Meta-Cognitive Neuro-Fuzzy Inference System Research Articles

A Metacognitive Neuro-Fuzzy Inference System (McFIS) for Sequential Classification Problems

In this paper, we present a metacognitive sequential learning algorithm for a neuro-fuzzy inference system for classification tasks, which is referred to as a “metacognitive neuro-fuzzy inference system (McFIS).” The McFIS learning algorithm is developed based on the principles of the best human learning strategy, viz., a self-regulatory learning strategy in a metacognitive framework. McFIS has two components: a cognitive component and a metacognitive component. A neuro-fuzzy inference system forms the cognitive component of the McFIS, and a self-regulatory learning mechanism forms its metacognitive component. The learning ability of the cognitive component is monitored and controlled by the self-regulatory learning mechanism. For each sample in the training dataset, the metacognitive component uses its self-adaptive thresholds to choose one of the following learning strategies based on the criteria that depends on class-specific knowledge: 1) sample deletion; 2) sample learning; and 3) sample reserve. Thus, the metacognitive component decides what-to-learn, when-to-learn, and how-to-learn the training samples. When a new rule is added, the parameters of the new rule are assigned such that the rule has minimum overlapping with the adjacent rules as well as the localization property of the Gaussian rules is efficiently exploited. Performance of the McFIS is evaluated using several well-known benchmark multicategory/binary classification datasets from the University of California, Irvine machine learning repository and on a practical human action recognition problem. The results clearly indicate that the proposed metacognitive learning helps the McFIS achieve better performance than other existing classifiers.

A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system

In this paper, we present a meta-cognitive sequential learning algorithm for a neuro-fuzzy inference system, referred to as, ‘Meta-Cognitive Neuro-Fuzzy Inference System’ (McFIS). McFIS has two components, viz., a cognitive component and a meta-cognitive component. The cognitive component employed is a Takagi–Sugeno–Kang type-0 neuro-fuzzy inference system. A self-regulatory learning mechanism that controls the learning process of the cognitive component, by deciding what-to-learn, when-to-learn and how-to-learn from sequential training data, forms the meta-cognitive component. McFIS realizes the above decision by employing sample deletion, sample reserve and sample learning strategy, respectively. The meta-cognitive component use the instantaneous error of the sample and spherical potential of the rule antecedents to select the best training strategy for the current sample. Also, in sample learning strategy, when a new rule is added the rule consequent is assigned such that the localization property of Gaussian rule is fully exploited. The performance of McFIS is evaluated on four regression and eight classification problems. The performance comparison shows the superior generalization performance of McFIS compared to existing algorithms.