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Swarming morlet wavelet neural network procedures for the mathematical robot system.

Peerapongpat Singkibud¹, Zulqurnain Sabir^2,3, Irwan Fathurrochman⁴

¹Department of Applied Mathematics and Statistics, Faculty of Sciences and Liberal Arts, Rajamangala University of Technology Isan, Nakhon Ratchasima, 30000, Thailand.

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Summary

This study introduces an artificial intelligence (AI) approach using a Morlet wavelet neural network (MWNN) optimized with particle swarm optimization (PSO) and active set procedure (ASP) to analyze mathematical robot systems (MRS) for coronavirus case examination. The AI model demonstrates reliable performance in solving the complex MRS.

Keywords:

Active set procedure Artificial intelligence Mathematical robot system Morlet wavelet Numerical solutions Particle swarm optimization

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Area of Science:

Computational mathematics
Artificial Intelligence
Robotics

Background:

Mathematical robot systems (MRS) require robust methods for analysis, particularly when applied to complex problems like examining infectious disease data.
Existing methods may lack the precision or adaptability needed for dynamic systems.

Purpose of the Study:

To develop and validate an artificial intelligence (AI) based Morlet wavelet neural network (MWNN) for solving the mathematical robot system (MRS).
To optimize the AI model using a hybrid approach combining particle swarm optimization (PSO) and active set procedure (ASP).
To assess the system's effectiveness in examining positive coronavirus cases.

Main Methods:

The study employs an AI-based MWNN model.
A fitness function for the MRS is designed using differential equations.
Optimization is achieved through a hybrid PSO and ASP algorithm.
The model's solutions are compared against reference solutions for accuracy.

Main Results:

The proposed AI-based MWNN-PSOASP model provides accurate solutions for the MRS.
Validation through 20 trials and statistical analysis confirms the reliability and performance of the scheme.
The model effectively uses 10 hidden neurons for solving the MRS.

Conclusions:

The AI-based MWNN-PSOASP is a reliable and effective method for solving the mathematical robot system.
This approach shows promise for applications in analyzing complex datasets, such as tracking infectious disease cases.