A Local Consensus Index Scheme for Random-Valued Impulse Noise Detection Systems

Adaptive Zeroing-Type Neural Dynamics for Solving Quadratic Minimization and Applied to Target Tracking

“…It can be generalized and concluded that i (t) globally converges to zero for each i ∈ 1, 2, ..., n. In summary, the error function (t) global converges to zero over time. In other words, the proposed RACZNN model (11) globally converges to the theoretical solution of the TDQM problem (1). The proof is thus completed.…”

“…Besides, the RACZNN model (11) inevitably is perturbed by various measurement noises in the practical application. Thereupon, the RACZNN model (11) for solving the TDQM problem (1) perturbed by noise is described as…”

“…Taking into account that convergence is a key criterion for the RACZNN model (11), we propose the following theorem and corresponding proof process to analyze the global convergence of the RACZNN model (11).…”

“…Theorem 1: Given any solvable TDQM problem (1), the computational solution vector z(t) of the proposed RACZNN model (11) globally converges to the theoretical solution of the TDQM problem (1) from any random initial state.…”

“…It is worth noting that although considerable research has been devoted to solving conventional QM problems, studies aimed explicitly at the time-dependent quadratic minimization (TDQM) problem are insufficient. Traditional solutions have serious lag errors when facing large-scale time-dependent issues, resulting in inadequate solution accuracy and even the collapse of the solution system [10], [11].…”