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Most predictive modeling techniques utilize all available data to build global models. This is despite the wellknown fact that for many problems, the targeted relationship varies greatly over the input space, thus suggesting that localized models may improve predictive performance. In this paper, we suggest and evaluate a technique inducing one predictive model for each test instance, using only neighboring...
This paper investigates the application of a novel approach for the parameter estimation of a Radial Basis Function (RBF) network model. The new concept (denoted as functional training) minimizes the integral of the analytical error between the process output and the model output [1]. In this paper, the analytical expressions needed to use this approach are introduced, both for the back-propagation...
Because the air compressor has too many fault types, so it is often difficult to make the fault diagnosis of air compressor. For example, the detected variables are too many then it is difficult to take fault classification. The method of making use of RBF neural networks to achieve the fault diagnosis of air compressor is proposed in the paper. For the sample data is used to train RBF neural networks,...
The analysis of the surface Electrocardiogram (ECG) is the most extended noninvasive technique in cardi-ological diagnosis. In order to properly use the ECG, we need to cancel out ectopic beats. These beats may occur in both normal subjects and patients with heart disease, and their presence represents an important source of error which must be handled before any other analysis. This paper presents...
In Taiwan, there are hundreds of accidents every day recorded by government due to the human factor and environmental factor. The accident usually involved the money dispute; therefore the accident appraisal must indicate the bilateral parties' blame clearly: all blame; major blame; minor blame and none blame. Although the local police can give a preliminary analysis report at first, the report cannot...
We use the radial basis function network (RBF) for well log data inversion. The first step of the network is the K-means clustering. For the second step, we adopt the 2-layer perceptron instead of conventional 1-layer perceptron. The 2-layer perceptron can do the more nonlinear mapping. The gradient descent method is used in the back propagation learning rule at the second step. The input of the network...
A marine electromagnetic survey is an engineering endeavour to discover the location and dimension of a hydrocarbon layer under an ocean floor. In this kind of survey, an array of electric and magnetic receivers are located on the sea floor and record the scattered, refracted and reflected electromagnetic wave, which has been transmitted by an electric dipole antenna towed by a vessel. The data recorded...
A simulator in order to calculate the rate of the reaction in the methanol oxidation to formaldehyde process is presented in this paper. Here the Radial Basis Function Network is used to model the process. To choose an optimum number of hidden neuron we use an algorithm called Minimal Resource allocation Network. It recruits hidden neuron based on the novelty of the input data. The training data were...
Traditional groundwater quality evaluation methods often need to set the weight of evaluation factors, and the evaluation results were to a large extent influenced by the subjective factors and there would be a poor grade of evaluation result because of one factor that had a larger content. To solve above problems, the artificial neural network theory and ideology was introduced, and use the RBF neural...
The paper presents the properties of two types of neural networks: traditional neural networks and radial basis function (RBF) networks, both of which are considered as universal approximators. In this paper, the advantages and disadvantages of the two types of neural network architectures are analyzed and compared based on four different examples. The comparison results indicate approaches to be...
This paper presents a nonlinear controller based on an inverse neural network model of the system under control. The neural controller is implemented as a Radial Basis Function (RBF) network trained with the powerful fuzzy means algorithm. The resulting controller is tested on a nonlinear DC motor control problem and the results illustrate the advantages of the proposed approach.
This paper presents an optimal radial basic function (RBF) neural network for fast restoration of distribution systems under different load levels. Basically, service restoration of distribution systems is a stressful and urgent task that must be performed by system operators. In this paper, a RBF network evolved by an enhanced differential evolution (EDE) algorithm is developed to achieve the fast...
The paper presents a novel two-step approach for constructing and training of optimally weighted Euclidean distance based Radial-Basis Function (RBF) neural network. Unlike other RBF learning algorithms, the proposed paradigms use Fuzzy C-means for initial clustering and optimal learning factors to train the network parameters (i.e. spread parameter and mean vector). We also introduce an optimized...
Automatic detection of network intrusion is a challenging task because of increasing types of attacks. Many of the existing approaches either are rigid, inflexible designs tailored to a specific situation or require manual setting of design parameters such as the initial number of clusters. In this paper we allow the design parameters to be determined dynamically by adopting a layered hybrid architecture,...
In this paper, a radial basis neural network (RBFN) for lung cancer screening algorithm is presented. Because of the learning characteristics of the radial basis neural network (RBFN), it has been selected to train the samples, which are the lung cancer examples, and then extracts the internal relations between the pathogenic factors and inducing lung cancer, and eventually it generates empirical...
The values of analog circuits' input and output signals and the component parameters are continuous, and meanwhile there are inevitable tolerance and non-linear components in analog circuits, therefore the presence of these factors increases complexity of the analog circuits fault diagnosis. RBF and BP neural network are two widely used feedforward neural networks, LabVIEW is a graphical programming...
The paper presents the design of three types of neural networks with different features, including traditional backpropagation networks, radial basis function networks and counterpropagation networks. Traditional backpropagation networks require very complex training process before being applied for classification or approximation. Radial basis function networks simplify the training process by the...
It is difficult to evaluate the technical condition for complicated structure, lack of samples and condition data. In order to solve the problem, a method based on support vector machine (SVM) which had its own advantages of solving the classification and evaluation in the case of limited examples is given. Take the canned motor pump (CMP) for example, the indices' grade model and code coding rules...
In this paper a novel approach for implementing isolated speech recognition is studied. While most of the literature on speech recognition (SR) is based on hidden Markov model (HMM), the present system is implemented by Radial Basis Function type neural network. The two phases of training and testing in a Radial Basis Function type neural network has been described. All of classifiers use Linear Predictive...
Local scour around bridge abutment is a time-dependent complex phenomenon encountered world-wide. It is difficult to establish a general empirical model that can be applied to all abutment conditions. In this paper, Radial basis function (RBF) Network has been used to predict the maximum scour depth around bridge abutment. An appropriate model is identified using experimental data from literature...
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