Access the full text.
Sign up today, get DeepDyve free for 14 days.
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.
Applied Intelligence – Springer Journals
Published: Jun 1, 2018
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.