Membership function optimization of a fuzzy controller using modified tabu search algorithm

Kaplan, Ahmet; Özer, Şaban; Sağıroğlu, Şeref

doi:10.1109/nafips.1998.715531

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…Using more integration of ANNs with other artificial intelligence techniques (fuzzy logic, genetic algorithms, expert systems, tabu search), investigate new neural network structures or architectures which may provide more precise solutions for more complex robotic problems. Some of the recent integration have been presented by [38,80,98,115,129,130,142,143] In spite of being successful in solving robotic problems, ANNs have some drawbacks in the area like setting the network parameters, finding convenient structure orland learning algorithms. The problems can be summarised as:-type of network to be used, learning algorithms, topology, number of layers, number of nodes, type of nonlinearities (sigmoid, sine), network parameters (seed, momentum and learning coefficients, initialisation), and the initial values of the weights and biases.…”

Section: Discussion and Analysismentioning

confidence: 99%

“…The following features make the ANNs more attractive for robotic applications-Adaptability and ability to learn [26,30,32,37,39,41,42,131], fast real-time applicability [9,41,59,132], generalisation [21,22,24], tolerance to noise in the input ipformation [67], less priori knowledge required [26,39,40,13 I], ease of integrating with other Artificia) Intelligence techniques such as Fuzzy logic, Expert Systems and Tabu search [70,79,80,88,89,98,129,142,143], and using several networks [71,115,[134][135][136][137][138][139][140][141].…”

Section: Discussion and Analysismentioning

confidence: 99%

See 1 more Smart Citation

Artificial Neural Networks in Robotic Applications

Sağiroğlu

1998

MCA

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There are a number of problems that their analytical solutions are difficult to obtain using conventional techniques in robotics. This-paper examines the use of Artificial Neural Networks (ANNs) as a new technique to solve such problems in the field of robotics. This paper presents an overview on ANNs applications to robot kinematics, dynamics, control, trajectory and task planning, and sensing. Moreover, the advantages and disadvantages of using and implementing ANNs to the robotic problems are outlined.

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Section: Discussion and Analysismentioning

confidence: 99%

Section: Discussion and Analysismentioning

confidence: 99%

Artificial Neural Networks in Robotic Applications

Sağiroğlu

1998

MCA

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“…Quantization factor, scale factor, fuzzy control rule and membership function are at the core of the fuzzy control, and greatly affect the dynamic characteristics of the control system. 18,19 Generally, the control rules are confirmed by experts' experience with considerable authority while the quantization factor, scale factor and the membership function are set subjectively with strong subjectivity and a blind eye to the selection process. Thus, the quantization factor, the scale factor and the membership function cannot achieve a favorable effect in the actual control, and the optimization of them is one of the most important concerns in the fuzzy control system.…”

Section: Design Of the Optimized Fuzzy Control Systemmentioning

confidence: 99%

“…17 In the fuzzy controller, quantization factor, scale factor, fuzzy control rule and membership function which are the core of fuzzy control and greatly affect the dynamic characteristics the control system, have a certain blindness and subjectivity because these parameters are set subjectively. 18,19 So, many scholars' research shows that an intelligent algorithm is used to optimize the fuzzy controller.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Control of Floating Raft Active Vibration Isolation System under Narrowband Interference

Jiang¹,

Duan²,

Liu³

2020

The International Journal of Acoustics and Vibration

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The harmonic signal generated by power equipment on naval vessels is a typical narrowband interference, which seriously affects the stealth performance of naval vessels. The float raft active vibration isolation system is characterized by strong adaptability and good controllability, which can effectively isolate low-frequency interference. For the active vibration isolation system, the control strategy is one of the core, and the precise mathematical model is difficult to be established. The fuzzy control does not rely on the precise mathematical model of the controlled system, which cannot only provide an intelligent path for the active control of the complex floating raft system, but also effectively suppresses the narrowband interference caused by naval vessels' power equipment. Therefore, depending on the existing float raft active vibration isolation platform, this paper outlines a design for a two-input/single-output optimized fuzzy control system based on vibration acceleration, variation of vibration acceleration and control voltage. In an optimized fuzzy control system, an algorithm of quantum behavior particle swarm optimization algorithm (QPSO) is proposed to optimize quantization factor and scale factor because quantization factor and scale factor are set subjectively with strong subjectivity and blindness, and the stability of the control system is roughly verified. Therefore, simulations and experiments are carried out to test and verify the performance of control system designs outlined in this paper. Through comparative analysis, it shows that the floating raft active vibration isolation system with a fuzzy control system based on the QPSO algorithm has a favorable effect on low-frequency narrowband interference.

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“…In first aspect, there are recently several proposed methods such as logic optimization [7], fuzzy linear optimization [8], upward search learning algorithm [9], genetic rule selection and lateral tuning [10], learning error feedback [11]. For second aspect, several methods have been proposed such as genetic algorithm [12], tabu search algorithm [13], gradient algorithm [14], partical swam algorithm [15], Ant colony algorithm [16], real-coded genetic algorithm [17].…”

Section: Background and Related Workmentioning

confidence: 99%

An algorithm for optimizing membership functions of fuzzy inference systems based on fuzzy associative memory

Ton-That

Cao

Choi

2014

Journal of Intelligent &Amp; Fuzzy Systems

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A fuzzy associative memory is a fuzzy logic tool for pattern recognition or control problems. Fuzzy inference systems based on fuzzy associative memory have a wide range of practical applications. These systems need to be determined how to form and how many membership functions for each input variable by analyzing its histogram. This paper proposes an algorithm for optimizing their membership functions by a threshold set and a method that finds out optimal membership functions whereby classification effects of the fuzzy inference systems can be improved. The algorithm is associated with a measure of useful degree of input membership functions to increase accurate classification rates of the system. The paper also based on the experiment to show criteria for collecting training data to improve the effect of recognition or classification of the fuzzy inference systems. To confirm the effectiveness, the proposed algorithm is applied to a pattern recognition problem with the iris data through a fuzzy inference system based on fuzzy associative memory.

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Membership function optimization of a fuzzy controller using modified tabu search algorithm

Cited by 8 publications

References 8 publications

Artificial Neural Networks in Robotic Applications

Artificial Neural Networks in Robotic Applications

Fuzzy Control of Floating Raft Active Vibration Isolation System under Narrowband Interference

An algorithm for optimizing membership functions of fuzzy inference systems based on fuzzy associative memory

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