Hybrid GA-SQP neural network frame for solving english language learning

A new way to compute the English Language Mathematical Model (ELMM) is described in this paper using a neural network hybridized with a genetic algorithm-based Sequential Quadratic Programming (SQP) optimization technique. The proposed GA-SQP-NN model for the ELMM is not just standard hybridization, it is for nonlinear linguistic learning processes when GA is used for global search and SQP for local refinement within the framework of a neural-network mapping structure. Another novelty is the use of error-based statistical measurements (TIC, MAD, RMSE) in the optimization cycle that creates convergence and precision than a GA-SQP. The results of the comparative measurements using the Lobatto method show that GA-SQP-NN is more reliable, more accurate, and more flexibly. There are also multi-run statistical verifications that prove the GA-SQP-NN model is stable. Therefore, the GA-SQP-NN structure is a usable and efficacious solver of complex mathematical models in the language acquisition area.