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1
Babu, N. S. C., and V. C. Prasad. "Radial Basis Function Networks for Analog Circuit Fault Isolation." Journal of Circuits, Systems and Computers 07, no. 06 (December 1997): 643–55. http://dx.doi.org/10.1142/s0218126697000462.
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The application of a radial basis function neural network (RBFN) for analog circuit fault isolation is presented. In this method the RBFN replaces the fault dictionary of analog circuits. The proposed method for analog circuit fault isolation takes the advantage of extremely fast training of RBFN compared to earlier neural network methods. A method is suggested to select centers and widths of RBF units. This selection procedure accounts for the component tolerances. The effectiveness of the RBFN for the fault isolation problem is demonstrated with an illustrative example. RBFN performed well even when the input patterns are drawn directly from the test node voltages of the analog circuit under consideration. A method is suggested to modify the RBF network in the event of occurrence of a new fault. The suggested modifications do not affect the previous training.
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HUANG, DE-SHUANG. "RADIAL BASIS PROBABILISTIC NEURAL NETWORKS: MODEL AND APPLICATION." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 07 (November 1999): 1083–101. http://dx.doi.org/10.1142/s0218001499000604.
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This paper investigates the capabilities of radial basis function networks (RBFN) and kernel neural networks (KNN), i.e. a specific probabilistic neural networks (PNN), and studies their similarities and differences. In order to avoid the huge amount of hidden units of the KNNs (or PNNs) and reduce the training time for the RBFNs, this paper proposes a new feedforward neural network model referred to as radial basis probabilistic neural network (RBPNN). This new network model inherits the merits of the two old odels to a great extent, and avoids their defects in some ways. Finally, we apply this new RBPNN to the recognition of one-dimensional cross-images of radar targets (five kinds of aircrafts), and the experimental results are given and discussed.
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Krishnamoorthy, Vinoth Kumar, Usha Nandini Duraisamy, Amruta S. Jondhale, Jaime Lloret, and Balaji Venkatesalu Ramasamy. "Energy-Constrained Target Localization Scheme for Wireless Sensor Networks Using Radial Basis Function Neural Network." International Journal of Distributed Sensor Networks 2023 (March 30, 2023): 1–12. http://dx.doi.org/10.1155/2023/1426430.
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The indoor object tracking by utilizing received signal strength indicator (RSSI) measurements with the help of wireless sensor network (WSN) is an interesting and important topic in the domain of location-based applications. Without the knowledge of location, the measurements obtained with WSN are of no use. The trilateration is a widely used technique to get location updates of target based on RSSI measurements from WSN. However, it suffers with high location estimation errors arising due to random variations in RSSI measurements. This paper presents a range-free radial basis function neural network (RBFN) and Kalman filtering- (KF-) based algorithm named RBFN+KF. The performance of the RBFN+KF algorithm is evaluated using simulated RSSIs and is compared against trilateration, multilayer perceptron (MLP), and RBFN-based estimations. The simulation results reveal that the proposed RBFN+KF algorithm shows very low location estimation errors compared to the rest of the three approaches. Additionally, it is also seen that RBFN-based approach is more energy efficient than trilateration and MLP-based localization approaches.
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Safavi, A., M. H. Esteki, S. M. Mirvakili, and M. Khaki. "Comparison of back propagation network and radial basis function network in Departure from Nucleate Boiling Ratio (DNBR) calculation." Kerntechnik 85, no. 1 (December 1, 2020): 15–25. http://dx.doi.org/10.1515/kern-2020-850105.
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Abstract Since estimating the minimum departure from nucleate boiling ratio (MDNBR) requires complex calculations, an alternative method has always been considered. One of these methods is neural network. In this study, the Back Propagation Neural network (BPN) and Radial Basis Function Neural network (RBFN) are introduced and compared in order to estimate MDNBR of the VVER-1000 light water reactor. In these networks, the MDNBR were predicted with the inputs including core mass flux, core inlet temperature, pressure, reactor power level and position of the control rods. To obtain the data required to design these neural networks, an externally coupledcode was developed and its ability to estimate the thermo-hydraulic parameters of the VVER-1000 reactor was compared with other numerical solutions of this benchmark and the Final Safety Analysis Report (FSAR). After ensuring the accuracy of this coupled-code, MDNBR was calculated for 272 different conditions of reactor operating, and it was used to design BPN and RBFN. Comparison of these two neural networks revealed that when the output SMEs of the two systems were approximately the same, the training process in RBFN was much faster than in BPN and the maximum network error in RBFN was less than in BPN.
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Dash, Ch Sanjeev Kumar, Ajit Kumar Behera, Satchidananda Dehuri, and Sung-Bae Cho. "Radial basis function neural networks: a topical state-of-the-art survey." Open Computer Science 6, no. 1 (May 2, 2016): 33–63. http://dx.doi.org/10.1515/comp-2016-0005.
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AbstractRadial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains. They have potential for hybridization and demonstrate some interesting emergent behaviors. This paper aims to offer a compendious and sensible survey on RBF networks. The advantages they offer, such as fast training and global approximation capability with local responses, are attracting many researchers to use them in diversified fields. The overall algorithmic development of RBF networks by giving special focus on their learning methods, novel kernels, and fine tuning of kernel parameters have been discussed. In addition, we have considered the recent research work on optimization of multi-criterions in RBF networks and a range of indicative application areas along with some open source RBFN tools.
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Aik, Lim Eng, Tan Wei Hong, and Ahmad Kadri Junoh. "An Improved Radial Basis Function Networks Based on Quantum Evolutionary Algorithm for Training Nonlinear Datasets." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 2 (June 1, 2019): 120. http://dx.doi.org/10.11591/ijai.v8.i2.pp120-131.
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In neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and spread value. Radial basis function network (RBFN) is a type of feedforward network that capable of perform nonlinear approximation on unknown dataset. It has been widely used in classification, pattern recognition, nonlinear control and image processing. Thus, with the increases in RBFN application, some problems and weakness of RBFN network is identified. Through the combination of quantum computing and RBFN provides a new research idea in design and performance improvement of RBFN system. This paper describes the theory and application of quantum computing and cloning operators, and discusses the superiority of these theories and the feasibility of their optimization algorithms. This proposed improved RBFN (I-RBFN) that combined with cloning operator and quantum computing algorithm demonstrated its ability in global search and local optimization to effectively speed up learning and provides better accuracy in prediction results. Both the algorithms that combined with RBFN optimize the centers and spread value of RBFN. The proposed I-RBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to I-RBFN for root mean square error (RMSE) values with standard RBFN. The proposed I-RBFN yielded better results with an average improvement percentage more than 90 percent in RMSE.
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Ibrahim, Ashraf Osman, Walaa Akif Hussien, Ayat Mohammoud Yagoop, and Mohd Arfian Ismail. "Feature Selection and Radial Basis Function Network for Parkinson Disease Classification." Kurdistan Journal of Applied Research 2, no. 3 (August 27, 2017): 167–71. http://dx.doi.org/10.24017/science.2017.3.121.
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Recently, several works have focused on detection of a different disease using computational intelligence techniques. In this paper, we applied feature selection method and radial basis function neural network (RBFN) to classify the diagnosis of Parkinson’s disease. The feature selection (FS) method used to reduce the number of attributes in Parkinson disease data. The Parkinson disease dataset is acquired from UCI repository of large well-known data sets. The experimental results have revealed significant improvement to detect Parkinson’s disease using feature selection method and RBF network.
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Ding, Shuo, and Xiao Heng Chang. "A MATLAB-Based Study on the Realization and Approximation Performance of RBF Neural Networks." Applied Mechanics and Materials 325-326 (June 2013): 1746–49. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1746.
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BP neural network is a kind of widely used feed-forward network. However its innate shortcomings are gradually giving rise to the study of other networks. Currently one of the research focuses in the area of feed-forward networks is radial basis function neural network. To test the radial basis function neural network for nonlinear function approximation capability, this paper first introduces the theories of RBF networks, as well as the structure, function approximation and learning algorithm of radial basis function neural network. Then a simulation test is carried out to compare BPNN and RBFNN. The simulation results indicate that RBFNN is simpler in structure, faster in speed and better in approximation performance. That is to say RBFNN is superior to BPNN in many aspects. But when solving the same problem, the structure of radial basis networks is more complicated than that of BP neural networks. Keywords: Radial basis function; Neural network; Function approximation; Simulation; MATLAB
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Aik, Lim Eng, Tan Wei Hong, and Ahmad Kadri Junoh. "An Improved Radial Basis Function Networks in Networks Weights Adjustment for Training Real-World Nonlinear Datasets." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 1 (March 1, 2019): 63. http://dx.doi.org/10.11591/ijai.v8.i1.pp63-76.
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In neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and the networks weight. The gradient descent algorithm is a widely used weight adjustment algorithm in most of neural networks training algorithm. However, the method is known for its weakness for easily trap in local minima. It suffers from a random weight generated for the networks during initial stage of training at input layer to hidden layer networks. The performance of radial basis function networks (RBFN) has been improved from different perspectives, including centroid initialization problem to weight correction stage over the years. Unfortunately, the solution does not provide a good trade-off between quality and efficiency of the weight produces by the algorithm. To solve this problem, an improved gradient descent algorithm for finding initial weight and improve the overall networks weight is proposed. This improved version algorithm is incorporated into RBFN training algorithm for updating weight. Hence, this paper presented an improved RBFN in term of algorithm for improving the weight adjustment in RBFN during training process. The proposed training algorithm, which uses improved gradient descent algorithm for weight adjustment for training RBFN, obtained significant improvement in predictions compared to the standard RBFN. The proposed training algorithm was implemented in MATLAB environment. The proposed improved network called IRBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to IRBFN for root mean square error (RMSE) values with standard RBFN. The IRBFN yielded a promising result with an average improvement percentage more than 40 percent in RMSE.
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Aik, Lim Eng, Tan Wei Hong, and Ahmad Kadri Junoh. "Distance Weighted K-Means Algorithm for Center Selection in Training Radial Basis Function Networks." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 1 (March 1, 2019): 54. http://dx.doi.org/10.11591/ijai.v8.i1.pp54-62.
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The accuracies rates of the neural networks mainly depend on the selection of the correct data centers. The K-means algorithm is a widely used clustering algorithm in various disciplines for centers selection. However, the method is known for its sensitivity to initial centers selection. It suffers not only from a high dependency on the algorithm's initial centers selection but, also from data points. The performance of K-means has been enhanced from different perspectives, including centroid initialization problem over the years. Unfortunately, the solution does not provide a good trade-off between quality and efficiency of the centers produces by the algorithm. To solve this problem, a new method to find the initial centers and improve the sensitivity to the initial centers of K-means algorithm is proposed. This paper presented a training algorithm for the radial basis function network (RBFN) using improved K-means (KM) algorithm, which is the modified version of KM algorithm based on distance-weighted adjustment for each centers, known as distance-weighted K-means (DWKM) algorithm. The proposed training algorithm, which uses DWKM algorithm select centers for training RBFN obtained better accuracy in predictions and reduced network architecture compared to the standard RBFN. The proposed training algorithm was implemented in MATLAB environment; hence, the new network was undergoing a hybrid learning process. The network called DWKM-RBFN was tested against the standard RBFN in predictions. The experimental models were tested on four literatures nonlinear function and four real-world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, Phytoplankton problem, and forex pair EURUSD. The results are compared to proposed method for root mean square error (RMSE) in radial basis function network (RBFN). The proposed method yielded a promising result with an average improvement percentage more than 50 percent in RMSE.
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Saxena, Akash, and Ankit Kumar Sharma. "Assessment of Global Voltage Stability Margin through Radial Basis Function Neural Network." Advances in Electrical Engineering 2016 (September 29, 2016): 1–11. http://dx.doi.org/10.1155/2016/4858431.
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Dynamic operating conditions along with contingencies often present formidable challenges to the power engineers. Decisions pertaining to the control strategies taken by the system operators at energy management centre are based on the information about the system’s behavior. The application of ANN as a tool for voltage stability assessment is empirical because of its ability to do parallel data processing with high accuracy, fast response, and capability to model dynamic, nonlinear, and noisy data. This paper presents an effective methodology based on Radial Basis Function Neural Network (RBFN) to predict Global Voltage Stability Margin (GVSM), for any unseen loading condition of the system. GVSM is used to assess the overall voltage stability status of the power system. A comparative analysis of different topologies of ANN, namely, Feedforward Backprop (FFBP), Cascade Forward Backprop (CFB), Generalized Regression (GR), Layer Recurrent (LR), Nonlinear Autoregressive Exogenous (NARX), ELMAN Backprop, and Feedforward Distributed Time Delay Network (FFDTDN), is carried out on the basis of capability of the prediction of GVSM. The efficacy of RBFN is better than other networks, which is validated by taking the predictions of GVSM at different levels of Additive White Gaussian Noise (AWGN) in input features. The results obtained from ANNs are validated through the offline Newton Raphson (N-R) method. The proposed methodology is tested over IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems.
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Tawfiq, Nada Elya. "Chronic Kidney Disease Diagnose using Radial Basis Function Network (RBFN)." Academic Journal of Nawroz University 11, no. 3 (August 25, 2022): 289–94. http://dx.doi.org/10.25007/ajnu.v11n3a1427.
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Fast and accurate diagnosis of the diseases consider one of the major challenges in giving proper treatment. Different techniques have their own limitations in terms of accuracy and time. Neural network technique used as a powerful discriminating classifier for tasks in medical diagnosis for early detection of diseases. It had already been applied in diagnose many diseases, like chronic kidney disease (CKD) which is one of the leading causes of death contributed by other illnesses such as diabetes, hypertension, lupus, anemia or weak bones that lead to bone fractures. In this paper, a deep learning method to perform a both feature extraction and the classification for CKD detection using Radial Basis Function Network as activation function . This network has great ability of accurate and speed diagnosing, so it is useful to use it in medicine to give the doctors or medical team the right diagnoses. Better performance in terms of accuracy, specificity and sensitivity will be selected as classification model. To test the performance of RBF model, a CDK dataset is employed which contains the clinical manifestations of six diseases as a sample. After applying training method, the network will match these manifestations with the manifestations obtained from sample patients to decide right disease which was entered to the program, the result, shows good performance, low error ratio, high accuracy.
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Aziz, K. A. A., Abdul Kadir, Rostam Affendi Hamzah, and Amat Amir Basari. "Product Identification Using Image Processing and Radial Basis Function Neural Networks." Applied Mechanics and Materials 761 (May 2015): 120–24. http://dx.doi.org/10.4028/www.scientific.net/amm.761.120.
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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.
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Anh Nguyen, Tuan Ngoc, Duy Cong Pham, Luu Hoang Minh, and Nguyen Huu Chan Thanh. "Combined RBFN based MPPT and d-axis stator current control for permanent magnet synchronous generators." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 4 (December 1, 2021): 2459. http://dx.doi.org/10.11591/ijpeds.v12.i4.pp2459-2469.
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This paper proposes a new radial basis function neural network maximum power point tracking controller based on a differential evolution algorithm for machine side converter of permanent magnet synchronous generator wind turbine under variable wind speed. Direct axis stator current control methods of permanent magnet synchronous machine are reviewed shortly. A combined radial basis function neural network-based network maximum power point tracking method and d axis stator current control techniques including zero d axis stator current, unity power factor, and constant stator flux-linkage have been implemented to control the machine side converter of permanent magnet synchronous generator wind turbine. The dynamic performance of the proposed approach is assessed under different operating conditions through a simulation model based on MATLAB. It has been seen that the radial basis function neural network controller can not only track well the maximum power point but also can be reduced costly.
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Ma, Y., A. Engeda, M. Cave, and J.-L. Di Liberti. "Improved centrifugal compressor impeller optimization with a radial basis function network and principle component analysis." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 224, no. 4 (April 1, 2010): 935–45. http://dx.doi.org/10.1243/09544062jmes1635.
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The development of a fast and reliable computer-aided design and optimization procedure for centrifugal compressors has attracted a great deal of attention both in the industry and in academia. Artificial neural networks (ANNs) have been widely used to create an approximate performance map to substitute the direct application of flow solvers in the optimization procedure. Although ANNs greatly decrease the computational time for the optimization, their accuracies still limit their applications. Furthermore, ANNs also bring errors to the final results. In this study, principal component analysis (PCA) or independent component analysis (ICA) is applied to transform the training database and make a radial basis function network (RBFN), a type of ANN, trained in a new coordinate system. The present study compares the accuracies of three different trained ANNs: RBFN, RBFN with PCA, and RBFN with ICA. Furthermore, the total performances of the centrifugal compressor impeller optimization procedures using these three different trained ANNs are compared. Genetic algorithm (GA) is used as an optimization method in the optimization procedure and influences of GA parameters on the optimization procedure performances are also studied. All results demonstrate that the application of PCA significantly increases the accuracy of trained ANN as well as the total performance of the centrifugal compressor impeller optimization procedure.
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Zheng, Dongxi, Wonsuk Jung, and Sunghoon Kim. "RBFNN Design Based on Modified Nearest Neighbor Clustering Algorithm for Path Tracking Control." Sensors 21, no. 24 (December 14, 2021): 8349. http://dx.doi.org/10.3390/s21248349.
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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.
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Samantaray, Sandeep, and Abinash Sahoo. "Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 2 (July 26, 2021): 227–34. http://dx.doi.org/10.3233/kes-210066.
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Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.
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Kaushik, Anupama, Devendra Kumar Tayal, and Kalpana Yadav. "The Role of Neural Networks and Metaheuristics in Agile Software Development Effort Estimation." International Journal of Information Technology Project Management 11, no. 2 (April 2020): 50–71. http://dx.doi.org/10.4018/ijitpm.2020040104.
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In any software development, accurate estimation of resources is one of the crucial tasks that leads to a successful project development. A lot of work has been done in estimation of effort in traditional software development. But, work on estimation of effort for agile software development is very scant. This paper provides an effort estimation technique for agile software development using artificial neural networks (ANN) and a metaheuristic technique. The artificial neural networks used are radial basis function neural network (RBFN) and functional link artificial neural network (FLANN). The metaheuristic technique used is whale optimization algorithm (WOA), which is a nature-inspired metaheuristic technique. The proposed techniques FLANN-WOA and RBFN-WOA are evaluated on three agile datasets, and it is found that these neural network models performed extremely well with the metaheuristic technique used. This is further empirically validated using non-parametric statistical tests.
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Fatichah, Chastine, and Nurina Indah Kemalasari. "AN EXPERIMENTAL STUDY ON BANK PERFORMANCE PREDICTION BASE ON FINANCIAL REPORT." CCIT Journal 5, no. 1 (September 5, 2011): 92–99. http://dx.doi.org/10.33050/ccit.v5i1.490.
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This paper presents an experimental study on bank performance prediction base on financial report. This research use Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and Radial Basis Function Neural Network (RBFN) methods to experiment the bank performance prediction. To improve accuracy prediction of both neural network methods, this research use Principal Component Analysis (PCA) to get best feature. This research work based on the bank’s financial report and financial variables predictions of several banks that registered in Bank Indonesia. The experimental results show that the accuracy rate of bank performance prediction of PCA-PNN or PCA-RBFN methods are higher than SVM method for Bank Persero, Bank Non Devisa and Bank Asing categories. But, the accuracy rate of SVM method is higher than PCA-PNN or PCA-RBFN methods for Bank Pembangunan Daerah and Bank Devisa categories. The accuracy rate of PCA-PNN method for all bank categories is comparable to that PCA-RBFN method.
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Alzaeemi, Shehab, Mohd Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Saratha Sathasivam, and Mustafa Mamat. "Radial basis function neural network for 2 satisfiability programming." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 1 (April 1, 2020): 459. http://dx.doi.org/10.11591/ijeecs.v18.i1.pp459-469.
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<span>Radial Basis Function Neural Network (RBFNN) is very prominent in data processing. However, improving this technique is vital for the NN training process. This paper presents an integrated 2 Satisfiability in radial basis function neural network (RBFNN-2SAT). There are two different types of training in RBFNN, namely no-training technique and half-training technique. The performance of the solutions via Genetic Algorithm (GA) training was investigated by comparing the Radial Basis Function Neural Network No-Training Technique (RBFNN- 2SATNT) and Radial Basis Function Neural Network Half-Training Technique (RBFNN- 2SATHT). The comparison of both techniques was examined on 2 Satisfiability problem by using a C# software that was developed for this experiment. The performance of the RBFNN-2SATNT and RBFNN-2SATHT in performing 2SAT is discussed in terms of root mean squared error (RMSE), sum squared error (SSE), mean absolute percentage error (MAPE), mean absolute error (MAE), number of the hidden neurons and CPU time. Results obtained from a computer simulation showed that RBFNN-2SATHT outperformed RBFNN-2SATNT.</span>
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Fong, Li Wei, Pi Ching Lou, and Kung Ting Lin. "On-Line Bayesian Classifier Design for Measurement Fusion." Advanced Materials Research 461 (February 2012): 826–29. http://dx.doi.org/10.4028/www.scientific.net/amr.461.826.
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A neural-network-based classifier design for adaptive Kalman filtering is introduced to fuse the measurements extracted from multiple sensors to improve tracking accuracy. The proposed method consists of a group of parallel Kalman filters and a classifier based on Radial Basis Function Network (RBFN). By incorporating Markov chain into Bayesian estimation scheme, a RBFN is used as a probabilistic neural network for classification. Based upon data compression technique and on-line classification algorithm, an adaptive estimator to measurement fusion is developed that can handle the switching plant in the multi-sensor environment. The simulation results are presented which demonstrate the effectiveness of the proposed method.
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El Shafie, Amr H., A. El-Shafie, A. Almukhtar, Mohd R. Taha, Hasan G. El Mazoghi, and A. Shehata. "Radial basis function neural networks for reliably forecasting rainfall." Journal of Water and Climate Change 3, no. 2 (June 1, 2012): 125–38. http://dx.doi.org/10.2166/wcc.2012.017.
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Rainfall forecasting is an interesting task especially in a modern city facing the problem of global warming; in addition rainfall is a necessary input for the analysis and design of hydrologic systems. Most rainfall real-time forecasting models are based on conceptual models simulating the complex hydrological process under climate variability. As there are a lot of variables and parameters with uncertainties and non-linear relationships, the calibration of conceptual or physically based models is often a difficult and time-consuming procedure. Simpler artificial neural network (ANN) forecasts may therefore seem attractive as an alternative model. The present research demonstrates the application of the radial basis function neural network (RBFNN) to rainfall forecasting for Alexandria City, Egypt. A significant feature of the input construction of the RBF network is based on the use of the average 10 year rainfall in each decade to forecast the next year. The results show the capability of the RBF network in forecasting the yearly rainfall and two highest rainfall monsoon months, January and December, compared with other statistical models. Based on these results, the use of the RBF model can be recommended as a viable alternative for forecasting the rainfall based on historical rainfall recorded data.
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Haviluddin, Haviluddin, and Imam Tahyudin. "Time Series Prediction Using Radial Basis Function Neural Network." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 4 (August 1, 2015): 765. http://dx.doi.org/10.11591/ijece.v5i4.pp765-771.
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This paper presents an approach for predicting daily network traffic using artificial neural networks (ANN), namely radial basis function neural network (RBFNN) method. The data is gained from 21-24 June 2013 (192 samples series data) in ICT Unit of Mulawarman University, East Kalimantan, Indonesia. The results of measurement are using statistical analysis, e.g. sum of square error (SSE), mean of square error (MSE), mean of absolute percentage error (MAPE), and mean of absolute deviation (MAD). The results show that values are the same, with different goals that have been set are 0.001, 0.002, and 0.003, and spread 200. The smallest MSE value indicates a good method for accuracy. Therefore, the RBFNN model illustrates the proposed best model to predict daily network traffic.
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Schmitt, Michael. "Descartes' Rule of Signs for Radial Basis Function Neural Networks." Neural Computation 14, no. 12 (December 1, 2002): 2997–3011. http://dx.doi.org/10.1162/089976602760805386.
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We establish versions of Descartes' rule of signs for radial basis function (RBF) neural networks. The RBF rules of signs provide tight bounds for the number of zeros of univariate networks with certain parameter restrictions. Moreover, they can be used to infer that the Vapnik-Chervonenkis (VC) dimension and pseudodimension of these networks are no more than linear. This contrasts with previous work showing that RBF neural networks with two or more input nodes have superlinear VC dimension. The rules also give rise to lower bounds for network sizes, thus demonstrating the relevance of network parameters for the complexity of computing with RBF neural networks.
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Tarigan, Rizki Brendita Br, Hasbi Yasin, and Alan Prahutama. "PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) DENGAN METODE RADIAL BASIS FUNCTION NEURAL NETWORK MENGGUNAKAN GUI MATLAB." Jurnal Gaussian 7, no. 4 (November 30, 2018): 431–42. http://dx.doi.org/10.14710/j.gauss.v7i4.28872.
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Capital market Indonesia is one of the important factors in the development of the national economy, proved to have many industries and companies that use these institutions as a medium to absorb investment to strengthen its financial position. The recent years, Jakarta Composite Index (JCI) in Capital Market tend to strengthen. JCI data are the time series data obtained from the past to predict the future with caracteristics of JCI data are non stationary and non linier. Neural network is a computational method that imitate the biological neural network. There are several types of methods that can be used in neural network that is: Radial Basis Function Neural Network (RBFNN) Generalized Regression Neural Network (GRNN), dan Probabilistic Neural Network (PNN). Model of Radial Basis Function Neural Network is suitable for time series data. This model has a network architecture in the form of input layer, hidden layer and output layer. This research is done with the help of GUI as a computation tool. The results of analysis by using GUI conducted on the size sample of data as much as 1211 taken as 100 the data thus obtained value of 2315,6 MSE training and training MAPE value of 0,72%, while for the testing of 28886,7 MSE and MAPE testing value is 0,70%. Based on the results of forecasting, JCI values on January 02, 2018 until January 08, 2018 at 6499,922 every day. Keywords: Radial Basis Function Neural Network (RBFNN), Jakarta Composite Index (JCI), MSE, MAPE, Time Series, GUI.
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Huang, Cong Hui, Chih Ming Hong, Yih Feng Su, Song Mao Lee, Chuan Sing Jhuang, Bo Yan Shi, and Bo Ren Shi. "Elman Neural Network for Dynamic Control of Wind Power Systems." Applied Mechanics and Materials 479-480 (December 2013): 570–74. http://dx.doi.org/10.4028/www.scientific.net/amm.479-480.570.
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This paper presents Elman neural network for the dynamic control strategies of a hybrid power system that include wind/photovoltaic/diesel system. Wind and PV power are the primary power sources of the system to take full advantages of renewable energy, and the diesel-engine is used as a backup system. A simulation model for the hybrid energy system has been developed using MATLAB/Simulink. To achieve a fast and stable response for the real power control, the intelligent controller consists of a Radial Basis Function Network (RBFN) and an modified Elman Neural Network (ENN) for maximum power point tracking (MPPT). The pitch angle of wind turbine is controlled by ENN, and the PV system uses RBFN, where the output signal is used to control the DC / DC boost converters to achieve the MPPT.
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Chen, Wei-Bo, and Wen-Cheng Liu. "Water Quality Modeling in Reservoirs Using Multivariate Linear Regression and Two Neural Network Models." Advances in Artificial Neural Systems 2015 (June 9, 2015): 1–12. http://dx.doi.org/10.1155/2015/521721.
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In this study, two artificial neural network models (i.e., a radial basis function neural network, RBFN, and an adaptive neurofuzzy inference system approach, ANFIS) and a multilinear regression (MLR) model were developed to simulate the DO, TP, Chl a, and SD in the Mingder Reservoir of central Taiwan. The input variables of the neural network and the MLR models were determined using linear regression. The performances were evaluated using the RBFN, ANFIS, and MLR models based on statistical errors, including the mean absolute error, the root mean square error, and the correlation coefficient, computed from the measured and the model-simulated DO, TP, Chl a, and SD values. The results indicate that the performance of the ANFIS model is superior to those of the MLR and RBFN models. The study results show that the neural network using the ANFIS model is suitable for simulating the water quality variables with reasonable accuracy, suggesting that the ANFIS model can be used as a valuable tool for reservoir management in Taiwan.
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Tavoosi, Jafar. "PMSM speed control based on intelligent sliding mode technique." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 39, no. 6 (September 24, 2020): 1315–28. http://dx.doi.org/10.1108/compel-04-2020-0137.
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Purpose The purpose of this paper is to present a novel intelligent backstepping sliding mode control for an experimental permanent magnet synchronous motor. Design/methodology/approach A novel recurrent radial basis function network (RBFN) is used to is used to approximate unknown nonlinear functions in permanent magnet synchronous motor (PMSM) dynamics. Then, using the functions obtained from the neural network, it is possible to design a model-based and precise controller for PMSM using the immersive modeling method. Findings Experimental results indicate the appropriate performance of the proposed method. Originality/value This paper presents a novel intelligent backstepping sliding mode control for an experimental permanent magnet synchronous motor. A novel recurrent RBFN is used to is used to approximate unknown nonlinear functions in PMSM dynamics.
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Bai, Wen Le, Yong Mei Zhang, and Bin Song. "Study on Optimization of Software Regressive Testing Based on RBF Neural Networks." Applied Mechanics and Materials 268-270 (December 2012): 1714–17. http://dx.doi.org/10.4028/www.scientific.net/amm.268-270.1714.
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In order to reduce times of software regression testing, a new research idea and method is proposed based on RBFN (Radial Basis Function Network). Using the adaptive ability of network study, regression testing is optimized by its learning strategy. The simulation results demonstrate the new method can forecast regressive testing effectively, and implement very little error. It means an important meaning for developing new effective method of soft testing in the future.
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AUGUSTEIJN, MARIJKE F., and KELLY A. SHAW. "CONSTRUCTING A QUERY-ABLE RADIAL BASIS FUNCTION ARTIFICIAL NEURAL NETWORK." International Journal of Neural Systems 12, no. 03n04 (June 2002): 159–75. http://dx.doi.org/10.1142/s0129065702001102.
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Artificial neural networks will be more widely accepted as standard engineering tools if their reasoning process can be made less opaque. This paper describes NetQuery, an explanation mechanism that extracts meaningful explanations from trained Radial Basis Function (RBF) networks. RBF networks are well suited for explanation generation because they contain a set of locally tuned units. Standard RBF networks are modified to identify dependencies between the inputs, to be sparsely connected, and to have an easily interpretable output layer. Given these modifications, the network architecture can be used to extract "Why?" and "Why not?" explanations from the network in terms of excitatory and inhibitory in-puts and their linear relationships, greatly simplified by a run-time pruning algorithm. These query results are validated by creating an expert system based on the explanations. NetQuery is also able to inform a user about a possible change in category for a given pattern by responding to a "How can I…?" query. This kind of query is extremely useful when analyzing the quality of a pattern set.
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Chen, Dong. "Evaluation Model of Physical Education Effect: On the Application of Radial Basis Function-Particle Swarm Optimization Neural Network (RBFNN-PSO)." Computational Intelligence and Neuroscience 2021 (July 30, 2021): 1–11. http://dx.doi.org/10.1155/2021/6819493.
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This study constructs a new radial basis function-particle swarm optimization neural network (RBFNN-PSO) system, which is applied to the evaluation system of physical education teaching effect. In order to verify the evaluation performance of the RBFNN-PSO system, the traditional RBF neural network system is used as the control, and the training is carried out. The results show that the RBFNN-PSO system can reach the convergence value faster than the traditional RBF neural network system in the training, and the training error is smaller. The results show that the scoring error of RBFNN-PSO system is smaller than that of RBF neural network system, with higher accuracy and smaller error. The experimental results show that the RBFNN-PSO is superior to the traditional RBF neural network in error and accuracy.
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Zhang, Qing, Abdul Rashid Abdullah, Choo Wei Chong, and Mass Hareeza Ali. "A Study on Regional GDP Forecasting Analysis Based on Radial Basis Function Neural Network with Genetic Algorithm (RBFNN-GA) for Shandong Economy." Computational Intelligence and Neuroscience 2022 (January 25, 2022): 1–12. http://dx.doi.org/10.1155/2022/8235308.
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Gross domestic product (GDP) is an important indicator for determining a country’s or region’s economic status and development level, and it is closely linked to inflation, unemployment, and economic growth rates. These basic indicators can comprehensively and effectively reflect a country’s or region’s future economic development. The center of radial basis function neural network and smoothing factor to take a uniform distribution of the random radial basis function artificial neural network will be the focus of this study. This stochastic learning method is a useful addition to the existing methods for determining the center and smoothing factors of radial basis function neural networks, and it can also help the network more efficiently train. GDP forecasting is aided by the genetic algorithm radial basis neural network, which allows the government to make timely and effective macrocontrol plans based on the forecast trend of GDP in the region. This study uses the genetic algorithm radial basis, neural network model, to make judgments on the relationships contained in this sequence and compare and analyze the prediction effect and generalization ability of the model to verify the applicability of the genetic algorithm radial basis, neural network model, based on the modeling of historical data, which may contain linear and nonlinear relationships by itself, so this study uses the genetic algorithm radial basis, neural network model, to make, compare, and analyze judgments on the relationships contained in this sequence.
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Kong, Hui Fang, Liang Jun Xiang, and Shun Li Xia. "SOC Estimation on Radial Basis Function." Advanced Materials Research 403-408 (November 2011): 3119–22. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3119.
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Power lithium battery state of charge (SOC) is an important parameter for the measure of battery charge remaining, SOC estimation of Power lithium battery accurate or not, affect its performance and service life directly. thus, it is particularly important to improving the accuracy estimation of SOC further, combined characteristics of battery time-varying, SOC estimation method was proposed based on radial basis function neural network (RBF) in this paper, experiment results show that the RBF network algorithm can improve the estimation accuracy of SOC.
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Shymkovych, Volodymyr, Sergii Telenyk, and Petro Kravets. "Hardware implementation of radial-basis neural networks with Gaussian activation functions on FPGA." Neural Computing and Applications 33, no. 15 (March 13, 2021): 9467–79. http://dx.doi.org/10.1007/s00521-021-05706-3.
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AbstractThis article introduces a method for realizing the Gaussian activation function of radial-basis (RBF) neural networks with their hardware implementation on field-programmable gaits area (FPGAs). The results of modeling of the Gaussian function on FPGA chips of different families have been presented. RBF neural networks of various topologies have been synthesized and investigated. The hardware component implemented by this algorithm is an RBF neural network with four neurons of the latent layer and one neuron with a sigmoid activation function on an FPGA using 16-bit numbers with a fixed point, which took 1193 logic matrix gate (LUTs—LookUpTable). Each hidden layer neuron of the RBF network is designed on an FPGA as a separate computing unit. The speed as a total delay of the combination scheme of the block RBF network was 101.579 ns. The implementation of the Gaussian activation functions of the hidden layer of the RBF network occupies 106 LUTs, and the speed of the Gaussian activation functions is 29.33 ns. The absolute error is ± 0.005. The Spartan 3 family of chips for modeling has been used to get these results. Modeling on chips of other series has been also introduced in the article. RBF neural networks of various topologies have been synthesized and investigated. Hardware implementation of RBF neural networks with such speed allows them to be used in real-time control systems for high-speed objects.
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EBRAHIMZADEH, ATAOLLAH, ABOLFAZL RANJBAR, and MEHRDAD ARDEBLILPOUR. "A NEW TECHNIQUE FOR CLASSIFICATION OF DIGITAL SIGNAL TYPES." Journal of Circuits, Systems and Computers 17, no. 05 (October 2008): 957–71. http://dx.doi.org/10.1142/s0218126608004617.
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Classification of the communication signals has seen under increasing demands. In this paper, we present a new technique that identifies a variety of digital communication signal types. This technique utilizes a radial basis function neural network (RBFN) as the classifier. Swarm intelligence, as an evolutionary algorithm, is used to construct RBFN. A combination of the higher-order moments and the higher-order cumulants up to eight are selected as the features of the considered digital signal types. In conjunction with RBFN, we have used k-fold cross-validation to improve the generalization potentiality. Simulation results show that the proposed technique has high performance for classification of different communication signals even at very low signal-to-noise ratios.
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Dawson, C. W., C. Harpham, R. L. Wilby, and Y. Chen. "Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China." Hydrology and Earth System Sciences 6, no. 4 (August 31, 2002): 619–26. http://dx.doi.org/10.5194/hess-6-619-2002.
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Abstract. While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP), and the radial basis function network (RBF). Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam) for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting
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Rinanto, Noorman, Mohammad Thoriq Wahyudi, and Agus Khumaidi. "Radial Basis Function Neural Network sebagai Pengklasifikasi Citra Cacat Pengelasan." Rekayasa 11, no. 2 (October 1, 2018): 118. http://dx.doi.org/10.21107/rekayasa.v11i2.4418.
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<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>
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Mansor, Mohd Asyraf, Siti Zulaikha Mohd Jamaludin, Mohd Shareduwan Mohd Kasihmuddin, Shehab Abdulhabib Alzaeemi, Md Faisal Md Basir, and Saratha Sathasivam. "Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network." Processes 8, no. 2 (February 10, 2020): 214. http://dx.doi.org/10.3390/pr8020214.
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Radial Basis Function Neural Network (RBFNN) is a class of Artificial Neural Network (ANN) that contains hidden layer processing units (neurons) with nonlinear, radially symmetric activation functions. Consequently, RBFNN has extensively suffered from significant computational error and difficulties in approximating the optimal hidden neuron, especially when dealing with Boolean Satisfiability logical rule. In this paper, we present a comprehensive investigation of the potential effect of systematic Satisfiability programming as a logical rule, namely 2 Satisfiability (2SAT) to optimize the output weights and parameters in RBFNN. The 2SAT logical rule has extensively applied in various disciplines, ranging from industrial automation to the complex management system. The core impetus of this study is to investigate the effectiveness of 2SAT logical rule in reducing the computational burden for RBFNN by obtaining the parameters in RBFNN. The comparison is made between RBFNN and the existing method, based on the Hopfield Neural Network (HNN) in searching for the optimal neuron state by utilizing different numbers of neurons. The comparison was made with the HNN as a benchmark to validate the final output of our proposed RBFNN with 2SAT logical rule. Note that the final output in HNN is represented in terms of the quality of the final states produced at the end of the simulation. The simulation dynamic was carried out by using the simulated data, randomly generated by the program. In terms of 2SAT logical rule, simulation revealed that RBFNN has two advantages over HNN model: RBFNN can obtain the correct final neuron state with the lowest error and does not require any approximation for the number of hidden layers. Furthermore, this study provides a new paradigm in the field feed-forward neural network by implementing a more systematic propositional logic rule.
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Haviluddin, Haviluddin, and Imam Tahyudin. "Prediction of Daily Network Traffic based on Radial Basis Function Neural Network." IAES International Journal of Artificial Intelligence (IJ-AI) 3, no. 4 (August 20, 2016): 145. http://dx.doi.org/10.11591/ijai.v3.i4.pp145-149.
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This paper presents an approach for predicting daily network traffic using artificial neural networks (ANN), namely radial basis function neural network (RBFNN) method. The data is gained from 21 – 24 June 2013 (192 samples series data) in ICT Unit Universitas Mulawarman, East Kalimantan, Indonesia. The results of measurement are using statistical analysis, e.g. sum of square error (SSE), mean of square error (MSE), mean of percentage error (MPE), mean of absolute percentage error (MAPE), and mean of absolute deviation (MAD). The results show that values are the same, with different goals that have been set are 0.001, 0.002, and 0.003, and spread 200. The smallest MSE value indicates a good method for accuracy. Therefore, the RBFNN model illustrates the proposed best model to predict daily network traffic.
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Shao, Changpeng. "Quantum speedup of training radial basis function networks." Quantum Information and Computation 19, no. 7&8 (June 2019): 609–25. http://dx.doi.org/10.26421/qic19.7-8-6.
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Radial basis function (RBF) network is a simple but useful neural network model that contains wide applications in machine learning. The training of an RBF network reduces to solve a linear system, which is time consuming classically. Based on HHL algorithm, we propose two quantum algorithms to train RBF networks. To apply the HHL algorithm, we choose using the Hamiltonian simulation algorithm proposed in [P. Rebentrost, A. Steffens, I. Marvian and S. Lloyd, Phys. Rev. A 97, 012327, 2018]. However, to use this result, an oracle to query the entries of the matrix of the network should be constructed. We apply the amplitude estimation technique to build this oracle. The final results indicate that if the centers of the RBF network are the training samples, then the quantum computer achieves exponential speedup at the number and the dimension of training samples over the classical computer; if the centers are determined by the K-means algorithm, then the quantum computer achieves quadratic speedup at the number of samples and exponential speedup at the dimension of samples.
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Luan, Tiantian, Mingxiao Sun, Guoqing Xia, and Daidai Chen. "Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate." Complexity 2018 (October 22, 2018): 1–19. http://dx.doi.org/10.1155/2018/6950124.
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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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Liu, Wei, Feifan Wang, Xiawei Yang, and Wenya Li. "Upset Prediction in Friction Welding Using Radial Basis Function Neural Network." Advances in Materials Science and Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/196382.
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This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW), a radial basis function (RBF) neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW) and continuous drive friction welding (CDFW). The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.
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Ch. Tah, Prakash, Anup K. Panda, and Bibhu P. Panigrahi. "Shunt Active Filter Based on Radial Basis Function Neural Network and p-q Power Theory." International Journal of Power Electronics and Drive Systems (IJPEDS) 8, no. 2 (June 1, 2017): 667. http://dx.doi.org/10.11591/ijpeds.v8.i2.pp667-676.
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In this paper a new combination Radial Basis Function Neural Network and p-q Power Theory (RBFNN-PQ) proposed to control shunt active power filters (SAPF). The recommended system has better specifications in comparison with other control methods. In the proposed combination an RBF neural network is employed to extract compensation reference current when supply voltages are distorted and/or unbalance sinusoidal. In order to make the employed model much simpler and tighter an adaptive algorithm for RBF network is proposed. The proposed RBFNN filtering algorithm is based on efficient training methods called hybrid learning method.The method requires a small size network, very robust, and the proposed algorithms are very effective. Extensive simulations are carried out with PI as well as RBFNN controller for p-q control strategies by considering different voltage conditions and adequate results were presented.
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Chen, Tongqing, Lei Wang, Xijuan Jiang, Yubin Wang, and Kai Yan. "Finite Element Model Modification of Arch Bridge Based on Radial Basis Function Neural Network." E3S Web of Conferences 136 (2019): 04033. http://dx.doi.org/10.1051/e3sconf/201913604033.
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Compared with other neural networks, Radial Basis Function (RBF) neural network has the advantages of simple structure and fast convergence. As long as there are enough hidden layer nodes in the hidden layer, it can approximate any non-linear function. In this paper, the finite element model of a through tied arch bridge is modified based on Neural Network. The approximation function of RBF neural network is utilized to fit the implicit function relationship between the response of the bridge and its design parameters. Then the finite element model of the bridge structure is modified. The results show that RBF neural network is efficient to modify the model of a through tied arch bridge.
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AISYAH, SITI, SRI WAHYUNINGSIH, and FDT AMIJAYA. "PERAMALAN JUMLAH TITIK PANAS PROVINSI KALIMANTAN TIMUR MENGGUNAKAN METODE RADIAL BASIS FUNCTION NEURAL NETWORK." Jambura Journal of Probability and Statistics 2, no. 2 (November 11, 2021): 64–74. http://dx.doi.org/10.34312/jjps.v2i2.10292.
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Radial Basis Function Neural Network (RBFNN) is a neural that uses a radial base function in hidden layers for classification and forecasting purposes. Neural Network is developed into a radial function base with an information processing system that has characteristics similar to biological neural networks, consisting of input layers, hidden layers, and output layers. The data used in this study is data on the number of hotspots in East Kalimantan Province obtained from the official website of the National Aeronautics and Space Administration (NASA). The purpose of this research is to obtain the RBFNN model and the results of forecasting the number of hotspots for the period January 2020 to March 2020. The radial basis function used is the local Gaussian function and the linear activation function. In this study using the proportion of training data and testing data 70: 30; 80:20; and 90:10. The results showed that the input network using significant Partial Autocorrelation Function (PACF) at lag 1 and lag 2, so that the RBFNN model that was formed involved Xt-1 and Xt-2. The best Mean Absolute Percentage Error (MAPE) minimum obtained the 80:20 data proportion with 2 hidden networks. The RBFNN architecture that is formed is 2 input layers, 2 hidden layers and 1 output layer. Data from forecasting the number of hotspots in East Kalimantan Province shows that from January 2020 to February 2020 there was a decline and March 2020 an increase.
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Kawale, Praniali Surendra. "High Voltage Gain Interleaved Boost Converter with Neural Network Based MPPT Controller for Fuel Cell Based Electric Vehicle Applications." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4728–32. http://dx.doi.org/10.22214/ijraset.2021.35499.
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As a result of the strict regulations on carbon emissions and the fuel economy, fuel cell electric vehicles (FCEV) vehicles are becoming increasingly popular in the automotive industry. This paper provides the Neural Network Maximum Power Point Tracking (MPPT) controller of the 1.26 kW Proton Exchange Membrane Fuel Cell (PEMFC), which provides electric vehicle powertrain using DC-DC power converters. The proposed neural network controls the MPPT Radial Basis Function Network (RBFN) using the PEMFC Maximum PowerPoint (MPP) tracking algorithm. High frequency switching and high DC-DC converting power are important for FCEV continuity. For maximum power gain, a three-phase power supply interleaved boost converter (IBC) is also designed for the FCEV system. The interleaving process reduces the current input pressure and electrical pressure in semiconductor electrical equipment. FCEV system performance analysis with RBFN based MPPT control compared to fuzzy logic controllers (FLC) on the MATLAB / Simulink platform.
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Yu, Fa Hong, Mei Jia Chen, and Wei Zhi Liao. "A Novel Learning Evaluation Method Based on RBF Neural Network." Applied Mechanics and Materials 385-386 (August 2013): 1697–700. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1697.
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There are many learning evaluation methods, but most of them are subjective, which contains a lot of man-made factors. This paper presents a new learning evaluation method based on radial basis function (RBF) neutral network. By analysis the orthogonal least squares for RBF and determines the center of the basis functions, the model of RBF neural network was constructed. Experimental studies show that the Method Based on RBF Neural Network is effective for learning Evaluation.
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Chaveesuk, Ravipim, and Natthamon Konjanattham. "Modeling approach for releasing a frankfurter production batch." British Food Journal 121, no. 8 (August 5, 2019): 1813–24. http://dx.doi.org/10.1108/bfj-09-2018-0602.
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Purpose The purpose of this paper is to model the relationship between 11 frankfurter physical properties and their sensory scores to classify a release of frankfurter production batches to the market. Design/methodology/approach Data from 209 frankfurter batches were collected. Market batch release classifications were based on 11 physical properties via predictive and direct classification models. The predictive models under study included a regression, backpropagation neural network (BPN) and radial basis function neural network (RBFN) whereas the direct classification models were logistic regression, BPN and RBFN. Model performance was evaluated via correct classification rate. Findings The 11-7-4 RBFN predictive model proved superior with a 90 percent correct classification rate and 0 percent producer risk while the 11-5-1 RBFN, as a classification model, outperformed with the same level of accuracy, 90 and 0 percent, respectively. Producers prefer the less time-consuming direct classifiers for evaluation. Furthermore, the 11-5-1 RBFN direct classifier revealed that color measurement greatly influenced frankfurter batch release. Increases in redness, yellowness and brownness increased batch release probability. Originality/value This research attempts to establish a novel production batch release model for sausage manufacturing. Key factors can then be optimized for improving batch release probability for implementation throughout the sausage industry.
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Rahmani, Reza, Saleh Mobayen, Afef Fekih, and Jong-Suk Ro. "Robust Passivity Cascade Technique-Based Control Using RBFN Approximators for the Stabilization of a Cart Inverted Pendulum." Mathematics 9, no. 11 (May 27, 2021): 1229. http://dx.doi.org/10.3390/math9111229.
Full textAbstract:
This paper proposes a novel passivity cascade technique (PCT)-based control for nonlinear inverted pendulum systems. Its main objective is to stabilize the pendulum’s upward states despite uncertainties and exogenous disturbances. The proposed framework combines the estimation properties of radial basis function neural networks (RBFNs) with the passivity attributes of the cascade control framework. The unknown terms of the nonlinear system are estimated using an RBFN approximator. The performance of the closed-loop system is further enhanced by using the integral of angular position as a virtual state variable. The lumped uncertainties (NN—Neural Network approximation, external disturbances and parametric uncertainty) are compensated for by adding a robustifying adaptive rule-based signal to the PCT-based control. The boundedness of the states is confirmed using the passivity theorem. The performance of the proposed approach was assessed using a nonlinear inverted pendulum system under both nominal and disturbed conditions.
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Leung, Henry, and Simon Haykin. "Rational Function Neural Network." Neural Computation 5, no. 6 (November 1993): 928–38. http://dx.doi.org/10.1162/neco.1993.5.6.928.
Full textAbstract:
In this paper we observe that a particular class of rational function (RF) approximations may be viewed as feedforward networks. Like the radial basis function (RBF) network, the training of the RF network may be performed using a linear adaptive filtering algorithm. We illustrate the application of the RF network by considering two nonlinear signal processing problems. The first problem concerns the one-step prediction of a time series consisting of a pair of complex sinusoid in the presence of colored non-gaussian noise. Simulated data were used for this problem. In the second problem, we use the RF network to build a nonlinear dynamic model of sea clutter (radar backscattering from a sea surface); here, real-life data were used for the study.