks at whether the error is increasing or decreasing, adjusting weights accordingly. If the error trend continues to go in the same direction, it increases or decreases the step size for changes; if it switches, it reduces the step size. This approach helps the network learn faster and more reliably, especially when facing noisy or complex data, by emphasizing the direction of improvement rather than the magnitude of errors.