Time series in forecasting and decision: an experiment in elman nn models

The paper examines the role of analytical tools in analysis of economic statistical data (commonly referred to as econometry) and artificial neural network (ANN) models for time series processing in forecasting, decision and control. The emphasis is put on the comparative analysis of classical econometric approach of pattern recognition (Box-Jenkins approach) and neural network models, especially the class of recurrent ones and Elman ANN in particular. A comprehensive experiment in applying the latter modeling has been carried out, some specific applications software developed, and a number of benchmark series from the literature processed. This paper reports on comparison findings in favor of Elman ANN modeling, and on the use of a designed program package that encompasses routines for regression, ARIMA and ANN analysis of time series. The analysis is illustrated by two sample examples known as difficult to model via any technique

A memory ann computing structure for nonlinear systems emulation identification

Currently, almost all efforts for using artificial neural networks for control oriented process identification are based on feed-forward networks. Provided the system order or the upper limit of the order is known, a neural network design is feasible for which all the collection of previous values of the inputs and outputs of the system to be identified can be used as input data to train in the network computing structures to learn the input-output map. This work reports on a novel technique that makes use of memory artificial neural network architecture that can learn and transform so as to emulate any non-linear input-output map for multi-input-multi-output systems when no prior knowledge on specific system features exists

How good ANN identification of post-stabilization inflation dynamics can be?

IEEEThe recent emerging trend in financial systems engineering relies on exploiting soft-computing technologies, and on employing neural-nets techniques, in particular. Simultaneously, recent empirical studies on economic stabilization programs implemented worldwide have clearly demonstrated that, after the successful disinflation, the inflationary process can no longer be captured and explained using the traditional variables and models provided by economic theory. This paper proposes a combined stochastic and artificial neural-nets approach in expert support systems to the identification of inflation dynamics by means of Box-Jenkins ARIMA and Elman-ANN models. The approach is illustrated by means of the case-study data set on inflation dynamics in the pre- and post-stabilization period in the Republic of Macedonia

Perception model of forecasting life exapectancy via insurance Lee-Carter mortality function

Dimirovski, Georgi M. (Dogus Author) -- Conference full title: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC); Miyazaki, Japan; 7 October 2018 through 10 October 2018.Forecasting of mortality function is important for many field of human work like insurance companies, government projections of the human assets, medical research. During past years many models were presented. Most common Lee-Carter model is based on the log function on mortality rate which includes as input variables age, year of mortality function and bias, which also enables predicting the life expectancy. In this paper a perceptron based model with minimum number of nodes in the network having custom transfer function is proposed. Results are compared with Lee-Carter and other neural network based models by using MSE type of error. This model is simpler than other neural networks and is easier to handle adjusting the weights while computing results are rather comparable with those of more complex neural network models

Modelling study on post-stabilization dynamics of inflation in Macedonia

Dimirovsk, Georgi M. (Dogus Autjor) -- Conference full title: 3rd IFAC Workshop on Automatic Systems for Building the Infrastructure in Developing Countries, DECOM-TT 2003; Istanbul; Turkey; 26 June 2003 through 28 June 2003.The experience from and recorded observations on economy stabilization programs implemented worldwide have clearly shown that there is sharp difference between the inflation dynamics during the implementation of such programs and the one during the post-stabilization period. In this context, it is observed that after the successful disinflation, the inflationary process can no longer be explained using the traditional variables provided by the standard theory of economy. Following these empirically established regularity phenomena, this paper explores the possibility of identification of the inflation process dynamics via of the system-theoretic, by means of both the traditional statistics and Box-Jenkins ARIMA methodologies. The application of this theoretic approach is to the real inflation dynamics in Rep. of Macedonia in the post-stabilization period, second half of the 1990s

Reliable adaptive control for switched Fuzzy Systems

Dimirovski, Georgi M. (Dogus Author) -- Conference full title: 17th World Congress, International Federation of Automatic Control, IFAC; Seoul; South Korea; 6 July 2008 through 11 July 2008.A model of a kind of uncertain switched fuzzy systems is presented first, in which each subsystem is an uncertain fuzzy system. Then the robust stabilization problem to the system is studied and a solution proposed. When the upper bounds of the disturbances are unknown, and the actuator is serious failure and the residual part of actuator can not make original system stable, a reliable robust adaptive controller is constructed to guarantee the closed-loop system is uniformly ultimately bounded via using switching technique and multiple Lyapunov function approach. The switching strategy achieving system uniformly ultimately bounded of the uncertain switched fuzzy system is given. An illustrative example is given that demonstrates the feasibility and the effectiveness of the proposed method

Elman NN and time series in forecasting models for decision making

Dimirovski, Georgi M. (Dogus Author) -- Conference full title: World Automation Congress: Budapest, Hungary, July 24-26, 2006This paper examines and compares analytical tools in analysis of economic statistical data, econometric modeling, and neural network, soft-computing modeling, as representation models for time series processing in forecasting, decision and control. In addition, a novel forecasting model using Elman networks is proposed. A comprehensive experiment in applying the latter modeling has been carried out, some specific applications software developed, and a number of benchmark series from the literature processed. This paper reports on comparison findings as well on the use of our application software package which encompasses routines for regression, ARIMA and NN analysis of time series. The comparison analysis is illustrated by a sample example known as difficult to model via any technique

A fuzzy cost function optimization in product mix selection problem

Dimirovski, Georgi M. (Dogus Author) -- Conference full title: 2006 World Automation Congress, WAC'06; Budapest; Hungary; 24 June 2006 through 26 June 2006The modern trend in industrial application problem deserves modeling of all relevant vague or fuzzy information involved in a real decision making problem. In this paper the usefulness of the proposed S-curve membership function is established using a real life industrial production planning of a chocolate manufacturing unit. The unit produces 8 products using 8 raw materials, mixed in various proportions by 9 different processes under 29 constraints. A solution to this problem establishes the usefulness of the suggested membership function for decision making in industrial production planning. The objective of this paper is to find the optimal cost to produce 8 products using modified S-curve membership function as a methodology. The fuzzy linear programming approach is used to solve this problem. The optimal cost function is obtained respect to two major factors of degree of satisfaction and vagueness