Swarm Intelligence Procedures Using Meyer Wavelets as a Neural Network for the Novel Fractional Order Pantograph Singular System

Sabir, Zulqurnain; Raja, Muhammad Asif Zahoor; Guirao, Juan Luis García; Saeed, Tareq

doi:10.3390/fractalfract5040277

Cited by 8 publications

(4 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This demanded a newer way of looking at the problem and, today, we can employ evolutionary computing to evolve the architectures of ANNs in an automated fashion. This is known as a neural architecture search (NAS) [24].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Evolving CNN with Paddy Field Algorithm for Geographical Landmark Recognition

et al. 2022

View full text Add to dashboard Cite

Convolutional Neural Networks (CNNs) operate within a wide variety of hyperparameters, the optimization of which can greatly improve the performance of CNNs when performing the task at hand. However, these hyperparameters can be very difficult to optimize, either manually or by brute force. Neural architecture search or NAS methods have been developed to address this problem and are used to find the best architectures for the deep learning paradigm. In this article, a CNN has been evolved with a well-known nature-inspired metaheuristic paddy field algorithm (PFA). It can be seen that PFA can evolve the neural architecture using the Google Landmarks Dataset V2, which is one of the toughest datasets available in the literature. The CNN’s performance, when evaluated based on the accuracy benchmark, increases from an accuracy of 0.53 to 0.76, which is an improvement of more than 40%. The evolved architecture also shows some major improvements in hyperparameters that are normally considered to be the best suited for the task.

show abstract

Section: Convolutional Neural Networkmentioning

confidence: 99%

Evolving CNN with Paddy Field Algorithm for Geographical Landmark Recognition

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In the previous paper [24], CNN was used to determine the accurate variable measure and fault-tolerant edge dimension of stupid tripod structure. Meyer wavelet can be regarded as CNN [25]. This new CNN can find the numerical solution of fractional pantograph singular system.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Search and Energy-Saving Continuous Tracking Algorithm for Underwater Targets Based on Prediction and Neural Network

Liu

2022

JMSE

View full text Add to dashboard Cite

Underwater target search and tracking has become a technical hotspot in underwater sensor networks (UWSNs). Unfortunately, the complex and changeable marine environment creates many obstacles for localization and tracking. This paper proposes an automatic search and energy-saving continuous tracking algorithm for underwater targets based on prediction and neural network (ST-BPN). Firstly, the network contains active sensor nodes that can transmit detection signal. When analyzing the reflected signal spectrum, a modified convolutional neural network M-CNN is built to search the target. Then, based on the relationship between propagation delay and target location, a localization algorithm which can resist the influence of clock asynchrony LA-AIC is designed. Thirdly, a scheme based on consensus filtering TS-PSMCF is used to track the target. It is worth mentioning that a predictive switching mechanism, PSM, is added to the tracking process to adjust the working state of nodes. Simulation results show that the recognition accuracy of M-CNN is as high as 99.7%, the location accuracy of LA-AIC is 92.3% higher than that of traditional methods, and the tracking error of TS-PSMCF is kept between 0 m and 5 m.

show abstract

“…Other spectral methods and recent research about numerical treatments of pantograph equations exist in Refs. [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Theta‐Method for Pantograph‐Type Delay Differential Equations

Rihan

2022

Complexity

View full text Add to dashboard Cite

The pantograph equation arises in electrodynamics as a delay differential equation (DDE). In this article, we provide the ϑ -method for numerical solutions of pantograph equations. We investigate the stability conditions for the numerical schemes. The theoretical results are verified by numerical simulations. The theoretical results and numerical simulations show that implicit or partially implicit ϑ -methods, with ϑ > 1 / 2 , are effective in resolving stiff pantograph problems.

show abstract

Swarm Intelligence Procedures Using Meyer Wavelets as a Neural Network for the Novel Fractional Order Pantograph Singular System

Cited by 8 publications

References 63 publications

Evolving CNN with Paddy Field Algorithm for Geographical Landmark Recognition

Evolving CNN with Paddy Field Algorithm for Geographical Landmark Recognition

An Automatic Search and Energy-Saving Continuous Tracking Algorithm for Underwater Targets Based on Prediction and Neural Network

An Analysis of the Theta‐Method for Pantograph‐Type Delay Differential Equations

Contact Info

Product

Resources

About