Parameters Estimation of Ultrasonics Echoes using the Cuckoo Search and Adaptive Cuckoo Search Algorithms

Chibane, Farid; Benammar, Abdessalem; Drai, Redouane

doi:10.23919/eusipco.2018.8553222

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…As a result, it is frequently used to solve optimization problems. However, the general CS algorithm has a problem of slow convergence speed and incomplete global search [39]. To upgrade the algorithm's performance, we optimize it with adaptive parasitic-failure probability and adaptive stepsize control.…”

Section: The Weight and Threshold Optimization Of Tmcs-enn Modelmentioning

confidence: 99%

TMCS-ENN: A Temporal Feature-Correlation Cuckoo Search-Elman Neural Network for Sugar Raw Materials Demands Prediction

et al. 2022

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The prediction of the demand for raw materials is of vital importance to modern industries. Most studies are based on traditional regression, linear programming, and other methods. Previous studies have often overlooked the characteristics of the sugar raw materials business and the influence of time factors on raw material demand, resulting in limited prediction accuracy. How to accurately predict the demand for sugar raw materials is one of the key issues for intelligent management. In view of the above problems, combined with the characteristics of the supply and demand cycle of sugar raw materials, this paper aims to predict the demand for raw materials based on their supply and demand in a real sugar company by optimizing the Elman neural network through the modified cuckoo search (MCS) algorithm with temporal features. This study proposes a temporal feature-correlation cuckoo search–Elman neural network (TMCS-ENN) for predicting the demand for sugar raw materials. The experimental results show that the accuracy of the TMCS-ENN model reaches 93.89%, a better performance than that achieved by existing models. Therefore, the study model effectively improves the accuracy of the demand forecast of sugar raw materials for companies. This output will be helpful for improving the production efficiency and automation level, as well as reducing costs.

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Section: The Weight and Threshold Optimization Of Tmcs-enn Modelmentioning

confidence: 99%

TMCS-ENN: A Temporal Feature-Correlation Cuckoo Search-Elman Neural Network for Sugar Raw Materials Demands Prediction

et al. 2022

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“…Depending on the initial starting, the EM algorithm can converge with the optimal solution with finite steps. Afterwards, the modified Artificial Bee Colony embedding [17], Adaptive Cuckoo Search [18], and the space alternating generalised expectation‐maximisation [19] are applied in the parametric solution, performing as the promotion on the initialisation guess acceleration direction optimisation and parallelisation requirements for the EM algorithm, individually. From another perspective, the base optimisation model derives the deconvolution [20] and matrix capture method [21], which utilise sparse prior information and a least‐squares cost function to obtain the time parameters directly.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised deep domain adaptation framework in remote acoustic time parametric imaging

Jun

et al. 2021

IET Signal Processing

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Intelligent waveform analysis surveying geological structures is a challenging task in remote acoustic measurement for formation detection, which has two problems: (1) time parametric imaging is disturbed by noisy environments and (2) manually annotated data for machine learning are unattainable. These restrict the deployment of advanced parameter extraction methods in imaging instruments. As a potential theory, domain adaptation makes the intelligent prediction implementable in the above situations. Hence, to counter high precision travel time extraction for acoustic imaging, a deep adaptation-picking network (DAPN) framework is proposed, which consists of three modules: signal-to-signal generator, domain adaptation encoder, and feature recognition decoder. The translation module preprocesses the different datasets to improve the training accuracy and confuses the distribution between source and target domains based on the adversarial network. The core convolution neural network achieves travel time measurement, where the backbone extracts content features with modified maximum mean discrepancy embedding. Meanwhile, the decoding structure preserves the signal features in the training process. The effectiveness and feasibility of DAPN are verified by experimental results, suggesting that DAPN accurately extracts time parameters. Compared with traditional parametric imaging schemes, the proposed method has the advantages of high accuracy and anti-noise ability.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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A Review on the Cuckoo Search Algorithm

Guerrero-Luis¹,

Valdez²,

Castillo³

2021

Studies in Computational Intelligence

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Parameters Estimation of Ultrasonics Echoes using the Cuckoo Search and Adaptive Cuckoo Search Algorithms

Cited by 6 publications

References 12 publications

TMCS-ENN: A Temporal Feature-Correlation Cuckoo Search-Elman Neural Network for Sugar Raw Materials Demands Prediction

TMCS-ENN: A Temporal Feature-Correlation Cuckoo Search-Elman Neural Network for Sugar Raw Materials Demands Prediction

Unsupervised deep domain adaptation framework in remote acoustic time parametric imaging

A Review on the Cuckoo Search Algorithm

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