Radial Basis Function Classifier Research Articles

Polymorphic Radial Basis Functions Neural Network

The work is devoted to the development of the radial basis functions (RBF networks) neural network new architecture – a polymorphic RBF network in which the one-dimensional radial basis functions (RBFs) in the hidden layer instead, multidimensional RBFs are used, which makes it possible to better approximate complex functions that depend on several independent variables. Moreover, in its second layer, the summing the RBF outputs one by one from each group instead, multiplication is used, which allows the polymorphic RBF network to better identify relations between independent variables. Based on the training classical RBF networks evolutionary algorithm, the polymorphic RBF network training algorithm was created, which, through the initializing weight coefficients methods use taking into account the tasks structure and preliminary values, using the mutations tournament selection, adding additional criteria to the fitness function to take into account stability and speed training a polymorphic RBF network, as well as using an evolutionary mutation strategy, allowed us to obtain the lowest errors in training and testing a polymorphic RBF network compared to known RBF network architectures. The created polymorphic RBF network practical application possibility is demonstrated experimentally using the helicopters turboshaft engines (using the example, the TV3-117 turboshaft engine) operating process parameters optimizing solving task using a multicriteria optimization algorithm. The optimal Pareto front was obtained, which made it possible to obtain the engine operation three additional modes: maximum reduction of specific fuel consumption at the total pressure in the compressor increase degree increased value by 5.0 %, specific fuel consumption minimization at the total pressure in the compressor increase degree reduced value by 1.0 %, the total pressure in the compressor increases degree optimal value with a slight increase in specific fuel consumption by 10.5 %. Future research prospects include adapting the developed methods and models into the general concept for monitoring and controlling helicopter turboshaft engines during flight operations. This concept is implemented in the neural network expert system and the on-board automatic control system.

Performance prediction of co-rotating disk cavity with finned vortex reducer based on machine learning

Machine learning method was applied for rapid and precise prediction of flow and heat transfer characteristics within co-rotating disk cavity with a finned vortex reducer. A new data preprocessing scheme was developed to reduce modeling cost. By using this scheme, classical radial basis function neural network (RBFNN) shows better prediction performance compared with deconvolutional neural network. Furthermore, RBFNN has a simpler topological structure and fewer hyperparameters. By testing, the relative root mean square error for total pressure, total temperature, and swirl ratio is 0.51 %, 0.32 %, and 1.99 %, and corresponding coefficient of determination reaches 99.68 %, 96.55 %, and 99.16 %. The effects of input parameters on the outputs of RBFNN were analyzed, and a sensitivity analysis was conducted. Within the investigated parameter range, the total pressure loss increases as dimensionless mass flow rate, rotational Reynolds number, and radial position of the fin increases. The total temperature drop increases with the increase of dimensionless mass flow rate and rotational Reynolds number, but the decrease of radial position of the fin. Additionally, an increase in dimensionless mass flow rate or a decrease in rotational Reynolds number causes the increase of swirl ratio. This research provides an efficient modeling method for components in secondary air system in gas turbines.

A Low-Power Analog Integrated Euclidean Distance Radial Basis Function Classifier

This study introduces a low-power analog integrated Euclidean distance radial basis function classifier. The high-level architecture is composed of several Manhattan distance circuits in connection with a current comparator circuit. Notably, each implementation was designed with modularity and scalability in mind, effectively accommodating variations in the classification parameters. The proposed classifier’s operational principles are meticulously detailed, tailored for low-power, low-voltage, and fully tunable implementations, specifically targeting biomedical applications. This design methodology materialized within a 90 nm CMOS process, utilizing the Cadence IC Suite for the comprehensive management of both the schematic and layout design aspects. During the verification phase, post-layout simulation results were meticulously cross-referenced with software-based classifier implementations. Also, a comparison study with related analog classifiers is provided. Through the simulation results and comparative study, the design architecture’s accuracy and sensitivity were effectively validated and confirmed.

Landslide susceptibility assessment using locally weighted learning integrated with machine learning algorithms

Assessing landslide susceptibility and predicting the possibility of landslide event is the foundation and prerequisite for emergency response and management of landslide disaster. The target of current paper is to propose five integration models based on integrating locally weighted learning (LWL) with a radial basis function classifier (RBF), Fisher's linear discriminant (FLDA), quadratic discriminant analysis (QDA), a Credal decision tree (CDT), an alternating decision tree (ADT) and random subspace (RS) and the performance of five integration models were compared for modeling landslide susceptibility. Yongxin County from China was employed as a study area, 364 landslide locations and fifteen environmental factors were applied. The results demonstrate that the proposed LWL-RS-ADT model is more reliable and stable than the other models. Among the fifteen environmental factors, NDVI, lithology, and altitude are the very significant factors in the six models. It is concluded that the proposed integration models provide an effective way to predict the susceptibility of landslides.

Ensemble models based on radial basis function network for landslide susceptibility mapping.

Ensemble learning techniques have shown promise in improving the accuracy of landslide models by combining multiple models to achieve better predictive performance. In this study, several ensemble methods (Dagging, Bagging, and Decorate) and a radial basis function classifier (RBFC) were combined to predict landslide susceptibility in the Trung Khanh district of the Cao Bang Province, Vietnam. The ensemble models were developed using a geospatial database containing 45 historical landslides (1074 points) and thirteen influencing variables characterizing the topography, geology, land use/cover, and human activities of the study area. The performance of the models was evaluated based on the area under the receiver operating characteristic curve (AUC) and several other performance metrics, including positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF), accuracy (ACC), and root mean square error (RMSE). The Bagging-RBFC model with PPV = 86%, NPV = 95%, SST = 95%, SPF = 87%, ACC = 91%, RMSE = 0.297, and AUC = 98% was found to be the most accurate model for the prediction of landslide susceptibility, followed by the Dagging-RBFC, Decorate-RBFC, and single RBFC models. The study demonstrates the efficacy of ensemble learning techniques in developing reliable landslide predictive models, which can ultimately save lives and reduce infrastructure damage in landslide-prone regions worldwide.

Multi-Spectral Satellite Image Classification Using an Evolving Neural Network Approach

This paper investigates a new evolving neural network classifier based on real coded genetic algorithm for automatic multi-spectral satellite image classification (land cover mapping problem). The evolving neural network classifier is designed using hybrid genetic operators with classification accuracy as a measure of performance. The evolving neural network methodology is implemented in Pentium clusters. The proposed methodology searches for the best neural network architecture and its connection weights for a given set of training patterns. The performance of the proposed evolving neural network based classifier is evaluated for Level-II classifier model using the Landsat 7 Thematic Mapper high resolution imagery. After evolving the neural network at pixel level, the system performance is tested with sites not seen during training. Results are compared with maximum likelihood classifier, gradient based fully connected multilayer perceptron and growing and pruning radial basis function classifier. The proposed classifier is more accurate, robust with respect to the noise in the input spectrum and also overcomes the common limitations of the standard neural based classifier models.

Meta-Heuristic Optimized Hybrid Wavelet Features for Arrhythmia Classification

The non-invasive evaluation of the heart through EectroCardioGraphy (ECG) has played a key role in detecting heart disease. The analysis of ECG signals requires years of learning and experience to interpret and extract useful information from them. Thus, a computerized system is needed to classify ECG signals with more accurate results effectively. Abnormal heart rhythms are called arrhythmias and cause sudden cardiac deaths. In this work, a Computerized Abnormal Heart Rhythms Detection (CAHRD) system is developed using ECG signals. It consists of four stages; preprocessing, feature extraction, feature optimization and classifier. At first, Pan and Tompkins algorithm is employed to detect the envelope of Q, R and S waves in the preprocessing stage. It uses a recursive filter to eliminate muscle noise, <i>T</i>-wave interference and baseline wander. As the analysis of ECG signal in the spatial domain does not provide a complete description of the signal, the feature extraction involves using frequency contents obtained from multiple wavelet filters; bi-orthogonal, Symlet and Daubechies at different resolution levels in the feature extraction stage. Then, Black Widow Optimization (BWO) is applied to optimize the hybrid wavelet features in the feature optimization stage. Finally, a kernel based Support Vector Machine (SVM) is employed to classify heartbeats into five classes. In SVM, Radial Basis Function (RBF), polynomial and linear kernels are used. A total of ∼15000 ECG signals are obtained from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database for performance evaluation of the proposed CAHRD system. Results show that the proposed CAHRD system proved to be a powerful tool for ECG analysis. It correctly classifies five classes of heartbeats with 99.91% accuracy using an RBF kernel with 2^nd level wavelet coefficients. The CAHRD system achieves an improvement of ∼6% over random projections with the ensemble SVM approach and ∼2% over morphological and ECG segment based features with the RBF classifier.

Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

The medical community has more concern on lung cancer analysis. Medical experts’ physical segmentation of lung cancers is time-consuming and needs to be automated. The research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning techniques. Computer-Aided Diagnostic (CAD) system aids in the diagnosis and shortens the time necessary to detect the tumor detected. The application of Deep Neural Networks (DNN) has also been exhibited as an excellent and effective method in classification and segmentation tasks. This research aims to separate lung cancers from images of Magnetic Resonance Imaging (MRI) with threshold segmentation. The Honey hook process categorizes lung cancer based on characteristics retrieved using several classifiers. Considering this principle, the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder (DWAE). The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using DNN. The proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function (RBF) classifier. The study reported promising results with an accuracy of 97.34%, whereas using the Decision Tree (DT) classifier has an accuracy of 94.24%. The proposed approach (DWAE-DNN) is found to classify the images with an accuracy of 98.67%, either as malignant or normal patients. In contrast to the accuracy requirements, the work also uses the benchmark standards like specificity, sensitivity, and precision to evaluate the efficiency of the network. It is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing, as shown by the data acquired by the categorizers themselves.

Influencing Factors Analysis and Prediction Model Development of Stroke: The Machine Learning Approach

Prediction is an important way to analyse stroke risk management. This study explored the critical influencing factors of stroke, used the classical multilayer perception (MLP) and radial basis function (RBF) machine learning (ML) algorithms to develop the model for stroke prediction. The two models were trained with Bagging and Boosting ensemble learning algorithms. The performances of the prediction models were also compared with other classical ML algorithms. The result showed that (1) total cholesterol (TC) and other nine factors were selected as principal factors for the stroke prediction; (2) the MLP model outperformed RBF model in terms of accuracy, generalization and inter-rater reliability; (3) ensemble algorithm was superior to single algorithms for high-dimension dataset in this study. It may come to the conclusion that this study improved the stroke prediction methods and contributed much to the prevention of stroke.

Support vector machine based methodology for classification of thermal images pertaining to breast cancer

Breast cancer has been and continues to be a cause of major health concern for women. It is more prevalent in old age, but its incidence has increased in recent years in groups below 50 years old, as in India. According to the Indian Council of Medical Research (ICMR) 2020, 50% of all the cases are in the age group of 25–50 where the numbers are staggering and constantly rising. The increase in incidence over the years indicates an urging need for innovative approaches to enhance breast cancer detection early. Thermography is non-contact imaging modalities and has potential to detect breast cancer at an early stage. Though thermography has capable of detecting breast cancer early, the challenge lies in the interpretation of the breast thermograms with respect to features and subsequent analysis. The present work discusses image acquisition, image processing related pre-processing, segmentation, and feature extraction. The extracted features were analyzed using ANOVA (Analysis of variance) statistical analysis. Statistical analyses were done in order to find the appropriate feature on the whole and quadrant breast. Statistical analysis results clearly reveal existence of thermal symmetry for the healthy subjects (p value > .05) in both whole and quadrant breast regions. In the case of abnormal subjects, whole breast analyses revealed the significance (p value < .05) for features like mean, variance, standard deviation, kurtosis, skewness, entropy, energy, homogeneity and contrast whereas upper outer quadrant analyses showed significance for all above features except contrast. The well correlated features of upper outer quadrant and whole breast were given as input for the Support Vector Machine – Radial Basis Function (SVM – RBF) classifier with grid search method. The results revealed that whole breast analysis has achieved 92.86% accuracy and upper outer quadrant breast analysis has achieved 85.71% accuracy. The results clearly indicate the involvement of upper outer quadrant and whole breast in early detection of breast cancer using thermal imaging.

Landslide susceptibility assessment using statistical and machine learning techniques: A case study in the upper reaches of the Minjiang River, southwestern China

Landslides have frequently occurred in deeply incised valleys in the upper reaches of the Minjiang River. Long-term interactions between rock uplift and river undercutting developed widely distributed landslides in this catchment, which recorded the typical tectonic geomorphology in the eastern margin of the Tibetan Plateau. In this study, we examined the landslides in the Minjiang catchment and aimed to compare the prediction ability of the statistical and machine learning (ML) models in landslide susceptibility assessment. We adopted the statistical models of the frequency ratio (FR) and information value (IV) models, and the ML models represented by a logistic model tree (LMT) and radial basis function classifier (RBFC) for landslide prediction. An inventory map of 668 landslides was compiled, and the landslides were randomly divided into training (80%) and validation (20%) datasets. Furthermore, 11 control factors of landslides based on topography, geology, hydrology, and other environments were applied for the analysis. The comprehensive performance of the four models was validated and compared using accuracy and area under the receiver operating characteristic curve (AUC). The results indicated that both sides of the valley along the Mingjiang and Heishuihe Rivers are in the high and very high susceptibility zones; in particular, the river segment from Wenchuan to Maoxian County has the highest susceptibility. The AUC values of the FR, IV, LMT, and RBFC models with the training data were 0.842, 0.862, 0.898, and 0.894, respectively, while the validation dataset illustrated the highest AUC value of 0.879 in the LMT model, followed by the RBFC (0.871), IV (0.869), and FR (0.839) models. Moreover, the LMT and RBFC models had higher accuracy values than the FR and IV models. This suggests that the ML models are superior to the statistical models in generating adequate landslide susceptibility maps, and the LMT model is the most efficient one for landslide prediction in the study region. This study provides a typical case in a landslide-prone region in the plateau margin to advance the understanding of landslide susceptibility assessment.

Development of a WENO scheme based on radial basis function with an improved convergence order

In this article, we present a novel RBF-WENO scheme improving the fifth-order WENO techniques for solving hyperbolic conservation laws. The numerical flux is implemented by incorporating radial basis function (RBF) interpolation to cell average data. To do this, the classical RBF interpolation is amended to be suitable for cell average data setting. With the aid of a locally fitting parameter in the RBF, the RBF-WENO reconstruction attains an additional one order of improvement, resulting in the sixth-order of accuracy. In addition, on the purpose of detecting small scale structures and steep gradients more accurately, we present new smoothness indicators by devising a method of generalized undivided differences with exponential vanishing moments. Several experimental results are performed to confirm the effectiveness of the proposed WENO method.

A novel end-to-end neural network for simultaneous filtering of task-unrelated named entities and fine-grained typing of task-related named entities

Recently, one emerging problem in Named Entity Typing (NET) is the fine-grained classification of task-related entities co-existing with task-unrelated entities. The traditional pipeline framework decomposes this problem into two sub-tasks. The first sub-task filters out the task-unrelated entities, while the second sub-task performs fine-grained classification for task-related entities. In the present study, we have developed an end-to-end neural network to solve the two sub-tasks simultaneously. The new model has two main merits. First, Mention–Mention (MM) relationship learning is developed to capture the interaction of task related and unrelated entities for producing more discriminative features. Second, an Improved Radial Basis Function classifier (ImRBF) with a novel training scheme is developed to jointly solve task-unrelated entity filtering and fine-grained classification of task-related entities. Experiments show that our model outperforms the pipeline methods by 3.3%–6% (F1 score) on the first sub-task and 1.8%–6.3% (F1 score) on the second sub-task.

Modeling and classifying the in-operando effects of wear and metal contaminations of lubricating oil on diesel engine: A machine learning approach

The lubricating oil analysis may be used to verify an assessment of the engine's health and operational conditions, as well as the need for oil changes. The wide sight of oil characteristics allows for the detection of elemental contaminations, additives, and wear for maintenance monitoring. The goal of this work is to investigate the in-operando effects of oil metal pollution on diesel engine conditions using large datasets and Support vector machine (SVM) and Radial basis function (RBF) models. To do this, 1948 datasets from engine lubricant spectral analysis were used. The greatest accuracy for forecasting engine conditions was obtained using cubic Polynomial (poly 3) and rbf kernel function in SVM; and RBF-NN classifiers used two training methods, trainbr, and trainlm. For engine condition predictions, RBF and SVM classifiers had an average accuracy of 99 percent and 97 percent, respectively. The capacity of models to generalize was tested by varying the dataset size (10% to 90%), with RBF demonstrating the greatest performance. Following that, RBF-NN used the confusion matrix approach to diagnose the engine's critical state with a 99.38 percent accuracy. The presence of metal contaminations such as Cr, Si, and Fe has a significant impact on the accuracy of engine identification in normal, critical, and numerous classes, according to the RBF-NN sensitivity study. Finally, RBF-NN has been chosen to define and monitor diesel engine conditions since it outperforms the SVM model.

A new oversampling method and improved radial basis function classifier for customer consumption behavior prediction

In practical applications, imbalanced data has brought great challenges to classification problems. In this paper, we propose two new methods: (1) a new oversampling method, named Tomek-CASUWO, to address the issue of class imbalance; (2) a new classifier, named ILS-RBFNN, to increase the accuracy of prediction of customer consumption behavior. Since current ASUWO algorithm is easily affected by noise samples and fuzzy class boundaries, we propose Tomek-CASUWO to address these problems: (i) the Tomek links algorithm is used to filter noise samples; (ii) CASUWO is used to avoid overlapping class boundaries; (iii) Tomek-CASUWO is used to synthesize new samples. Also, we propose a new classifier based on RBFNN, named ILS-RBFNN, to improve the prediction accuracy: (i) the hybrid kernel is developed by combining Gaussian and Polynomial; (ii) an Immune Algorithm (IA) is used to optimize the centers of RBFNN; (iii) Least-Squares (LS) is used to calculate the biases and weights. Wine-consumer behavior data is used to compare our Tomek-CASUWO with other oversampling methods. We compare ILS-RBFNN with several well-known kernel functions and parameter update methods. The experimental results show that Tomek-CASUWO can significantly improve the prediction accuracy of a classifier, and ILS-RBFNN outperforms other classification methods. We also conduct experiments on the extended real-world dataset. Finally, the robustness and applicability of ILS-RBFNN are verified on the eleven UCI datasets. All results show that the proposed two methods outperform existing models. The experimental results also demonstrate the effectiveness and practicability of ILS-RBFNN for predicting customer behavior.

Satellite coverage traffic volume prediction using a new surrogate model

The space-based Automatic Dependent Surveillance-Broadcast (ADS-B) can significantly improve aircraft traffic efficiency and safety but suffers from the issue of signal collision. A potential solution for this limitation is employing an adaptive ADS-B antenna, e.g., the phased array antenna, which can adjust its coverage area in real-time during the satellite movement according to the satellite coverage traffic volume (SCTV). This paper proposes a new surrogate modeling technique, i.e., subregion radial basis function (RBF), and applies the proposed surrogate to predict the SCTV so that the adaptive strategy for the ADS-B antenna can be designed. Instead of training one model using all training samples as the classical RBF does, the proposed subregion RBF approach first partitions the input space into multiple fuzzy regions, then trains a RBF model in each region, and finally integrates the multiple subregion RBF models in a weighted average manner. By using this partition of unity strategy, the proposed method can greatly reduce the computational complexity of training models meanwhile maintain the interpolation property and high accuracy of RBF models. Further, the proposed subregion RBF is applied to construct a surrogate for the SCTV model to guide the design of the adaptive strategy for the space-based ADS-B. Simulation results show that compared with the classical and state-of-the-art methods, the proposed method has arrived a better balance between predictive accuracy and computational cost. In the simulation scenarios, it is also observed that by using the adaptive strategy, the detection probability of ADS-B receiver can always be greater than 95% and nearly 1/3 of power consumption can be saved. The simulations demonstrate the effectiveness of the proposed method.

Flood susceptibility modeling using Radial Basis Function Classifier and Fisher’s linear discriminant function

Floods are among the most frequent highly disastrous hazards affecting life, property, and the environment worldwide. While various models are available to predict flood susceptibility, no model is accurate enough to be used for all flood-prone areas. Model development using different algorithms is a continuous process to improve the prediction accuracy of flood susceptibility. In the study, we used the Radial Basis Function and Fisher’s linear discriminant function to develop a flood susceptibility map for a case study of Quang Binh Province. The model development used ten variables (elevation, slope, curvature, river density, distance from river, geomorphology, land use, flow accumulation, flow direction, and rainfall). For model training and validation, input data was split into a 70:30 ratio according to flood locations. Statistical indexes were used to evaluate model performance such as Receiver Operating Characteristic, the Area Under the ROC Curve, Root Mean Square Error, Accuracy, Sensitivity, Specificity, and Kappa index. Results indicated that the radial basis function classifier model had better performance in predicting flood susceptible areas based on the statistical measures (PPV = 92.00%, NPV = 87.00%, SST = 87.62%, SPF = 91.58%, ACC = 89.50%, Kappa = 0.790, MAE = 0.204, RMSE = 0.292 and AUC = 0.957. Therefore, the radial basis function classifier algorithm model is appropriate for predicting flood susceptibility in Quang Binh Province.

ADASYN and ABC-optimized RBF convergence network for classification of electroencephalograph signal

Electroencephalograph (EEG) is supposed to be a major challenge in the area of biomedical signal processing. Being one of the widely used invasive techniques, it is capable to find many cases of brain disorder problems like epileptic seizures and sleep disorder. This work follows the procedure of convergence computing where there are different computing techniques have been combined together to achieve our final goal with much perfection. radial basis function (RBF) being one of the simplest forms of neural network (NN) can be used for the purpose of EEG signal classification, where the network uses the radial basis function as an activation function. In this study, artificial bee colony (ABC) technique was applied for optimizing the parameters that were required in the RBF network for the EEG signal classification. Adaptive synthetic oversampling (ADASYN) process was considered for improving the learning method of managing the class imbalance problem in the EEG dataset. An EEG dataset for normal and epileptic person is typically seen and is processed where five datasets are combined together in three different ways. The new datasets were grouped and were created as set 1 (A+D & E), set 2 (B+D & E), and set 3 (C+D & E). The ABC optimized RBF classifier is applied along with ADASYN method where the resolution for the above stated datasets states that the classification accuracy for set 1 and set 3 were increasing significantly as compared to the standard RBF classifier network.

Classification of Soybean Pubescence from Multispectral Aerial Imagery.

The accurate determination of soybean pubescence is essential for plant breeding programs and cultivar registration. Currently, soybean pubescence is classified visually, which is a labor-intensive and time-consuming activity. Additionally, the three classes of phenotypes (tawny, light tawny, and gray) may be difficult to visually distinguish, especially the light tawny class where misclassification with tawny frequently occurs. The objectives of this study were to solve both the throughput and accuracy issues in the plant breeding workflow, develop a set of indices for distinguishing pubescence classes, and test a machine learning (ML) classification approach. A principal component analysis (PCA) on hyperspectral soybean plot data identified clusters related to pubescence classes, while a Jeffries-Matusita distance analysis indicated that all bands were important for pubescence class separability. Aerial images from 2018, 2019, and 2020 were analyzed in this study. A 60-plot test (2019) of genotypes with known pubescence was used as reference data, while whole-field images from 2018, 2019, and 2020 were used to examine the broad applicability of the classification methodology. Two indices, a red/blue ratio and blue normalized difference vegetation index (blue NDVI), were effective at differentiating tawny and gray pubescence types in high-resolution imagery. A ML approach using a support vector machine (SVM) radial basis function (RBF) classifier was able to differentiate the gray and tawny types (83.1% accuracy and kappa = 0.740 on a pixel basis) on images where reference training data was present. The tested indices and ML model did not generalize across years to imagery that did not contain the reference training panel, indicating limitations of using aerial imagery for pubescence classification in some environmental conditions. High-throughput classification of gray and tawny pubescence types is possible using aerial imagery, but light tawny soybeans remain difficult to classify and may require training data from each field season.

Data classification by quantum radial-basis-function networks

Radial basis function (RBF) network is a third layered neural network that is widely used in function approximation and data classification. Here we propose a quantum model of the RBF network. Similar to the classical case, we still use the radial basis functions as the activation functions. Quantum linear algebraic techniques and coherent states can be applied to implement these functions. Differently, we define the state of the weight as a tensor product of single-qubit states. This gives a simple approach to implement the quantum RBF network in the quantum circuits. Theoretically, we prove that the training is almost quadratic faster than the classical one. Numerically, we demonstrate that the quantum RBF network can solve binary classification problems as good as the classical RBF network. While the time used for training is much shorter.