2011/09/21 by Farnood Merrikh-Bayat, Merrikh-Bayat, Farnood, Saeed Bagheri Shouraki +1
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #Artificial Intelligence (cs.AI) #CCD and CMOS Imaging Sensors #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Neural dynamics and brain function #cs.AI #cs.AR #cs.LG #cs.NE
paper · pdf · doi:10.48550/arxiv.1109.4609
21 pages, 6 figures, submitted to IET Image Processing Journal
arxiv created 2011/09/21 · openalex publication_date 2011/09/21 · arxiv updated 2011/09/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Fuzzy inference systems always suffer from the lack of efficient structures or platforms for their hardware implementation. In this paper, we tried to overcome this problem by proposing new method for the implementation of those fuzzy inference systems which use fuzzy rule base to make inference. To achieve this goal, we have designed a multi-layer neuro-fuzzy computing system based on the memristor crossbar structure by introducing some new concepts like fuzzy minterms. Although many applications can be realized through the use of our proposed system, in this study we show how the fuzzy XOR function can be constructed and how it can be used to extract edges from grayscale images. Our memristive fuzzy edge detector (implemented in analog form) compared with other common edge detectors has this advantage that it can extract edges of any given image all at once in real-time.