Performance Comparison of Parallel PSO-GA Algorithm with Dynamic Parameter Adjustment Using Type-1 and Interval Type-2 Fuzzy Systems

In this paper, the optimal designs of type-1 and interval type-2 fuzzy systems for the classification of the heart rate level are presented. The contribution of this work is a proposed approach for achieving the optimal design of interval type-2 fuzzy systems for the classification of the heart rate in patients. The fuzzy rule base was designed based on the knowledge of experts. Optimization of the membership functions of the fuzzy systems is done in order to improve the classification rate and provide a more accurate diagnosis, and for this goal the Bird Swarm Algorithm was used. Two different type-1 fuzzy systems are designed and optimized, the first one with trapezoidal membership functions and the second with Gaussian membership functions. Once the best type-1 fuzzy systems have been obtained, these are considered as a basis for designing the interval type-2 fuzzy systems, where the footprint of uncertainty was optimized to find the optimal representation of uncertainty. After performing different tests with patients and comparing the classification rate of each fuzzy system, it is concluded that fuzzy systems with Gaussian membership functions provide a better classification than those designed with trapezoidal membership functions. Additionally, tests were performed with the Crow Search Algorithm to carry out a performance comparison, with Bird Swarm Algorithm being the one with the best results.

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

Advances in Operations Research ◽

10.1155/2018/3092872 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 18

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.

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Bidding Strategies Based on Type-1 and Interval Type-2 Fuzzy Systems for Google AdWords Advertising Campaigns

Nature-Inspired Design of Hybrid Intelligent Systems - Studies in Computational Intelligence ◽

10.1007/978-3-319-47054-2_6 ◽

2016 ◽

pp. 99-113 ◽

Cited By ~ 3

Author(s):

Quetzali Madera ◽

Oscar Castillo ◽

Mario Garcia ◽

Alejandra Mancilla

Keyword(s):

Fuzzy Systems ◽

Bidding Strategies ◽

Interval Type

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Uncertainty in Interval Type-2 Fuzzy Systems

Mathematical Problems in Engineering ◽

10.1155/2013/452780 ◽

2013 ◽

Vol 2013 ◽

pp. 1-16 ◽

Cited By ~ 3

Author(s):

Sadegh Aminifar ◽

Arjuna Marzuki

Keyword(s):

Fuzzy Systems ◽

System Response ◽

Membership Functions ◽

Degree Reduction ◽

Word Meanings ◽

Lower Complexity ◽

Interval Type

This paper studies uncertainty and its effect on system response displacement. The paper also describes how IT2MFs (interval type-2 membership functions) differentiate from T1MFs (type-1 membership functions) by adding uncertainty. The effect of uncertainty is modeled clearly by introducing a technique that describes how uncertainty causes membership degree reduction and changing the fuzzy word meanings in fuzzy logic controllers (FLCs). Several criteria are discussed for the measurement of the imbalance rate of internal uncertainty and its effect on system behavior. Uncertainty removal is introduced to observe the effect of uncertainty on the output. The theorem of uncertainty avoidance is presented for describing the role of uncertainty in interval type-2 fuzzy systems (IT2FSs). Another objective of this paper is to derive a novel uncertainty measure for IT2MFs with lower complexity and clearer presentation. Finally, for proving the affectivity of novel interpretation of uncertainty in IT2FSs, several investigations are done.

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