scholarly journals Research on a proportional-integral-derivative neural network decoupling control based on genetic algorithm optimization for unified chaotic system

2007 ◽  
Vol 56 (5) ◽  
pp. 2493
Author(s):  
Niu Pei_Feng ◽  
Zhang Jun ◽  
Guan Xin_Ping
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Chaotic System ◽  
Decoupling Control ◽  
Proportional Integral Derivative ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Proportional Integral ◽  
Unified Chaotic System

Hangzhou Housing Demand Forecasting Model Based on BP Neural Network of Genetic Algorithm Optimization

2014 ◽  
Vol 587-589 ◽  
pp. 37-41 ◽  
Author(s):  
Yi Hua Mao ◽  
Meng Bo Zhang ◽  
Ning Bo Yao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Real Estate ◽  
Housing Market ◽  
Bp Neural Network ◽  
Housing Demand ◽  
Forecasting Model ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
The Impact

Hangzhou, the capital of Zhejiang province and a famous scenic tourist city in China, goes at the forefront of the country for its high real estate prices, which hold a very important position of orientation to pricing in the real estate markets of the Yangtze River Delta region and of the whole country as well. The price trend of Hangzhou's real estate is even related to the sustainable development of the city. This paper uses the macro data on the housing market in Hangzhou during 1999-2012 to establish a forecasting model which is based on BP neural network of genetic algorithm optimization. With MATLAB software exploited for programming and simulation, the prediction made by the model about the housing demand in Hangzhou and the subsequent re-examination show that the model has high precision. But due to the impact of the national macro-control policies on housing market, the predictive value of some years may fluctuate to a certain extent.

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