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Active Learning Method (ALM) is one of the powerful tools in soft computingthat is inspired by human brain capabilities in processing complicatedinformation. ALM, which is in essence an adaptive fuzzy learning method, modelsa Multi-Input Single-Output (MISO) system with several Single-InputSingle-Output (SISO) subsystems. Ink Drop Spread (IDS) operator, which is themain processing engine of this method, extracts useful features from the datawithout complicated computations and provides stability and convergence aswell. Despite great performance of ALM in applications such as classification,clustering, and modelling, an efficient hardware implementation has remained achallenging problem. Large amount of memory required to store the informationof IDS planes as well as the high computational cost of the IDS computingsystem are two main barriers to ALM becoming more popular. In this paper, anovel learning method is proposed based on the idea of IDS, but with a novelapproach that eliminates the computational cost of IDS operator. Unliketraditional approaches, our proposed method finds functions to describe the IDSplane 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 inferenceengine of ALM, are then extracted from IDS planes with minimum amount of memoryusage and power consumption. Our proposed algorithm is fully compatible withmemristor-crossbar implementation that leads to a significant decrease in thenumber of required memristors (from O(n^2) to O(3n)). Simpler algorithm andhigher speed make our algorithm suitable for applications where real-timeprocess, low-cost and small implementation are paramount. Applications inclustering and function approximation are provided, which reveals the effectiveperformance of our proposed algorithm.

Fast IDS Computing System Method and its Memristor Crossbar-based Hardware Implementation