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

Area of Science:

• Computational Linguistics
• Artificial Intelligence
• Optimization Techniques

Background:

• The English Language Mathematical Model (ELMM) is crucial for understanding language acquisition.
• Existing computational methods may lack efficiency in handling nonlinear linguistic learning processes.

Purpose of the Study:

• To introduce a novel hybrid computational model, GA-SQP-NN, for the ELMM.
• To enhance the accuracy, reliability, and flexibility of ELMM computation.
• To validate the stability and efficacy of the proposed model.

Main Methods:

• Hybridization of a neural network with a genetic algorithm (GA) and Sequential Quadratic Programming (SQP).
• GA employed for global search, SQP for local refinement within the neural network framework.
• Integration of error-based statistical measurements (TIC, MAD, RMSE) into the optimization cycle.

Main Results:

• The GA-SQP-NN model demonstrated superior convergence and precision compared to standard GA-SQP.
• Comparative measurements using the Lobatto method confirmed GA-SQP-NN's enhanced reliability, accuracy, and flexibility.
• Multi-run statistical verifications proved the stability of the GA-SQP-NN model.

Conclusions:

• The GA-SQP-NN structure is an effective and usable solver for complex mathematical models in language acquisition.
• This hybrid approach offers significant improvements for computational linguistics and natural language processing tasks.
• The model's stability and performance highlight its potential for advanced language modeling applications.