Optimization of diesel engine dual-variable geometry turbocharger regulated two-stage turbocharging system based on radial basis function neural network-quantum genetic algorithm

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.

Download Full-text

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

Computational and Mathematical Methods in Medicine ◽

10.1155/2016/7493535 ◽

2016 ◽

Vol 2016 ◽

pp. 1-15 ◽

Cited By ~ 1

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.

Download Full-text

Genetic Algorithm-Based COD Calibration Method Research

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.341-342.748 ◽

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.

Download Full-text

A New Approach to Software Project Cost Estimation using a Hybrid Model of Radial Basis Function Neural Network and Genetic Algorithm

Indian Journal of Science and Technology ◽

10.17485/ijst/2014/v7i6.3 ◽

2014 ◽

Vol 7 (6) ◽

pp. 838-843

Author(s):

Maryam Molani

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Radial Basis Function ◽

Hybrid Model ◽

Basis Function ◽

Cost Estimation ◽

Software Project ◽

Project Cost ◽

New Approach ◽

Radial Basis

Download Full-text

The genetic algorithm-radial basis function neural network to quickly predict aerodynamic performance of compressors

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/0954410020948977 ◽

2020 ◽

pp. 095441002094897

Author(s):

Tianquan Tang ◽

Bo Liu

Keyword(s):

Neural Network ◽

Experimental Data ◽

Genetic Algorithm ◽

Radial Basis Function ◽

Basis Function ◽

Surrogate Model ◽

Pressure Ratio ◽

Aerodynamic Performance ◽

Mixing Model ◽

Deviation Angle

In this paper, compressor aerodynamic performance has been predicted based on throughflow theory, combined with a surrogate model, which is a combination of the Genetic Algorithm (GA) and generalized Radial Basis function (RBF) neural network. And the predicting results have been compared with those from the traditional models and spanwise mixing model, which still widely be used to predict the aerodynamic performance. We first predicted the deviation angle and total-pressure loss coefficient (TPLC) by the surrogate model, and then using these two intermediate variables connected the model with throughflow theory. The pressure ratio and efficiency, representing the compressors’ total performance parameters, are predicted and compared with experimental data. In order to increase the accuracy of prediction, a data augmentation method based on the piecewise cubic Hermite interpolation (PCHIP) algorithm is introduced to enlarge the training database. At the same time, considering the vast differences of deviation angle and loss in different working conditions as well as aerodynamic and geometric differences of rotor and stator, the database and the network should be split into six components based on the choke, the normal and the stall conditions as well as rotor and stator. Then, the performance curves of pressure ratio and efficiency can be determined by an iteration process. The predicting results are compared with experimental data, which shows that the surrogate model matches experiments much better than those from the traditional models and spanwise mixing model.

Download Full-text