A fuzzy neural network prediction model based on manifold learning to reduce dimensions for typhoon intensity

In order to predict the new residential property market price, a Fuzzy Neural Network (FNN) prediction model was proposed. It was used to estimate the appropriate price level for a new property by learning from historical data on the correlations between various factors that influence the prices of properties and the actual selling prices. In particular, an artificial neural network prediction model was developed to compare it accuracy with the fuzzy neural network prediction model. The experimental results show that the fuzzy neural network prediction model has strong function approximation ability and is suitable for residential properties price prediction depending on the quality of the available data.

Download Full-text

The Study of Prediction Model Based on Morlet Wavelet Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.4847 ◽

2011 ◽

Vol 121-126 ◽

pp. 4847-4851 ◽

Cited By ~ 3

Author(s):

Hui Zhen Yang ◽

Wen Guang Zhao ◽

Wei Chen ◽

Xu Quan Chen

Keyword(s):

Neural Network ◽

Prediction Model ◽

Network Model ◽

Neural Network Model ◽

Learning Algorithm ◽

Morlet Wavelet ◽

Wavelet Neural Network ◽

Model Based ◽

Neural Network Prediction ◽

Network Prediction

Wavelet Neural Network (WNN) is a new form of neural network combined with the wavelet theory and artificial neural network. The wavelet neural network model based on Morlet wavelet and the corresponding learning algorithm were studied in this paper. And through learning the wavelet neural network model is applied to all kinds of engineering examples, it proved that the wavelet neural network prediction model which has a more flexible and efficient function approximation ability and strong fault tolerance, and with high predicting precision.

Download Full-text

Prediction and Analysis of Strawberry Moisture Content based on BP Neural Network Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190429161911 ◽

2020 ◽

Vol 13 (4) ◽

pp. 657-671

Author(s):

Wei Jiang ◽

Hongmei Xu ◽

Elnaz Akbari ◽

Jiang Wen ◽

Shuang Liu ◽

...

Keyword(s):

Neural Network ◽

Moisture Content ◽

Prediction Model ◽

Bp Neural Network ◽

Prediction Method ◽

Principal Component ◽

Spectral Difference ◽

Model Based ◽

Neural Network Prediction ◽

Network Prediction

Background: Moisture content is one of the most important indicators for the quality of fresh strawberries. Currently, several methods are usually employed to detect the moisture content in strawberry. However, these methods are relatively simple and can only be used to detect the moisture content of single samples but not batches of samples. Besides, the integrity of the samples may be destroyed. Therefore, it is important to develop a simple and efficient prediction method for strawberry moisture to facilitate the market circulation of strawberry. Objective: This study aims to establish a novel BP neural network prediction model to predict and analyze strawberry moisture. Methods: Toyonoka and Jingyao strawberries were taken as the research objects. The hyperspectral technology, spectral difference analysis, correlation coefficient method, principal component analysis and artificial neural network technology were combined to predict the moisture content of strawberry. Results: The characteristic wavelengths were highly correlated with the strawberry moisture content. The stability and prediction effect of the BP neural network prediction model based on characteristic wavelengths are superior to those of the prediction model based on principal components, and the correlation coefficients of the calibration set for Toyonaka and Jingyao respectively reached up to 0.9532 and 0.9846 with low levels of standard deviations (0.3204 and 0.3010, respectively). Conclusion: The BP neural network prediction model of strawberry moisture has certain practicability and can provide some reference for the on-line and non-destructive detection of fruits and vegetables.

Download Full-text