Crisscross Optimization Algorithm for the Designing of Quadrature Mirror Filter Bank

Chauhan, Sumika; Singh, Manmohan; Agarwal, Ashwani Kumar

doi:10.1109/icct46177.2019.8968779

Cited by 16 publications

(3 citation statements)

References 39 publications

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“…Here, G is bivariate standard normal density, h represents kernel height, and N indicates number of samples. The p (g) and p (j) are marginal of p (z) and accordingly, the KEMI is given in equation (36):…”

Section: Kernel Estimate For Mutual Information (Kemi)mentioning

confidence: 99%

“…Swarm intelligence approaches are inspired by nature and the behaviour of numerous species such as ants, swarms, fish schooling, and so on [1,[27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44], are used to solve a variety of problems. The African vulture optimization algorithm (AVOA) is one of them that has received special attention because of its ease of implementation, low storage and computational needs, faster convergence due to continuous search space reduction, and fewer choices criteria.…”

Section: Introductionmentioning

confidence: 99%

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An ameliorated African vulture optimization algorithm to diagnose the rolling bearing defects

Vashishtha

Chauhan

Kumar

et al. 2022

Meas. Sci. Technol.

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In this work, a novel bearing fault identification scheme making use of deep learning has been proposed. Initially, the raw vibration signal is passed through a time-varying filter based empirical mode decomposition (TVF-EMD) to obtain different modes. Filter parameters of TVF-EMD are optimized by a newly developed optimization algorithm i.e., ameliorated African vulture optimization algorithm (A-AVOA). The Kernel estimate for mutual information (KEMI) has been considered as the fitness index for the developed optimization algorithm. The mode having the least value of fitness index is known as a prominent mode from which sensitive features representing different bearing conditions are extracted. These extracted features help in preparing the data matrix which is further utilised to build fuzzy-based classification models. The results obtained revealed that the linguistic hedge neuro-fuzzy classifier obtained maximum performance with the least computational time. The comparison of the developed method has also been done with other classification models viz., KNN, SVM, ELM and random forest that revealed the superiority of the developed method.

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Section: Kernel Estimate For Mutual Information (Kemi)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An ameliorated African vulture optimization algorithm to diagnose the rolling bearing defects

Vashishtha

Chauhan

Kumar

et al. 2022

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Numerous metaheuristic algorithms are available in the literature to optimize the parameters of controllers such as slime mould algorithm (SMA) [13], evolutionary-based algorithm [14,15], coral reef optimizer (CRO) [16], grey wolf optimization (GWO) and its variants [17][18][19][20], Harris hawk optimization [21,22], volleyball premier league [23], whale optimization [24][25][26], ant lion optimizer (ALO) [27,28], salp swarm algorithm (SSA) [29][30][31], sine-cosine algorithm (SCA) [32][33][34], gravitational search algorithm (GSA) [35,36], multi-verse optimizer [37], crisscross optimization [38,39], artificial bee colony (ABC) [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Modified ant colony optimization based PID controller design for coupled tank system

Chauhan

Singh

2021

Eng. Res. Express

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Most of the optimization algorithm suffers from slow convergence and poor solution accuracy due to nonlinearity characteristics of the problem. This paper proposes a meta-heuristic method, namely the modified Ant Colony Optimization (m-ACO) technique to address these issues. The conventional ant colony optimization algorithm is improved by using multiple random initializations to increase the probability of getting a better start population and hence increase the possibility to achieve near-global optimum. The efficacy of the proposed algorithm is tested on classical benchmark functions and on the PID controllers. The performance of controllers depends on the selection of tuning parameters that are optimized to obtain an optimum solution. The m-ACO technique is used to optimize parameters of PID controller for Coupled Tank System. It is one of the benchmark control problems due to its nonlinear characteristics. The obtained results of the proposed algorithm are compared with the Zeigler Nichols tuned PID controller, and PSO tuned PID controller in terms of transient performances including rise-time, peak overshoot and performance parameters such as Integral Square Error (ISE) and Integral Absolute Error (IAE). The proposed method is also tested for showing effectiveness in set-point tracking and disturbance rejection.

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Design of a Two-Channel Quadrature Mirror Filter Bank Through a Diversity-Driven Multi-Parent Evolutionary Algorithm

Chauhan

Singh

Aggarwal

2021

Circuits Syst Signal Process

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Crisscross Optimization Algorithm for the Designing of Quadrature Mirror Filter Bank

Cited by 16 publications

References 39 publications

An ameliorated African vulture optimization algorithm to diagnose the rolling bearing defects

An ameliorated African vulture optimization algorithm to diagnose the rolling bearing defects

Modified ant colony optimization based PID controller design for coupled tank system

Design of a Two-Channel Quadrature Mirror Filter Bank Through a Diversity-Driven Multi-Parent Evolutionary Algorithm

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