Intelligence theory studies the connection between the subjective and objective worlds perceived and analyzed by human intellect. Therefore, on the one hand, intelligence theory must correspond to the objective requirements adopted in physical sciences as science; on the other hand, it is compelled to rely on introspective intelligence data. Like other exact sciences, intelligence theory needs a special mathematical language corresponding to the intelligence theory object; special methods suitable for objective study of human intelligence. The basic method of objective analysis and modeling of human intelligence is comparative identification method. In the method the subject realizes a certain final predicate by his behavior. In accordance with the method, an experimental study of this predicates' properties is conducted, then, basing on the results a mathematical the subject's reactions model subjective states of its intelligence is constructed. Comparative identification method accurate of isomorphism allows t find a function transforming physical situations into subjective images generated by them. In this article a comparator predicate decomposition is performed and its functional structure is analyzed, the process of the human intellect subjective states multiplicities factorization is studied. K e ywor d s : intelligence theory; algebra of finite predicates; comparative identification.
In order to objectively and completely analyze the state of the monitored object with the required level of efficiency, the method for estimating and forecasting the state of the monitored object in intelligent decision support systems was improved. The essence of the method is to provide an analysis of the current state of the monitored object and short-term forecasting of the state of the monitored object. Objective and complete analysis is achieved using advanced fuzzy temporal models of the object state, taking into account the type of uncertainty and noise of initial data. The novelty of the method is the use of an improved procedure for processing initial data in conditions of uncertainty, an improved procedure for training artificial neural networks and an improved procedure for topological analysis of the structure of fuzzy cognitive models. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function and the architecture of individual elements and the architecture of the artificial neural network as a whole. The procedure of forecasting the state of the monitored object allows for multidimensional analysis, accounting and indirect influence of all components of the multidimensional time series with their different time shifts relative to each other in conditions of uncertainty. The method allows increasing the efficiency of data processing at the level of 12–18 % using additional advanced procedures. The proposed method can be used in decision support systems of automated control systems (ACS DSS) for artillery units, special-purpose geographic information systems. It can also be used in ACS DSS for aviation and air defense and ACS DSS for logistics of the Armed Forces of Ukraine
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