scholarly journals Shadowed Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search and Differential Evolution Algorithms

Algorithms ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 17 ◽  
Author(s):  
Oscar Castillo ◽  
Patricia Melin ◽  
Fevrier Valdez ◽  
Jose Soria ◽  
Emanuel Ontiveros-Robles ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Fuzzy Inference ◽  
Harmony Search ◽  
Dynamic Parameter ◽  
Optimization Methods ◽  
Dynamic Adaptation ◽  
Fuzzy Inference Systems ◽  
Parameter Adaptation ◽  
Inference Systems

Nowadays, dynamic parameter adaptation has been shown to provide a significant improvement in several metaheuristic optimization methods, and one of the main ways to realize this dynamic adaptation is the implementation of Fuzzy Inference Systems. The main reason for this is because Fuzzy Inference Systems can be designed based on human knowledge, and this can provide an intelligent dynamic adaptation of parameters in metaheuristics. In addition, with the coming forth of Type-2 Fuzzy Logic, the capability of uncertainty handling offers an attractive improvement for dynamic parameter adaptation in metaheuristic methods, and, in fact, the use of Interval Type-2 Fuzzy Inference Systems (IT2 FIS) has been shown to provide better results with respect to Type-1 Fuzzy Inference Systems (T1 FIS) in recent works. Based on the performance improvement exhibited by IT2 FIS, the present paper aims to implement the Shadowed Type-2 Fuzzy Inference System (ST2 FIS) for further improvements in dynamic parameter adaptation in Harmony Search and Differential Evolution optimization methods. The ST2 FIS is an approximation of General Type-2 Fuzzy Inference Systems (GT2 FIS), and is based on the principles of Shadowed Fuzzy Sets. The main reason for using ST2 FIS and not GT2 FIS is because the computational cost of GT2 FIS represents a time limitation in this application. The paper presents a comparison of the conventional methods with static parameters and the dynamic parameter adaptation based on ST2 FIS, and the approaches are compared in solving mathematical functions and in controller optimization.

scholarly journals Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Cinthia Peraza ◽  
Fevrier Valdez ◽  
Juan R. Castro ◽  
Oscar Castillo
Keyword(s):  
Fuzzy Systems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Dynamic Parameter ◽  
Harmony Search Algorithm ◽  
Parameter Adaptation ◽  
Error Metrics ◽  
Interval Type ◽  
Following Error

This paper presents a method for dynamic parameter adaptation in the harmony search algorithm (HS) based on fuzzy logic. The adaptation is performed using Type 1 (FHS), interval Type 2 (IT2FHS), and generalized Type 2 (GT2FHS) fuzzy systems as the number of improvisations or iterations advances, achieving a better intensification and diversification. The main contribution of this work is the dynamic parameter adaptation using different types of fuzzy systems in the harmony search algorithm applied to optimization of the membership functions for a benchmark control problem; in this case it is focused on the ball and beam controller. Experiments are presented with the HS, FHS, IT2FHS, and GT2FHS with noise (uniform random number) and without noise for the controller, and the following error metrics are obtained: ITAE, ITSE, IAE, ISE, and RMSE, to validate the efficacy of the proposed methods.

scholarly journals Artificial Hydrocarbon Networks Fuzzy Inference System

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Hiram Ponce ◽  
Pedro Ponce ◽  
Arturo Molina
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Inference Model ◽  
Inference System ◽  
Position Controller ◽  
Inference Systems ◽  
Linguistic Units ◽  
Output Space

This paper presents a novel fuzzy inference model based on artificial hydrocarbon networks, a computational algorithm for modeling problems based on chemical hydrocarbon compounds. In particular, the proposed fuzzy-molecular inference model (FIM-model) uses molecular units of information to partition the output space in the defuzzification step. Moreover, these molecules are linguistic units that can be partially understandable due to the organized structure of the topology and metadata parameters involved in artificial hydrocarbon networks. In addition, a position controller for a direct current (DC) motor was implemented using the proposed FIM-model in type-1 and type-2 fuzzy inference systems. Experimental results demonstrate that the fuzzy-molecular inference model can be applied as an alternative of type-2 Mamdani’s fuzzy control systems because the set of molecular units can deal with dynamic uncertainties mostly present in real-world control applications.

scholarly journals Building Java Intelligent Applications Data Mining for Java Type-2 Fuzzy Inference Systems

2015 ◽  
Vol 51 ◽  
pp. 2719-2728 ◽  
Author(s):  
Manuel Castañón-Puga ◽  
Josué Miguel Flores-Parra ◽  
Juan Ramón Castro ◽  
Carelia Gaxiola-Pacheco ◽  
Luis Enrique Palafox-Maestre
Keyword(s):  
Data Mining ◽  
Fuzzy Inference ◽  
Fuzzy Inference Systems ◽  
Inference Systems

An approach for non-singleton generalized Type-2 fuzzy classifiers

2020 ◽  
Vol 39 (5) ◽  
pp. 7203-7215
Author(s):  
Emanuel Ontiveros-Robles ◽  
Oscar Castillo ◽  
Patricia Melin
Keyword(s):  
Fuzzy Systems ◽  
General Type ◽  
Fuzzy Inference ◽  
Classification Problems ◽  
Fuzzy Inference Systems ◽  
Classification Rate ◽  
Inference System ◽  
Fuzzy Classifiers ◽  
Inference Systems

In recent years, successful applications of singleton fuzzy inference systems have been made in a plethora of different kinds of problems, for example in the areas of control, digital image processing, time series prediction, fault detection and classification. However, there exists another relatively less explored approach, which is the use of non-singleton fuzzy inference systems. This approach offers an interesting way for handling uncertainty in complex problems by considering inputs with uncertainty, while the conventional Fuzzy Systems have their inputs with crisp values (singleton systems). Non-singleton systems have as inputs Type-1 membership functions, and this difference increases the complexity of the fuzzification, but provides the systems with additional non-linearities and robustness. The main limitations of using a non-singleton fuzzy inference system is that it requires an additional computational overhead and are usually more difficult to apply in some problems. Based on these limitations, we propose in this work an approach for efficiently processing non-singleton fuzzy systems. To verify the advantages of the proposed approach we consider the case of general type-2 fuzzy systems with non-singleton inputs and their application in the classification area. The main contribution of the paper is the implementation of non-singleton General Type-2 Fuzzy Inference Systems for the classification task, aiming at analyzing its potential advantage in classification problems. In the present paper we propose that the use of non-singleton inputs in Type-2 Fuzzy Classifiers can improve the classification rate and based on the realized experiments we can observe that General Type-2 Fuzzy Classifiers, but with non-singleton fuzzification, obtain better results in comparison with respect to their singleton counterparts.

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