Adaptive Memory-enhanced Time Delay Reservoir and Its Memristive Implementation
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Shi:ieeeTC,
-
author = "Xinming Shi and Leandro L. Minku and Xin Yao",
-
title = "Adaptive Memory-enhanced Time Delay Reservoir and Its
Memristive Implementation",
-
journal = "IEEE Transactions on Computers",
-
year = "2022",
-
volume = "71",
-
number = "11",
-
pages = "2766--2777",
-
keywords = "genetic algorithms, genetic programming, Time Delay
Reservoir, memristor, time series prediction",
-
ISSN = "0018-9340",
-
DOI = "doi:10.1109/TC.2022.3173151",
-
size = "11 pages",
-
abstract = "Time Delay Reservoir (TDR) is a hardware-friendly
machine learning approach from two perspectives. First,
it can prevent the connection overhead of neural
networks with increasing neurons. Second, through its
dynamic system representation, it can also be
implemented in hardware by different systems. However,
it performs poorly on tasks that involve long-term
dependency. In this work, we first introduce a
higher-order delay unit, which is capable to accumulate
and transfer the long history states in an adaptive
manner to further enhance the reservoir memory.
Particle Swarm Optimisation is applied to optimize the
enhanced degree of memory adaptivity and also decouple
the different given tasks. Our experiments demonstrate
its superiority both for short- and long-term memory
datasets over seven existing approaches. In light of
hardware-friendly feature of TDR, we further propose a
memristive implementation of our adaptive
memory-enhanced TDR, where a dynamic memristor and the
memristor-based delay element are applied to construct
the reservoir. Through circuit simulation, the
feasibility of our proposed memristive implementation
is verified. The comparisons with different hardware
reservoirs show that our proposed memristive
implementation is effective both for short- and
long-term memory datasets, while exhibiting benefits in
terms of smaller circuit area and lower power
consumption compared with traditional hardware
reservoirs.",
-
notes = "also known as \cite{9773007}",
- }
Genetic Programming entries for
Xinming Shi
Leandro L Minku
Xin Yao
Citations