Title :
A transient learning comparison of Rosenblatt, backpropagation, and LMS algorithms for a single-layer perceptron for system identification
Author :
Engel, Isaac ; Bershad, Neil J.
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA
fDate :
5/1/1994 12:00:00 AM
Abstract :
Presents a transient performance comparison of the Rosenblatt (1962), backpropagation, and LMS training algorithms for a single layer perceptron. The perceptron is attempting to identify a specific nonlinear system with Gaussian inputs. A summary of the first and second moment behaviors of the three algorithms is presented. With the criteria of probability of correct classification versus the number of algorithm iterations, the statistical results are used to compare the learning performance of the algorithms for some specific parameter values. By both the theoretical analysis and by Monte Carlo simulations, it is shown that there are no significant learning performance differences among these three algorithms for these parameter selections
Keywords :
backpropagation; identification; least squares approximations; linear systems; neural nets; probability; stochastic processes; Gaussian inputs; LMS algorithm; Monte Carlo simulations; Rosenblatt algorithm; algorithm iterations; backpropagation algorithm; correct classification probability; first moment behavior; learning performance; nonlinear system; second moment behavior; single-layer perceptron; statistical results; system identification; transient learning; transient performance comparison; Adaptive algorithm; Algorithm design and analysis; Backpropagation algorithms; Convergence; Least squares approximation; Nonlinear systems; Performance analysis; Probability; Signal processing algorithms; System identification;
Journal_Title :
Signal Processing, IEEE Transactions on
