# Memristor Crossbar-Based Hardware Implementation of the IDS Method

@article{MerrikhBayat2011MemristorCH, title={Memristor Crossbar-Based Hardware Implementation of the IDS Method}, author={Farnood Merrikh-Bayat and Saeed Bagheri Shouraki and Ali Rohani}, journal={IEEE Transactions on Fuzzy Systems}, year={2011}, volume={19}, pages={1083-1096} }

Ink drop spread (IDS) is the engine of an active learning method, which is the methodology of soft computing. IDS, as a pattern-based processing unit, extracts useful information from a system that is subjected to modeling. In spite of its excellent potential to solve problems such as classification and modeling compared with other soft-computing tools, finding its simple and fast hardware implementation is still a challenge. This paper describes a new hardware implementation of the IDS method…

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## 46 Citations

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

- Computer ScienceArXiv
- 2016

Simpler algorithm and higher speed make the algorithm suitable for applications where real-time process, low-cost and small implementation are paramount, and reveals the effective performance of the proposed algorithm.

An Optimal Hardware Implementation for Active Learning Method Based on Memristor Crossbar Structures

- Computer ScienceIEEE Systems Journal
- 2014

A fuzzy number is extracted from each IDS plane rather than from the narrow path and the spread, which leads to a significant reduction in the hardware required to implement the inference part of the algorithm and real-time computation of the implemented hardware.

Memristor Crossbar-Based Hardware Implementation of Type-2 Fuzzy Membership Function and On-Chip Learning

- Computer Science
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The proposed implementation of type-2 fuzzy membership function has the potential to learn (On-Chip learning capability regardless of host system), and the proposed hardware is analog and can be used as a basis in the construction of evolutionary systems.

An adaptive efficient memristive ink drop spread (IDS) computing system

- Computer ScienceNeural Computing and Applications
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An abstract representation of the IDS planes is proposed which minimizes the memory requirement and the computational cost, and consequently, benefits the hardware implementation in terms of area and speed and concludes that the proposed computing structure provides a synergy between artificial neural networks and fuzzy domains.

Memristive fuzzy edge detector

- Computer ScienceJournal of Real-Time Image Processing
- 2012

This paper has designed a multi-layer neuro-fuzzy computing system based on the memristor crossbar structure by introducing a new concept called the fuzzy minterm, and shows how the fuzzy XOR function can be constructed and how it can be used to extract edges from grayscale images.

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- Computer ScienceApplied Intelligence
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This 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 and can be efficiently implemented on memristor crossbar/CMOS hardware platform in terms of area and speed.

The Neuro-Fuzzy Computing System With the Capacity of Implementation on a Memristor Crossbar and Optimization-Free Hardware Training

- Computer ScienceIEEE Transactions on Fuzzy Systems
- 2014

It is shown that neural networks are working in the same way as logical circuits when the connection between them is through the fuzzy logic, and a new neuro-fuzzy computing system is proposed that has the potential to be directly trained using the Hebbian learning rule and without the need for any optimization.

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- Computer ScienceFuzzy Sets Syst.
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Efficient neuro-fuzzy system and its Memristor Crossbar-based Hardware Implementation

- Computer ScienceArXiv
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A novel neuro-fuzzy system is proposed where its learning is based on the creation of fuzzy relations by using new implication method without utilizing any exact mathematical techniques, and this system can be a good candidate to be used for creating artificial brain.

Memristive Neuro-Fuzzy System

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Simulation results indicate that this neuro-fuzzy computing system can be a good candidate to be used for creating artificial brain and all synaptic weights in this method are always non-negative, and there is no need to adjust them precisely.

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