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In the training of the radial basis function network (RBFN), feature selection and classifier design are two tasks commonly addressed in separated processes. The former is related to the number of input nodes, whereas the latter is associated with the design of the hidden layer. Hence, this paper presents an algorithm to train a RBFN based on differential evolution (DE), which simultaneously adjusts...
Anemia is a condition in which the hemoglobin (Hb) content becomes less than that of the normal value. In this project, hemoglobin value is estimated using ANN (Artificial Neural Network). Database of blood sample images and their actual Hb values is collected from a local laboratory. Red, green and blue normalized values of images' samples are fed to the ANN as input. Cyanemethemoglobin method based...
This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The training data patterns are processed incrementally. If a data pattern is not similar enough to an existing cluster, it forms a new cluster of its own. However, if a data pattern is similar enough to an existing cluster, it is added to the most similar cluster. During the clustering...
This paper investigates an event-triggered distributed cooperative learning (DCL) algorithm using radial basis function networks (RBFNs), where training samples are often extremely large-scale, high-dimensional and located on distributed nodes over strongly connected and weight-balanced networks. The algorithm is based on Zero-Gradient-Sum (ZGS) distributed optimization strategy and works in a fully...
This paper deals with a design methodology for a neural network with improved robust qualities in notion to handling uncertain input data space variations. The proposed network topology combines the simplicity of the radial basis functions networks to interpret or classify data pairs and the abilities of the intuitionistic fuzzy logic to deal with the vagueness of the data space. A simplified gradient...
ADP is an effective optimal method. However, the optimality depends on its network structure and training algorithm. This paper adopts RBF neural network to realize its critic and action networks after a detailed analysis on ADP. The LSM method is introduced as training algorithm, and a novel basis function is defined, which achieves global optimization and online control. The validity is verified...
Membrane pollution is the main obstacle to the popularization and application of MBR. In order to solve the problem that the influence factors of membrane fouling are more complicated, three kinds of membrane fouling factors with the contribution rate of more than 95% are selected by principal component analysis(PCA) method: The mixed solution suspended solids (MLSS), operating pressure (AP) and temperature...
Gesture recognition enables human to communicate with machine and interact naturally without any mechanical devices. The ultimate aim of gesture recognition system is to create a system which understands human gesture and use them to control various other devices. This research focuses on gesture recognition system with a radial basis function network. The radial basis function network is a 3 layer...
Induction machines are extensively used in industries and are subject to unexpected breakdowns. It is necessary, therefore, to prevent them from such breakdown through the maintenance that works according to a well-trained planning. A considerable number of diagnosis techniques have been used such as Motor Current Signature Analysis (MCSA), Axial Flux Monitoring and Vibration Monitoring. This paper...
In this paper, the author has developed a new theoretical model called the Adaptive Learning and Thinking Style for E-learning System using Neural Network (ALTENN) model. Determining intangible human behavior such as preference or motional aspect is very difficult due to many factors featured in the person's personality, taste, gender, age, and mood. Therefore, determining individuals learning style...
Brain-Computer Interface (BCI) is a specific type of human-computer interface that stablish the direct communication between human and computers by analyzing brain activities. Oddball paradigms are used in BCI to generate Event-Related Potentials (ERPs), like the P300 response, on targets selected by the user. A P300 speller is based on this principle, where the detection of P300, allows the user...
In the earth there is distressing number of people who suffer from neurological disorders. Electroencephalogram EEG signal are chaotic time series signals and tends to change rapidly with the patient condition. From normal to severe conditions the nature of signals has drastic difference and with change in amplitude as well as frequencies. Prediction of these signals in the early stage is mere a complex...
This paper focuses on account stolen case of smart phone users. In order to enhance the credibility of user authentication, an authentication method based on virtual keystroke dynamics behavior of touch screen is proposed in this paper. The proposed method extracts time dependent characteristics and pressure related characteristics of users' virtual keystroke dynamics behavior, builds combined authentication...
In this paper we approach from a machine learningperspective the problem of identifying the sex of archaeologicalremains from anthropometric data, an important problem withinthe field of bioarchaeology. As the conditions for detecting thesex of a skeleton are not entirely known, machine learning baseddata mining models are appropriate to address this problem sincethey are able to capture unobservable...
In this paper, a new online learning algorithm is proposed to learn a data sample in hybrid mode. This new algorithm is developed and referred as Growing and Pruning — Fuzzy ARTMAP-radial basis function (GAP-FAM-RBF) neural network. In this algorithm, fuzzy ARTMAP (FAM) network learns from training samples and radial basis function (RBF) network provides viable solutions. The GAP-FAM-RBF that proposed...
In the multilayer perceptron (MLP), there was a theorem about the maximum number of separable regions (M) given the number of hidden nodes (H) in the input d-dimensional space. We propose a recurrence relation to prove the theorem using the expansion of recurrence relation instead of proof by induction. We use three-layer radial basis function net (RBF) on the well log data inversion to test the number...
The paper presents an improved redial basis function network to degrade the influence of the heteroscedasticity noises in the training data. A general purpose learning algorithm is regarded as the statistical nonlinear regression model which is assumed the constant noise level. However, the heteroscedasticity noises always exist in the real data. The transformation based least trimmed squares-support...
The Synthetic Aperture Radar (SAR) can work on all weather conditions for high-resolution monitoring of oil spills. Efficient eigenvectors can be extracted to optimize the RBF network model which is used for distinguishing oil spill from SAR images. The eigenvectors can be computed using the Measured Dark zone boundary determined SAR images. Such eigenvectors could be valid input parameters for building...
The root-finding problem is one of the most important computational problems and applications. In this paper we introduced the modify artificial neural network is represented depending on radial basis function networks which have been three layers: input layer, hidden layer and output layer, where the hidden layer based on Gaussian function, these neural network techniques are developed to obtain...
A recursive network is proposed by introducing memory neurons based on the RBF network. Due to the current output value of the network is related to the past input value in the network, the network will be able to identify the dynamics of the system without the need of explicitly feedback of input and output in the past. Thus, the network is able to identify a system has an unknown order or an unknown...
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