Residential Property Price Prediction with FNN Network Model

2011 ◽  
Vol 271-273 ◽  
pp. 1638-1643
Author(s):  
Zhang Xiaoli

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.

2007 ◽  
Vol 329 ◽  
pp. 93-98
Author(s):  
Ning Ding ◽  
Long Shan Wang ◽  
Guang Fu Li

A surface roughness intelligent prediction control system during grinding is built. The system is composed of fuzzy neural network prediction subsystem and fuzzy neural network controller. In the fuzzy neural network prediction subsystem, the vibration data are added to the inputs besides the grinding condition, such as feed and speed, so as to improve the dynamic performance of the prediction subsystem. The fuzzy neural network controller is able to adapt grinding parameters in process to improve the surface roughness of machined parts when the roughness is not meeting requirements. Experiment verifies that the developed prediction control system is feasible and has high prediction and control accuracy.


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