scholarly journals LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network

2020 ◽  
Vol 16 (2) ◽  
pp. 155014772090783
Author(s):  
Jingqiu Ren ◽  
Ke Bao ◽  
Guanghua Zhang ◽  
Li Chu ◽  
Weidang Lu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Spatial Clustering ◽  
Indoor Positioning ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Radial Basis ◽  
Positioning Algorithm

In recent years, fifth-generation communication technology has begun to experiment successfully. As an indoor positioning technology of the Internet of things, it changes with each passing day and shows great vitality in the development of smart cities. Aiming at the problem that existing radio frequency identification indoor positioning algorithm is prone to environmental interference and poor positioning accuracy, a LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is proposed. In this article, the signal intensity value is processed by Gaussian filter, and the noise points and boundary points are removed by density-based clustering algorithm. The threshold and weight of radial basis function neural network were optimized by genetic algorithm. With less data information, the relationship between the value of label signal strength and position coordinate could be established to improve the positioning accuracy of LANDMARC positioning algorithm. Experimental research shows that the average positioning error of the proposed LANDMARC algorithm based on density-based spatial clustering of applications with noise–genetic algorithm–radial basis function neural network is about 0.9 m, which is 64% lower than the average positioning error of the traditional LANDMARC algorithm and improves the indoor positioning accuracy.

An Improved Prediction Method of SOC Based on the GA-RBF Neural Network

2014 ◽  
Vol 953-954 ◽  
pp. 800-805 ◽  
Author(s):  
Meng Di Liang ◽  
Tie Zhou Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Prediction Method ◽  
Charged State ◽  
Improved Genetic Algorithm ◽  
State Prediction ◽  
Radial Basis

Concerning the prediction problems’accuracy of the state-of-charge(SOC) of the battery，this paper proposes a prediction method based on an improved genetic algorithm-radial basis function neural network for power battery charged state. The prediction method, based on intensity of information interaction and neural activity, adjusts the size of the neural network online and solves the problem that radial basis function neural network structure adjustment influences the accuracy of charged state prediction. The simulation results show that，compared with the method of radial basis function neural network based on genetic algorithm , the accuracy of charged state prediction is more stable and more precise.

scholarly journals Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural Network Classifier

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
I. Jasmine Selvakumari Jeya ◽  
S. N. Deepa
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Minimum Mean Square Error ◽  
Gaussian Kernel ◽  
Neural Network Classifier ◽  
Real Coded Genetic Algorithm ◽  
Radial Basis

A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen as a classifier model because of its Gaussian Kernel function and its effective learning process to avoid local and global minima problem and enable faster convergence. This paper specifically focused on tuning the weights and bias of RBFNN classifier employing the proposed RCGA. The operators used in RCGA enable the algorithm flow to compute weights and bias value so that minimum Mean Square Error (MSE) is obtained. With both the lung healthy and cancer images from Lung Image Database Consortium (LIDC) database and Real time database, it is noted that the proposed RCGA based RBFNN classifier has performed effective classification of the healthy lung tissues and that of the cancer affected lung nodules. The classification accuracy computed using the proposed approach is noted to be higher in comparison with that of the classifiers proposed earlier in the literatures.

Genetic Algorithm-Based COD Calibration Method Research

2013 ◽  
Vol 341-342 ◽  
pp. 748-753
Author(s):  
Jia Ni Qian ◽  
Tian Wang ◽  
Xi Peng Lv ◽  
Yun Long Tang ◽  
Xiu Fen Ye
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Evolutionary Process ◽  
Function Optimization ◽  
Measuring System ◽  
Good Effect ◽  
Fitness Value ◽  
Radial Basis

For better realization of the function of Chemical oxygen demand (COD) online measuring instrument and improving its measurement accuracy , a good calibration and identification of signals collected is needed. During the process, the problem on parameter identification of undetermined function can be transformed into function optimization. Considering the characteristics of genetic algorithm It is introduced into the function identification of the measuring system and compare it with the radial basis function neural network. As for the premature of population evolutionary process, this article presents the method to select operators according to genetic fitness value of each individual and designs a set of system identifier based on Genetic Algorithm to identify the system. Finally, test the experimental data get from water bath in the lab dish. The relative error of output value does not exceed 8%.The experiment results show that genetic algorithm has a good effect in the system identifier on the calibration and identification of COD measuring system, better than radial basis function neural network.

An Integrated Hybrid Algorithm Based on Nature Inspired Evolutionary for Radial Basis Function Neural Network Learning

2016 ◽  
Vol 25 (02) ◽  
pp. 1650004 ◽  
Author(s):  
Zhen-Yao Chen ◽  
R. J. Kuo ◽  
Tung-Lai Hu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Learning Performance ◽  
Benchmark Problems ◽  
Neural Network Learning ◽  
Radial Basis

This paper intends to propose an integrated hybrid algorithm for training radial basis function neural network (RBFNN) learning. The proposed integrated of particle swarm and genetic algorithm based optimization (IPGO) algorithm is composed of two approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) for gathering both their virtues to improve the learning performance of RBFNN. The diversity of individuals results in higher chance to search in the direction of global optimal instead of being confined to local optimal particularly in problem with higher complexity. The IPGO algorithm with PSO-based and GA-based approaches has shown promising results in some benchmark problems with three continuous test functions. After proposing the algorithm for these problems with result providing its outperforming performance, this paper supplements a practical application case for the papaya milk sales forecasting to expound the superiority of the IPGO algorithm. In addition, model evaluation results of the case have showed that the IPGO algorithm outperforms other algorithms and auto-regressive moving average (ARMA) models in terms of forecasting accuracy and execution time.

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