RANDOM WEIGHTED-PSEUDO-ALMOST PERIODICITY AND COSTING CONTROLS FOR TIME-SPACE DIFFERENCING STOCHASTIC CNNS WITH INFINITE DISTRIBUTED DELAYS

Ting Yuan; Tianwei Zhang

doi:10.11948/20250169

2026 Volume 16 Issue 4

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Article navigation > Journal of Applied Analysis & Computation > 2026 > 16(4): 1643-1667

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Ting Yuan, Tianwei Zhang. RANDOM WEIGHTED-PSEUDO-ALMOST PERIODICITY AND COSTING CONTROLS FOR TIME-SPACE DIFFERENCING STOCHASTIC CNNS WITH INFINITE DISTRIBUTED DELAYS[J]. Journal of Applied Analysis & Computation, 2026, 16(4): 1643-1667. doi: 10.11948/20250169

Citation:

Ting Yuan, Tianwei Zhang. RANDOM WEIGHTED-PSEUDO-ALMOST PERIODICITY AND COSTING CONTROLS FOR TIME-SPACE DIFFERENCING STOCHASTIC CNNS WITH INFINITE DISTRIBUTED DELAYS[J]. Journal of Applied Analysis & Computation, 2026, 16(4): 1643-1667. doi: 10.11948/20250169

RANDOM WEIGHTED-PSEUDO-ALMOST PERIODICITY AND COSTING CONTROLS FOR TIME-SPACE DIFFERENCING STOCHASTIC CNNS WITH INFINITE DISTRIBUTED DELAYS

Ting Yuan^1,,
Tianwei Zhang^2, ,

1.
Department of Basic Education, Yunnan Tourism College, Kunming 650221, China
2.
School of Mathematics and Statistics, Yunnan University, Yunnan, Kunming 650500, China

Author Bio: Email: hbtyuan@163.com(T. Yuan)
Corresponding author: Email: zhang@ynu.edu.cn(T. Zhang)
Fund Project: The authors were supported by National Natural Science Foundation of China (62463032) and Yunnan Fundamental Research Projects (202501AT070209)

Abstract

Abstract

This paper investigates weighted pseudo $ \theta $-almost periodicity in discrete-time and -space stochastic competitive neural networks with time delays (IDD-SCNNs) driven by two-sided Brownian motions. Using the variation-of-constants formula and fixed-point theory, new results are established on the existence, uniqueness, and boundedness of weighted pseudo almost periodic solutions. A guaranteed cost controller is further designed for the drive–response–error IDD-SCNNs to ensure global mean-square exponential stability. The study also reveals how spatial diffusion influences stochastic dynamics and control performance. These findings extend existing results on stochastic neural networks to the discrete spatio-temporal framework.
- Recurrent oscillation /
- weighted-pseudo dynamics /
- competitive neural network /
- exponential convergence /
- guaranteed cost
MSC: 34C27, 34K14

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References

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