Elman Neural Network‐Based Direct Lift Automatic Carrier Landing Nonsingular Terminal Sliding Mode Fault‐Tolerant Control System Design

Wu, Qilong; Zhu, Qidan; Han, Shihao

doi:10.1155/2023/3560441

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…The IDS outcomes of the BAFWO-MLIDS approach can be tested on the BoT-IoT Database [24][25][26][27], which comprises a mixture of benign and botnet traffic, and the original PCAP files comprise over 72 million records. In this work, a set of 3673 samples was taken with 10 classes as depicted in Tab.…”

Section: Resultsmentioning

confidence: 99%

Blockchain Assisted Fireworks Optimization with Machine Learning based Intrusion Detection System (IDS)

2024

Teh. vjesn.

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In order to cope with the growing complexity of cyber attacks, it is imperative to have efficient intrusion detection systems (IDSs) that can monitor computer resources and produce data on abnormal or suspicious activities. The security of IoT networks is increasingly becoming a crucial concern as the Internet of Things (IoT) technology receives widespread use. Protecting the IoT framework with a conventional Intrusion Detection System (IDS) might be challenging due to the vast quantity and diversity of IoT devices. Traditional Intrusion Detection Systems (IDSs) face limitations when deployed in IoT networks due to resource limitations and the inherent complexity of these networks.This research proposed the Blockchain Assisted Fireworks Optimization with Machine Learning based Intrusion Detection System (BAFWO-MLIDS) technique in the healthcare platform. The major purpose of the BAFWO-MLIDS system is to apply BC technology (BCT) with IDS for enhanced security in the healthcare sector. The BCT enables to achieve secure data transmission in the healthcare platform. The BCT enables to achieve secure data broadcast in the healthcare environment. The BAFWO-MLIDS technique involves a three-stage procedure: FWO based FS process, ENN-based detection, and BO-based parameter optimization. In the proposed BAFWO-MLIDS technique, the FWO-based feature selection process is involved to select optimal features. For intrusion detection, the BAFWO-MLIDS technique uses Elman Neural Network (ENN) model. Finally, the Bayesian optimization (BO) technique is applied to modify the parameters compared with the ENN model and thereby it accomplishes enhanced detection performance. The simulation results of the BAFWO-MLIDS system can be inspected in a series of experiments and the obtained results ensured greater efficiency of the BAFWO-MLIDS methodology with other recent algorithms.

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Section: Resultsmentioning

confidence: 99%

Blockchain Assisted Fireworks Optimization with Machine Learning based Intrusion Detection System (IDS)

2024

Teh. vjesn.

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“…The core components of the network are analogous to those in a feed-forward neural network [12]; the junctions between the input layer ( ), the hidden layer ( ), and the output layer ( ) are similar to what's found in a multi-layer neural network. As seen in Fig.…”

Section: Elman Neural Networkmentioning

confidence: 90%

“…The might of the colonizing country as well as a ratio of the entire might of its colonies, is the entire power of an empire; Hence, the whole cost of an empire is calculated through Eq. ( 12): (12) where, describes the whole cost of the empire and signifies the mean amount of the cost of the colonizer , is a positive integer. Any empire that cannot improve its power and lose its competitive power during the colonizer's competition will be eliminated.…”

Section: Hidden Layermentioning

confidence: 99%

Enhancing Breast Cancer Diagnosis using a Modified Elman Neural Network with Optimized Algorithm Integration

Chen,

You,

Fan

et al. 2023

IJACSA

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Breast cancer is a class of cancer that starts in the cells of the breast. It happens once the cells of the breast divide and amplify abnormally and uncontrollably. Other parts of the body, including lymph nodes, bones, lungs, and liver, can be affected by breast cancer. Early diagnosis and treatment are critical in helping to lessen the risk of death from breast cancer. Machine learning is a type of artificial intelligence that can be used to diagnose breast cancer. It uses algorithms to analyze data and assess patterns associated with breast cancer. Machine learning models can help improve diagnostic accuracy, reduce false-positive results, and improve the efficiency of diagnosis. Elman Neural Networks (ENNs) are machine learning algorithms that can be used to diagnose breast cancer. ENNs use medical data to detect patterns that are associated with the presence of cancer. The accuracy of ENNs in diagnosing breast cancer is still being researched, but they have the potential to help improve diagnostic accuracy and reduce false-positive results. In the existing study, a new modified version of ENN founded on a combination of an upgraded version of the imperialist competitive algorithm is proposed for this objective.Likewise, the results of the model compared with some other methods illustrated the proposed method's higher efficiency.

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“…The stability of the control system is analyzed using the Lyapunov stability theory, and the simulation results are compared with ADRC and PID. Compared with the literature [29], the model in this paper is more accurate and the control method is more practical. The results show that the method can quickly and stably track the ideal glide slope and has good robustness to external disturbances.…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Control for Carrier-Based Aircraft Automatic Landing Subject to Multiple Disturbances and Actuator Faults

Wu,

Zhu

2024

International Journal of Aerospace Engineering

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This paper introduces a fault-tolerant control scheme for the automatic carrier landing of carrier-based aircraft using direct lift control. The scheme combines radial basis function neural network and active disturbance rejection control (RBF-ADRC) to overcome the impact of actuator failures and external disturbances. First, the carrier-based aircraft model, the carrier air-wake model, and the actuator fault model were established. Secondly, ADRC is designed to estimate and compensate for actuator faults and disturbances in real time. RBFNN adjusts the ADRC controller parameters based on the system state. Then, the Lyapunov function is constructed to prove the stability of the closed-loop system. The controller is applied to the direct lift control channel, auxiliary attitude channel, and approach power compensation system. The direct lift control improves the performance of fixed-wing aircraft. Finally, comparative simulations were conducted under various actuator failures. The results demonstrate the remarkable fault tolerance of the RBF-ADRC scheme, enabling precise tracking of the desired glide path by the shipboard aircraft even in the presence of actuator failures.

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Elman Neural Network‐Based Direct Lift Automatic Carrier Landing Nonsingular Terminal Sliding Mode Fault‐Tolerant Control System Design

Cited by 8 publications

References 35 publications

Blockchain Assisted Fireworks Optimization with Machine Learning based Intrusion Detection System (IDS)

Blockchain Assisted Fireworks Optimization with Machine Learning based Intrusion Detection System (IDS)

Enhancing Breast Cancer Diagnosis using a Modified Elman Neural Network with Optimized Algorithm Integration

Fault-Tolerant Control for Carrier-Based Aircraft Automatic Landing Subject to Multiple Disturbances and Actuator Faults

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