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Abstract

Content

Introduction

Competing in today’s economic environment is increasingly becoming a struggle not of resources and strategies, and investment companies are increasingly directed at ensuring the development of new methods of doing business. All the important role played by the innovative potential of the enterprise, the ability to generate more effective strategies and develop constantly, updating its structure and key business processes.

Desire to win the competition makes new demands on managers of metallurgical enterprises: they should put the ambitious but achievable goals, create a vision of the future of the enterprise and make it available to all employees, encourage the search for and find new non-trivial solutions in all the areas.

The result is an objective need for restructuring, the use of innovative approaches to the management, methods of planning and implementing strategies and tactics of industrial and economic activities on the basis of the relevant organizational level, the laws and regularities.

1. Theme urgency

Since restructuring – a complex process, which has large amounts of the factors affecting its occurrence, then to analyze the impact of various factors, control flow and evaluate the effectiveness of the restructuring process it is advisable to use different types of intelligent systems.

Thus, the need of generalization and the development of theoretical propositions as well as the development of methodological approaches and models for decision support systems (DSS) during the restructuring process that determines the relevance of the research topic, its theoretical and practical importance.

2. Goal and tasks of the research

The goal of the research is the creation a computer system to enhance the information content analysis of the influence factors on the production process of the company, forecasting financial results and decision support.

Main tasks of the research:

  1. Consider issues of restructuring the company and identify areas to improve the informativeness of the influence factors on the production process.
  2. Select the basic parameters that enable you to assess the quality of the production process.
  3. Design and development of computer subsystems.
  4. Testing the system.

Research object: Business restructuring of industrial enterprises of metallurgical profile, experiencing financial and operational difficulties.

3. An approach to the unification of synthesis of Moore FSM on FPGA

One of the biggest advantages of neural networks is its adaptation to a dynamically changing process parameters investigated the possibility of retraining on new data.

The study examined the different structures of neural networks to achieve the best possible training, followed by exact calculations. Results of neural network training are listed in Table 1.

Table 1 – Analysis of the NN training

Network Type Number of layers Number of neurons Accuracy of the result
newff210 8 8 30,02604
newff210 8 5 30,00114
newff210 8 3 30,00692
newff210 10 3 30,44759
newff310 12 12 12 30,02587
newff310 10 10 8 30,13357
newff310 10 8 8 30,07915
newff310 8 8 3 30,03656

As a result, has been designed following the model of a neural network with two hidden layers (Fig. 1).

Figure 1 – A neural network model.

Figure 1 – A neural network model.

first layer contains 10 neurons, which correspond to the number of input factors:

X1–X8 – input data: the most liquid assets, assets that are quickly implemented, etc.

X9 – product price on the stock exchange

X10 – the cost of production on the world market

second layer (hidden) contains 8 neurons, the third layer (the hidden) contains three neuron and the fourth layer (output) – 3 neuron:

Y1 – cover ratio;

Y2 – quick ratio;

Y3 – absolute liquidity ratio

Conclusion

The study financial restructuring were identified factors, including random, which directly affect the financial outcome of the enterprise. To assess the influence of various factors of the enterprise have been allocated the most important indicators of the enterprise: the coverage ratio, quick ratio, ratio of the absolute liquidity.

Master's thesis deals with the problem of restructuring industrial enterprises to improve financial stability and the production process using neural networks.

In the trials carried out:

  1. Questions enterprise restructuring, and reflects the main direction of more informative to analyze the influence of factors on the production process.
  2. As a result, research and analysis process as the main object is selected financial restructuring of the company.
  3. Selected key parameters and mathematical tools that enable you to assess the quality of the production process.

Further developed model can be complicated by the introduction of new factors involved, and used to analyze the effects of changing them on the production process.

This master's work is not completed yet. Final completion: December 2011. The full text of the work and materials on the topic can be obtained from the author or his head after this date.

References

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