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Invited researcher Wang Jun
Time span of the project
General information

Name of the project: Multi-scale intelligent neurodynamic systems for multi-dimensional optimisation in the field of machine learning and data processing

Strategy for Scientific and Technological Development Priority Level: а
Goals and objectives

The scientific goal of the proposed project is the development of fundamentally new optimisation algorithms based on multi-scale collaborative neurodynamic systems and modern scientific approaches related to them. A special attention will be paid to the development of methods of solution of the following complex optimisation problems:

  • Problems involving non-convex and discontinuous target functions and special limitations that guarantee smoothness and sparsity of the solutions;
  • Problems involving integer and continuous variables produced from constrains on the allowable set of solutions;
  • Problems of multi-criteria optimisation with multiple target functions in which it is necessary to efficiently find the solution suitable for all the target functions;
  • Problems of multi-level optimisation in a hierarchical decision-making structure as well as multi-period optimisation with dynamic modelling in the context of a decreasing time horizon of planning that will be investigated from the point of view of dynamic optimisation.
The practical value of the study

The created scientific laboratory will become a world-class research centre in the field of multi-scale collaborative neurodynamic models for solving problems of optimisation in a wide range of applications of machine learning and data analysis.

Projected research results:

  • We will solve problems related to the determination of the correlation between adaptive neurodynamic systems, artificial neural networks, and neural ordinary differential equations and their applications to a wide range of problems of training both with and without a teacher;
  • We will review a number of important practical domains, including robotics and robust control, surrogate models, and reverse engineering problems as well as the optimisation of the architecture of neural networks.
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