A computationally fast Interval Type-2 Neuro-Fuzzy Inference System and its Meta-Cognitive projection based learning algorithm

In this paper, a computationally efficient Interval Type-2 Neuro-Fuzzy Inference System (IT2FIS) and its Meta-Cognitive projection based learning (PBL) algorithm is presented, together referred as PBL-McIT2FIS. A six layered network with computationally cheap type-reduction technique is proposed, rendering the inference mechanism faster. During learning, the projection based learning algorithm assumes that IT2FIS has no rules in the beginning, and the learning algorithm adds rules to the network and updates it depending on the prediction error and relative knowledge present in the current sample. As each sample is presented to the network, the meta-cognitive component of the learning algorithm decides what-to-learn, when-to-learn and how-to-learn it, depending on the instantaneous error and spherical potential of the current sample. Whenever a new rule is added or an existing rule is updated, a projection based learning algorithm computes the optimal output weights by minimizing the total error in the network in a computationally efficient manner. The performance of PBL-McIT2FIS is evaluated on a set of benchmark problem and compared to other state-of-the-art algorithms available in literature. The results indicate superior performance of PBL-McIT2FIS.