Hierarchical genetic algorithm based RBF neural networks and application for modelling of the automatic depth control electrohydraulic system

Xing, Zongyi; Pang, Xuemiao; Ji, Haiyan; Qin, Yong; Jia, Limin

doi:10.1007/s12555-011-0418-6

Cited by 8 publications

(4 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hierarchical genetic algorithms (HGA) (Xing et al 2011; Barreto et al 2006) were put forward based on the hierarchy of biological chromosome which has two parts: controlling genes and parameter genes. The controlling genes which determine whether the parameter genes are activated, are expressed in binary form, in which “1” indicates that the underlayer genes are active, while “0” indicates that the underlayer genes are inactive.…”

Section: Hhga Optimizes Rbf Neural Networkmentioning

confidence: 99%

Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic algorithm

et al. 2016

View full text Add to dashboard Cite

With the continuous expansion of the cloud computing platform scale and rapid growth of users and applications, how to efficiently use system resources to improve the overall performance of cloud computing has become a crucial issue. To address this issue, this paper proposes a method that uses an analytic hierarchy process group decision (AHPGD) to evaluate the load state of server nodes. Training was carried out by using a hybrid hierarchical genetic algorithm (HHGA) for optimizing a radial basis function neural network (RBFNN). The AHPGD makes the aggregative indicator of virtual machines in cloud, and become input parameters of predicted RBFNN. Also, this paper proposes a new dynamic load balancing scheduling algorithm combined with a weighted round-robin algorithm, which uses the predictive periodical load value of nodes based on AHPPGD and RBFNN optimized by HHGA, then calculates the corresponding weight values of nodes and makes constant updates. Meanwhile, it keeps the advantages and avoids the shortcomings of static weighted round-robin algorithm.

show abstract

Section: Hhga Optimizes Rbf Neural Networkmentioning

confidence: 99%

Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic algorithm

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Moreover, genetic algorithm was adopted in the calculation and the calculation results were compared with the experimental data. The comparison results indicated that this method allowed satisfactory results [18]. Shi and Reitz studied NSGAII applications under different strategies and indicated that the NSGAII was superior to other commonly used algorithms for engine performance optimization [19,20].…”

Section: Introductionmentioning

confidence: 97%

Multiobjective Optimization Design of Double-Row Blades Hydraulic Retarder with Surrogate Model

Liu

Linshan

et al. 2014

Advances in Mechanical Engineering

View full text Add to dashboard Cite

For the design of double-row blades hydraulic retarder involves too many parameters, the solution process of the optimal parameter combination is characterized by the large calculation load, the long calculation time, and the high cost. In this paper, we proposed a multiobjective optimization method to obtain the optimal balanced solution between the braking torque and volume of double-row blades hydraulic retarder. Moreover, we established the surrogate model for objective function with radial basis function (RBF), thus avoiding the time-consuming three-dimensional modeling and fluid simulation. Then, nondominated sorting genetic algorithm-II (NSGA-II) was adopted to obtain the optimal combination solution of design variables. Moreover, the comparison results of computational fluid dynamics (CFD) values of the optimal combination parameters and original design parameters indicated that the multiobjective optimization method based on surrogate model was applicable for the design of double-row blades hydraulic retarder.

show abstract

“…The use of Neural Networks (NN) for identification of nonlinear models has already been explored [10][11][12]. Suthradar et al [13] demonstrates both identification and control of nonlinear dynamical systems using static and dynamic back-propagation methods.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Identification of Inverted Pendulum System Using Volterra Polynomials

Ronquillo

Ríos-Moreno

Martínez

et al. 2014

Multibody Mechatronic Systems

View full text Add to dashboard Cite

The inverted pendulum problem is one of the most important problems in control theory and has been studied excessively in control literatures. When control systems have strong requirements, the adjustment of the controller is a complex problem. The nonlinear model is useful for control design. In the present work, Volterra polynomial basis function (VPBF) networks have been used to identify a single inverted pendulum on a moving cart (SIPC) system. The inverted pendulum is a benchmark problem of nonlinear multivariable systems with inherent instability. The multivariable system has been considered with a force produced by a DC motor as the input, and four states variables as the outputs. A Fuzzy Logic controller has been used to stabilize the system for closed-loop identification. Here, the nonlinear model of the inverted pendulum has been implemented. The offline structure selection through orthogonal least square algorithm is used for the nonlinear system identification via the basis function selection of Volterra polynomial networks. The neural network is trained using the error between the model's outputs and the plant's actual outputs. The results show good match between predicted and actual outputs.

show abstract

Hierarchical genetic algorithm based RBF neural networks and application for modelling of the automatic depth control electrohydraulic system

Cited by 8 publications

References 25 publications

Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic algorithm

Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic algorithm

Multiobjective Optimization Design of Double-Row Blades Hydraulic Retarder with Surrogate Model

Nonlinear Identification of Inverted Pendulum System Using Volterra Polynomials

Contact Info

Product

Resources

About