Using Fruit Fly Optimization Algorithm Optimized Grey Model Neural Network to Perform Satisfaction Analysis for E-Business Service

2013 ◽  
Vol 7 (2L) ◽  
pp. 459-465 ◽  
Author(s):  
Peng-Wen Chen ◽  
Wei-Yuan Lin ◽  
Tsui-Hua Huang ◽  
Wen-Tsao Pan
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Grey Model ◽  
Fruit Fly Optimization Algorithm ◽  
Business Service ◽  
Fruit Fly Optimization ◽  
Satisfaction Analysis

A Mutual Fund Investment Method Using Fruit Fly Optimization Algorithm and Neural Network

2014 ◽  
Vol 571-572 ◽  
pp. 318-325 ◽  
Author(s):  
Tsu Hua Huang ◽  
Yung Ho Leu
Keyword(s):  
Neural Network ◽  
Mutual Funds ◽  
Optimization Algorithm ◽  
Mutual Fund ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Asset Value ◽  
Net Asset Value ◽  
Fund Portfolio

This paper presents a method to construct a profitable portfolio of mutual funds for investors. This method comprises two stages. In the first stage, the DEA, Sharpe and Treynor indices of mutual funds and the monthly rates of return (ROR) of mutual funds are used to select a mutual fund portfolio. In the second stage, the linear regression model, the Fruit Fly Optimization Algorithm (FOA) and the General Regression Neural Network (GRNN) are used to construct a prediction model for the net asset values of each of the constituent mutual funds of the portfolio. The trade decision of a selected mutual fund is then made based on the rise or fall of its net asset value. The empirical results showed that, compared to other combinations, the combination of using Sharpe index for portfolio selection and the GRNN optimized with FOA for net asset value prediction offered the best accumulated return rate for the mutual fund portfolio investment.

Prediction Research on the Failure of Steam Turbine Based on Fruit Fly Optimization Algorithm Support Vector Regression

2012 ◽  
Vol 614-615 ◽  
pp. 409-413 ◽  
Author(s):  
Zhi Biao Shi ◽  
Ying Miao
Keyword(s):  
Neural Network ◽  
Support Vector Regression ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Fruit Fly ◽  
Support Vector ◽  
Fruit Fly Optimization Algorithm ◽  
Global Optimal Solution ◽  
Fruit Fly Optimization ◽  
Global Optimal

In order to solve the blindness of the parameter selection in the Support Vector Regression (SVR) algorithm, we use the Fruit Fly Optimization Algorithm (FOA) to optimize the parameters in SVR, and then propose the optimization algorithm on the parameters in SVR based on FOA to fitting and simulate the experimental data of the turbine’s failures. This algorithm could optimize the parameters in SVR automatically, and achieve ideal global optimal solution. By comparing with the commonly used methods such as Support Vector Regression and Radial Basis Function neural network, it can be shown that the forecast results of FOA_SVR more accurate and the forecast speed is the fastest.

scholarly journals Application of the fruit fly optimization algorithm to an optimized neural network model in radar target recognition

2021 ◽  
Vol 45 (2) ◽  
pp. 296-300
Author(s):  
M. Liu ◽  
Z.H. Sun
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Target Recognition ◽  
Recognition Rate ◽  
Fruit Fly ◽  
Generalized Regression Neural Network ◽  
Radar Target ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Radar Target Recognition

With the development of computer technology, there are more and more algorithms and models for data processing and analysis, which brings a new direction to radar target recognition. This study mainly analyzed the recognition of high resolution range profile (HRRP) in radar target recognition and applied the generalized regression neural network (GRNN) model for HRRP recognition. In order to improve the performance of HRRP, the fruit fly optimization algorithm (FOA) algorithm was improved to optimize the parameters of the GRNN model. Simulation experiments were carried out on three types of aircraft. The improved FOA-GRNN (IFOA-GRNN) model was compared with the radial basis function (RBF) and GRNN models. The results showed that the IFOA-GRNN model had a better convergence accuracy, the highest average recognition rate (96.4 %), the shortest average calculation time (275 s), and a good recognition rate under noise interference. The experimental results show that the IFOA-GRNN model has a good performance in radar target recognition and can be further promoted and applied in practice.

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