QSPR prediction of polymers’ solubility parameters by radial basis functional link net

2020 ◽  
pp. 1-16
Author(s):  
Dilek İmren Koç ◽  
Mehmet Levent Koç
Keyword(s):  
Solubility Parameters ◽  
Functional Link ◽  
Radial Basis

scholarly journals Radial Basis Functional Link Network and Hamilton Jacobi Issacs for Force/Position Control in Robotic Manipulation

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Shuhuan Wen ◽  
Junying Yuan ◽  
Jinghai Zhu
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Robotic Manipulation ◽  
System Stability ◽  
External Interference ◽  
Functional Link ◽  
Rbf Network ◽  
Radial Basis

This paper works on hybrid force/position control in robotic manipulation and proposes an improved radial basis functional (RBF) neural network, which is a robust relying on the Hamilton Jacobi Issacs principle of the force control loop. The method compensates uncertainties in a robot system by using the property of RBF neural network. The error approximation of neural network is regarded as an external interference of the system, and it is eliminated by the robust control method. Since the conventionally fixed structure of RBF network is not optimal, resource allocating network (RAN) is proposed in this paper to adjust the network structure in time and avoid the underfit. Finally the advantage of system stability and transient performance is demonstrated by the numerical simulations.

Sliding Mode Control Based on Chemical Reaction Optimization and Radial Basis Functional Link Net for De-Icing Robot Manipulator

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Thuy Van Tran ◽  
YaoNan Wang ◽  
HungLinh Ao ◽  
Tung Khac Truong
Keyword(s):  
Sliding Mode Control ◽  
Chemical Reaction ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Stability And Convergence ◽  
Chemical Reaction Optimization ◽  
Functional Link ◽  
Mode Control ◽  
Reaction Optimization ◽  
Radial Basis

In this paper, a sliding mode control (SMC) system based on combining chemical reaction optimization (CRO) algorithm with radial basis functional link net (RBFLN) for an n-link robot manipulator is proposed to achieve the high-precision position tracking. In the proposed scheme, a three-layer RBFLN with powerful approximation ability is employed to approximate the uncertainties, such as parameter variations, friction forces, and external disturbances, and to eliminate chattering phenomenon of the SMC. In order to achieve the expected performance in the initial phase as well as the improved convergence rate, the RBFLN parameters need to be optimized in advance. Therefore, the initial parameters of the RBFLN are optimized offline by CRO algorithm instead of random selection. Furthermore, the RBFLN weights are determined online according to adaptive tuning laws in the sense of a projection algorithm and the Lyapunov stability theorem to guarantee the stability and convergence of the system. The simulation results of three-link de-icing robot manipulator (DIRM) are provided to verify the robustness and effectiveness of the proposed methodology.

Radial basis functional link nets and fuzzy reasoning

2002 ◽  
Vol 48 (1-4) ◽  
pp. 489-509 ◽  
Author(s):  
Carl G. Looney
Keyword(s):  
Fuzzy Reasoning ◽  
Functional Link ◽  
Radial Basis
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