Modeling of Neuro-Fuzzy System With Optimization Algorithm as a Support in System Boundary Capability Online Assessment

Abstract

The system’s ability to execute the task reflects the overall attributes of the equipment system. The mission capability of the equipment system is the result of each system’s interaction; nevertheless, the development of the subsystem has not yet produced a specific study scheme or measuring methodology for the overall system’s capability. This study investigates the real-time feature parameters of equipment systems, as well as the structure and interaction between the task capability of the system. Furthermore, it develops an evaluation model for the formation of the equipment system’s task competence based on the structural equation model. It can reflect the generation characteristics of the system’s mission capability and the emerging sources, provide a scientific basis for enhancing the efficiency of the system, and also support for evaluating the contribution of the subsystems. All of these factors contribute to improving the overall effectiveness of the system. The neuro-fuzzy system is given the learning ability through the application of modified particle swarm optimization. This enabled the elimination of experts or experiences from the process of determining unknown weight coefficients in the system. The neuro-fuzzy model’s inherent adaptability serves as the basis for this evaluation framework. The results of the tests conducted on the model demonstrate its learning efficiency and precision. The results of the experiment using the provided online assessment method demonstrate effectiveness and rationality.