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The paper is a study of a business recessions. On the ground of the actual financial crisis, which has broad international influence, many questions about possibilities of efficient solutions of the economic recessions have been opened. In the economics history is possible to find many examples of business recessions and more or less successful solution methods. Each recession has several analogical characters with other recessions. The similarity of the recessions is utilized in this study as the base for fuzzy model of the business recessions. The fuzzy logic is very useful tool for modeling, because absolute as well as relative quantitative indicators can be used in the model. The fuzzy model of the business recession is based on the data of the American's recessions and also covers the data about the Japanese crisis and one example of the Czech business recession. The model was utilized for research of the recessions' similarities and for the prediction of the aspect of the future business recessions.

Purpose of the article: Relations among parameters of High-tech projects are very complex, vague, partially inconsistent and multidimensional. Optimal decisions to invest into High-tech companies require top field experts and knowledgeable investors. Therefore the conventional methods of investments analysis are not relevant. Therefore fuzzy logic is introduced. Methodology/methods: A fuzzy knowledge base is a flexible framework for acquisition of vague inconsistent knowledge items which are typical for knowledge economics and consequently for High-tech projects. The pooling of the records and / or observations represents a trade-off between minimal modification of the original data and elimination of inconsistencies among available sets of data. Scientific aim: The paper presents a detailed description of fuzzy model of investment decision making into High-tech firm’s projects. A set of conditional statements was used to formalize the effects of selected variables on investment feasibility of High-tech projects. The main aim is to quantify feasibilities of High-tech projects risk investors make good /not bad decisions. Findings: A set of 50 observations of High-tech companies was transformed into a set of 50 conditional statements using 14 variables. The result is the fuzzy model, which can be used to answer investors’ queries. Two queries are answered and presented in details as an example and as a nucleus of a fuzzy dialogue investor – computer. Conclusions: The main problem is the sparseness of the fuzzy model. Many fuzzy similarities are relatively low and the decision process is therefore often problematic. A much more complex set of variables must be applied to specify the fuzzy model to increase reliability of predictions and decisions.

The problem of dynamic optimal allocation of firms finance assets is important decision process. With this importance corresponds an attention dedicated to it. In spite of fuzzy, concerning the allocation tasks this side of problem is usually leave out. The text describes general linear optimisation fuzzy model for the solution of dynamic optimal allocation tasks of company finance. The text branches out a mentioned model by fuzzy aspects.