Currently, the specifics of external conditions and peculiarities of innovation activity main subjects development determine not only the need for close, long-term scientific and technical cooperation with the state for the sustainable development of territories, but also the need to develop and substantiate proposals for managing the development of innovation processes in such a system as a whole. The article proposes a model for the representation of scientific and industrial interaction in the implementation of regional innovation processes in the form of a three-dimensional "slice" of the triple helix as a resource VRIO-profile of cooperative formation, which allows to clearly demonstrate the system of relations, identify in which direction the problem area is, influencing which it will be possible to return the system to an equilibrium state of sustainable development in a strategic perspective. The analysis of modern scientific works shows the relevance, necessity and effectiveness of using methods based on neural networks to predict changes in the state of complex socio-economic systems, such as regional innovation systems. Existing approaches, as a rule, demonstrate a narrow focus and belonging to a separate enterprise or organization, and therefore do not meet all the requirements from both the implementation of the innovation process itself and the modification of the external environment. In this connection, the authors proposed an information and analytical solution for using the described model to support decision-making on the management of cooperative formations. The developed program is based on predicting the future state (position in a three-dimensional coordinate system) of the system using deep neural networks, namely recurrent. The described practical approbation of the model can in the future serve as a basis for decision-making on the choice of forms and directions of interaction of cooperative formations in the strategic perspective.
neural network model, forecasting, LSTM-networks, innovation process, triple helix, innovative ecosystems, resource approach