Intensive Noise-Tolerant Zeroing Neural Network Based on a Novel Fuzzy Control Approach

To overcome the disadvantages of the current zeroing neural network (ZNN) in noise tolerance, this article first proposes an intensive noise-tolerant ZNN (INT-ZNN) by introducing a novel fuzzy control approach (FCA). This FCA is designed dexterously according to the variation of two errors related to the INT-ZNN. Thus, the most feature of the INT-ZNN is that the added fuzzy control can inherently restrain the various noises. Compared with the previous noise-tolerant ZNN derived by the integral design formula, the INT-ZNN with a much simpler structure can tolerate the noise in finite/fixed time. That is, the INT-ZNN activated by nonlinear functions possesses finite/fixed-time convergence while suppressing the noise, which is guaranteed by the presented theorems. Besides, it also theoretically proves that the INT-ZNN has global stability under the interference of noise. In the simulative experiment, the INT-ZNN is used to solve the time-varying Sylvester matrix equation problem and the experimental results verify the excellent noise-tolerance of the INT-ZNN. Meanwhile, the INT-ZNN is successfully applied to image processing.