Improving the Performance of Adaptive Neural Fuzzy Inference System (ANFIS) Using a New Meta-Heuristic Algorithm

محورهای موضوعی : فصلنامه ریاضی

Mehdi Khadem ¹ , Abbas Toloie Eshlaghy ² , Kiamars Fathi hafshejani ³

1 - Department of industrial management, science and research branch, Islamic Azad university, Tehran, Iran
2 - Department of industrial management, science and research branch, Islamic Azad university, Tehran, Iran
3 - Department of industrial management, south tehran branch, Islamic Azad university , Tehran, Iran

تاریخ دریافت : 1401/01/05 تاریخ پذیرش : 1401/10/04 تاریخ انتشار : 1401/10/06

کلید واژه: Meta-heuristic Algorithm, Optimization, Genetic Algorithm (GA), Adaptive Neural Fuzzy Inference System (ANFIS), Particle swarm algorithm (PSO), Qashqai algorithm (QA),

چکیده مقاله :

The adaptive fuzzy neural inference system (ANFIS) is an efficient estimation model not only among fuzzy neural systems but also among other types of machine learning techniques. Despite its acceptance among researchers, ANFIS cited limitations such as inefficiencies in large data and data problems, cost of computation, processing time and optimization, and error training. The ANFIS structural design is a complex optimization problem that can be improved using meta-heuristic algorithms. In this study, to optimize and reduce errors, a new meta-heuristic algorithm inspired by nomadic migration was designed and used to design an adaptive fuzzy neural system called the Qashqai nomadic meta-heuristic algorithm. The results of the hypothesis test showed that the Qashqai optimization algorithm is not defeated by the genetic algorithm and particle swarm and works well in terms of convergence to the optimal answer. In this hybrid algorithm, random data set are first generated and then trained by designing a basic fuzzy neural system. Subsequently, the parameters of the basic fuzzy system were adjusted according to the modeling error using the meta-heuristic optimization algorithm of Qashqai nomads. The fuzzy nervous system with the best values was obtained as the final result.The main achievements of the study are:• Improving ANFIS accuracy using a novel meta-heuristic algorithm.• Fix and remove some problems and Limitations in the Anfis model, such as inefficiencies in large data, cost of computation, Answer accuracy, and reduce errors.• Comparing the proposed ANFIS+QA with some recent related work such as ANFIS+QA and ANFIS+Pso.

چکیده انگلیسی:

The adaptive fuzzy neural inference system (ANFIS) is an efficient estimation model not only among fuzzy neural systems but also among other types of machine learning techniques. Despite its acceptance among researchers, ANFIS cited limitations such as inefficiencies in large data and data problems, cost of computation, processing time and optimization, and error training. The ANFIS structural design is a complex optimization problem that can be improved using meta-heuristic algorithms. In this study, to optimize and reduce errors, a new meta-heuristic algorithm inspired by nomadic migration was designed and used to design an adaptive fuzzy neural system called the Qashqai nomadic meta-heuristic algorithm. The results of the hypothesis test showed that the Qashqai optimization algorithm is not defeated by the genetic algorithm and particle swarm and works well in terms of convergence to the optimal answer. In this hybrid algorithm, random data set are first generated and then trained by designing a basic fuzzy neural system. Subsequently, the parameters of the basic fuzzy system were adjusted according to the modeling error using the meta-heuristic optimization algorithm of Qashqai nomads. The fuzzy nervous system with the best values was obtained as the final result.The main achievements of the study are:• Improving ANFIS accuracy using a novel meta-heuristic algorithm.• Fix and remove some problems and Limitations in the Anfis model, such as inefficiencies in large data, cost of computation, Answer accuracy, and reduce errors.• Comparing the proposed ANFIS+QA with some recent related work such as ANFIS+QA and ANFIS+Pso.

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