A Novel Alternative Operator for Ink Drop Spread (IDS) in Active Learning Method (ALM)

Active Learning method is one of the fuzzy learning methods inspired from the computation of human brain. Ink drop spread operator is the main computational engine in ALM which without using any complex formula, seeks for the relationship between the output and the inputs of the system. One of the challenges of IDS operator is that not only a large memory is required for implementing IDS planes, but also extracting features imposes high computational costs. In this paper, one learning method, as a replacement for IDS operator, is represented that considerable reduces the computational complexity. The represented algorithm defines the IDS planes with only two memory vectors and solves the problem of huge wastage of memory in these planes. This algorithm starts to learn the available patterns in learning data to find two features of Narrow Path and Spread in planes which are the most important concepts in the level of active inference learning with minimum computational and time cost. To investigate the accuracy of algorithm’s performance, some simulations in modelling and classification have been done on standard data sets. Time and accuracy of proposed algorithm is compared with traditional ALM, MLP and ANFIS algorithms.