Hardware implementation of an active learning self-organizing neural network to predict the power fluctuation events of a photovoltaic grid-tied system.

2022 ◽  
pp. 104448
Author(s):  
Shu Mei Goh ◽  
Ken Weng Kow ◽  
Michelle Tan ◽  
Rajprasad Rajkumar ◽  
Yee Wan Wong
Keyword(s):  
Neural Network ◽  
Active Learning ◽  
Hardware Implementation ◽  
Power Fluctuation ◽  
Self Organizing

Self-Organizing Fusion Neural Networks

2007 ◽  
Vol 11 (6) ◽  
pp. 610-619
Author(s):  
Jung-Hua Wang ◽  
◽  
Chun-Shun Tseng ◽  
Sih-Yin Shen ◽  
Ya-Yun Jheng
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Noise Tolerance ◽  
Training Process ◽  
Local Statistics ◽  
Input Noise ◽  
Manual Intervention ◽  
Two Parameters ◽  
Parallel Fashion ◽  
Self Organizing

This paper presents a self-organizing fusion neural network (SOFNN) effective in performing fast clustering and segmentation. Based on a counteracting learning scheme, SOFNN employs two parameters that together control the training in a counteracting manner to obviate problems of over-segmentation and under-segmentation. In particular, a simultaneous region-based updating strategy is adopted to facilitate an interesting fusion effect useful for identifying regions comprising an object in a self-organizing way. To achieve reliable merging, a dynamic merging criterion based on both intra-regional and inter-regional local statistics is used. Such extension in adjacency not only helps achieve more accurate segmentation results, but also improves input noise tolerance. Through iterating the three phases of simultaneous updating, self-organizing fusion, and extended merging, the training process converges without manual intervention, thereby conveniently obviating the need of pre-specifying the terminating number of objects. Unlike existing methods that sequentially merge regions, all regions in SOFNN can be processed in parallel fashion, thus providing great potentiality for a fully parallel hardware implementation.

An incremental online semi-supervised active learning algorithm based on self-organizing incremental neural network

2010 ◽  
Vol 20 (7) ◽  
pp. 1061-1074 ◽  
Author(s):  
Furao Shen ◽  
Hui Yu ◽  
Keisuke Sakurai ◽  
Osamu Hasegawa
Keyword(s):  
Neural Network ◽  
Active Learning ◽  
Learning Algorithm ◽  
Self Organizing

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

scholarly journals Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Segmentation Method ◽  
Semantic Image Segmentation ◽  
Batch Normalization ◽  
Set Up ◽  
Image Datasets

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.

An improved self-organizing incremental neural network model for short-term time-series load prediction

2021 ◽  
Vol 292 ◽  
pp. 116912
Author(s):  
Rong Wang Ng ◽  
Kasim Mumtaj Begam ◽  
Rajprasad Kumar Rajkumar ◽  
Yee Wan Wong ◽  
Lee Wai Chong
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Load Prediction ◽  
Short Term ◽  
Self Organizing
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