A Multi-objective Learning Algorithm for RBF Neural Network

2008 ◽  
Author(s):  
Illya Kokshenev ◽  
Antonio Padua Braga
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Multi Objective

An efficient multi-objective learning algorithm for RBF neural network

2010 ◽  
Vol 73 (16-18) ◽  
pp. 2799-2808 ◽  
Author(s):  
Illya Kokshenev ◽  
Antonio Padua Braga
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Multi Objective

Neural Network Sliding Mode Control of 5DOFs Robotic Manipulator

2014 ◽  
Vol 898 ◽  
pp. 514-520 ◽  
Author(s):  
Chang Kai Xu ◽  
Ming Li ◽  
Jian Yin
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Dynamic Performance ◽  
Robotic Manipulator ◽  
Terminal Sliding Mode ◽  
Mode Control ◽  
The Stability

In this paper, a neural network sliding mode controller for a kind of 5DOFs robotic manipulator is proposed. A radial basis function (RBF) neural network is used as an estimator to approximate uncertainties of the system. The learning algorithm of the neural network improves the performance of the system. A globle terminal sliding mode control (GTSMC) is designed to guarantee the stability and improve the dynamic performance of the robotic manipulator. Simulation results show that the proposed NNSMC strategy is effective to ensure the robustness and dynamic performance of the 5DOFs robotic manipulator.

AN Improved RBF Neural Network Model Based on Hybrid Learning Algorithm

2013 ◽  
Vol 718-720 ◽  
pp. 2202-2207
Author(s):  
Zhao Hu Deng ◽  
Yan Qin Zhang
Keyword(s):  
Neural Network ◽  
Traditional Method ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Clustering Algorithms ◽  
Hybrid Learning ◽  
Descent Method ◽  
Learning Ability ◽  
Steepest Descent Method ◽  
Training Samples

When building the radial basis function (RBF) neural network with traditional method, the property of the network is easily influenced by the distribution of training samples. The learning ability and generalization ability are hard to achieve the optimum. In this paper, it presents a new method to solve this problem. In the method it replaced the traditional clustering algorithms with genetic algorithms to optimize the distribution of RBF. At the same time it combined the steepest descent method with GA to solve the binary defect of GA encoding. After experiments the results showed that the constructed neural network has a better architecture and more accuracy than that built with traditional method.

Fault Diagnosis of Cascaded Converter Based on Wavelet Packet RBF Neural Network

2013 ◽  
Vol 273 ◽  
pp. 300-304
Author(s):  
Xin Wang ◽  
Juan Xu ◽  
Guo Dong Zhang ◽  
Rui Min Qi
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Open Circuit ◽  
Learning Performance ◽  
Cascaded Converter ◽  
Diagnosis Method ◽  
Faults Diagnosis

To study the power component open circuit faults diagnosis method of the cascaded converter. Aiming at the insufficiency of the BP learning algorithm in the machinery fault diagnosis, such as the low learning convergence speed, the easily appearing local minimum, the instability learning performance caused by the initial value, to proposed a new method applied to the cascaded converter based on radial basis function (RBF) neural network. Experiments show that the method based on wavelet packet analysis and RBF neural network has better learning and fault identification capability, and it can meet the online real-time fault diagnosis of the cascaded converter.

scholarly journals A Constrained Multi-Objective Learning Algorithm for Feed-Forward Neural Network Classifiers

2017 ◽  
Vol 7 (3) ◽  
pp. 1685-1693
Author(s):  
M. Njah ◽  
R. El Hamdi
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Benchmark Problems ◽  
Feed Forward Neural Network ◽  
New Approach ◽  
Feed Forward ◽  
Multi Objective ◽  
Adaptive Penalty ◽  
Neural Network Classifiers

This paper proposes a new approach to address the optimal design of a Feed-forward Neural Network (FNN) based classifier. The originality of the proposed methodology, called CMOA, lie in the use of a new constraint handling technique based on a self-adaptive penalty procedure in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Neurons and connections of the FNN Classifier are dynamically built during the learning process. The approach includes differential evolution to create new individuals and then keeps only the non-dominated ones as the basis for the next generation. The designed FNN Classifier is applied to six binary classification benchmark problems, obtained from the UCI repository, and results indicated the advantages of the proposed approach over other existing multi-objective evolutionary neural networks classifiers reported recently in the literature.

scholarly journals Design of Picking Robot Manipulator Control System Based on Fuzzy Compensation RBF Neural Network

CONVERTER ◽  
2021 ◽  
pp. 685-692
Author(s):  
Na Wang, Qinghui Meng, Jie Yang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Learning Algorithm ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Nonlinear Approximation ◽  
Basic Structure ◽  
Euler Method ◽  
Important Position ◽  
Manipulator Control

Industrial manipulator occupies a very important position in industrial production. The tracking control of its control system and joint trajectory has always been a research hotspot. But the manipulator is a multi input multi output system, which has the characteristics of nonlinearity and strong coupling. Radial basis function (RBF) neural network has high nonlinear mapping ability. In this paper, the structure characteristics, learning algorithm and application of RBF neural network in manipulator control are analyzed. In this paper, the nonlinear approximation property of RBF neural network is theoretically verified. This paper analyzes the basic structure of picking manipulator system in detail. At the same time, the Lagrange Euler method is used to deduce the dynamic equation of the two degree of freedom series manipulator, and the inertia characteristics, Coriolis force and centripetal force characteristics, heavy torque characteristics are analyzed. The nonlinear system model of manipulator based on S-function is established in MATLAB, and the dynamic model is transformed into the form of second-order differential equation to facilitate the introduction of the designed algorithm.

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