Impulse activated sparse cell array network in non-linear autoregressive process modeling

Abstract

This paper introduces new neural network/fuzzy logic architecture called the impulse activated sparse cell array network (IASCAN) architecture. The motivation behind and origins of the new architecture are discussed. Its performance in non-linear dynamic system modeling using the Mackey Glass process which exhibits chaotic behavior is compared with that of Adaptive-Neuro Fuzzy Inference Systems (ANFIS). This is done by comparing the root mean squared errors (RMSE) using training and checking data sets after the networks are trained with data taken from simulations of the Mackey Glass process. The same data sets are used in training and checking of errors for both the ANFIS networks and the new architecture for a direct comparison of performance. The results shows that the RMSEs of the new architecture were lower than that of the ANFIS networks with standard configuration of the problem even after many epochs of training. The errors were shown to approach that of the new architecture for equivalent order models only after increasing the numbers of Membership Functions (MFs) in the ANFIS model after which it took an extremely long time comparatively to produce a solution. The method is also uniquely extensible to the practical solution of high order problems. There are also other features that make this new architecture very promising.