This paper applied a novel global meta-heuristic optimization algorithm, cuckoo search algorithm (CSA) to determine optimal coefficients of a fractional delay-infinite impulse response (FD-IIR) filter and trying to meet the ideal frequency response characteristics. Since fractional delay-IIR filter design is a multi-modal optimization problem, it cannot be computed efficiently using conventional gradient based optimization techniques. A weighted least square (WLS) based fitness function is used to improve the performance to a great extent. FD-IIR filters of different orders have been designed using the CSA. The simulation results of the proposed CSA based approach have been compared to those of well accepted evolutionary algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performance of the CSA based FD-IIR filter is superior to those obtained by GA and PSO. The simulation and statistical results affirm that the proposed approach using CSA outperforms GA and PSO, not only in the convergence rate but also in optimal performance of the designed FD-IIR filter (i.e., smaller magnitude error, smaller phase error, higher percentage improvement in magnitude and phase error, fast convergence rate). The absolute magnitude and phase error obtained for the designed 5th order FD-IIR filter are as low as 0.0037 and 0.0046, respectively. The percentage improvement in magnitude error for CSA based 5th order FD-IIR design with respect to GA and PSO are 80.93% and 74.83% respectively, and phase error are 76.04% and 71.25%, respectively.

Optimal fractional delay-IIR filter design using cuckoo search algorithm.

This paper presents a global heuristic search optimization technique known as Gravitation Search Algorithm (GSA) for the design of 8th order Infinite Impulse Response (IIR), low pass (LP), high pass (HP), band pass (BP) and band stop (BS) filters considering various non-linear characteristics of the filter design problems. This paper also adopts a novel fitness function in order to improve the stop band attenuation to a great extent. In GSA, law of gravity and mass interactions among different particles are adopted for handling the non-linear IIR filter design optimization problem. In this optimization technique, searcher agents are the collection of masses and interactions among them are governed by the Newtonian gravity and the laws of motion. The performances of the GSA based IIR filter designs have proven to be superior as compared to those obtained by real coded genetic algorithm (RGA) and standard Particle Swarm Optimization (PSO). Extensive simulation results affirm that the proposed approach using GSA outperforms over its counterparts not only in terms of quality output, i.e., sharpness at cut-off, smaller pass band ripple, higher stop band attenuation, but also the fastest convergence speed with assured stability.

Gravitation search algorithm: Application to the optimal IIR filter design

The goodness of Infinite Impulse Response (IIR) digital filters design depends on pass band ripple, stop band ripple and transition band values. The main problem is defining a suitable error fitness function that depends on these parameters. This fitness function can be optimized by search algorithms such as evolutionary algorithms. This paper proposes an intelligent algorithm for the design of optimal 8th order IIR filters. The main contribution is the design of Fuzzy Inference Systems able to tune key parameters of a revisited version of the Gravitational Search Algorithm (GSA). In this way, a Fuzzy Gravitational Search Algorithm (FGSA) is designed. The optimization performances of FGSA are compared with those of Differential Evolution (DE) and GSA. The results show that FGSA is the algorithm that gives the best compromise between goodness, robustness and convergence rate for the design of 8th order IIR filters. Moreover, FGSA assures a good stability of the designed filters.

A Fuzzy Gravitational Search Algorithm to Design Optimal IIR Filters

In this paper, a new design method for digital infinite impulse response &#x0028 IIR &#x0029 filters with nearly linear-phase response is presented using fractional derivative constraints &#x0028 FDCs &#x0029 . In the proposed method, design problem of an IIR filter is constructed as the minimization of phase error between the desired and designed phase response of an allpass filter &#x0028 APF &#x0029 such that the designed lowpass filter &#x0028 LPF &#x0029 or highpass filter &#x0028 HPF &#x0029 yields less passband &#x0028 ep &#x0029 , and stopband errors &#x0028 es &#x0029 with optimal stopband attenuation &#x0028 As &#x0029 . In order to have accurate passband &#x0028 pb &#x0029 response, FDCs are imposed on appropriate reference frequency, where the optimality of these FDCs are ensured by using a new greedy based sorting mechanism. The simulated results reflect the efficiency of the proposed method in term of improved passband response along with better transition width. However, small reduction in As is observed within the allowable limit, when compared to non-fractional design approach, but the designed filter remains immune to wordlength &#x0028 WL &#x0029 effect.

A new design approach for nearly linear phase stable IIR filter using fractional derivative

A solution methodology for the design of digital infinite impulse response (II R) filter considering multiple conflicting objectives has been proposed. The nucleus of the method is that multiple objectives can be attuned suitably, to optimize the performance in pass bands, stop bands and transition bands of IIR digital filters. The p roposed method uses multiobjective optimization approach for designing stable IIR filter using real-coded genetic algorithm (RCGA). Digital IIR filters are designed by minimizing magnitude response and phase response simultaneously, using weighted sum approach. The value of weights are searched using RCGA along with filter coefficients thus assigning different weight vector to each indiv idual population thereby finding multiple pareto-optimal solutions in one simulation run. The order of the filter is controlled by a contro l gene whose value is also optimized along with the filter coefficients to obtain optimum order of designed IIR filter. The compu tational experiments show that the proposed approach gives better digital IIR filters than the existing genetic algorithm based methods.

/pdf/real-coded-genetic-algorithm-for-design-of-iir-digital-za86na6gpm.pdf

Real Coded Genetic Algorithm for Design of IIR Digital Filter with Conflicting Objectives

Large-scale global optimization (LSGO) is a very important and challenging task in optimization domain, which is embedded in many scientific and engineering applications. Previously, the cooperative co-evolution (CC) is a usual and effective choice for LSGO problems. In this paper, aim at more fully exploring the flexibility and potential of CC strategy, an adaptive CC (ACC) is designed to handle LSGO problems. The advantages of ACC compared with the classical CC strategies are experimentally verified on a set of widely used large scale function optimization problems.

Adaptive cooperative co-evolution for large scale global optimization

The research of designing floating-point digital infinite-impulse response (IIR) filter using multi-objective optimization evolutionary algorithm (MOEA) is a newly emerged research field However, none of previous work takes notice of the fixed-point representation in the MOEA based digital IIR filter design research In this paper, we introduce fixed-point representation in a new local search operator enhanced multi-objective evolutionary algorithm (LS-MOEA), which is specifically proposed for multi-objective optimization digital IIR filter design problems, to form a new version LS-MOEA with floating-point representation (LS-MOEA-FR) The effectiveness and efficiency of LS-MOEA-FR is verified on four types of digital IIR filter design problems, including low pass (LP), high pass (HP), band pass (BP) and band stop (BS)

Fixed-point digital IIR filter design using multi-objective optimization evolutionary algorithm

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Zhengdong Li

Author Tools

Chat about Author

Papers

Adaptive cooperative co-evolution for large scale global optimization

Fixed-point digital IIR filter design using multi-objective optimization evolutionary algorithm