scholarly journals RBFNN Design Based on Modified Nearest Neighbor Clustering Algorithm for Path Tracking Control

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8349
Author(s):  
Dongxi Zheng ◽  
Wonsuk Jung ◽  
Sunghoon Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Tracking Control ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Path Tracking ◽  
Test Accuracy ◽  
Radial Basis

Radial basis function neural networks are a widely used type of artificial neural network. The number and centers of basis functions directly affect the accuracy and speed of radial basis function neural networks. Many studies use supervised learning algorithms to obtain these parameters, but this leads to more parameters that need to be determined, thereby making the system more complex. This study proposes a modified nearest neighbor-based clustering algorithm for training radial basis function neural networks. The calculation of this clustering algorithm is not large, and it can adapt to varying densities. Furthermore, it does not require researchers to set parameters based on experience. Simulation proves that the clustering algorithm can effectively cluster samples and optimize the abnormal samples. The radial basis function neural network based on modified nearest neighbor-based clustering has higher accuracy in curve fitting than the conventional radial basis function neural network. Finally, the path tracking control based on a radial basis function neural network of a magnetic microrobot is investigated, and its effectiveness is verified through simulation. The test accuracy and training accuracy of the radial basis function neural network was improved by 23.5% and 7.5%, respectively.

Product Identification Using Image Processing and Radial Basis Function Neural Networks

2015 ◽  
Vol 761 ◽  
pp. 120-124
Author(s):  
K.A.A. Aziz ◽  
Abdul Kadir ◽  
Rostam Affendi Hamzah ◽  
Amat Amir Basari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Architecture ◽  
Rbf Neural Networks ◽  
Specific Product ◽  
Product Identification ◽  
Radial Basis

This paper presents a product identification using image processing and radial basis function neural networks. The system identified a specific product based on the shape of the product. An image processing had been applied to the acquired image and the product was recognized using the Radial Basis Function Neural Network (RBFNN). The RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using a fast two-stage training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable values of the center and the spread of RBF. In this paper, fixed spread value was used for every cluster. The system can detect all the four products with 100% successful rate using ±0.2 tolerance.

scholarly journals Improved neural network-based adaptive tracking control for manipulators with uncertain dynamics

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094756
Author(s):  
Dong-hui Wang ◽  
Shi-jie Zhang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Tracking Control ◽  
Robot Manipulators ◽  
External Disturbance ◽  
Tracking Error ◽  
Adaptive Tracking ◽  
Uncertain Dynamics ◽  
Radial Basis

In this article, a robust adaptive tracking controller is developed for robot manipulators with uncertain dynamics using radial basis function neural network. The design of tracking control systems for robot manipulators is a highly challenging task due to external disturbance and the uncertainties in their dynamics. The improved radial basis function neural network is chosen to approximate the uncertain dynamics of robot manipulators and learn the upper bound of the uncertainty. The adaptive law based on the Lyapunov stability theory is used to solve the uniform final bounded problem of the radial basis function neural network weights, which guarantees the stability and the consistent bounded tracking error of the closed-loop system. Finally, the simulation results are provided to demonstrate the practicability and effectiveness of the proposed method.

Quantum-Behaved Particle Swarm Optimization Based Radial Basis Function Network for Classification of Clinical Datasets

2018 ◽  
Vol 9 (2) ◽  
pp. 32-52
Author(s):  
N. Leema ◽  
H. Khanna Nehemiah ◽  
A. Kannan
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Clustering Algorithm ◽  
Radial Basis Function Network ◽  
Approximation Error ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Radial Basis

In this article, a classification framework that uses quantum-behaved particle swarm optimization neural network (QPSONN) classifiers for diagnosing a disease is discussed. The neural network used for classification is radial basis function neural network (RBFNN). For training the RBFNN K-means clustering algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm has been used. The K-means clustering algorithm is used to find the optimal number of clusters which determines the number of neurons in the hidden layer. The cluster approximation error is used to find the optimal clusters. The weights between the hidden and the output layer is determined using QPSO algorithm based on the mean squared error (MSE). The performance of the developed classifier model has been tested with five clinical datasets, namely Pima Indian Diabetes, Hepatitis, Bupa Liver Disease, Wisconsin Breast Cancer and Cleveland Heart Disease were obtained from the University of California, Irvine (UCI) machine learning repository.

scholarly journals A Comparison between Multi-Layer Perceptron and Radial Basis Function Networks in Detecting Humans Based on Object Shape

2018 ◽  
Vol 31 (2) ◽  
pp. 210
Author(s):  
Laith Jasim Saud ◽  
Zainab Kudair Abass
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Mean Squared Error ◽  
Rbf Neural Network ◽  
Human Detection ◽  
Geometric Information ◽  
Radial Basis ◽  
Mlp Model

       Human detection represents a main problem of interest when using video based monitoring. In this paper, artificial neural networks, namely multilayer perceptron (MLP) and radial basis function (RBF) are used to detect humans among different objects in a sequence of frames (images) using classification approach. The classification used is based on the shape of the object instead of depending on the contents of the frame. Initially, background subtraction is depended to extract objects of interest from the frame, then statistical and geometric information are obtained from vertical and horizontal projections of the objects that are detected to stand for the shape of the object. Next to this step, two types of neural networks are used to classify the extracted objects. Tests have been performed on a sequence of frames, and the simulation results by MATLAB showed that the RBF neural network gave a better performance compared with the MLP neural network where the RBF model gave a mean squared error (MSE) equals to 2.36811e-18 against MSE equals to 2.6937e-11 achieved by the MLP model. The more important thing observed is that the RBF approach required less time to classify the detected object as human compared to the MLP, where the RBF took approximately 86.2% lesser time to give the decision.

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