Tuning of a neuro-fuzzy controller by genetic algorithm

Seng, Teo Lian; Khalid, Marzuki; Yusof, Rubiyah

doi:10.1109/3477.752795

Cited by 200 publications

(15 citation statements)

References 30 publications

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“…However, finding an appropriate sigma for each of the variables of the pattern nodes could be a difficult task [20,21] since it depends on the network input variables and the Web page characteristics. The initialisation method used for the neural network is the dynamic sigma initialisation, which does not require statistical calculations [22,23]. This method assigns the centre and width of the Gaussian kernel for each of the input variables effectively and with less computation procedures compared to other clustering methods [24,25].…”

Section: Initialisation Methodsmentioning

confidence: 99%

A generalised regression algorithm for Web page categorisation

Anagnostopoulos

Kouzas

et al. 2004

Neural Comput & Applic

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This paper proposes an information system that classifies Web pages according a taxonomy, which is mainly used from seven search engines/directories. The proposed classifier is a four-layer generalised regression neural network (GRNN) that aims to perform the information segmentation according to information filtering techniques using content descriptor vectors. Eight categories of Web pages were used in order to evaluate the robustness of the method, while no restrictions were imposed except for the language of the content, which is English. The system can be used as an assistant and consultative tool for classification purposes as well as for estimating the population of Web pages at any given point in time.

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Section: Initialisation Methodsmentioning

confidence: 99%

A generalised regression algorithm for Web page categorisation

Anagnostopoulos

Kouzas

et al. 2004

Neural Comput & Applic

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“…PID controller tuning based on fuzzy logic, neural networks and other evolutionary computation methods are commonly used. 1 A conventional PID controller is not appropriate for nonlinear systems that are used in many of the industries. For fixed values of parameters the PID controller, is incapable of adjusting the nonlinear factors to enhance the control performance.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of a non linear PID controller using chaotic gravitational search algorithm for a twin rotor system

Sivadasan,

Shiney

2023

Adv Control Appl

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A novel strategy using a chaotic gravitational search algorithm (CGSA) based nonlinear PID control scheme, which is validated through a laboratory helicopter model called the twin rotor system, is presented in this paper. In this work, CGSA is used as a stochastic based global optimization algorithm for controller design in the twin rotor system adopted. The fine chaotic search process used in CGSA obtains the optimal solution in the iterative process based on the current best solution. The goal of the controller design in this paper is to stabilize the twin rotor system with considerable cross couplings to reach the selected position and follow the desired trajectory effectively. The addition of nonlinear functions to the PID controller structure initiates better error tracking and facilitates smooth output under changing input conditions. The design objective is to implement a nonlinear PID control scheme for the angular displacements of the twin rotor system with minimization of the integral square error (ISE) as the fitness function in the algorithm. The statistical performance of the controller is analyzed by considering the best, worst, mean, and standard deviations of ISE. In this work, simultaneous control of pitch and yaw angles is considered to get rid of the coupling effect between the two rotors. From the simulation results it is observed that the proposed work shows better performance than the other evolutionary computation techniques. The results also indicate the advantage of the proposed CGSA based tuning for the two degree of freedom MIMO control with standard reference trajectories as per the TRMS330‐10 model.

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“…Numerous control methods have been proposed, such as fuzzy control [1], neural control [2] and adaptive control [3]. Among these, the proportion integration differentiation (PID) controller is the most popular.…”

Section: Introductionmentioning

confidence: 99%

“…However, the performance of Z-N on the practical engineering was poor [11]. In recent decades, artificial intelligence algorithms have developed and play an important role in optimization fields, such as artificial neural network (ANN) [12], genetic algorithm (GA) [13], fuzzy logic [1] and neural-fuzzy system [2]. Due to the poor performance of the Z-N method, intelligence algorithms were put into use for tuning the PID controller.…”

Section: Introductionmentioning

confidence: 99%

Pressure Control for a Hydraulic Cylinder Based on a Self-Tuning PID Controller Optimized by a Hybrid Optimization Algorithm

Wang

Tan

et al. 2017

Algorithms

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Abstract:In order to improve the performance of the hydraulic support electro-hydraulic control system test platform, a self-tuning proportion integration differentiation (PID) controller is proposed to imitate the actual pressure of the hydraulic support. To avoid the premature convergence and to improve the convergence velocity for tuning PID parameters, the PID controller is optimized with a hybrid optimization algorithm integrated with the particle swarm algorithm (PSO) and genetic algorithm (GA). A selection probability and an adaptive cross probability are introduced into the PSO to enhance the diversity of particles. The proportional overflow valve is installed to control the pressure of the pillar cylinder. The data of the control voltage of the proportional relief valve amplifier and pillar pressure are collected to acquire the system transfer function. Several simulations with different methods are performed on the hydraulic cylinder pressure system. The results demonstrate that the hybrid algorithm for a PID controller has comparatively better global search ability and faster convergence velocity on the pressure control of the hydraulic cylinder. Finally, an experiment is conducted to verify the validity of the proposed method.

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Tuning of a neuro-fuzzy controller by genetic algorithm

Cited by 200 publications

References 30 publications

A generalised regression algorithm for Web page categorisation

A generalised regression algorithm for Web page categorisation

Performance evaluation of a non linear PID controller using chaotic gravitational search algorithm for a twin rotor system

Pressure Control for a Hydraulic Cylinder Based on a Self-Tuning PID Controller Optimized by a Hybrid Optimization Algorithm

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