Theoretical Foundations of Neural Network Integration of System Software Modules

The theoretical foundations of neural network integration of system software modules lie in the field of combining formal methods for automation, optimization and management of development, integration and maintenance of complex software systems and systems engineering. The article proposes a formalism of operator schemes for constructing programs with deterministically connected modules for any class of algorithms. The structures of description and execution of programs are considered. In programs, there are data (information) and control transfer links between operators. When training neural networks, program structures indicate only the links that control their execution. Distributed links between inputs and outputs of sets of operators are associated with modules. Program operator modules are numbered. This numbering is preserved in program execution. Programs of modules can be sequential, parallel and sequential-parallel. The structure of programs with deterministically connected modules is considered. In the proposed formalism of operator schemes, the solvability of the problem of constructing programs with deterministically connected modules is proved. These fundamentals provide a theoretical basis for developing systems in which neural networks act as tools for complex automation and optimization of design, integration, and management of system software modules. In the future, the development of this concept will contribute to the creation of self-regulating, adaptive, and scalable system architectures.