Guidance Systematic Error Separation for Mobile Launch Vehicles Using Artificial Fish Swarm Algorithm

Zhou, Xuanying; Wang, Zhengming; Liu, Dong; Zhou, Hongyan; Qin, Yongrui; Wang, Jiongqi

doi:10.1109/access.2019.2893765

Cited by 7 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the CPAFSA optimized for underwater 3D coverage problem in WQMSNs in Section 4, the field of view and step length parameters are set. And based on the comprehensive evaluation of relevant literature, the crowding factor and the maximum number of swarming are set [29][30][31]. Table 1 shows these parameters. PSO is a swarm optimization algorithm inspired by the phenomenon of bird swarm preying.…”

Section: Resultsmentioning

confidence: 99%

A Chaotic Parallel Artificial Fish Swarm Algorithm for Water Quality Monitoring Sensor Networks 3D Coverage Optimization

Zhou

Liu

2021

Journal of Sensors

View full text Add to dashboard Cite

In recent years, the increasingly severe water pollution problem encouraged researchers to optimize water quality monitoring sensor networks (WQMSNs) by creating new underwater sensor coverage algorithms. Since the sensor is limited by the monitoring range and the number of targets, optimizing the 3D target coverage of heterogeneous multisensors is essential to maximize the 3D target coverage rate of the monitored waters. To enhance the target coverage rate, the target allocation needs to be searched in all possible combinations. To optimize the 3D coverage of underwater targets, this research proposes a chaotic parallel artificial fish swarm algorithm (CPAFSA). CPAFSA uses chaotic selection to initialize parameters and integrates the global search capabilities of parallel operators. It also applies the elite selection which effectively avoiding local optimization and solving the problem of 3D target coverage. Ultimately, CPAFSA is compared with genetic algorithm (GA) and particle swarm optimization (PSO). The results of the simulation experiment demonstrated the excellent performance of CPAFSA in achieving underwater 3D target coverage.

show abstract

Section: Resultsmentioning

confidence: 99%

A Chaotic Parallel Artificial Fish Swarm Algorithm for Water Quality Monitoring Sensor Networks 3D Coverage Optimization

Zhou

Liu

2021

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…2. Due to the bandpass characteristic in frequency domain, the desired wavelet scales can be realized by denormalizing (20) to related center frequency f0. Herein, to extract the features of bio-signals commonly in 0-100 Hz range, e.g., ECG and EEG, scale a=0.1 (f0 =2.5Hz) is chosen as a design example.…”

Section: Construction Of Analog Marr Wavelet Base Using Hafsamentioning

confidence: 99%

Design of programmable Gaussian‐derived wavelet filter for wearable biomedical sensor

Zhang

Zhao

Sun

2021

Circuit Theory & Apps

View full text Add to dashboard Cite

To provide multiple options for specific application in biosignal processing, the programmable Gaussian‐derived Gm‐C wavelet filter has been proposed. To realize the programmable characteristic, the analog wavelet base with one numerator term is constructed by using hybrid artificial fish swarm algorithm. Also, the inverse follow‐the‐leader feedback Gm‐C filter structure with a switch array is employed. By programming switches only, Gaussian and Marr wavelet transforms can be realized flexibly with all component parameters unchanged. The seventh‐order programmable wavelet filter is designed as an example. Simulation results show that power consumption is only 141.68 pW at scale a = 0.1, with dynamic range of 42.6 dB and figure‐of‐merit of 2.05 × 10−13. Due to the programmability, the proposed design method can implement two wavelet filters with very low circuit complexity.

show abstract

“…Some researchers began to pay attention to the improvement and optimization of the selected algorithms [17]. Some optimization algorithms used to solve the optimal solution problem of the algorithm model have been applied, which mainly include the gradient descent method [18], Newton method [19], and the meta-heuristic algorithm [20][21][22][23][24][25]. Gradient descent is one of the most commonly used methods when solving for the model parameters of machine learning algorithms, especially unconstrained optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Power System Fault Classification and Prediction Based on a Three-Layer Data Mining Structure

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In traditional fault diagnosis methods in power systems, it is difficult to accurately classify and predict the types of faults. With the emergence of big data technology, the fault classification and prediction methods based on big data analysis and processing have been applied in power systems. To make the classification and prediction of the fault types more accurate, this paper proposes a hybrid data mining method for power system fault classification and prediction based on clustering, association rules and stochastic gradient descent. This method uses a three-layer data mining model: The first layer uses the K-means clustering algorithm to preprocess the original fault data source, and it proposes to use self-encoding to simplify the data form. The second layer effectively eliminates the data that have little impact on the prediction results by using association rules, and the highly correlated data are mined to become the regression training data. The third layer first uses the cross-validation method to obtain the optimal parameters of each fault model, and then, it uses stochastic gradient descent for data regression training to obtain a classification and prediction model for each fault type. Finally, a verification example shows that compared with a single data mining algorithm model, the proposed method is more comparative in terms of the data mining, and the established power system fault classification and prediction model has global optimality and higher prediction accuracy, which has a certain feasibility for real-time online power system fault classification and prediction. This method reduces the disturbances from low-impact or irrelevant data by mining the fault data three times, and it uses cross-validation to optimize the multiple regression parameters of the regression model to solve the problems of low accuracy, large errors and easily falling into a local optimum, given the conduct of fault classification and prediction.

show abstract

Guidance Systematic Error Separation for Mobile Launch Vehicles Using Artificial Fish Swarm Algorithm

Cited by 7 publications

References 22 publications

A Chaotic Parallel Artificial Fish Swarm Algorithm for Water Quality Monitoring Sensor Networks 3D Coverage Optimization

A Chaotic Parallel Artificial Fish Swarm Algorithm for Water Quality Monitoring Sensor Networks 3D Coverage Optimization

Design of programmable Gaussian‐derived wavelet filter for wearable biomedical sensor

Power System Fault Classification and Prediction Based on a Three-Layer Data Mining Structure

Contact Info

Product

Resources

About