Genetic Algorithm Optimization for Type-2 Non-singleton Fuzzy Logic Controllers

Martínez-Soto, Ricardo; Castillo, Oscar; Castro, Juan R.

doi:10.1007/978-3-319-05170-3_1

Cited by 26 publications

(12 citation statements)

References 38 publications

(36 reference statements)

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“…It has also been shown that in the presence of perturbations, the generalized type-2 fuzzy controllers outperform their type-1 and interval type-2 counterparts [30], [31], and that interval type-2 NSFLSs outperform type-1 NSFLSs in different application domains [32], [33]. Incorporating other AI techniques with type-2 FLSs has also resulted in hybrid solutions for predicting the behaviour of non-linear complex systems (e.g., using neural networks in [34] and genetic algorithms in [35]). …”

Section: Other Related Workmentioning

confidence: 99%

Improved Uncertainty Capture for Nonsingleton Fuzzy Systems

Pourabdollah

Wagner

Aladi

et al. 2016

IEEE Trans. Fuzzy Syst.

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(2016) Improved uncertainty capture for non-singleton fuzzy systems. IEEE Transactions on Fuzzy Systems, 24 (6). pp. 1513 -1524 . ISSN 1941 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/45444/1/Improved%20Uncertainty%20Capture%20for %20Non-Singleton%20Fuzzy%20Systems.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the University of Nottingham End User licence and may be reused according to the conditions of the licence. For more details see: http://eprints.nottingham.ac.uk/end_user_agreement.pdf A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk 1 Improved Uncertainty Capture for Non-Singleton Fuzzy SystemsAmir Pourabdollah, Member, IEEE, Christian Wagner, Senior Member, IEEE, Jabran Hussain Aladi, Student Member, IEEE and Jonathan M. Garibaldi, Member, IEEE Abstract-In non-singleton fuzzy logic systems (NSFLSs), input uncertainties are modelled with input fuzzy sets in order to capture input uncertainty (e.g., sensor noise). The performance of NSFLSs in handling such uncertainties depends on both: the appropriate modelling in the input fuzzy sets of the uncertainties present in the system's inputs, and on how the input fuzzy sets (and their inherent model of uncertainty) interact with the antecedent and thus affect the inference within the remainder of the NSFLS. This paper proposes a novel development on the latter. Specifically, an alteration to the standard composition method of type-1 fuzzy relations is proposed, and applied to build a new type of NSFLS. The proposed approach is based on employing the centroid of the intersection of input and antecedent sets as origin of the firing degree, rather than the traditional maximum of their intersection, thus making the NSFLS more sensitive to changes in the input's uncertainty characteristics. The traditional and novel approach to NSFLSs are experimentally compared for two well-known problems of Mackey-Glass and Lorenz chaotic time series predictions, where the NSFLSs' inputs have been perturbed with different levels of Gaussian noise. Experiments are repeated for system training under noisy and noise-free conditions. Analyses of the results show that the new method outperforms the traditional approach. Moreover, it is shown that while formally more complex, in practice, the new method has no significant computational overhead compared to the standard approach.

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Section: Other Related Workmentioning

confidence: 99%

Improved Uncertainty Capture for Nonsingleton Fuzzy Systems

Pourabdollah

Wagner

Aladi

et al. 2016

IEEE Trans. Fuzzy Syst.

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“…The chromosomes with higher fitness value are more likely to be selected and go through the next parts of the GA cycle, which are crossover and mutation that help the algorithm to culminate in a new generation. A typical GA works as follows [13]:…”

Section: A Genetic Algorithm (Ga)mentioning

confidence: 99%

Fuzzy membership functions optimization of fuzzy controllers for a quad rotor using particle swarm optimization and genetic algorithm

Safaee

Mashhadi

2016

2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA)

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Quad rotor as an Unmanned Aerial Vehicle (UAV) has always drawn attention due to its widespread military and civilian applications during the recent decades. Despite the simplicity in design, the vehicle is inherently unstable. Hence, designing a proper controller to reach the stability purpose is of a great importance. In this paper fuzzy controllers have been proposed to control a quad rotor. Due to the high reliance of fuzzy controller's performance on the membership functions, the membership functions are optimized using particle swarm optimization and genetic algorithm. Finally, the results of the proposed methods are compared to verify their effectiveness on the fuzzy controllers.

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“…The Genetic Algorithm (GA) is one of the most wellknown evolutionary optimization techniques, which has been adopted by many researchers to optimize complex problems [54,55]. Briefly, the optimization process by GA can be divided into 6 steps, which are given as follows [46]:…”

Section: Fitness Functionmentioning

confidence: 99%

Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

Azizi

2017

Complexity

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Recent advances in modern manufacturing industries have created a great need to track and identify objects and parts by obtaining real-time information. One of the main technologies which has been utilized for this need is the Radio Frequency Identification (RFID) system. As a result of adopting this technology to the manufacturing industry environment, RFID Network Planning (RNP) has become a challenge. Mainly RNP deals with calculating the number and position of antennas which should be deployed in the RFID network to achieve full coverage of the tags that need to be read. The ultimate goal of this paper is to present and evaluate a way of modelling and optimizing nonlinear RNP problems utilizing artificial intelligence (AI) techniques. This effort has led the author to propose a novel AI algorithm, which has been named "hybrid AI optimization technique," to perform optimization of RNP as a hard learning problem. The proposed algorithm is composed of two different optimization algorithms: Redundant Antenna Elimination (RAE) and Ring Probabilistic Logic Neural Networks (RPLNN). The proposed hybrid paradigm has been explored using a flexible manufacturing system (FMS), and results have been compared with Genetic Algorithm (GA) that demonstrates the feasibility of the proposed architecture successfully.

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Genetic Algorithm Optimization for Type-2 Non-singleton Fuzzy Logic Controllers

Cited by 26 publications

References 38 publications

Improved Uncertainty Capture for Nonsingleton Fuzzy Systems

Improved Uncertainty Capture for Nonsingleton Fuzzy Systems

Fuzzy membership functions optimization of fuzzy controllers for a quad rotor using particle swarm optimization and genetic algorithm

Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

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