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The article is devoted to description of structure and operation algorithms of a multilevel intellectual system for analysis and forecast of scientific-technological and innovative development of objects and territories based on expert methods, trainable models of fuzzy logic and fuzzy cognitive analysis. The article analyses distinctions and advantages of the system in making forecast evaluation and scenarios of the dynamics of multidimensional feature objects and processes. The authors have built a neutrosophic cognitive map for interaction of technologies based on the results of foresight studies.

The article is devoted to research and development of adaptive algorithms for neuro-fuzzy inference when solving multicriteria problems connected with analysis of expert (foresight) data to identify technological breakthroughs and strategic perspectives of scientific, technological and innovative development. The article describes the optimized structuralfunctional scheme of the high-performance adaptive neuro-fuzzy classifier with a logical output, which has such specific features as a block of decision tree-based fuzzy rules and a hybrid algorithm for neural network adaptation of parameters based on the error back-propagation to the root of the decision tree.

The article considers the problem of measuring the level and rate of technical and economic development of countries in terms of technological change. Advantages of cognitive neural network approach to monitoring and quality analysis for integrating into a single model of economic, scientific-technological, innovative and other quantitative and qualitative components of growth, not amenable to traditional statistical analysis, with calculation of the forecast evaluation time of the reference trajectory of technical and economic development. Presents the results of the calculations. To improve the accuracy of the model trajectories are encouraged to use self-organizing Kohonen maps.

We consider a new type of hybrid neuro-fuzzy system based on fuzzy and neural computing in hierarchical sequential structure, the total effect exceeds the effect of each component separately. The proposed system can be applied to multi-criteria analysis, automatic classification on signs and obtain evidence-based estimates of the efficiency of scientific and technical solutions and technologies, engineering and robotics. An example of a neuro-fuzzy system measuring the intensity of the emotions of a robot, with the extraction of diagnostic decision rules “If & then”.