Journal articles: 'Radial Basis Function Neural Network Classifier'

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Mohammadi, Mahnaz, Akhil Krishna, Nalesh S., and S. K. Nandy. "A Hardware Architecture for Radial Basis Function Neural Network Classifier." IEEE Transactions on Parallel and Distributed Systems 29, no. 3 (March 1, 2018): 481–95. http://dx.doi.org/10.1109/tpds.2017.2768366.

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Lee, Yuchun. "Handwritten Digit Recognition Using K Nearest-Neighbor, Radial-Basis Function, and Backpropagation Neural Networks." Neural Computation 3, no. 3 (September 1991): 440–49. http://dx.doi.org/10.1162/neco.1991.3.3.440.

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Results of recent research suggest that carefully designed multilayer neural networks with local “receptive fields” and shared weights may be unique in providing low error rates on handwritten digit recognition tasks. This study, however, demonstrates that these networks, radial basis function (RBF) networks, and k nearest-neighbor (kNN) classifiers, all provide similar low error rates on a large handwritten digit database. The backpropagation network is overall superior in memory usage and classification time but can provide “false positive” classifications when the input is not a digit. The backpropagation network also has the longest training time. The RBF classifier requires more memory and more classification time, but less training time. When high accuracy is warranted, the RBF classifier can generate a more effective confidence judgment for rejecting ambiguous inputs. The simple kNN classifier can also perform handwritten digit recognition, but requires a prohibitively large amount of memory and is much slower at classification. Nevertheless, the simplicity of the algorithm and fast training characteristics makes the kNN classifier an attractive candidate in hardware-assisted classification tasks. These results on a large, high input dimensional problem demonstrate that practical constraints including training time, memory usage, and classification time often constrain classifier selection more strongly than small differences in overall error rate.

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Rana, Anurag, Arjun Kumar, and Ankur Sharma. "Neural Network Radial Basis Function classifier for earthquake data using aFOA." International Journal of Advanced Research 4, no. 8 (August 31, 2016): 537–40. http://dx.doi.org/10.21474/ijar01/1244.

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RADHIKA, K. R., S. V. SHEELA, and G. N. SEKHAR. "OFF-LINE SIGNATURE AUTHENTICATION USING RADIAL BASIS FUNCTION." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 02 (March 2011): 207–25. http://dx.doi.org/10.1142/s0218001411008580.

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A system is proposed that considers minimal features using subpattern analysis which leads to less response time in a real time scenario. Using training samples, with a high degree of certainty, the minimum variance quadtree components [MVQC] of a signature for a person are listed to be applied on a testing sample. Initially the experiment was conducted on wavelet decomposed information for a signature. The non-MVQCs and core components were analyzed. To characterize the local details Gaussian-Hermite moment was applied. Later Hu moments were applied on the selected subsections. The summation values of the subsections are provided as feature to radial basis function [RBF] and feed forward neural network classifiers. Results indicate that the RBF classifier yielded 7% false rejection rate and feed forward neural network classification technique produced 9% false rejection rate. Promising results were achieved, by experimenting on the list of most prominent minimum variance components which are core components using RBF.

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Manson, Graeme, Gareth Pierce, Keith Worden, and Daley Chetwynd. "Classification Using Radial Basis Function Networks with Uncertain Weights." Key Engineering Materials 293-294 (September 2005): 135–42. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.135.

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This paper considers the performance of radial basis function neural networks for the purpose of data classification. The methods are illustrated using a simple two class problem. Two techniques for reducing the rate of misclassifications, via the introduction of an “unable to classify” label, are presented. The first of these considers the imposition of a threshold value on the classifier outputs whilst the second considers the replacement of the crisp network weights with interval ranges. Two network training techniques are investigated and it is found that, although thresholding and uncertain weights give similar results, the level of variability of network performance is dependent upon the training approach

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Amini, Mohammad, Jalal Rezaeenour, and Esmaeil Hadavandi. "A Neural Network Ensemble Classifier for Effective Intrusion Detection Using Fuzzy Clustering and Radial Basis Function Networks." International Journal on Artificial Intelligence Tools 25, no. 02 (April 2016): 1550033. http://dx.doi.org/10.1142/s0218213015500335.

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Intrusion Detection Systems have considerable importance in preventing security threats and protecting computer networks against attackers. So far, various classification approaches using data mining and machine learning techniques have been proposed to the problem of intrusion detection. However, using single classifier systems for intrusion detection suffers from some limitations including lower detection rate for low-frequent attacks, detection instability, and complexity in training process. Ensemble classifier systems combine several individual classifiers and obtain a classifier with higher performance. In this paper, we propose a new ensemble classifier using Radial Basis Function (RBF) neural networks and fuzzy clustering in order to increase detection accuracy and stability, reduce false positives, and provide higher detection rate for low-frequent attacks. We also use a hybrid combination method to aggregate the individual predictions of the base classifiers, which helps to increase detection accuracy. The experimental results on NSL-KDD data set demonstrate that our proposed system has a higher detection accuracy compared to other wellknown classification systems. It also performs more effectively for detection of low-frequent attacks. Furthermore, the proposed ensemble method offers better performance compared to popular ensemble methods.

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Hwang, Young-Sup, and Sung-Yang Bang. "An Efficient Method to Construct a Radial Basis Function Neural Network Classifier." Neural Networks 10, no. 8 (November 1997): 1495–503. http://dx.doi.org/10.1016/s0893-6080(97)00002-6.

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Kumudha, P., and R. Venkatesan. "Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction." Scientific World Journal 2016 (2016): 1–20. http://dx.doi.org/10.1155/2016/2401496.

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Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.

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JIAO Hongqiang, JIA Limin, and JIN Yanhua. "A New Network Intrusion Detection Algorithm based on Radial Basis Function Neural Networks Classifier." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 1 (January 31, 2012): 170–76. http://dx.doi.org/10.4156/aiss.vol4.issue1.22.

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Selvakumari Jeya, I. Jasmine, and S. N. Deepa. "Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural Network Classifier." Computational and Mathematical Methods in Medicine 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/7493535.

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A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen as a classifier model because of its Gaussian Kernel function and its effective learning process to avoid local and global minima problem and enable faster convergence. This paper specifically focused on tuning the weights and bias of RBFNN classifier employing the proposed RCGA. The operators used in RCGA enable the algorithm flow to compute weights and bias value so that minimum Mean Square Error (MSE) is obtained. With both the lung healthy and cancer images from Lung Image Database Consortium (LIDC) database and Real time database, it is noted that the proposed RCGA based RBFNN classifier has performed effective classification of the healthy lung tissues and that of the cancer affected lung nodules. The classification accuracy computed using the proposed approach is noted to be higher in comparison with that of the classifiers proposed earlier in the literatures.

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Pogorilyi, Oleksandr, Mohammad Fard, John Davy, Mechanical and Automotive Engineering, School, Mechanical and Automotive Engineering, School, Mechanical and Automotive Engineering, School, Mechanical and Automotive Engineering, School, and Mechanical and Automotive Engineering, School. "Squeak and rattle noise classification using radial basis function neural networks." Noise Control Engineering Journal 68, no. 4 (July 1, 2020): 283–93. http://dx.doi.org/10.3397/1/376824.

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In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

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Park, Sang-Beom, and Sung-Kwun Oh. "Design of Compensator-based Radial Basis Function Neural Network Classifier for Error Compensation." Journal of Korean Institute of Intelligent Systems 29, no. 3 (June 30, 2019): 163–69. http://dx.doi.org/10.5391/jkiis.2019.29.3.163.

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Young-Sup Hwang and Sung-Yang Bang. "Recognition of unconstrained handwritten numerals by a radial basis function neural network classifier." Pattern Recognition Letters 18, no. 7 (July 1997): 657–64. http://dx.doi.org/10.1016/s0167-8655(97)00056-1.

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Kokkinos, Yiannis, and Konstantinos G. Margaritis. "Topology and simulations of a Hierarchical Markovian Radial Basis Function Neural Network classifier." Information Sciences 294 (February 2015): 612–27. http://dx.doi.org/10.1016/j.ins.2014.08.025.

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Gevaert, Wouter, Georgi Tsenov, and Valeri Mladenov. "Neural networks used for speech recognition." Journal of Automatic Control 20, no. 1 (2010): 1–7. http://dx.doi.org/10.2298/jac1001001g.

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In this paper is presented an investigation of the speech recognition classification performance. This investigation on the speech recognition classification performance is performed using two standard neural networks structures as the classifier. The utilized standard neural network types include Feed-forward Neural Network (NN) with back propagation algorithm and a Radial Basis Functions Neural Networks.

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Gruszczynski, Stanislaw. "An Ensemble of Neural Classifiers and Constructivist Algorithms in the Identification of Agricultural Suitability Complexes of Soils on the Basis of Physiographic Information." ISRN Soil Science 2012 (May 8, 2012): 1–9. http://dx.doi.org/10.5402/2012/610567.

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The ensemble of classifiers for identification of agricultural suitability of soils on the basis of physiographic information was created in accordance with the stacking algorithm. It is comprised of five neural networks of various structures. The deciding element was a neural classifier optimised on the basis of input vectors composed of the indications of five classifiers making up the lower level. Among the architectures studied, the best result was achieved using the Radial Basis Function network as the decisive classifier, composed with the use of the constructivist Feature Space Mapping algorithm. In this configuration, the group correctly identified more than 99% of the elements of the validation set. The models may be used as tools for predicting expected soil condition, which is helpful in assessment of the range of substantial transformations.

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Leema, N., H. Khanna Nehemiah, and A. Kannan. "Quantum-Behaved Particle Swarm Optimization Based Radial Basis Function Network for Classification of Clinical Datasets." International Journal of Operations Research and Information Systems 9, no. 2 (April 2018): 32–52. http://dx.doi.org/10.4018/ijoris.2018040102.

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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.

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Vinay, Kumar Jain. "A comparative analysis of neural network function: resilient back propagation algorithm (BPA) and radial basis functions (RBF) in multilingual environment." i-manager's Journal on Digital Signal Processing 10, no. 1 (2022): 9. http://dx.doi.org/10.26634/jdp.10.1.18639.

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The most convenient speech processing tool is Artificial Neural Networks (ANNs). The effectiveness has been tested with various real-time applications. The classifier using artificial neural networks identifies utterances based on features extracted from the speech signal. The proposed approach to multilingual speaker identification consists of two parts, such as a training part and a testing part. In the training part, the classifier is trained using speech feature vectors. The spoken language contains complete information, such as details about the content of the message and details about the speaker of that message. In the present work, the speech signal databases of different speakers in a multilingual environment were recorded in three Indian languages, i.e., Hindi, Marathi, and Rajasthani. The cepstral characteristics of the speech signal were extracted: Mel-Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC). The system is designed for speaker recognition through multilingual speech signals using MFCC, GFCC, and combined functions as acoustic characteristics. Training and testing were performed using the Neural Network (NN) function, robust Backpropagation Algorithm (BPA), and Radial Basis Functions (RBF), and the results were compared. The accuracy of the speaker identification system is 94.89% using BPA and 96.62% using the RBF neural network.

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REYNOLDS, JAKE, and LIONEL TARASSENKO. "SPOKEN LETTER RECOGNITION WITH NEURAL NETWORKS." International Journal of Neural Systems 03, no. 03 (January 1992): 219–35. http://dx.doi.org/10.1142/s0129065792000188.

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Neural networks have recently been applied to real-world speech recognition problems with a great deal of success. This paper develops a strategy for optimising a neural network known as the Radial Basis Function classifier (RBF) on a large spoken letter recognition problem designed by British Telecom Research Laboratories. The strategy developed can be viewed as a compromise between a fully adaptive approach involving prohibitively large amounts of computation and a heuristic approach resulting in poor generalisation. A value for the optimal number of kernel functions is suggested and methods for determining the positions of the centres and the values of the kernel function widths are provided. During the evolution of the optimisation strategy, it was demonstrated that spatial organisation of the centres does not adversely affect the ability of the classifier to generalise. An RBF employing the optimisation strategy achieved a lower error rate than Woodland’s multilayer perceptron26 and two traditional static pattern classifiers on the same problem. The error rate of the RBF was very close to the estimated minimum error rate obtainable with an optimal Bayesian classifier. An examination of the computational requirements of the classifiers illustrated a significant trade-off between the computational investment in training and level of generalisation achieved.

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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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Chen, Jun Ying, Jing Chen, and Zeng Xi Feng. "Shape Classification Using Multiple Classifiers with Different Feature Sets." Advanced Materials Research 368-373 (October 2011): 1583–87. http://dx.doi.org/10.4028/www.scientific.net/amr.368-373.1583.

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In this paper, a new shape classification method based on different feature sets using multiple classifiers is proposed. Different feature sets are derived from the shapes by using different extraction methods. The implements of feature extraction are based on two ways: Fourier descriptors and Zernike moments. Multiple classifiers comprise Normal densities based linear classifier, k-nearest neighbor classifier, Feed-Forward neural network, Radial Basis Function neural network classifier. Each classifier is trained by two feature sets respectively to form two classification results. The final classification results are a combined response of the individual classifier using six different classifier combination rules and the results were compared with those derived from multiple classifiers based on the same feature sets and individual classifier. In this study we examined the different classification tasks on Kimia dataset. For the tasks the best combination strategy was found using the product rule, giving an average recognition rate of 95.83%.

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Wu, Hong Qi, and Xiao Bin Li. "Research on Intelligent Diagnosis Technology of Transformer Fault." Applied Mechanics and Materials 385-386 (August 2013): 589–92. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.589.

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In order to improve the diagnosis rates of transformer fault, a research on application of RBF neural network is carried out. The structure and working principle of radial basis function (RBF) neural network are analyzed and a three layer RBF network is also designed for transformer fault diagnosis. It is proved by MATLAB experiment that RBF neural network is a strong classifier which is used to diagnose transformer fault effectively.

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Wongsathan, Rati, and Pasit Pothong. "Heart Disease Classification Using Artificial Neural Networks." Applied Mechanics and Materials 781 (August 2015): 624–27. http://dx.doi.org/10.4028/www.scientific.net/amm.781.624.

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Neural Networks (NNs) has emerged as an importance tool for classification in the field of decision making. The main objective of this work is to design the structure and select the optimized parameter in the neural networks to implement the heart disease classifier. Three types of neural networks, i.e. Multi-layered Perceptron Neural Network (MLP-NN), Radial Basis Function Neural Networks (RBF-NN), and Generalized Regression Neural Network (GR-NN) have been used to test the performance of heart disease classification. The classification accuracy obtained by RBFNN gave a very high performance than MLP-NN and GR-NN respectively. The performance of accuracy is very promising compared with the previously reported another type of neural networks.

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Urva, Gellysa. "K-Means Clustering to Design Radial Basis Function Neural Network (RBFNN) Classifiers." JURNAL UNITEK 9, no. 2 (January 9, 2017): 16–24. http://dx.doi.org/10.52072/unitek.v9i2.59.

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Radial Basis Function Neural Network (RBFNN) is hybrid network. It is combinationtraining both supervised and unsuvervised. RBFNN does not use generally activationfunction in Neural Network. It use radial based function. Dalam proses membangunarsitektur JSTRBF membutuhkan proses cluster. Pada penelitian ini, penulismenggunakan algoritma K-Means Clustering, dimana algoritma ini menjadi algoritma yangefisien dan efektif dalam mengolah data. K-Means Clustering merupakan salah satualgoritma clustering dengan tujuan untuk membagi data menjadi beberapa kelompok.Algoritma ini akan mengelompokkan data atau objek ke dalam k buah kelompok (cluster)yang diinginkan. Penentuan jumlah cluster pada JSTRBF mempengaruhi akurasi dariJSTRBF dalam melakukan klasifikasi. Hasil penelitian ini menyatakan desain arsitekturJSTRBF yang memiliki akurasi terbaik pada jumlah 3 cluster dengan nilai akurasi sebesar87,06%. Akan tetapi jika jumlah cluster pada JSTRBF di atas 4 cluster nilai akurasi dibawah 50%.

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PATEL, PRETESH B., and TSHILIDZI MARWALA. "CALLER BEHAVIOUR CLASSIFICATION USING COMPUTATIONAL INTELLIGENCE METHODS." International Journal of Neural Systems 20, no. 01 (February 2010): 87–93. http://dx.doi.org/10.1142/s0129065710002255.

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A classification system that accurately categorizes caller interaction within Interactive Voice Response systems is essential in determining caller behaviour. Field and call performance classifier for pay beneficiary application are developed. Genetic Algorithms, Multi-Layer Perceptron neural network, Radial Basis Function neural network, Fuzzy Inference Systems and Support Vector Machine computational intelligent techniques were considered in this research. Exceptional results were achieved. Classifiers with accuracy values greater than 90% were developed. The preferred models for field 'Say amount', 'Say confirmation' and call performance classification are the ensemble of classifiers. However, the Multi-Layer Perceptron classifiers performed the best in field 'Say account' and 'Select beneficiary' classification.

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Richard, Michael D., and Richard P. Lippmann. "Neural Network Classifiers Estimate Bayesian a posteriori Probabilities." Neural Computation 3, no. 4 (December 1991): 461–83. http://dx.doi.org/10.1162/neco.1991.3.4.461.

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Many neural network classifiers provide outputs which estimate Bayesian a posteriori probabilities. When the estimation is accurate, network outputs can be treated as probabilities and sum to one. Simple proofs show that Bayesian probabilities are estimated when desired network outputs are 1 of M (one output unity, all others zero) and a squared-error or cross-entropy cost function is used. Results of Monte Carlo simulations performed using multilayer perceptron (MLP) networks trained with backpropagation, radial basis function (RBF) networks, and high-order polynomial networks graphically demonstrate that network outputs provide good estimates of Bayesian probabilities. Estimation accuracy depends on network complexity, the amount of training data, and the degree to which training data reflect true likelihood distributions and a priori class probabilities. Interpretation of network outputs as Bayesian probabilities allows outputs from multiple networks to be combined for higher level decision making, simplifies creation of rejection thresholds, makes it possible to compensate for differences between pattern class probabilities in training and test data, allows outputs to be used to minimize alternative risk functions, and suggests alternative measures of network performance.

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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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Zaborski, D., and W. Grzesiak. "Detection of difficult calvings in dairy cows using neural classifier." Archives Animal Breeding 54, no. 5 (October 10, 2011): 477–89. http://dx.doi.org/10.5194/aab-54-477-2011.

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Abstract. In this study, the detection of dairy cows with difficult calvings using artificial neural networks (ANN) and classification functions (CF) is presented. The set of 15 classification variables was used. The dependent variable was the class of calving difficulty: difficult or easy. Perceptrons with one (MLP1) and two (MLP2) hidden layers as well as radial basis function (RBF) networks were analyzed. The prepared classifiers were characterized by good quality. The accuracy amounted to 75–92 %. Only the RBF network had somewhat worse quality. The level of correct detection by ANN was also high. The sensitivity on a test set was 67–80 % at specificity of 61- 81 %. In the case of CF, a considerable disproportion between sensitivity (6 %) and specificity (99 %) was found. The variables with the greatest contribution to the determination of calving difficulty class were calving season, CYP19-PvuII genotype, pregnancy length and, to a lesser degree, other variables. The performed analyses proved the usefulness of ANN for the detection of cows with difficult calvings, whereas the detection by CF was inaccurate.

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Appavu alias Balamurugan, S., and S. Gilbert Nancy. "An Efficient Feature Selection and Classification Using Optimal Radial Basis Function Neural Network." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, no. 05 (September 28, 2018): 695–715. http://dx.doi.org/10.1142/s0218488518500320.

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Feature selection is the process of identifying and removing many irrelevant and redundant features. Irrelevant features, along with redundant features, severely affect the accuracy of the learning machines. In high dimensional space finding clusters of data objects is challenging due to the curse of dimensionality. When the dimensionality increases, data in the irrelevant dimensions may produce much noise. And also, time complexity is the major issues in existing approach. In order to rectify these issues our proposed method made use of efficient feature subset selection in high dimensional data. Here we are considering the input dataset is the high dimensional micro array dataset. Initially, we have to select the optimal features so that our proposed technique employed Modified Social Spider Optimization (MSSO) algorithm. Here the traditional Social Spider Optimization is modified with the help of fruit fly optimization algorithm. Next the selected features are the input for the classifier. Here the classification is performed using Optimized Radial basis Function based neural network (ORBFNN) technique to classify the micro array data as normal or abnormal data. The effectiveness of RBFNN is optimized by means of artificial bee colony algorithm (ABC). Experimental results indicate that the proposed classification framework have outperformed by having better accuracy for five benchmark dataset 93.66%, 97.09%, 98.66%, 98.28% and 98.93% which is minimum value when compared to the existing technique. The proposed method is executed in MATLAB platform.

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Bondarenko, Andrey, and Ludmila Aleksejeva. "Methodology for Knowledge Extraction from Trained Artificial Neural Networks." Information Technology and Management Science 21 (December 14, 2018): 6–14. http://dx.doi.org/10.7250/itms-2018-0001.

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Artificial neural networks are widely spread models that outperform more basic, but explainable machine learning models like classification decision tree. Although their lack of explainability severely limits their area of application. All mission critical areas or law regulated areas (like European GDPR) require model to be explained. Explainability allows model validation for correctness and lack of bias. Thus methods for knowledge extraction from artificial neural networks have gained attention and development efforts. Current paper addresses this problem and describes knowledge extraction methodology which can be applied to classification problems. It is based on previous research and allows knowledge to be extracted from trained fully connected feed-forward artificial neural network, from radial basis function neural network and from hyper-polytope based classifier in the form of binary classification decision tree, elliptical rules and If-Then rules.

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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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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 Radial Basis Function Neural Network (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 Fast Fourier Transform (FFT) dan Descreate Cosine Transform (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.Kata Kunci: Radial Basis Function Neural Network (RBFNN), FFT, DCT, cacat las, klasifikasi.Radial Basis Function Neural Network as a Weld Defect Classifiers ABSTRACTThe 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.Keywords: Radial Basis Function Neural Network (RBFNN), FFT, DCT, weld defect, classification.

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Jin, Zhihao, Qicheng Han, Kai Zhang, and Yimin Zhang. "An intelligent fault diagnosis method of rolling bearings based on Welch power spectrum transformation with radial basis function neural network." Journal of Vibration and Control 26, no. 9-10 (January 9, 2020): 629–42. http://dx.doi.org/10.1177/1077546319889859.

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In the intelligent fault diagnosis of rolling bearings, the high recognition accuracy is hardly achieved when small training samples and strong noise happen. In this article, a novel fault diagnosis method is proposed, that is radial basis function neural network with power spectrum of Welch method. This fault diagnosis model adopts the way of end-to-end operating mode. It takes the original vibration signal (time-domain signal) as input, and Welch method transforms the data from time-domain signals to power spectrums and suppresses high strength noise. Then the results of Welch method are classified by radial basis function neural network. To test the performance of radial basis function neural network with power spectrum of Welch method, the method is compared with some advanced fault diagnosis methods, and the limit performance test for radial basis function neural network with power spectrum of Welch method is carried out to obtain its ultimate diagnosis ability. The results show that the proposed method can realize the high diagnostic precision without the complex feature extraction from the signal. At the same time, in the case of a small amount of training data, this method also can achieve the diagnosis in high precision. Moreover, the anti-noise performance of radial basis function neural network with power spectrum of Welch method is better than the performance of some fault diagnosis methods proposed in recent years.

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Mahajan, Payal, and Zaheeruddin Zaheeruddin. "Analysis of back propagation and radial basis function neural networks for handover decisions in wireless communication." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (October 1, 2020): 4835. http://dx.doi.org/10.11591/ijece.v10i5.pp4835-4843.

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In mobile systems, handoff is a vital process, referring to a process of allocating an ongoing call from one BS to another BS. The handover technique is very important to maintain the Quality of service. Handover algorithms, based on neural networks, fuzzy logic etc. can be used for the same purpose to keep Quality of service as high as possible. In this paper, it is proposed that back propagation networks and radial basis functions may be used for taking handover decision in wireless communication networks. The performance of these classifiers is evaluated on the basis of neurons in hidden layer, training time and classification accuracy. The proposed approach shows that radial basis function neural network give better results for making handover decisions in wireless heterogeneous networks with classification accuracy of 90%.

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Panda, Mrutyunjaya. "Software Defect Prediction Using Hybrid Distribution Base Balance Instance Selection and Radial Basis Function Classifier." International Journal of System Dynamics Applications 8, no. 3 (July 2019): 53–75. http://dx.doi.org/10.4018/ijsda.2019070103.

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Software is an important part of human life and with the rapid development of software engineering the demands for software to be reliable with low defects is increasingly pressing. The building of a software defect prediction model is proposed in this article by using various software metrics with publicly available historical software defect datasets collected from several projects. Such a prediction model can enable the software engineers to take proactive actions in enhancing software quality from the early stages of the software development cycle. This article introduces a hybrid classification method (DBBRBF) by combining distribution base balance (DBB) based instance selection and radial basis function (RBF) neural network classifier to obtain the best prediction compared to the existing research. The experimental results with post-hoc statistical significance tests shows the effectiveness of the proposed approach.

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van der Schaar, M., E. Delory, A. Català, and M. André. "Neural network-based sperm whale click classification." Journal of the Marine Biological Association of the United Kingdom 87, no. 1 (February 2007): 35–38. http://dx.doi.org/10.1017/s0025315407054756.

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Recordings of a group of foraging sperm whales usually result in a mixture of clicks from different animals. To analyse the click sequences of individual whales these clicks need to be separated, and for this an automatic classifier would be preferred. Here we study the use of a radial basis function network to perform the separation. The neural network's ability to discriminate between different whales was tested with six data sets of individually diving males. The data consisted of five shorter click trains and one complete dive which was especially important to evaluate the capacity of the network to generalize. The network was trained with characteristics extracted from the six click series with the help of a wavelet packet-based local discriminant basis. The selected features were separated in a training set containing 50 clicks of each data set and a validation set with the remaining clicks. After the network was trained it could correctly classify around 90% of the short click series, while for the entire dive this percentage was around 78%.

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Panda, Mrutyunjaya, Aboul Ella Hassanien, and Ajith Abraham. "Hybrid Data Mining Approach for Image Segmentation Based Classification." International Journal of Rough Sets and Data Analysis 3, no. 2 (April 2016): 65–81. http://dx.doi.org/10.4018/ijrsda.2016040105.

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Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function (RBF) neural network (WRBF) and feature subset harmony search based fuzzy discernibility classifier (HSFD) approaches are proposed as a data mining technique for image segmentation based classification. In this paper, the authors use Lena RGB image; Magnetic resonance image (MR) and Computed Tomography (CT) Image for analysis. It is observed from the obtained simulation results that Wavelet based RBF neural network outperforms the harmony search based fuzzy discernibility classifiers.

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Lim, King Hann, Kah Phooi Seng, and Li-Minn Ang. "MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–7. http://dx.doi.org/10.1155/2012/793176.

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Lyapunov theory-based radial basis function neural network (RBFNN) is developed for traffic sign recognition in this paper to perform multiple inputs multiple outputs (MIMO) classification. Multidimensional input is inserted into RBF nodes and these nodes are linked with multiple weights. An iterative weight adaptation scheme is hence designed with regards to the Lyapunov stability theory to obtain a set of optimum weights. In the design, the Lyapunov function has to be well selected to construct an energy space with a single global minimum. Weight gain is formed later to obey the Lyapunov stability theory. Detail analysis and discussion on the proposed classifier’s properties are included in the paper. The performance comparisons between the proposed classifier and some existing conventional techniques are evaluated using traffic sign patterns. Simulation results reveal that our proposed system achieved better performance with lower number of training iterations.

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Hua, Chi, Li Liu, Liang Kuang, and Dechang Pi. "Identification of Epileptic Electroencephalograms Signals Using an Integrated Transfer Radius Basis Function Neural Network." Journal of Medical Imaging and Health Informatics 10, no. 7 (July 1, 2020): 1584–89. http://dx.doi.org/10.1166/jmihi.2020.3084.

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As a common brain disease, epilepsy is rapidly increasing in terms of the number of patients. Long-term repeated sudden seizures seriously affect the physical and mental health of patients. Epileptic electroencephalogram (EEG) signals are an effective tool in the hands of clinicians for diagnosing epilepsy, and how to use computer technology to automatically analyze and detect epileptic EEG signals has become very meaningful. This article proposes a method for effectively identifying epileptic EEGs for further diagnosis of epilepsy. The traditional modeling method default is to train on training samples and test samples that obey the same distribution, which usually does not match the actual situation. Therefore, a transfer learning (TL) mechanism is introduced to a classical radial basis function neural network (RBFNN). Considering the limited stability of a single classifier, this article introduces an integration strategy and proposes an integrated transfer RBFNN (ITRBFNN) algorithm. Experimental results of EEG signal recognition for epilepsy show that the algorithm has better adaptability of scene transfer and stability.

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Dash, Ch Sanjeev Kumar, Amitav Saran, Pulak Sahoo, Satchidananda Dehuri, and Sung-Bae Cho. "Design of self-adaptive and equilibrium differential evolution optimized radial basis function neural network classifier for imputed database." Pattern Recognition Letters 80 (September 2016): 76–83. http://dx.doi.org/10.1016/j.patrec.2016.05.002.

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Novizon and Zulkurnain Abdul-Malek. "Neutral Networks for Fault Classification: Comparison between Feed-Forward Back-Propagation, RBF and LVQ Neural Network." Applied Mechanics and Materials 818 (January 2016): 96–100. http://dx.doi.org/10.4028/www.scientific.net/amm.818.96.

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— Neural networks are frequently used as a classifier for tasks in many classifications. However there are disadvantages in terms of amount of training data required, and length of training time. This paper, develop an intelligent diagnosis system for zinc oxide (ZnO) surge arrester fault classification. First the features were extracted from 600 ZnO surge arrester thermal images and leakage currents. Then these features were presented to several neural network architectures to investigate the most suitable network model for classifying the ZnO surge arrester fault condition effectively. Three classification models were used namely feed forward back propagation (FFBP), radial basis function (RBF) and learning vector quantization (LVQ) algorithm. The performance of the networks was compared based on resulted of misclassify and correct rate. The method was evaluated using 24 testing datasets. Comparison results showed that LVQ was the best training algorithm for the ZnO surge arrester fault classification compared to the others system. Also the LVQ is faster than FFBP and RBF.

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Xu, Zeng Bing, Jian Ping Xuan, Tie Lin Shi, Bo Wu, and You Min Hu. "A Novel Fault Diagnosis Method Using PCA and ART-Similarity Classifier Based on Yu’s Norm." Key Engineering Materials 413-414 (June 2009): 569–74. http://dx.doi.org/10.4028/www.scientific.net/kem.413-414.569.

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In this paper, a novel similarity classifier which synthesizes the adaptive resonance theory (ART) and the similarity classifier based on the Yu’s norm is proposed. The proposed ART-similarity classifier can not only carry out training without forgetting previously trained patterns but also be adaptive to changes in the environment. In order to test the proposed classifier, it is applied to the fault diagnosis of rolling element bearings. Before application to the fault diagnosis of bearings, considering computation burden principal component analysis (PCA) is proposed to reduce the number of features. The PCs are input the proposed classifier to diagnose the faulty bearings. The experiment results testify that the proposed classifier can identify the faults accurately. Furthermore, in order to validate the effectiveness of the proposed classifier further, it compares with other neural networks, such as the fuzzy ART, self-organising feature maps (SOFMs) and radial basis function (RBF) neural network through diagnosing the bearings under the same conditions. The comparison results confirm the superiority of the proposed method.

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Zhang, Chu, Chang Wang, Fei Liu, and Yong He. "Mid-Infrared Spectroscopy for Coffee Variety Identification: Comparison of Pattern Recognition Methods." Journal of Spectroscopy 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7927286.

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The potential of using mid-infrared transmittance spectroscopy combined with pattern recognition algorithm to identify coffee variety was investigated. Four coffee varieties in China were studied, including Typica Arabica coffee from Yunnan Province, Catimor Arabica coffee from Yunnan Province, Fushan Robusta coffee from Hainan Province, and Xinglong Robusta coffee from Hainan Province. Ten different pattern recognition methods were applied on the optimal wavenumbers selected by principal component analysis loadings. These methods were classified as highly effective methods (soft independent modelling of class analogy, support vector machine, back propagation neural network, radial basis function neural network, extreme learning machine, and relevance vector machine), methods of medium effectiveness (partial least squares-discrimination analysis,Knearest neighbors, and random forest), and methods of low effectiveness (Naive Bayes classifier) according to the classification accuracy for coffee variety identification.

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Emperuman, Malathy, and Srimathi Chandrasekaran. "Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network." Sensors 20, no. 3 (January 29, 2020): 745. http://dx.doi.org/10.3390/s20030745.

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Sensor devices in wireless sensor networks are vulnerable to faults during their operation in unmonitored and hazardous environments. Though various methods have been proposed by researchers to detect sensor faults, only very few research studies have reported on capturing the dynamics of the inherent states in sensor data during fault occurrence. The continuous density hidden Markov model (CDHMM) is proposed in this research to determine the dynamics of the state transitions due to fault occurrence, while neural networks are utilized to classify the faults based on the state transition probability density generated by the CDHMM. Therefore, this paper focuses on the fault detection and classification using the hybridization of CDHMM and various neural networks (NNs), namely the learning vector quantization, probabilistic neural network, adaptive probabilistic neural network, and radial basis function. The hybrid models of each NN are used for the classification of sensor faults, namely bias, drift, random, and spike. The proposed methods are evaluated using four performance metrics which includes detection accuracy, false positive rate, F1-score, and the Matthews correlation coefficient. The simulation results show that the learning vector quantization NN classifier outperforms the detection accuracy rate when compared to the other classifiers. In addition, an ensemble NN framework based on the hybrid CDHMM classifier is built with majority voting scheme for decision making and classification. The results of the hybrid CDHMM ensemble classifiers clearly indicates the efficacy of the proposed scheme in capturing the dynamics of change of statesm which is the vital aspect in determining rapidly-evolving instant faults that occur in wireless sensor networks.

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Ng, Wing W. Y., Shichao Xu, Ting Wang, Shuai Zhang, and Chris Nugent. "Radial Basis Function Neural Network with Localized Stochastic-Sensitive Autoencoder for Home-Based Activity Recognition." Sensors 20, no. 5 (March 8, 2020): 1479. http://dx.doi.org/10.3390/s20051479.

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Over the past few years, the Internet of Things (IoT) has been greatly developed with one instance being smart home devices gradually entering into people’s lives. To maximize the impact of such deployments, home-based activity recognition is required to initially recognize behaviors within smart home environments and to use this information to provide better health and social care services. Activity recognition has the ability to recognize people’s activities from the information about their interaction with the environment collected by sensors embedded within the home. In this paper, binary data collected by anonymous binary sensors such as pressure sensors, contact sensors, passive infrared sensors etc. are used to recognize activities. A radial basis function neural network (RBFNN) with localized stochastic-sensitive autoencoder (LiSSA) method is proposed for the purposes of home-based activity recognition. An autoencoder (AE) is introduced to extract useful features from the binary sensor data by converting binary inputs into continuous inputs to extract increased levels of hidden information. The generalization capability of the proposed method is enhanced by minimizing both the training error and the stochastic sensitivity measure in an attempt to improve the ability of the classifier to tolerate uncertainties in the sensor data. Four binary home-based activity recognition datasets including OrdonezA, OrdonezB, Ulster, and activities of daily living data from van Kasteren (vanKasterenADL) are used to evaluate the effectiveness of the proposed method. Compared with well-known benchmarking approaches including support vector machine (SVM), multilayer perceptron neural network (MLPNN), random forest and an RBFNN-based method, the proposed method yielded the best performance with 98.35%, 86.26%, 96.31%, 92.31% accuracy on four datasets, respectively.

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Li, L., J. Ma, and Q. Wen. "Comparison of local transfer function classifier and radial basis function neural network with and without an exhaustively defined set of classes." International Journal of Remote Sensing 30, no. 1 (December 2, 2008): 85–96. http://dx.doi.org/10.1080/01431160802261189.

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Chheepa, Tarun Kumar, and Tanuj Manglani. "Power Quality Events Classification using ANN with Hilbert Transform." International Journal of Emerging Research in Management and Technology 6, no. 6 (June 29, 2018): 227. http://dx.doi.org/10.23956/ijermt.v6i6.274.

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With the evolution of Smart Grid, Power Quality issues have become prominent. The urban development involves usage of computers, microprocessor controlled electronic loads and power electronic devices. These devices are the source of power quality disturbances. PQ problems are characterized by the variations in the magnitude and frequency in the system voltages and currents from their nominal values. To decide a control action, a proper classification mechanism is required to classify different PQ events. In this paper we propose a hybrid approach to perform this task. Different Neural topologies namely Cascade Forward Backprop Neural Network (CFBNN), Elman Backprop Neural Network (EBPNN), Feed Forward Backprop Neural Network (FFBPNN), Feed Forward Distributed Time Delay Neural Network (FFDTDNN) , Layer Recurrent Neural Network (LRNN), Nonlinear Autoregressive Exogenous Neural Network (NARX), Radial Basis Function Neural Network (RBFNN) along with the application of Hilbert Transform are employed to classify the PQ events. A meaningful comparison of these neural topologies is presented and it is found that Radial Basis Function Neural Network (RBFNN) is the most efficient topology to perform the classification task. Different levels of Additive White Gaussian Noise (AWGN) are added in the input features to present the comparison of classifiers.

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Wen, Hui, Tongbin Li, Deli Chen, Jianlu Yang, and Yan Che. "An Optimized Neural Network Classification Method Based on Kernel Holistic Learning and Division." Mathematical Problems in Engineering 2021 (February 26, 2021): 1–16. http://dx.doi.org/10.1155/2021/8857818.

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An optimized neural network classification method based on kernel holistic learning and division (KHLD) is presented. The proposed method is based on the learned radial basis function (RBF) kernel as the research object. The kernel proposed here can be considered a subspace region consisting of the same pattern category in the training sample space. By extending the region of the sample space of the original instances, relevant information between instances can be obtained from the subspace, and the classifier’s boundary can be far from the original instances; thus, the robustness and generalization performance of the classifier are enhanced. In concrete implementation, a new pattern vector is generated within each RBF kernel according to the instance optimization and screening method to characterize KHLD. Experiments on artificial datasets and several UCI benchmark datasets show the effectiveness of our method.

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Oh, Sung-Kwun, Wook-Dong Kim, and Witold Pedrycz. "Design of radial basis function neural network classifier realized with the aid of data preprocessing techniques: design and analysis." International Journal of General Systems 45, no. 4 (December 29, 2015): 434–54. http://dx.doi.org/10.1080/03081079.2015.1072523.

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Anitha, V. "An Operative Acute Brain Tumor Recognition by Jointure Inward Unswerving Probabilistic Neural Network Classifier." Journal of Medical Imaging and Health Informatics 12, no. 2 (February 1, 2022): 155–67. http://dx.doi.org/10.1166/jmihi.2022.3935.

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Brain tumors have to be predicted earlier to avoid the risk of being mortal. For an effective detection an adaptive segmentation with two-tier tumors region extraction is needed. This framework offers preprocessing to avoid noise occurrence by fusing median and wiener filter also utilizes adaptive pillar C-means algorithm for obtaining the essential feature set thus the processing time is reduced. Thus the attained essential feature sets are then classified by means of unswerving PNN (Probabilistic Neural network) classifier where classification is done twice initially to classify whether benign or malignant, Sub sequently to classify different sorts of brain tumor such as Astrocytoma, Meningioma, Glioblastoma and Medulloblastoma. Since the non-linearity of PNN due to distance factor consumes more computation time which is tackled by intruding the radial basis function resulted in LS-SVM (Least Square-Support Vector Machine) as a distance factor which is linear one. Thus computation time is further reduced.

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HADDADNIA, JAVAD, KARIM FAEZ, and MAJID AHMADI. "AN EFFICIENT HUMAN FACE RECOGNITION SYSTEM USING PSEUDO ZERNIKE MOMENT INVARIANT AND RADIAL BASIS FUNCTION NEURAL NETWORK." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 01 (February 2003): 41–62. http://dx.doi.org/10.1142/s0218001403002265.

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This paper introduces a novel method for the recognition of human faces in two-dimensional digital images using a new feature extraction method and Radial Basis Function (RBF) neural network with a Hybrid Learning Algorithm (HLA) as classifier. The proposed feature extraction method includes human face localization derived from the shape information using a proposed distance measure as Facial Candidate Threshold (FCT) as well as Pseudo Zernike Moment Invariant (PZMI) with a newly defined parameter named Correct Information Ratio (CIR) of images for disregarding irrelevant information of face images. In this paper, the effect of these parameters in disregarding irrelevant information in recognition rate improvement is studied. Also we evaluate the effect of orders of PZMI in recognition rate of the proposed technique as well as learning speed. Simulation results on the face database of Olivetti Research Laboratory (ORL) indicate that high order PZMI together with the derived face localization technique for extraction of feature data yielded a recognition rate of 99.3%.

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Journal articles on the topic 'Radial Basis Function Neural Network Classifier'

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