The perceptron algorithm, introduced in the late fifties in the machine learning community, is a simple greedy algorithm for finding a solution to a finite set of linear inequalities. The algorithm’s main advantages are its simplicity and noise tolerance. The algorithm’s main disadvantage is its slow convergence rate. We propose a modified version of the perceptron algorithm that retains the al...

This paper presents a class of neural networks suitable for the application of decoding error-correcting codes.The neural model is basically a perceptron with a high-order polynomial as its discriminant function. A single layer of high-order perceptrons is shown to be able to decode a binary linear block code with at most 2 weights in each perceptron, where m is the parity length. For some subc...