Phase locking of ultra-low power consumption stochastic magnetic bits induced by colored noise

Liao, Zhiqiang; Ma, Ke; Tang, Siyi; Sarker, Shamim; Yamahara, Hiroyasu; Tabata, Hitoshi

doi:10.1016/j.chaos.2021.111262

Cited by 19 publications

(10 citation statements)

References 47 publications

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“…(4) For the fractional-order six-dimensional chaotic power system model under consideration, the estimated parameters were very close to the real values under both ideal and noisy conditions, showing the effectiveness, robustness and versatility of this algorithm. However, the impact of real-world noise (such as color noise [34] and Levy noise [35]) on power systems is worth studying and is another promising direction.…”

Section: Discussionmentioning

confidence: 99%

Parameter Estimation of Fractional-Order Chaotic Power System Based on Lens Imaging Learning Strategy State Transition Algorithm

Fan

2023

IEEE Access

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：Parameter identification of fractional-order chaotic power systems is a multidimensional optimization problem that plays a decisive role in the synchronization and control of fractional-order chaotic power systems. In this paper, a state transition algorithm based on the lens imaging learning strategy is proposed for parameter identification of fractional-order chaotic power systems. Taking a fractional-order six-dimensional chaotic power system mathematical model as an example, the mathematical model and chaotic state are analyzed. First, the Tent chaotic mapping is used to initialize the population, thus increasing population diversity. The randomness and ergodicity of the Tent chaotic sequence are used to enhance the global searching ability of the algorithm. Second, a maturity index is employed to determine population maturity. The lens imaging learning strategy is used to suppress the premature convergence of the state transition algorithm effectively and help the population jump out of local optima. Finally, the improved state transition algorithm is used to identify the parameters of the fractional-order six-dimensional chaotic power system model. The proposed improved state transition algorithm shows high estimation accuracy and convergence speed, and is superior to the traditional state transition and particle swarm optimization algorithms. The simulation results show that the parameters of the fractional-order chaotic power system are identified accurately even in the presence of white noise, demonstrating the strong robustness and versatility of the proposed algorithm.

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Section: Discussionmentioning

confidence: 99%

Parameter Estimation of Fractional-Order Chaotic Power System Based on Lens Imaging Learning Strategy State Transition Algorithm

Fan

2023

IEEE Access

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“…There are still some shortcomings in this paper. In the future, we will further study fractional power system model due to the following reasons: As the high‐dimensional system is closer to the actual power system, it is practically useful to study the chaos control of the fractional‐order high‐dimensional power system. It is valuable to further explore the control research for the noisy background in real environments, such as coloured noise [41] and Levy noise [42]. …”

Section: Discussionmentioning

confidence: 99%

“…2. It is valuable to further explore the control research for the noisy background in real environments, such as coloured noise [41] and Levy noise [42].…”

Section: Discussionmentioning

confidence: 99%

Finite time adaptive synchronous control for fractional‐order chaotic power systems

Wang

et al. 2023

IET Generation Trans & Dist

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As a complex non‐linear system, the chaotic oscillation of power system seriously threatens the safe and stable operation of power grids. In this paper, a finite time adaptive synchronization control method is proposed to mitigate the problem of chaotic oscillation in the power system. The proposed method can realize chaos control and parameter identification by completely synchronizing the fractional‐order chaotic power system with the stable fractional‐order power system to identify parameters within a finite time. The fractional Lyapunov stability theory is used for numerical simulation. Theoretical and simulation results show that this method can effectively stabilize the system in a finite time. It is proved that compared with the adaptive synchronous control method, the control method is simpler in design, shorter in action time, and more meaningful in engineering practice.

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“…However, the separation of memory and computation of traditional computers restricts the embedded interaction between artificial intelligence systems and modern devices and significantly increases the power consumption of computing 1 . In recent years, to overcome this bottleneck, researchers have developed neuromorphic devices with high integration and low power consumption by simulating the function of neurons and synapses 2 – 5 . Among the existing neuromorphic devices, a framework called reservoir computing has been favored by researchers 6 – 8 .…”

Section: Introductionmentioning

confidence: 99%

Short-term memory capacity analysis of Lu3Fe4Co0.5Si0.5O12-based spin cluster glass towards reservoir computing

Liao

Yamahara

Terao

et al. 2023

Sci Rep

Self Cite

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Reservoir computing is a brain heuristic computing paradigm that can complete training at a high speed. The learning performance of a reservoir computing system relies on its nonlinearity and short-term memory ability. As physical implementation, spintronic reservoir computing has attracted considerable attention because of its low power consumption and small size. However, few studies have focused on developing the short-term memory ability of the material itself in spintronics reservoir computing. Among various magnetic materials, spin glass is known to exhibit slow magnetic relaxation that has the potential to offer the short-term memory capability. In this research, we have quantitatively investigated the short-term memory capability of spin cluster glass based on the prevalent benchmark. The results reveal that the magnetization relaxation of Co, Si-substituted Lu3Fe5O12 with spin glass behavior can provide higher short-term memory capacity than ferrimagnetic material without substitution. Therefore, materials with spin glass behavior can be considered as potential candidates for constructing next-generation spintronic reservoir computing with better performance.

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Phase locking of ultra-low power consumption stochastic magnetic bits induced by colored noise

Cited by 19 publications

References 47 publications

Parameter Estimation of Fractional-Order Chaotic Power System Based on Lens Imaging Learning Strategy State Transition Algorithm

Parameter Estimation of Fractional-Order Chaotic Power System Based on Lens Imaging Learning Strategy State Transition Algorithm

Finite time adaptive synchronous control for fractional‐order chaotic power systems

Short-term memory capacity analysis of Lu3Fe4Co0.5Si0.5O12-based spin cluster glass towards reservoir computing

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