ESOFCMAC: Evolving Self-Organizing Fuzzy Cerebellar Model Articulation Controller

This paper proposes an evolving fuzzy associative memory neural network model based on the fuzzy CMAC (FCMAC). FCMAC is an auto-associate memory feed forward neural network with attractive properties of fast learning and simple computation. Evolving techniques aim at building adaptive intelligent systems that evolve both their structure and parameters through incremental online learning. During fuzzification phase, the proposed ESOFCMAC uses raw numerical values of a training data set without any preprocessing and obtains dynamic partition-base clusters with no prior knowledge of the number of clusters. The performance of ESOFCMAC is illustrated on several benchmark data sets and compared with traditional models.