Variables Screening Method Based on the Algorithm of Combining Fruit Fly Optimization Algorithm and RBF Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 3225-3230
Author(s):  
Fu Qiang Xu ◽  
You Tian Tao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Optimization Algorithm ◽  
Rbf Neural Network ◽  
Screening Method ◽  
Fruit Fly ◽  
Local Optimum ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
The Mean

The form of fruit fly optimization algorithm (FOA) is easy to learn and has the characteristics of quick convergence and not readily dropping into local optimum. This paper presents the optimization of RBF neural network by means of FOA and establishment of network model, adopting it with the combination of the evaluation of the mean impact value (MIV) to select variables. The validity of this model is tested by two actual examples, furthermore, it is simpler to learn, more stable and practical.

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.

Sign in / Sign up

Export Citation Format

Share Document