A New Ridgelet Neural Network Training Algorithm Based on Improved Particle Swarm Optimization

2007 ◽  
Author(s):  
Rijian Su ◽  
Li Kong ◽  
Shengli Song ◽  
Pu Zhang ◽  
Kaibo Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Neural Network Training ◽  
Training Algorithm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Network Training

scholarly journals Design of a-based smart meters to monitor electricity usage in the household sector using hybrid particle swarm optimization - neural network

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
Muhammad Yusuf Yunus ◽  
Marhatang Marhatang ◽  
Andareas Pangkung ◽  
Muhammad Ruswandi Djalal
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Data Sampling ◽  
Neural Network Training ◽  
Swarm Optimization ◽  
Load Combination ◽  
Network Training ◽  
Hybrid Particle Swarm Optimization ◽  
Iron Load

The procedure is training and testing the nerves that will be made. Matlab software has a Neural Network tool, which in this study will be used. Load sampling data is used as input data for neural network training. As output / target load classification is used. Load classification method, which is 1 for TV load classification, 2 for fan load, 3 for iron load, 4 for water pump load, 5 for lamp load, 6 for dispenser load, and 7 for fan iron load combination. The total load is 6 single loads and 1 combination load. One load combination was chosen because, on the combination load characteristics after the fan has characteristics that are not the same as the others. Data sampling of the current of each load will be used as neural network training. Load data used is 30 samples or for 30 seconds, with every minute the data is taken. From the results of the training, it can be seen that the biggest training error is in the seventh data, namely the identification of the load on the classification of the fan-iron load. This is because the current pattern on the iron and fan with the iron or fan itself has almost the same characteristics. However, for this process networks will be used and then the PSO optimization method is used to reduce the error, in the next study. From the test results, it is shown that by varying the input current data of each load, the network has been able to identify well, even though in the data classification load 7, the load of the iron-fan combination still has a large error. This will be corrected in subsequent studies with Particle Swarm Optimization (PSO) algorithm optimization.

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