IDEAS home Printed from
   My bibliography  Save this article

Neural Networks Architectures For Modeling And Simulation Of The Economy System Dynamics


  • Nicolae Tudoroiu
  • Claudiu Chiru
  • Manuela Grigore


This research work investigates the possibility to apply several neural network architectures for simulation and prediction of the dynamic behavior of the complex economic processes. Therefore we will explore different neural networks architectures to build several neural models of the complex dynamic economy system. In future work we will use these architectures to be trained by well-known training algorithms, such as Levenberg-Marquardt back-propagation error and Radial Basic Function (RBF), to compare their results and to decide at the end, which one is the best among the different applications from the economy field. The results presented in this work are based on the experience accumulated by the authors in the field of identification, modeling and control of the industrial and economic processes, namely chemical, HVAC, automotive industry and satellites constellation. The neural networks are strongly recommended for the highly nonlinear processes for which an analytic description is almost impossible. It is well known that the single-index economic models and selection of leading indicator variables are normally based on linear regression methods. Moreover, in statisti- cal modeling of the business cycle, it has been well established that cycles are asymmetric; therefore it is doubtful that linear models can adequately describe them. With recent development in nonlinear time series analysis, several authors have begun to examine the forecasting properties of nonlinear models in economics. Probably the largest share of economic appli- cations of nonlinear models can be found in the field of prediction of time series capital markets. Furthermore recently, the neural network architectures use financial variables to forecast industrial production by estimating a nonlinear, non- parametric nearest-neighbor regression model, and are very useful for fault detection, diagnosis and isolation ( FDDI) of the models fault in the control systems.The simulation results reveal a high capability of the neural networks to capture more accurate the nonlinear dynamics behavior of the process and to yield high performance, comparable to the Kalman filters techniques and all other control strategies developed in literature. The nonlinear mapping and self-learning abilities of neural networks have been motivating factors for development of intelligent contol strategies. The neural networks approach is very interesting because don`t need the linear model of the process that means time consuming and increasing the risk to reduce the accuracy in capturing the appropriate dynamics of the process.

Suggested Citation

  • Nicolae Tudoroiu & Claudiu Chiru & Manuela Grigore, 2009. "Neural Networks Architectures For Modeling And Simulation Of The Economy System Dynamics," Journal of Academic Research in Economics, Spiru Haret University, Faculty of Accounting and Financial Management Constanta, vol. 1(1 (June)), pages 93-116.
  • Handle: RePEc:shc:jaresh:v:1:y:2009:i:1:p:93-116

    Download full text from publisher

    File URL:
    Download Restriction: no

    More about this item


    Dynamic Systems; Kalman Filers; Neural Networks Architectures; ARMA Models; Estimation; Neural-Models; Neural-Control Strategy; Inverse Neural-Control Strategy; MIMO Control Strategies. Market-Oriented Economy;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:shc:jaresh:v:1:y:2009:i:1:p:93-116. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Claudiu Chiru). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.