  Active Learning Method (ALM) is one of the powerful tools in soft computing
that is inspired by human brain capabilities in processing complicated
information. ALM, which is in essence an adaptive fuzzy learning method, models
a Multi-Input Single-Output (MISO) system with several Single-Input
Single-Output (SISO) subsystems. Ink Drop Spread (IDS) operator, which is the
main processing engine of this method, extracts useful features from the data
without complicated computations and provides stability and convergence as
well. Despite great performance of ALM in applications such as classification,
clustering, and modelling, an efficient hardware implementation has remained a
challenging problem. Large amount of memory required to store the information
of IDS planes as well as the high computational cost of the IDS computing
system are two main barriers to ALM becoming more popular. In this paper, a
novel learning method is proposed based on the idea of IDS, but with a novel
approach that eliminates the computational cost of IDS operator. Unlike
traditional approaches, our proposed method finds functions to describe the IDS
plane that eliminates the need for large amount of memory to a great extent.
Narrow Path and Spread, which are two main features used in the inference
engine of ALM, are then extracted from IDS planes with minimum amount of memory
usage and power consumption. Our proposed algorithm is fully compatible with
memristor-crossbar implementation that leads to a significant decrease in the
number of required memristors (from O(n^2) to O(3n)). Simpler algorithm and
higher speed make our algorithm suitable for applications where real-time
process, low-cost and small implementation are paramount. Applications in
clustering and function approximation are provided, which reveals the effective
performance of our proposed algorithm.
