Add to ORKGAbstract — In this paper, we introduce a new evolutionary methodology to design fuzzy inference systems. An innovative hybrid stages of learning method and tuning method, contains Subtractive clustering, Adaptive Neuro-Fuzzy Inference System (ANFIS) and particle swarm optimization (PSO), is developed to generate evolutional fuzzy modeling systems with high accuracy. For the purpose of illustration and validation of the approach, some data sets have been exploited. Empirical results illustrate that the proposed method is efficient
The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve g...
In the structure of ANFIS, there are two different parameter groups: premise and consequence. Traini...
This paper introduces a new approach for training the adaptive network based fuzzy inference system ...
An innovative hybrid stages particle swarm optimization (HSPSO) learning method, contains fuzzy c-me...
In recent years, the use of hybrid soft computing methods has shown that in various applications the...
Summary. In recent years, the use of hybrid soft computing methods has shown that in various applica...
Abstract—This study proposes an efficient self-evolving evolu-tionary learning algorithm (SEELA) for...
In this work an evolutionary fuzzy system (EFS) is presented and applied to an environmental problem...
The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method t...
In this paper, a novel self-constructing evolutionary algorithm (SCEA) for designing a TSK-type fuzz...
A key characteristic of intelligent systems is their ability to deduce new knowledge, to predict and...
While using automated learning methods, the lack of accuracy and poor knowledge generalization are b...
This paper presents a framework for studying the effectiveness of evolutionary strategies for genera...
This paper proposes a differential evolution with local information for TSK-type neuro-fuzzy system ...
The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve g...
The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve g...
In the structure of ANFIS, there are two different parameter groups: premise and consequence. Traini...
This paper introduces a new approach for training the adaptive network based fuzzy inference system ...
An innovative hybrid stages particle swarm optimization (HSPSO) learning method, contains fuzzy c-me...
In recent years, the use of hybrid soft computing methods has shown that in various applications the...
Summary. In recent years, the use of hybrid soft computing methods has shown that in various applica...
Abstract—This study proposes an efficient self-evolving evolu-tionary learning algorithm (SEELA) for...
In this work an evolutionary fuzzy system (EFS) is presented and applied to an environmental problem...
The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method t...
In this paper, a novel self-constructing evolutionary algorithm (SCEA) for designing a TSK-type fuzz...
A key characteristic of intelligent systems is their ability to deduce new knowledge, to predict and...
While using automated learning methods, the lack of accuracy and poor knowledge generalization are b...
This paper presents a framework for studying the effectiveness of evolutionary strategies for genera...
This paper proposes a differential evolution with local information for TSK-type neuro-fuzzy system ...
The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve g...
The main purpose of using the hybrid evolutionary algorithm is to reach optimal values and achieve g...
In the structure of ANFIS, there are two different parameter groups: premise and consequence. Traini...
This paper introduces a new approach for training the adaptive network based fuzzy inference system ...