A Novel Artificial Neural Network Ensemble Model Based on K--Nearest Neighbor Nonparametric Estimation of Regression Function and Its Application for Rainfall Forecasting

2009 ◽  
Author(s):  
Jiansheng Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nonparametric Estimation ◽  
Nearest Neighbor ◽  
Regression Function ◽  
Ensemble Model ◽  
Neural Network Ensemble ◽  
K Nearest Neighbor ◽  
Rainfall Forecasting ◽  
Artificial Neural Network Ensemble

An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery

Energy ◽  
2015 ◽  
Vol 91 ◽  
pp. 264-273 ◽  
Author(s):  
Alvaro Linares-Rodriguez ◽  
Samuel Quesada-Ruiz ◽  
David Pozo-Vazquez ◽  
Joaquin Tovar-Pescador
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ensemble Model ◽  
Neural Network Ensemble ◽  
Artificial Neural ◽  
Artificial Neural Network Ensemble

scholarly journals A novel time difference of arrival localization algorithm using a neural network ensemble model

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881579 ◽  
Author(s):  
Zhenkai Zhang ◽  
Feng Jiang ◽  
Boyuan Li ◽  
Bing Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mobile Station ◽  
Time Difference ◽  
Ensemble Model ◽  
Neural Network Ensemble ◽  
Time Difference Of Arrival ◽  
Generalization Ability ◽  
Artificial Neural ◽  
Artificial Neural Network Ensemble

In a localization system, time difference of arrival technique is widely used to estimate the location of a mobile station. To improve the performance of mobile station location estimation, a novel algorithm-based artificial neural network ensemble and time difference of arrival information is proposed in non-line-of-sight environments. Back propagation neural network is a classic artificial neural network and may be effectively used for mathematical modeling and prediction, and an artificial neural network ensemble has better generalization ability and stability than a single network. First, the parameters, such as the weights and biases of the single neural network are optimized by the ant lion optimization method which is novel and effective. Then four types of different information from the time difference of arrival measurements are respectively used to train the individual neural network. Finally, the weighted average method is improved to combine the outputs of the different individual neural network, where weights are determined by the training errors. The estimation accuracy of the locating system is evaluated through experimental measurements. The simulation results show that the proposed algorithm is efficient in improving the generalization ability and localization precision of the neural network ensemble model.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

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