A novel adaptive learning algorithm for low-dimensional feature space using memristor-crossbar implementation and on-chip training

Proposing an efficient algorithm with an appropriate hardware implementation has always been an interesting and a rather challenging field of research in Artificial Intelligence (AI). Fuzzy logic is one of the techniques that can be used for accurate and high-speed modeling as well as controlling complex and nonlinear systems. The defuzzification process during the test phase as well as the repetitive processes in order to find the optimal parameters during the training phase, lead to some serious limitations in real-time applications and hardware implementation of these algorithms. The proposed algorithm employs Ink Drop Spread (IDS) concept to mimic the functionality of human brain. In this algorithm, learning is based on the distance between training data and the learning plane. Unlike previous algorithms, the new one does not need to partition nor the input space neither the calculation of IDS plane features. Besides, the output is obtained without using the optimization methods. The proposed algorithm is a numerical foundation that does not encounter a processing problem and lack of memory in dealing with different datasets consisting of a large number of samples. This algorithm can be efficiently implemented on memristor crossbar/CMOS hardware platform in terms of area and speed. This hardware has the ability to learn and adapt to the environment regardless of a host system (on-chip learning capability). Finally, to verify the performance of the proposed algorithm, it has been compared to ALM, RBF and PNN algorithms which have a similar functionality.