Showing papers on "Radial basis function network published in 2018"
TL;DR: A hybrid model that uses MARS to evaluate the importance of every parameter in the prediction and these important parameters have been fed to the ELM to build hybrid model and it can be seen that this boosts the ELm performance to match up to the accuracy of MARS with lesser computation time.
Abstract: Heating load and cooling forecasting are essential for estimating energy consumption, and consequently, helping engineers in improving the energy performance right from the design phase of buildings. The capacity of heating ventilation and air-conditioning system of the building contributes to the operation cost. Moreover, building being one of the sectors with heavy energy use, it is required to develop an accurate model for energy forecasting of building and constructing energy-efficient buildings. This paper explores different machine learning techniques for predicting the heating load and cooling load of residential buildings. Among these methods, we focus on advanced techniques like Multivariate Adaptive Regression Splines (MARS), Extreme Learning Machine (ELM) and a hybrid model of MARS and ELM along with a comparison of the results with those of more conventional methods like linear regression, neural network, Gaussian processes and Radial Basis Function Network. The MARS model is a non-parametric regression model that splits the data and fits each interval into a basis function and ELM is similar to a Single Layer Feed-forward Neural Network except that in ELM randomly assigned input weights are not updated. As an improvement, we have tried a hybrid model that uses MARS to evaluate the importance of every parameter in the prediction and these important parameters have been fed to the ELM to build hybrid model and it can be seen that this boosts the ELM performance to match up to the accuracy of MARS with lesser computation time. Finally, a comparative study examines the performances of the different techniques by measuring different performance metrics.
137 citations
TL;DR: Deep Learning Methods to Process fMRI Data and Their Application in the Diagnosis of Cognitive Impairment: A Brief Overview and The authors' Opinion.
Abstract: Citation: Wen D, Wei Z, Zhou Y, Li G, Zhang X and Han W (2018) Deep Learning Methods to Process fMRI Data and Their Application in the Diagnosis of Cognitive Impairment: A Brief Overview and Our Opinion. Front. Neuroinform. 12:23. doi: 10.3389/fninf.2018.00023 Deep Learning Methods to Process fMRI Data and Their Application in the Diagnosis of Cognitive Impairment: A Brief Overview and Our Opinion
94 citations
TL;DR: A robust adaptive system for a ship-mounted container crane with the disadvantages of a highly under-actuated nonlinear system is proposed and the adaptive RBFN algorithm approximates almost all the structure of a crane model, including system parameters.
91 citations
TL;DR: Comparisons indicate that the proposed RBFN-based NFAC algorithm is capable of obtaining lower position tracking error and better frequency response characteristic, compared to those of cascaded proportional-integral-derivative controller (CPID) and fuzzy sliding mode controller (FSMC).
87 citations
TL;DR: The experimental results show that the proposed models learn better and outperform other popular machine learning approaches such as the artificial neural network (ANNs), support vector machine (SVM), radial basis function network (RBFN), random forest(RF) and existing work in the energy and building domain.
78 citations
TL;DR: Three types of deep belief network (DBN)-based models to estimate NO X emission in coal-fired power plants by a new data acquisition method are developed and the results indicate that the DBN-based models have a greater prediction accuracy and greater robustness compared to the three other NO X prediction models.
69 citations
TL;DR: An artificial neural network (ANN) based maximum power point tracking (MPPT) control strategy for wind energy conversion system (WECS) implemented with a DC/DC converter using a radial basis function network (RBFN) based neural network control strategy.
Abstract: This paper proposes an artificial neural network (ANN) based maximum power point tracking (MPPT) control strategy for wind energy conversion system (WECS) implemented with a DC/DC converter. The proposed topology utilizes a radial basis function network (RBFN) based neural network control strategy to extract the maximum available power from the wind velocity. The results are compared with a classical Perturb and Observe (P&O) method and Back propagation network (BPN) method. In order to achieve a high voltage rating, the system is implemented with a quadratic boost converter and the performance of the converter is validated with a boost and single ended primary inductance converter (SEPIC). The performance of the MPPT technique along with a DC/DC converter is demonstrated using MATLAB/Simulink.
67 citations
TL;DR: A diagonal recurrent neural network based identification model for approximating the unknown dynamics of the nonlinear plants and a dynamic back-propagation learning algorithm for tuning the parameters of DRNN are proposed.
61 citations
TL;DR: Results demonstrate the remarkable predictive accuracy of the proposed ship roll prediction model as well as the effectiveness of the IFGG-based SDW in terms of representing time-varying dynamics.
Abstract: A ship roll prediction scheme is proposed using an adaptive sliding data window (SDW), which is designed to represent time-varying nonlinear dynamics of ship roll motion. The adjustment of SDW is realized by developing an improved fuzzy Gath-Geva (IFGG) segmentation approach, which detects the changes of system dynamics and thereby automatically adapting the scale of SDW. By virtue of the learning scheme with an adaptive SDW, the variable-structure radial basis function network is constructed sequentially to online predict ship roll dynamics. Experimental studies on online ship roll prediction are conducted on measured data from YuKun ’s full-scale sea trial. Results demonstrate the remarkable predictive accuracy of the proposed ship roll prediction model as well as the effectiveness of the IFGG-based SDW in terms of representing time-varying dynamics.
56 citations
TL;DR: The comparative study using the cutting-edge neural network-based control systems confirms that the proposed network is capable of producing better control performances with high computational efficiency.
Abstract: The trajectory tracking ability of mobile robots suffers from uncertain disturbances. This paper proposes an adaptive control system consisting of a new type of self-organizing neural network controller for mobile robot control. The newly designed neural network contains the key mechanisms of a typical brain emotional learning controller network and a self-organizing radial basis function network. In this system, the input values are delivered to a sensory channel and an emotional channel, and the two channels interact with each other to generate the final outputs of the proposed network. The proposed network possesses the ability of online generation and elimination of fuzzy rules to achieve an optimal neural structure. The parameters of the proposed network are online tunable by the brain emotional learning rules and gradient descent method; in addition, the stability analysis theory is used to guarantee the convergence of the proposed controller. In the experimentation, a simulated mobile robot was applied to verify the feasibility and effectiveness of the proposed control system. The comparative study using the cutting-edge neural network-based control systems confirms that the proposed network is capable of producing better control performances with high computational efficiency.
53 citations
TL;DR: This study evaluates the proposed features in three bio-inspired machine learning classifiers in artificial neural network (ANN) category to signify the usefulness of ANN type in uncovering unknown malware.
Abstract: Recently, people rely on mobile devices to conduct their daily fundamental activities. Simultaneously, most of the people prefer devices with Android operating system. As the demand expands, deceitful authors develop malware to compromise Android for private and money purposes. Consequently, security analysts have to conduct static and dynamic analyses to counter malware violation. In this paper, we adopt static analysis which only requests minimal resource consumption and rapid processing. However, finding a minimum set of features in the static analysis are vital because it removes irrelevant data, reduces the runtime of machine learning detection and reduces the dimensionality of datasets. Therefore, in this paper, we investigate three categories of features, which are permissions, directory path, and telephony. This investigation considers the features frequency as well as repeatedly used in each application. Subsequently, this study evaluates the proposed features in three bio-inspired machine learning classifiers in artificial neural network (ANN) category to signify the usefulness of ANN type in uncovering unknown malware. The classifiers are multilayer perceptron (MLP), voted perceptron (VP) and radial basis function network (RBFN). Among all these three classifiers, the outstanding outcomes acquire is the MLP, which achieves 90% in accuracy and 87% in true positive rate (TPR), as well as 97% accuracy in our Bio Analyzer prediction system.
TL;DR: The multicolumn RBF network (MCRN) is proposed as a method to improve the accuracy and speed of a traditional radial basis function network (RBFN) and shows better accuracy and great improvements in training and testing times compared with a single RBFN.
Abstract: This paper proposes the multicolumn RBF network (MCRN) as a method to improve the accuracy and speed of a traditional radial basis function network (RBFN). The RBFN, as a fully connected artificial neural network (ANN), suffers from costly kernel inner-product calculations due to the use of many instances as the centers of hidden units. This issue is not critical for small datasets, as adding more hidden units will not burden the computation time. However, for larger datasets, the RBFN requires many hidden units with several kernel computations to generalize the problem. The MCRN mechanism is constructed based on dividing a dataset into smaller subsets using the k-d tree algorithm. $N$ resultant subsets are considered as separate training datasets to train $N$ individual RBFNs. Those small RBFNs are stacked in parallel and bulged into the MCRN structure during testing. The MCRN is considered as a well-developed and easy-to-use parallel structure, because each individual ANN has been trained on its own subsets and is completely separate from the other ANNs. This parallelized structure reduces the testing time compared with that of a single but larger RBFN, which cannot be easily parallelized due to its fully connected structure. Small informative subsets provide the MCRN with a regional experience to specify the problem instead of generalizing it. The MCRN has been tested on many benchmark datasets and has shown better accuracy and great improvements in training and testing times compared with a single RBFN. The MCRN also shows good results compared with those of some machine learning techniques, such as the support vector machine and k-nearest neighbors.
TL;DR: The goal of this paper is to propose a statistical strategy to initiate the hidden nodes of a single-hidden layer feedforward neural network (SLFN) by using both the knowledge embedded in data and a filtering mechanism for attribute relevance.
1 Jan 2018
TL;DR: An unsupervised machine learning algorithm—tree-seed optimization algorithm tuned radial basis function network for segmentation of gallbladder in ultrasound images for diagnosis of acute cholecystitis.
Abstract: In this paper, computerized scheme for automatic volume estimation of inflamed gallbladder in ultrasound images has been investigated. Diagnosis of acute cholecystitis at an early stage is an arduous task as the difference between normal shape and inflamed gallbladder shape cannot be visualized in ultrasound images. This paper comes out with an unsupervised machine learning algorithm—tree-seed optimization algorithm tuned radial basis function network for segmentation of gallbladder in ultrasound images. Tree-seed optimization algorithm, which optimizes function and parameters in real values, is a population-based stochastic search algorithm. Prior to the classification, speckle reduction and feature extraction process were successfully used. These features are then used in classification process to define the gallbladder and non-gallbladder regions. The proposed optimized classifier system is evaluated in real-time clinical datasets with cholecystitis and cholelithiasis. The inherent differentiation of the proposed intelligent classifier is analyzed using standard evaluation parameters. Comparison with expert decisions provides further evidence that the optimally tuned radial basis function network has important implications for diagnosis of acute cholecystitis.
TL;DR: An enhancedRBFNN model that depends on the standard RBF built-in MATLAB (newrb) and performs more precisely than the traditional RBFNN and multilayer perceptron neural network methods, with low mean square error of relatively few neurons on the hidden layer.
Abstract: A radial basis function neural network is an effective technique for function approximation and prediction. It has been used in many applications in the real world; one of them is the time series prediction which is a relatively complex problem. In this paper, we propose an enhanced radial basis function neural network (RBFNN) model that depends on the standard RBF built-in MATLAB (newrb). The enhancement on newrb depends on the use of intelligent algorithms like K-means clustering, K-nearest neighbor, and singular value decomposition, to optimize the centers c, radii r, and weights w of the RBFNN. These algorithms replace the mathematical calculation used to find these parameters in newrb. The proposed enhanced model is applied to predict the solar cells energy production in Palestine using already installed solar panels in Jericho city. Solar irradiance and daily temperature are used as an input training data set for the proposed model, with the real output power of (2015) as the training supervisor. The model is applied to predict the output power within 1 month and for 1 year. Finally, a power output equation was optimized to calculate the solar energy depending on the irradiance and temperature with an acceptable accuracy. The experimental results show that the enhanced model performs more precisely than the traditional RBFNN and multilayer perceptron neural network methods, with low mean square error of relatively few neurons on the hidden layer.
TL;DR: It was found that the hybrid decision tree-based method significantly improved RBF neural network performance in forecasting the velocity and free-surface water profiles in a 90° open channel sharp bend.
Abstract: Open channel bends have fascinated engineers and scientists for decades while providing water for domestic, irrigation and industrial consumption. The presence of curvature in a channel impacts the flow pattern, velocity and water surface profile. Simulating flow variables such as velocity and water surface depth is one of the most important matters in the design and application of open channel bends. This study investigates a new neural network method using the radial basis function (RBF) based on decision trees (DT-RBF) to predict velocity and free-surface water profiles in a 90° open channel bend. In this study, 506 flow depth and 520 depth-averaged velocity field data obtained at 5 different discharges (5, 7.8, 13.6, 19.1 and 25.3 l/s) in a 90° sharp bend were used for training and testing purposes. The obtained results showed that the proposed DT-RBF models were more accurate than RBF models in estimating flow depth and depth-averaged velocity in the bend. The RBF root-mean-square error (RMSE), mean absolute error (MAE) and relative error (δ) were reduced by 20, 24 and 23.5%, respectively, when using the hybrid DT-RBF model to estimate the depth-averaged velocity. For water surface prediction, the RMSE, MAE and δ decreased by 33, 27.5 and 37%, respectively, when using the proposed DT-RBF hybrid model. For the longitudinal profiles of water surface profile prediction at the outer edge, MAE (0.018) improved to MAE (0.0084) with DT-RBF. It was found that the hybrid decision tree-based method significantly improved RBF neural network performance in forecasting the velocity and free-surface water profiles in a 90° open channel sharp bend.
TL;DR: This paper introduces a new family of infinitely smooth and “nearly” locally supported radial basis functions (RBFs), derived from the general solution of a heat equation arising from the American option pricing problem, and introduces an integral operator with a function-dependent lower limit to prove the radial positive definiteness of the proposed basis functions.
1 Aug 2018
TL;DR: The aim of this paper is to present a complete step‐by‐step algorithm for determining the significance of particular input neurons of the probabilistic neural network (PNN) based on the sensitivity analysis procedure applied to a trained PNN.
Abstract: In classical feedforward neural networks such as multilayer perceptron, radial basis function network, or counter-propagation network, the neurons in the input layer correspond to features of the training patterns. The number of these features may be large, and their meaningfulness can be various. Therefore, the selection of appropriate input neurons should be regarded. The aim of this paper is to present a complete step-by-step algorithm for determining the significance of particular input neurons of the probabilistic neural network (PNN). It is based on the sensitivity analysis procedure applied to a trained PNN. The proposed algorithm is utilized in the task of reduction of the input layer of the considered network, which is achieved by removing appropriately indicated features from the data set. For comparison purposes, the PNN's input neuron significance is established by using the ReliefF and variable importance procedures that provide the relevance of the input features in the data set. The performance of the reduced PNN is verified against a full structure network in classification problems using real benchmark data sets from an available machine learning repository. The achieved results are also referred to the ones attained by entropy-based algorithms. The prediction ability expressed in terms of misclassifications is obtained by means of a 10-fold cross-validation procedure. Received outcomes point out interesting properties of the proposed algorithm. It is shown that the efficiency determined by all tested reduction methods is comparable.
TL;DR: Radial basis function network (RBFN) is used in this paper for predefined trajectory control of both one-link and two-link robotic manipulators and reveals the superior performance of RBFN over MLFFNN over multilayer feed-forward neural network in both identification and control aspects.
Abstract: Radial basis function network (RBFN) is used in this paper for predefined trajectory control of both one-link and two-link robotic manipulators. The updating equations for the RBFN parameters were derived using the gradient descent principle. The other advantage of using this principle is that it shows the clustering effect in distributing the radial centres. To increase the complexity, the dynamics of robotic manipulator is assumed to be unknown, and hence, simultaneous control and identification steps were performed using the RBFNs. The performance of the RBFN is compared with the multilayer feed-forward neural network (MLFFNN) in terms of mean square error, tolerance to disturbance and parameter variations in the system. The efficacy of RBFN as a controller and identification tool is verified by performing the simulation study, and the results obtained reveal the superior performance of RBFN over MLFFNN in both identification and control aspects for one-link and two-link robotic manipulators.
TL;DR: A three-step approach is used to develop and evaluate prediction models and it is found that the MLR and BPNN models outperform the others in the prediction of thermal load with average absolute error of less than 1.19%, and the BPNN model is the best at predicting discomfort degree hour.
Abstract: Thermal load and indoor comfort level are two important building performance indicators, rapid predictions of which can help significantly reduce the computation time during design optimization. In this paper, a three-step approach is used to develop and evaluate prediction models. Firstly, the Latin Hypercube Sampling Method (LHSM) is used to generate a representative 19-dimensional design database and DesignBuilder is then used to obtain the thermal load and discomfort degree hours through simulation. Secondly, samples from the database are used to develop and validate seven prediction models, using data mining approaches including multilinear regression (MLR), chi-square automatic interaction detector (CHAID), exhaustive CHAID (ECHAID), back-propagation neural network (BPNN), radial basis function network (RBFN), classification and regression trees (CART), and support vector machines (SVM). It is found that the MLR and BPNN models outperform the others in the prediction of thermal load with average absolute error of less than 1.19%, and the BPNN model is the best at predicting discomfort degree hour with 0.62% average absolute error. Finally, two hybrid models—MLR (MLR + BPNN) and MLR-BPNN—are developed. The MLR-BPNN models are found to be the best prediction models, with average absolute error of 0.82% in thermal load and 0.59% in discomfort degree hour.
TL;DR: Numerical simulations are performed, which demonstrate that the DE algorithm can be effectively used for the search of the global optimal model parameters in the GKT model, while appears to be a promising method for the calibration of other similar traffic models.
TL;DR: The radial basis function network and extreme learning machine methods to classify available IRA measurements are introduced and the suitability of the proposed classification method is verified by applying it to the bank of Kalman filter) and particle filter-based TAN.
Abstract: The purpose of this study is to propose a measurement classification method necessary to implement precision terrain-aided navigation (TAN) by using an interferometric radar altimeter (IRA) as a technology that can replace global positioning system/inertial navigation system integrated navigation. IRA is a sensor that extracts the angle perpendicular to the direction of flight, the look angle, and the slant range from the aircraft to the nearest terrain point. Unlike the radio altimeter which only measures the direct downward distance, IRA can be converted into three-dimensional coordinates in the navigation system. However, the IRA output has a disadvantage that it has uncertainty that cannot be predicted due to the signal processing and environmental factors. Therefore, a useful navigation technique for classifying sensor outputs is needed to implement precision TAN. This study introduces the radial basis function network and extreme learning machine methods to classify available IRA measurements and verifies the suitability of the proposed classification method by applying it to the bank of Kalman filter) and particle filter-based TAN.
TL;DR: Comparisons of two feed-forward neural networks reveal that RBFN-based controller is performing better than that of NARX- and MLFFNN-based controllers.
Abstract: This paper performs the comparative study of two feed-forward neural networks: radial basis function network (RBFN), multilayer feed-forward neural network (MLFFNN) and a recurrent neural network: nonlinear auto-regressive with exogenous inputs (NARX) neural network for their ability to provide an adaptive control of nonlinear systems. Dynamic back-propagation algorithm is used to derive parameter update equations. To ensure stability and faster convergence, an adaptive learning rate is developed in the sense of discrete Lyapunov stability method. Both parameter variation and disturbance signal cases are considered for checking and comparing the robustness of controller. Three simulation examples are considered for carrying out this study. The results so obtained reveal that RBFN-based controller is performing better than that of NARX- and MLFFNN-based controllers.
1 Mar 2018
TL;DR: Artificial Neural Networks (ANNs) are used for assessment of voltage stability or to confirm secure and insecure mode of the power system.
Abstract: In deregulated environment voltage stability has become very important factor for the purpose of analysis. In this paper some important features associated with voltage stability use in power system have discussed. Line Stability index is used for estimation of the maximum loadability and in other words index is used to recognise the weak bus in electrical power system. In this paper Artificial Neural Networks (ANNs) are used for assessment of voltage stability or to confirm secure and insecure mode of the power system. The input data of neural network are yield from the Newton-Raphson (NR) load flow analysis in the platform of MATLAB R2015b. The result obtained from the N-R method also validates through Feed-Forward Back Propagation (FFBP) Layer Recurrent (LR) and Radial Basis Function Network (RBFN) in terms of accuracy to foresee the status of the power system. The effectiveness of the analyzed methods is validated through IEEE 14 test system and IEEE 30 test bus system, using Fast Voltage Stability Index (FVSI).
TL;DR: The purpose of this research is to analyze the application of neural networks and specific features of training radial basis functions for solving 2‐dimensional Navier‐Stokes equations.
Abstract: Summary
The purpose of this research is to analyze the application of neural networks and specific features of training radial basis functions for solving two-dimensional Navier–Stokes equations.
The authors developed an algorithm for solving hydrodynamic equations with representation of their solution by the method of weighted residuals upon the general neural network approximation throughout the entire computational domain. The article deals with testing of the developed algorithm through solving the two-dimensional Navier-Stokes equations.
Artificial neural networks are widely used for solving problems of mathematical physics; however, their use for modeling of hydrodynamic problems is very limited. At the same time, the problem of hydrodynamic modeling can be solved through neural network modeling and our study demonstrates an example of its solution.
The choice of neural networks based on radial basis functions is due to the ease of implementation and organization of the training process, the accuracy of the approximations and smoothness of solutions. Radial basis neural networks in the solution of differential equations in partial derivatives allow obtaining a sufficiently accurate solution with a relatively small size of the neural network model. The authors propose to consider the neural network as an approximation of the unknown solution of the equation. The Gaussian distribution is used as the activation function.
1 Sep 2018
TL;DR: This work compares the performance of three prominent Neural Network based classifiers for speech emotion recognition (SER) and shows the PNN has shown to outperform all others in classifying the chosen emotional states using the extracted feature sets.
Abstract: This work compares the performance of three prominent Neural Network based classifiers for speech emotion recognition (SER). The classifiers such as the Multilayer Perceptron (MLP), the Radial Basis Function Network (RBFN) and the Probabilistic Neural Network (PNN) have been tested for their effectiveness in recognizing speech emotions such as angry, sad, bore and happy states from Berlin (EMO-DB) database. The self-learning ability of these classifiers to capture complex input and output relationship effectively makes them versatile in the field of SER. Hence, these parallel structured networks are expected to provide the intended accuracy for the proposed task as speech frequencies occur in parallel. Extensive simulation of these classifiers has been carried out using the popular Vector Quantized based Linear Predictive Cepstral Coefficients (LPCC_VQ) and the excitation source Hurst components. The PNN has shown to outperform all others in classifying the chosen emotional states using the extracted feature sets. The LPCC_VQ remains more discriminating for the proposed SER system than the pH as our results reveal. The average classification error using the LPCC-VQ feature sets has been 0.173 for the PNN as compared to 0.185 with RBFN and 0.227 with MLP as observed from our results.
1 Feb 2018
TL;DR: The proposed tuned support vector regression by modified particle swarm optimization (TSVR-MPSO) method is compared with several techniques, such as SVR-PSO, support vectors regression grid search (SVR-GS), radial basis function network (RBFN), and multi-layer feedforward neural network (MLFN).
Abstract: Secure operation of power systems requires fast, efficient, and accurate contingency screening, selection, and ranking. Different machine learning methods have been proposed in the literature to replace the traditional numerical Newton-Raphson load flow (NRLF) method for online static security evaluation (SSE). Recently, a technique using support vector regression and particle swarm optimization (SVR-PSO) was proposed for a different application, namely the reliability prediction of systems. The performance of SVR heavily depends on the tuning of its three parameters. This is an optimization problem which SVR-PSO solved by modifying the process of adapting the inertial weight of PSO. In this paper, the SVR-PSO approach is extended and employed for online SSE of power systems. In particular, adaptation of the inertia weight is modified further, so that it is different for each one of the particle dimensions. The proposed tuned support vector regression by modified particle swarm optimization (TSVR-MPSO) method is compared with several techniques, such as SVR-PSO, support vector regression grid search (SVR-GS), radial basis function network (RBFN), and multi-layer feedforward neural network (MLFN). Experimental results demonstrate that TSVR-MPSO method provides a lower RMSE compared to other methods. The IEEE-14 bus and IEEE-118 bus test systems have been used in the simulations.
8 Jul 2018
TL;DR: The experimental results show that the spiking neural network can achieve a satisfactory classification accuracy by using only 15 neurons, and the classification accuracy of the spiken neural network is higher than that of the multilayer perceptron, radial basis function network and support vector machine.
Abstract: Hand rehabilitation robot can assist the patients in completing rehabilitation exercises. Usually these rehabilitation exercises are designed according to Fugl-Meyer Assessment(FMA). Surface electromyography(sEMG) signal is the most commonly used physiological signal to identify the patient’s movement intention. However, recognizing the hand gesture based on the sEMG signal is still a challenging problem due to the low amplitude and non-stationary characteristics of the sEMG signal. In this paper, eight standard hand movements in FMA are selected for the active exercises by hand rehabilitation robots. A total of 15 volunteers’ sEMG signals are collected in the course of the experiment. Four time domain features, integral EMG(IEGM), root mean square(RMS), zero crossings(ZC) and energy percentage(EP), are used to identify hand gestures. A feedforward spiking neural network receives the above time domain feature data, and combines the population coding with the Spikeprop learning algorithm to realize the accurate recognition of hand gestures. The experimental results show that: (1) the spiking neural network can achieve a satisfactory classification accuracy by using only 15 neurons; (2) the classification accuracy using all four features are highest with an accuracy of 96.5%; (3) under the same number of neurons, the classification accuracy of the spiking neural network is higher than that of the multilayer perceptron, radial basis function network and support vector machine. This demonstrates the fact that spiking neural networks can achieve a satisfactory classification accuracy with a smaller network size.
TL;DR: The results showed that BAGCT-RBFN can find a shortlist of discriminatory variables with reliability while attain more satisfactory classification accuracy than traditional CT and RBFN.
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TL;DR: A new approach to the RBF approximation of large datasets is introduced and experimental results for different real datasets and different RBFs are presented with respect to the accuracy of computation.
Abstract: Approximation of scattered data is often a task in many engineering problems. The Radial Basis Function (RBF) approximation is appropriate for large scattered datasets in d-dimensional space. It is non-separable approximation, as it is based on a distance between two points. This method leads to a solution of overdetermined linear system of equations.
In this paper a new approach to the RBF approximation of large datasets is introduced and experimental results for different real datasets and different RBFs are presented with respect to the accuracy of computation. The proposed approach uses symmetry of matrix and partitioning matrix into blocks.