Power and resource allocation using modified binary particle swarm optimization in neural network

2016 ◽  
Author(s):  
K. Anusha ◽  
G.I. Shamini
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization

scholarly journals Monitoring of OSNR Using an Improved Binary Particle Swarm Optimization and Deep Neural Network in Coherent Optical Systems

Photonics ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. 111 ◽  
Author(s):  
Xiaoyong Sun ◽  
Shaojing Su ◽  
Junyu Wei ◽  
Xiaojun Guo ◽  
Xiaopeng Tan
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Network Structure ◽  
Deep Neural Network ◽  
Estimation Error ◽  
Particle Swarm ◽  
Original Data ◽  
Binary Particle Swarm Optimization ◽  
Optical Performance Monitoring ◽  
Swarm Optimization

A novel technique is proposed to implement optical signal-to-noise ratio (OSNR) estimation by using an improved binary particle swarm optimization (IBPSO) and deep neural network (DNN) based on amplitude histograms (AHs) of signals obtained after constant modulus algorithm (CMA) equalization in an optical coherent system. For existing OSNR estimation models of DNN and AHs, sparse AHs with valid features of original data are selected by IBPSO algorithm to replace the original, and the sparse sets are used as input vector to train and test the particle swarm optimization (PSO) optimized DNN (PSO-DNN) network structure. Numerical simulations have been carried out in the OSNR ranges from 10 dB to 30 dB for 112 Gbps PM-RZ-QPSK and 112 Gbps PM-NRZ-16QAM signals, and results show that the proposed algorithm achieves a high OSNR estimation accuracy with the maximum estimation error is less than 0.5 dB. In addition, the simulation results with different data input into the deep neural network structure show that the mean OSNR estimation error is 0.29 dB and 0.39 dB under original data and 0.29 dB and 0.37 dB under sparse data for the two signals, respectively. In the future dynamic optical network, it is of more practical significance to reconstruct the original signal and analyze the data using sparse observation information in the face of multiple impairment and serious interference. The proposed technique has the potential to be applied for optical performance monitoring (OPM) and is helpful for better management of optical networks.

Analysis of Feature Reduction Techniques for Online News Popularity Prediction

2018 ◽  
Vol 4 (10) ◽  
pp. 6
Author(s):  
Shivangi Bhargava ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Online News ◽  
Feature Reduction ◽  
Support Vector ◽  
Swarm Optimization

News popularity is the maximum growth of attention given for particular news article. The popularity of online news depends on various factors such as the number of social media, the number of visitor comments, the number of Likes, etc. It is therefore necessary to build an automatic decision support system to predict the popularity of the news as it will help in business intelligence too. The work presented in this study aims to find the best model to predict the popularity of online news using machine learning methods. In this work, the result analysis is performed by applying Co-relation algorithm, particle swarm optimization and principal component analysis. For performance evaluation support vector machine, naïve bayes, k-nearest neighbor and neural network classifiers are used to classify the popular and unpopular data. From the experimental results, it is observed that support vector machine and naïve bayes outperforms better with co-relation algorithm as well as k-NN and neural network outperforms better with particle swarm optimization.

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