In artificial intelligence (AI), proposing an efficient algorithm with an appropriate hardware implementation has always been a challenge because of the well-accepted fact that AI hardware implementations should ideally be comparable to biological systems in terms of hardware area. Active learning method (ALM) is a fuzzy learning algorithm inspired by human brain computations. Unlike traditional algorithms, which employ complicated computations, ALM tries to model human brain computations using qualitative and behavioral descriptions of the problem. The main computational engine in ALM is the ink drop spread (IDS) operator, but this operator imposes high memory requirements and computational costs, making the ALM algorithm and its hardware implementation unsuitable for some of the applications. This paper proposes an adaptive alternative method for implementing the IDS operator; a method which results in a marked reduction in the algorithm’s computational complexity and in the amount of memory required and hardware. To check its validity and performance, the method was used to carry out modeling and pattern classification tasks. This paper used challenging and real-world datasets and compared with well-known algorithms (adaptive neuro-fuzzy inference system and multi-layer perceptron) in software simulation and hardware implementation. Compared to traditional implementations of the ALM algorithm and other learning algorithms, the proposed FPGA implementation offers higher speed, less hardware, and improved performance, thus facilitating real-time application. Our ultimate goal in this paper was to present a hardware implementation with an on-chip training that allows it to adapt to its environment without dependency on the host system (on-chip learning).