Программная реализация нейросетевого арифметического сумматора
The construction of integrated sub-symbolic systems is considered to be an important scientific direction where the expression of symbolic rules in the form of a neural network plays a key role. At the same time, there is an actual task of creating neural architec- tures for solving complex intellectual tasks without preliminary testing for modeling of computational processes in perspective mas- sively parallel computational environments. The very first step towards solving this task is the creation of a neural network that is ca- pable to perform an exact solution for the specifically selected motivating problem that incorporates various intellectual operations on symbolic structures. The task of Multi-Attribute Linguistic Decision Making can be selected as an example of an appropriate motivat- ing problem. Linguistic assessment aggregation is a key element of fuzzy decision-making models and includes several stages: as- sessments translation from a form of 2-tuple to a numerical representation, application of aggregation operators, and reverse transla- tion of numerical results to 2-tuple structures. Linguistic assessments are encoded and decoded with the help of rules defined by the Tensor Representations framework. The current work is a continuation of the research dedicated to the implementation of arithmetic operations in a neural form. The neural design is proposed that is capable of performing the arithmetic sum of two numbers, encoded with Tensor Representations without a training stage. The proposed method was implemented and analyzed with the help of the Keras framework. The design of the neural primitive that takes distributed representation of symbolic structures as an input proves the hypothesis about expressing various symbolic rules in a form of neural architectures, such as aggregation of linguistic assess- ments during the decision-making process. The proposed primitive is based on the arbitrary symbolic structures analysis and can be used as a neural adder. Such a network can be easily extended and supported as well as there is a huge potential for re-use of this network for implementation of other sub-symbolic operations on the neural level. Moreover, the generation approach to network cre- ation that can manipulate structures on the tensor level, can be used in a wide range of cognitive systems.