EFFECT OF NOISE ON GENERALISATION IN MASSIVELY PARALLEL FUZZY SYSTEMS

摘要

This paper studies the performance of Massively Parallel Fuzzy Systems (MPFS) on the two spiral benchmark. Spiral data is contaminated with five different noise distributions. The recognition rates of the system are reported with varying levels of different types of noise. The behaviour of the system is investigated with additive, multiplicative, cumulative and non-cumulative noise. The results show that the MPFS system remains stable to different noise variations and the generalisation error remains consistently low. As the total noise in the system increases, the system witnesses a linear decrease in entropy and the generalisation error is easier to predict. The error rate is found to have two separate patterns of variation for cumulative and non-cumulative noise.