Matching algorithm of missile tail flame based on back-propagation neural network

Huang, Diangui; Huang, Shucai; Tang, Yidong; Zhao, Wei; Cao, Wenhuan

doi:10.1117/12.2305884

Cited by 2 publications

(2 citation statements)

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“…The study confirmed that ANN with R 2 of 0.9921 exhibited better accuracy than the RSM with R 2 of 0.9583 in predicting the hardness values of the composites [4]. The ANN model is generally based upon artificial intelligence (machine learning), under which a predefined set of data is being trained [5,6], validated, and tested for prediction purposes. Due to this constraint, it is worthy to note that the values predicted by ANN will not often be the best predicted values, but will be within the range of the experimental study [7,8].…”

Section: Of 19mentioning

confidence: 56%

Sustainable Synthesis Processes for Carbon Dots through Response Surface Methodology and Artificial Neural Network

et al. 2019

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Nowadays, to ensure sustainability of smart materials, it is imperative to eliminate or reduce carbon footprint related to nano material production. The concept of design of experiment to provide an optimal synthesis process, with a desired yield, is indispensable. It is the researcher’s goal to get optimum value for experiments that requires multiple runs and multiple inputs. Herein, is a reliable approach of utilizing design of experiment (DOE) for response surface methodology (RSM). Thus, to optimize a facile and effective synthesis process for fluorescent carbon dots (CDs) derived from tapioca that is in line with green chemistry principles for sustainable synthesis. The predictions for fluorescent CDs synthesis from RSM were in excellent agreement with the artificial neural network (ANN) model prediction by the Levenberg–Marquardt back propagation (LMBP) algorithm. Considering R2, root mean square error (RMSE) and mean absolute error (MAE) have all revealed a positive hidden layer size. The best hidden layer of neurons were discovered at point 4-8, to confirm the validity of carbon dots, characterization of surface morphology and particles sizes of CDs were conducted with favorable confirmations of the unique characteristics and attributes of synthesized CDs by hydrothermal route.

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Section: Of 19mentioning

confidence: 56%

Sustainable Synthesis Processes for Carbon Dots through Response Surface Methodology and Artificial Neural Network

et al. 2019

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“…Where backpropagation is a supervised learning algorithm and is usually used by perceptron with many layers to change the weights that are connected with neurons in the hidden layer [26]. The implantation of the backpropagation neural network algorithm in missiles has been developed by Da Huang et al Regarding activation controls on the missile tail to match the exact spectrum volume with strong resistance [27]. The backpropagation algorithm uses error output to change its value.…”

Section: B Backpropagation Neural Network Missile Algorithmmentioning

confidence: 99%

Smart guided missile using accelerometer and gyroscope based on backpropagation neural network method for optimal control output feedback

Faqih

Sujito

Sendari

et al. 2020

J. Mechatron. Electr. Power Veh. Technol.

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As a maritime country with a large area, besides the need to defend itself with the military, it also needs to protect itself with aerospace technology that can be controlled automatically. This research aims to develop an air defense system that can control guided missiles automatically with high accuracy. The right method can provide a high level of accuracy in controlling missiles to the targeted object. With the backpropagation neural network method for optimal control output feedback, it can process information data from the radar to control missile’s movement with a high degree of accuracy. The controller uses optimal control output feedback, which is equipped with a lock system and utilizes an accelerometer that can detect the slope of the missile and a gyroscope that can detect the slope between the target direction of the missile to follow the target, control the position, and direction of the missile. The target speed of movement can be easily identified and followed by the missile through the lock system. Sampling data comes from signals generated by radars located in defense areas and from missiles. Each part’s data processing speed is calculated using a fast algorithm that is reliable and has a level of accuracy and fast processing. Data processing impacts on the accuracy of missile movements on any change in the position and motion of targets and target speed. Improved maneuvering accuracy in the first training system can detect 1000 files with a load of 273, while in the last training, the system can detect 1000 files without a load period. So the missile can be guided to hit the target without obstacles when maneuvering.

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Matching algorithm of missile tail flame based on back-propagation neural network

Cited by 2 publications

References 0 publications

Sustainable Synthesis Processes for Carbon Dots through Response Surface Methodology and Artificial Neural Network

Sustainable Synthesis Processes for Carbon Dots through Response Surface Methodology and Artificial Neural Network

Smart guided missile using accelerometer and gyroscope based on backpropagation neural network method for optimal control output feedback

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