Efficient Construction of Reformed Erudition Automata using Neural Networks and Adaptive Optimization Algorithms

The development of sophisticated computational models that mimic human-like learning and adaptation has become necessary as intelligent systems have evolved. The construction framework for Reformed Erudition Automata (REA), a novel class of intelligent automata intended for highlevel cognitive tasks like adaptive decision-making, real-time reasoning, and inference, is presented in this paper. REAs use neural networks to dynamically modify their transitions based on learned patterns, in contrast to traditional automata with rigid state transitions. To improve convergence speed, computational efficiency, and learning robustness, the suggested framework integrates adaptive optimization techniques, namely Adam, RMSProp, and a specially designed gradient-based reinforcement feedback algorithm. Layered learning is further supported by a hierarchical neural architecture, which allows feedback to refine abstract representations. Experiments on classification, sequence prediction, and anomaly detection tasks show that the REA model performs better in terms of accuracy, generalization, and adaptability than static neural systems and conventional finite automata. The findings highlight REA’s potential for use in intelligent real-time applications such as adaptive control, natural language processing, and autonomous systems. Future studies in neural-symbolic integration and cognitive model fusion are made possible by this framework.