NEURAL NETWORK-BASED METHODS FOR FINDING THE SHORTEST PATH and establishing associative connections between objects - Eugene Fedorov; Olga Nechyporenko; Maryna Chychuzhko; Vladyslav Chychuzhko; Ruslan Leshchenko (Радіоелектронні і комп'ютерні системи, 2023)
Nowadays, solving optimizations problems is one of the tasks for intelligent computer systems. Currently, there is a problem of insufficient efficiency of optimizations tasks solving methods (for example, high computing time and/or accuracy). The object of the research is the process of finding the shortest path and establishing associative connections between objects. The subject of the research is the methods of finding the shortest path and establishing associative connections between objects based on neural networks with associative memory and neural network reinforcement training. The objective of this work is to improve the efficiency of finding the shortest path and establishing associative connections between objects through neural networks with associative memory and neural network reinforcement training. To achieve this goal, a neuro-associative method and a neural network reinforcement training method was developed. The advantages of the proposed methods include the following. First, the proposed bi-directional recurrent correlative associative memory, which uses hetero-associative and auto-associative memory and an exponential weighting function, allows for increasing the associative memory capacity while preserving learning accuracy. Second, the Deep Q-Network (DQN) reinforcement learning method with dynamic parameters uses the ε-greedy approach, which in the initial iterations is close to random search, and in the final iterations is close to directed search, which is ensured by using dynamic parameters and allows increasing the learning speed while preserving learning accuracy. Conducted numerical research allowed us to estimate both methods (for the first method, the root mean square error was 0.02, and for the second method it was 0.05). The proposed methods allow expanding the field of application of neural networks with associative memory and neural network reinforcement learning, which is confirmed by their adaptation for the tasks of finding the shortest path and establishing associative connections between objects and contribute to the effectiveness of intelligent computer systems of general and special purpose. Prospects for further research are to investigate the proposed methods for a wide class of artificial intelligence problems.