Convergence analysis of the ε NSRLMMN algorithm
نویسندگان
چکیده
In this work, the ε−normalized sign regressor least mean mixed-norm (NSRLMMN) adaptive algorithm is proposed. The proposed algorithm exhibits increased convergence rate as compared to the least mean mixed-norm (LMMN) and the sign regressor least mean mixed-norm (SRLMMN) algorithms. Also, the steady-state analysis and convergence analysis are presented. Moreover, the proposed ε−NSRLMMN algorithm substantially reduces the computational load, a major drawback of the ε−normalized least mean mixednorm (NLMMN) algorithm. Finally, simulation results are presented to support the theoretical findings.
منابع مشابه
On the Convergence Analysis of Gravitational Search Algorithm
Gravitational search algorithm (GSA) is one of the newest swarm based optimization algorithms, which has been inspired by the Newtonian laws of gravity and motion. GSA has empirically shown to be an efficient and robust stochastic search algorithm. Since introducing GSA a convergence analysis of this algorithm has not yet been developed. This paper introduces the first attempt to a formal conve...
متن کاملOn the Convergence Analysis of Gravitational Search Algorithm
Gravitational search algorithm (GSA) is one of the newest swarm based optimization algorithms, which has been inspired by the Newtonian laws of gravity and motion. GSA has empirically shown to be an efficient and robust stochastic search algorithm. Since introducing GSA a convergence analysis of this algorithm has not yet been developed. This paper introduces the first attempt to a formal conve...
متن کاملA filter-based artificial fish swarm algorithm for constrained global optimization: theoretical and practical issues
This paper presents a filter-based artificial fish swarm algorithm for solving nonconvex constrained global optimization problems. Convergence to an ε-global minimizer is guaranteed. At each iteration k, the algorithm requires a (ρ(k),ε(k))-global minimizer of a bound constrained bi-objective subproblem, where as k→ ∞, ρ(k)→ 0 gives the constraint violation tolerance and ε(k)→ ε is the error bo...
متن کاملStrong convergence of modified iterative algorithm for family of asymptotically nonexpansive mappings
In this paper we introduce new modified implicit and explicit algorithms and prove strong convergence of the two algorithms to a common fixed point of a family of uniformly asymptotically regular asymptotically nonexpansive mappings in a real reflexive Banach space with a uniformly G$hat{a}$teaux differentiable norm. Our result is applicable in $L_{p}(ell_{p})$ spaces, $1 < p
متن کاملKrylov Approximation of Linear ODEs with Polynomial Parameterization
We propose a new numerical method to solve linear ordinary differential equations of the type ∂u ∂t (t, ε) = A(ε)u(t, ε), where A : C → C is a matrix polynomial with large and sparse matrix coefficients. The algorithm computes an explicit parameterization of approximations of u(t, ε) such that approximations for many different values of ε and t can be obtained with a very small additional compu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2012