Design of ANN (artificial neural networks)-fast backpropagation algorithm gain scheduling controller of active filtering

2002 ◽  
Author(s):  
K. Gulez ◽  
H. Watanabe ◽  
F. Harashima
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gain Scheduling ◽  
Backpropagation Algorithm ◽  
Artificial Neural ◽  
Active Filtering

scholarly journals Analisis Jaringan Syaraf Tiruan untuk Memprediksi Jumlah Narapidana pada Lembaga Pemasyarakatan Simalungun dengan Metode Backpropagation

2019 ◽  
Vol 1 ◽  
pp. 762
Author(s):  
Vicky Adriani ◽  
Irfan Sudahri Damanik ◽  
Jaya Tata Hardinata
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Network Architecture ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Artificial Neural ◽  
Detention Facilities

The author has conducted research at the Simalungun District Prosecutor's Office and found the problem of prison rooms that did not match the number of prisoners which caused a lack of security and a lack of detention facilities and risked inmates to flee. Artificial Neural Network which is one of the artificial representations of the human brain that always tries to simulate the learning process of the human brain. The application uses the Backpropagation algorithm where the data entered is the number of prisoners. Then Artificial Neural Networks are formed by determining the number of units per layer. Once formed, training is carried out from the data that has been grouped. Experiments are carried out with a network architecture consisting of input units, hidden units, and output units. Testing using Matlab software. For now, the number of prisoners continues to increase. Predictions with the best accuracy use the 12-3-1 architecture with an accuracy rate of 75% and the lowest level of accuracy using 12-4-1 architecture with an accuracy rate of 25%.

Multilayer Backpropagation Neural Networks for Implementation of Logic Gates

2021 ◽  
Vol 12 (1) ◽  
pp. 1-12
Author(s):  
Santosh Giri ◽  
Basanta Joshi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Specific Problem ◽  
Logic Gates ◽  
Backpropagation Algorithm ◽  
Processing Elements ◽  
Artificial Neural ◽  
Hidden Neurons

ANN is a computational model that is composed of several processing elements (neurons) that tries to solve a specific problem. Like the human brain, it provides the ability to learn from experiences without being explicitly programmed. This article is based on the implementation of artificial neural networks for logic gates. At first, the 3 layers Artificial Neural Network is designed with 2 input neurons, 2 hidden neurons & 1 output neuron. after that model is trained by using a backpropagation algorithm until the model satisfies the predefined error criteria (e) which set 0.01 in this experiment. The learning rate (α) used for this experiment was 0.01. The NN model produces correct output at iteration (p)= 20000 for AND, NAND & NOR gate. For OR & XOR the correct output is predicted at iteration (p)=15000 & 80000 respectively.

scholarly journals Analysis of Artificial Neural Network Accuracy Using Backpropagation Algorithm In Predicting Process (Forecasting)

2017 ◽  
Vol 1 (1) ◽  
pp. 34 ◽  
Author(s):  
Sandy Putra Siregar ◽  
Anjar Wanto
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Backpropagation Algorithm ◽  
Neural Structure ◽  
Data Prediction ◽  
Architectural Models ◽  
Artificial Neural ◽  
North Sumatra

Artificial Neural Networks are a computational paradigm formed based on the neural structure of intelligent organisms to gain better knowledge. Artificial neural networks are often used for various computing purposes. One of them is for prediction (forecasting) data. The type of artificial neural network that is often used for prediction is the artificial neural network backpropagation because the backpropagation algorithm is able to learn from previous data and recognize the data pattern. So from this pattern backpropagation able to analyze and predict what will happen in the future. In this study, the data to be predicted is Human Development Index data from 2011 to 2015. Data sourced from the Central Bureau of Statistics of North Sumatra. This research uses 5 architectural models: 3-8-1, 3-18-1, 3-28-1, 3-16-1 and 3-48-1. From the 5 models of this architecture, the best accuracy is obtained from the architectural model 3-48-1 with 100% accuracy rate, with the epoch of 5480 iterations and MSE 0.0006386600 with error level 0.001 to 0.05. Thus, backpropagation algorithm using 3-48-1 model is good enough when used for data prediction.

scholarly journals Jaringan Syaraf Tiruan untuk Memprediksi Penjualan Kelapa Sawit Menggunakan Algoritma Backpropagation

2019 ◽  
Vol 1 ◽  
pp. 418
Author(s):  
Delima Sinaga ◽  
Solikhun Solikhun ◽  
Iin Parlina
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Palm Oil ◽  
Learning Process ◽  
Grouped Data ◽  
Accuracy Rate ◽  
Backpropagation Algorithm ◽  
Matlab Software ◽  
Artificial Neural

This study discusses the prediction of palm oil sales using artificial neural networks, which is one of the artificial representations of the human brain that always tries to simulate the learning process of the human brain. The application uses a backpropagation algorithm where the data entered is the number of sold. Then artificial neural networks are formed by determining the number of units per layer. After the networks is formed, training is carried out from the grouped data. Experiments are carried out with an architecture consisting of input units, hidden units, output units and architecture. Testing is done with matlab software. For now the competition for palm oil sales is getting tougher. Predictions with the best accuracy use the 12-2-1 architecture with an accuracy rate of 92% and the lowest level of accuracy using 12-6-1 architecture with an accuracy rate of 58%

Performance Analysis of Cellular Radio System Using Artificial Neural Networks

2015 ◽  
Vol 2 (9) ◽  
pp. 15
Author(s):  
Kriti Priya Gupta
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Heavy Traffic ◽  
Fixed Number ◽  
Cellular Radio ◽  
Backpropagation Algorithm ◽  
Radio System ◽  
System A ◽  
Blocking Probabilities ◽  
Artificial Neural

In this paper, we exploit one of the fastest growing techniques of Soft Computing, i.e.  Artificial Neural Networks (ANNs) for obtaining various performance measures of a cellular radio system. A prioritized channel scheme with subrating is considered in which a fixed number of channels are reserved for handoff calls and in case of heavy traffic, these reserved channels are subrated into two channels of equal frequency to deal with more handoff calls. Two models dealing with infinite and finite number of subscribers are considered and the blocking probabilities of new and handoff calls are computed analytically as well as by using ANNs. A feedforward two-layer ANN is considered for obtaining the blocking probabilities. The backpropagation algorithm is used for training the ANN. The analytical and ANN results are compared by taking the numerical illustrations.

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