A species-based improved electromagnetism-like mechanism algorithm for TSK-type interval-valued neural fuzzy system optimization

Lee, Ching‐Hung; Li, Chung-Ta; Chang, Feng-Yu

doi:10.1016/j.fss.2011.02.004

Cited by 22 publications

(23 citation statements)

References 30 publications

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“…Like Genetic Algorithms, PSO is initialized with a population of random solutions. It has been applied on optimization problems such as neural network learning, fuzzy control, and evolutionary algorithms [24][25][26][27][28][29][30]. Compared with other …”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…It has been applied on optimization problems such as neural network learning, fuzzy control, and evolutionary algorithms [24][25][26][27][28][29][30]. Compared with other evolutionary algorithms [24][25][26][27][28][29][30], PSO has less selected parameters to provide efficiency. At first, the optimization problem is formulated as minimize f (X) subject to X = {x 1 ,…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

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Remote Servo Tuning System for Multi-Axis CNC Machine Tools Using a Virtual Machine Tool Approach

Lin

Lee

2017

Applied Sciences

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Servo systems affect the performances of machining in accuracy and surface quality for high speed and precision machine tools. This study introduces an efficient servo tuning technique for Computer Numerical Control (CNC) feed drive systems using particle swarm optimization (PSO) algorithm by virtual machine tool approach. The proposed approach contained a system identification phase and a servo tuning phase based on the same bandwidth for all axes feed drive systems. The PSO algorithm was adopted to obtain the system parameters and maximize the corresponding bandwidth. An efficient two-step servo tuning method based on gain and phase margins was proposed for high speed and precision requirements. All feed drive systems controller gains were optimized simultaneously for synchronization. A remote system called Machine Dr. was established for servo tuning and monitoring. Simulation and experimental results were introduced to illustrate the effectiveness of the proposed approach.

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Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Remote Servo Tuning System for Multi-Axis CNC Machine Tools Using a Virtual Machine Tool Approach

Lin

Lee

2017

Applied Sciences

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“…The rule of simplified FIS is expressed as (11) where j ∈ Z c is a rule number, i∈Z n is a variable number, M i j is a membership function of the antecedent part, and w j is the weight of the consequent part.…”

Section: Fuzzy Inference System and Learning Algorithmmentioning

confidence: 99%

“…EM is known as one of random search algorithms [5,6,10,11]. The basic idea is that a set of parameters is regarded as position of charged particles and the charge of the particles is corresponding to the value of the objective function for the optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Hybrid Electromagnetism-Like Mechanism Algorithms with Descent Method

Miyajima

Shigei

2015

Journal of Artificial Intelligence and Soft Computing Research

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Electromagnetism-like Mechanism (EM) method is known as one of metaheuristics. The basic idea is one that a set of parameters is regarded as charged particles and the strength of particles is corresponding to the value of the objective function for the optimization problem. Starting from any set of initial assignment of parameters, the parameters converge to a value including the optimal or semi-optimal parameter based on EM method. One of its drawbacks is that it takes too much time to the convergence of the parameters like other meta-heuristics. In this paper, we introduce hybrid methods combining EM and the descent method such as BP, k-means and FIS and show the performance comparison among some hybrid methods. As a result, it is shown that the hybrid EM method is superior in learning speed and accuracy to the conventional methods.

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“…INTRODUCTION Over the decades, fuzzy systems and neural networks have been successfully applied to system identification, nonlinear control, and classification [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. The advantages of fuzzy systems and neural networks are combined in fuzzy neural networks (FNNs), including neural-fuzzy systems and interval type-2 fuzzy neural systems [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15].…”

mentioning

confidence: 99%

Dynamic solution agent algorithm for TSK-type interval-valued fuzzy system optimization

Lee

Kuo

2012

2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)

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In this paper, a new heuristic learning algorithm, the modified water flow-like algorithm (MWFA), is proposed for TSK-type interval-valued fuzzy system (TIVFS) optimization. The water flow-like algorithm (WFA) is inspired by the natural behavior of water flows; the splitting, moving, merging, evaporation, and precipitation operations were developed for optimization. However, the original WFA is not valid for the continuous optimization problem. Therefore, some modifications, including some moving strategies, such as applying the tabu searching and gradient-descent techniques, are proposed to enhance the performance of MWFA. In addition, the modified strategies in evaporation and precipitation operations are more consistent with the natural behavior of water flows and they increase the diversity of solution agents. Finally, the MWFA is applied in the design of TIVFS for nonlinear system identification to demonstrate the effectiveness and performance.

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A species-based improved electromagnetism-like mechanism algorithm for TSK-type interval-valued neural fuzzy system optimization

Cited by 22 publications

References 30 publications

Remote Servo Tuning System for Multi-Axis CNC Machine Tools Using a Virtual Machine Tool Approach

Remote Servo Tuning System for Multi-Axis CNC Machine Tools Using a Virtual Machine Tool Approach

Performance Comparison of Hybrid Electromagnetism-Like Mechanism Algorithms with Descent Method

Dynamic solution agent algorithm for TSK-type interval-valued fuzzy system optimization

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