The smallest universal nonlinear connective operators

A novel class of highly adaptive nonlinear digital aggregation connectives called Q-aggregates is described in this paper. The Q-aggregate approach relies on a unique control parameter lambda to characterize the three conventional fuzzy intersection, union, and averaging aggregation operators. In addition to this universal coverage property of the proposed model, a distinguishing and extremely interesting characteristic of the Q-aggregate connective is that even for a fixed value of the control parameter lambda, a given operator can behave as "more than one" family of the conventional connectives depending on the input values. We present the Q-aggregate in application to a real-valued signal processing task, with an optimization algorithm, so that the parameters of the operators can be tuned automatically. The Q-aggregate operator is tested on electrocardiogram (EKG) data. The experiments show that the proposed model can be used to map input signals to their corresponding target signals through learning.

Research paper insights: Q-Aggregates: The smallest universal nonlinear connective operators. Available from: https://www.researchgate.net/publication/4304045_Q-Aggregates_The_smallest_universal_nonlinear_connective_operators [accessed Apr 5, 2017].