Home            Contact us            FAQs
    
      Journal Home      |      Aim & Scope     |     Author(s) Information      |      Editorial Board      |      MSP Download Statistics

     Research Journal of Applied Sciences, Engineering and Technology

    Abstract
2011(Vol.3, Issue:11)
Article Information:

Nonlinear Analysis of Core Competence for Real Estate Enterprise Using Evolutionary Fuzzy Neural Inference Model

Huawang Shi and Wei Hou
Corresponding Author:  Huawang Shi 
Submitted: 2011 July, 20
Accepted: 2011 September, 07
Published: 2011 November, 25
Abstract:
The real estate development trade is a pillar industry of our national economy. The real estate companies must focus on core competence in order to succeed in the fierce competition. This paper presents a probabilistic model for core competence estimation of real estate firms. According to the nonlinear feature of real estate core competence, this paper establishes the fuzzy and neural network model to core competence assessment of real estate. We analyze the features of core competence, thus define the core competence in real estate industry. The main competition factors of real estate industry include R&|D ability, innovation capability, strategic management capacity, marketing capacity and management capacity. We confirmed the weight of each index quantitatively by means of Analytical Hierarchy Process(AHP) according to an established index system. Then analyze the ability of artificial neural network to real estate core competence assessment, and lays the theoretical foundation of artificial neural network using in the systematic optimization of real estate core competence assessment and getting reasonable accurate core competence assessment result.

Key words:  Analytical Hierarchy Process (AHP), artificial neural network, core competence assessment, fuzzy method, real estate, ,
Abstract PDF HTML
Cite this Reference:
Huawang Shi and Wei Hou, . Nonlinear Analysis of Core Competence for Real Estate Enterprise Using Evolutionary Fuzzy Neural Inference Model. Research Journal of Applied Sciences, Engineering and Technology, (11): 1221-1226.
ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
Submit Manuscript
   Information
   Sales & Services
Home   |  Contact us   |  About us   |  Privacy Policy
Copyright © 2024. MAXWELL Scientific Publication Corp., All rights reserved