Optimal design of Cosine Modulated Filter Banks using quantum-behaved particle swarm optimization algorithm

Abstract

In the paper, a new method for designing Cosine Modulated Filter Banks (CMFB) is proposed, the prototype filter is designed by optimization method. Firstly, the method appropriately relaxes the limit conditions for perfect reconstruction, the design problem of CMFB is formulated as a nonlinear and unconstrained optimization of an objective function, which stopband cutoff frequency is fixed, passband cutoff frequency is adjusted to minimize the cost function which satisfies reconstruction condition, and directly designs the prototype filter. The simulation results illustrate the proposed method and its improvement over other methods in terms of amplitude distortion, aliasing distortion, signal to noise ratio and reconstruction performance.

An Efficient Method for Designing Multiplier-Less Non-uniform Filter Bank Based on Hybrid Method Using CSE Technique

“…Evolutionary algorithms, also referred as nature's inspired mathematical algorithm, are based on the evolutionary process of species, i.e., cognition behavior of swarms such as bird flocking, swarming of social insects, fish schooling and forming colony of bees [21,27,29,32,38]. The motivation behind the exertion of these algorithms for optimization is due to their unique mechanism of solving their complex linear or nonlinear cost function in multidimensional and multi-parametric environment.…”

“…The position of particle is selected on the basis of iterative updation in fitness function. Modification in position of each particle is based on their current positions and current velocities as [21,27,29,32,38]:…”

“…(4), V i j represents the velocity of ith particle in jth dimension at kth time step, and r 1 and r 2 are the random generated numbers between 0 and 1. C 1 is a cognitive coefficient that defines the attraction toward its p best position, while C 2 is a social coefficient, which determines the influence among swarms [21,27,29,32,38]. In Eq.…”

“…Employed bees are assigned with food source through which fitness function is evaluated. New food source is determined by its current position to the neighborhood position as [19,21,27,29,32,38]:…”

“…In this context, numerous design methods for efficient design of NUFB have been reported such as iterative methods, in which the cutoff frequency (ω c ) is linearly optimized for perfect reconstruction [17,22,30,31,37]. Recently, several evolutionary algorithms such as cuckoo search optimization [21], quantum behaved PSO [32], and memetic algorithm [38] have been exploited for 2 G 0 (z) 2 Processing Unit 2 2 H 0 (z)…”