State of charge estimation for a lead-acid battery using backpropagation neural network method

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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Implementation of Backpropagation Neural Network Method in Classification System of Timeliness of Graduation

Proceedings of the International Conference of Science and Technology for the Internet of Things ◽

10.4108/eai.19-10-2018.2282527 ◽

2019 ◽

Author(s):

Yanuar Nurdiansyah ◽

Diksi Media ◽

Fadhel Hizam

Keyword(s):

Neural Network ◽

Classification System ◽

Backpropagation Neural Network ◽

Neural Network Method ◽

Network Method

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Identifikasi Mutu Buah Pepaya California (Carica Papaya L.) Menggunakan Metode Jaringan Syaraf Tiruan

Jurnal Sisfokom (Sistem Informasi dan Komputer) ◽

10.32736/sisfokom.v10i1.1105 ◽

2021 ◽

Vol 10 (1) ◽

pp. 113-119

Author(s):

Muhammad Ezar Al Rivan ◽

Gabriela Repca Sung

Keyword(s):

Neural Network ◽

Carica Papaya ◽

Training Data ◽

Backpropagation Neural Network ◽

Neural Network Method ◽

Network Method ◽

Hidden Layer ◽

The Tropics

Papaya is one of the fruits that grows in the tropics area, one of the kinds that people’s love the most is papaya California. The quality identification of papaya California fruit can be measured using color, defect, and size. Color, defect and size extracted from image of papaya. The dataset that used in this research are 150 images papaya California. The dataset consist of 3 quality there are good, fair and low. Identification of papaya using the backpropagation neural network method with 17 training function in each training data with 3 different neurons in the hidden layer. The best result of the test is using training function trainrp with 10 neurons is 81,33% for accuracy, 73,37% for precision, and 72% for recall, with 20 neurons is 82,67% for accuracy, 75,24% for precision, and 74% for recall, and with 25 neurons is 80,89% for accuracy, 74,42% for precision, and 71,33% for recall.

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