scholarly journals Radial Basis Function Neural Network sebagai Pengklasifikasi Citra Cacat Pengelasan

Rekayasa ◽  
2018 ◽  
Vol 11 (2) ◽  
pp. 118
Author(s):  
Noorman Rinanto ◽  
Mohammad Thoriq Wahyudi ◽  
Agus Khumaidi
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Radial Basis Function ◽  
Basis Function ◽  
Extraction Methods ◽  
Weld Defect ◽  
Welding Defects ◽  
Radial Basis ◽  
Welding Image ◽  
Hidden Layer

<p>Tingginya resiko kesalahan manusia dalam inspeksi visual untuk cacat pengelasan yang masih mengandalkan kemampuan manusia sulit untuk dihindari. Oleh sebab itu, penelitian ini mengusulkan sebuah klasifikasi cacat las visual dengan menggunakan algoritma <em>Radial Basis Function Neural Network</em> (RBFNN). Masukan RBFNN berupa citra las yang terdiri dari 5 (lima) kelas cacat las visual dan 1 (satu) kelas citra las normal. Citra las tersebut diproses terlebih dahulu menggunakan metode ekstraksi fitur <em>Fast Fourier Transform</em> (FFT) dan <em>Descreate Cosine Transform</em> (DCT). Hasil kedua metode ekstraksi fitur tersebut kemudian akan saling dibandingkan untuk mengetahui kinerja RBFNN. Hasil pengujian menunjukkan bahwa sistem dengan metode FFT-RBFNN dapat menggolongkan citra cacat las dengan akurasi sebesar 91.67% dan DCT-RBFNN sekitar 83.33% dengan jumlah neuron hidden layer sebanyak 15 dan parameter spread adalah 4.<em></em></p><p>Kata Kunci: <em>Radial Basis Function Neural Network</em> (RBFNN), FFT, DCT, cacat las, klasifikasi.</p><p align="center">Radial Basis Function Neural Network as a Weld Defect Classifiers<strong></strong></p><p><strong> </strong></p><p><strong>ABSTRACT</strong></p><p><em>The high risk of human error in visual inspection of welding defects that still rely on human capabilities is difficult to avoid. Therefore, this study proposes a classification of visual welding defects using the Radial Base Function Neural Network (RBFNN) algorithm. The RBFNN input is in the form of a welding image consisting of 5 (five) visual welding defect classes and 1 (one) normal welding image class. The weld image is processed first using the Fast Fourier Transform (FFT) and Descreate Cosine Transform (DCT) feature extraction methods. The results of these two feature extraction methods will be compared to find out the RBFNN performance. The test results show that the system with FFT-RBFNN method can classify the image of weld defects with an accuracy of 91.67% and DCT-RBFNN around 83.33% with the number of hidden layer neurons as much as 15 and the parameters of spread are 4.</em></p><p><em>Keywords: Radial Basis Function Neural Network (RBFNN), FFT, DCT, weld defect, classification.</em></p>

scholarly journals Artificial Intelligence and Neural Network-Based Shooting Accuracy Prediction Analysis in Basketball

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongfei Li ◽  
Maolin Zhang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Prediction Accuracy ◽  
Learning Algorithm ◽  
Prediction Method ◽  
Rbf Network ◽  
Radial Basis ◽  
Hidden Layer ◽  
Time Required

In order to improve the accuracy of shooting in basketball. A shooting accuracy prediction method based on the convergent improved resource allocating network (CIRAN) online radial basis function neural network (RBFNN) is proposed, and the RBFNN learning algorithm is improved. Through the collection of shooting motion images, feature point extraction, and edge contour feature extraction, the shooting motion trajectory is obtained. Using the online neural network based on the CIRAN learning algorithm to predict the accuracy of shooting, this method analyzes the radial basis function (RBF) network. Based on the RBF analysis, the number of network layers and the number of hidden layer neurons are adjusted and optimized. In order to improve the prediction accuracy of shooting in basketball, a method based on. Through the analysis, it can be known that the accuracy of both the traditional RBFNN and the CIRAN-based online neural network for the prediction of shooting accuracy is above 70%. The prediction accuracy of the online neural network for shooting is higher than that of the traditional one. This is mainly because the online update function of the learning algorithm can better adjust the corresponding structure with the development of the game and has a better generalization ability. In addition, because the CIRAN learning algorithm introduces the hidden layer neuron deletion strategy, its network structure is simpler than that of the traditional one, the number of hidden layer neurons is less, and the running time required is less, which can better meet the real-time requirements and provide a more scientific method for basketball training.

Building up Embodiment in Learning Agents Using A Gaussian Radial Basis Function Neural Network

2000 ◽  
Vol 12 (6) ◽  
pp. 656-663
Author(s):  
Hajime Murao ◽  
◽  
Shinzo Kitamura
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
State Space ◽  
Performance Improvement ◽  
Basis Function ◽  
The State ◽  
Learning Agents ◽  
Radial Basis ◽  
Space Construction ◽  
Hidden Layer

In this paper, we propose actor-critic learning with adaptive state space construction. The gaussian radial basis function neural network is employed for both the actor and the critic modules, where each hidden neuron covers a subspace of the sensor space and so the hidden layer corresponds to the state space. In the proposed algorithm, a robot starts without any states and a new state is generated incrementally by adding a new hidden neuron. One clear advantage of the proposed algorithm to others is the performance improvement by the minimal training after adding a new state, i.e. the adjustment of the connective strength between the new neuron and others is only required after adding a new hidden neuron. This provides an efficient method to construct the state space during the learning in the real world. Resulting state space represents aspects of the environment in which the robot works and the characteristic of the robot itself. In this sense, the obtained state space is said to represent the embodiment of the robot.

scholarly journals Prediction of Flight Status of Logistics UAVs Based on an Information Entropy Radial Basis Function Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3651
Author(s):  
Qin Yang ◽  
Zhaofa Ye ◽  
Xuzheng Li ◽  
Daozhu Wei ◽  
Shunhua Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Network Structure ◽  
Information Entropy ◽  
Basis Function ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Output Layer ◽  
Radial Basis ◽  
Hidden Layer

Aiming at addressing the problems of short battery life, low payload and unmeasured load ratio of logistics Unmanned Aerial Vehicles (UAVs), the Radial Basis Function (RBF) neural network was trained with the flight data of logistics UAV from the Internet of Things to predict the flight status of logistics UAVs. Under the condition that there are few available input samples and the convergence of RBF neural network is not accurate, a dynamic adjustment method of RBF neural network structure based on information entropy is proposed. This method calculates the information entropy of hidden layer neurons and output layer neurons, and quantifies the output information of hidden layer neurons and the interaction information between hidden layer neurons and output layer neurons. The structural design and optimization of RBF neural network were solved by increasing the hidden layer neurons or disconnecting unnecessary connections, according to the connection strength between neurons. The steepest descent learning algorithm was used to correct the parameters of the network structure to ensure the convergence accuracy of the RBF neural network. By predicting the regression values of the flight status of logistics UAVs, it is demonstrated that the information entropy-based RBF neural network proposed in this paper has good approximation ability for the prediction of nonlinear systems.

scholarly journals Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN-GA)

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Mingxun Zhu ◽  
Zhigang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Radial Basis Function ◽  
Basis Function ◽  
Algorithm Optimization ◽  
Change Mechanism ◽  
Image Prediction ◽  
Radial Basis ◽  
Hidden Layer

The prediction of gross domestic product (GDP) is a research hotspot, and its importance is self-evident. Its complex internal change mechanism also increases the difficulty of analyzing GDP data. The genetic algorithm (GA) is applied to the parameter design of the radial basis function neural network (RBFNN) based on genetic algorithm optimization (RBFNN-GA). An economic zone GDP image prediction model is proposed, which realizes the optimal design of the center vector, the base width vector of the RBFNN node function, and the weight between the hidden layer and output layer. Based on the GDP data over the years, this paper uses the RBFNN-GA prediction model to analyze and predict the GDP image and compares the image prediction results. The results show that the genetic algorithm is used to optimize RBFNN, which gives full play to the advantages of the two algorithms. The relative error of the RBFNN-GA prediction model is only 3.52%. Compared with the prediction results, the prediction accuracy is significantly higher than the ARIMA time series model and GM (1,1) model.

scholarly journals Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Tiantian Luan ◽  
Mingxiao Sun ◽  
Guoqing Xia ◽  
Daidai Chen
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Variable Structure ◽  
Learning Rate ◽  
Rbf Neural Networks ◽  
Fast Learning ◽  
Radial Basis ◽  
Hidden Layer

The neural network has the advantages of self-learning, self-adaptation, and fault tolerance. It can establish a qualitative and quantitative evaluation model which is closer to human thought patterns. However, the structure and the convergence rate of the radial basis function (RBF) neural network need to be improved. This paper proposes a new variable structure radial basis function (VS-RBF) with a fast learning rate, in order to solve the problem of structural optimization design and parameter learning algorithm for the radial basis function neural network. The number of neurons in the hidden layer is adjusted by calculating the output information of neurons in the hidden layer and the multi-information between neurons in the hidden layer and output layer. This method effectively solves the problem that the RBF neural network structure is too large or too small. The convergence rate of the RBF neural network is improved by using the robust regression algorithm and the fast learning rate algorithm. At the same time, the convergence analysis of the VS-RBF neural network is given to ensure the stability of the RBF neural network. Compared with other self-organizing RBF neural networks (self-organizing RBF (SORBF) and rough RBF neural networks (RS-RBF)), VS-RBF has a more compact structure, faster dynamic response speed, and better generalization ability. The simulations of approximating a typical nonlinear function, identifying UCI datasets, and evaluating sortie generation capacity of an carrier aircraft show the effectiveness of VS-RBF.

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