Construct the prediction model for China agricultural output value based on the optimization neural network of fruit fly optimization algorithm

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.

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A New Hybrid Model Based on Fruit Fly Optimization Algorithm and Wavelet Neural Network and Its Application to Underwater Acoustic Signal Prediction

Mathematical Problems in Engineering ◽

10.1155/2018/3136267 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 4

Author(s):

Hong Yang ◽

Siliang Wang ◽

Guohui Li ◽

Tongtong Mao

Keyword(s):

Neural Network ◽

Prediction Model ◽

Acoustic Signal ◽

Fruit Fly ◽

Wavelet Neural Network ◽

Fruit Fly Optimization Algorithm ◽

Underwater Acoustic ◽

Fruit Fly Optimization ◽

Neural Network Prediction ◽

Underwater Acoustic Signal

The local predictability of underwater acoustic signal plays an important role in underwater acoustic signal processing, and it is the basis of nonstationary signal detection. Wavelet neural network model, with the advantages of both wavelet analysis and artificial neural network, makes full use of the time-frequency localization characteristics of wavelet analysis and the self-learning ability of artificial neural network; however, this model is prone to fall into local minima or creates convergence. To overcome these disadvantages, a new hybrid model based on fruit fly optimization algorithm (FOA) and wavelet neural network (WNN) is proposed in this paper. The FOA-WNN prediction model is constructed by optimizing the weights and thresholds of wavelet neural network, and the model is applied to underwater acoustic signal prediction. The experimental results show that the FOA-WNN prediction model has higher prediction accuracy and smaller prediction error, compared with wavelet neural network prediction model and BP neural network prediction model.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.614-615.409 ◽

2012 ◽

Vol 614-615 ◽

pp. 409-413 ◽

Cited By ~ 2

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.

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