This paper presents modification of Radial Basis Function Artificial Neural Network (RBF ANN)-based temperature compensation models for Interferometric Fiber Optical Gyroscopes (IFOGs). Based on the mathematical expression of IFOG output, three temperature relevant terms are extracted, which include: (1) temperature of fiber loops; (2) temperature variation of fiber loops; (3) temperature produ...

The behavior of Radial Basis Function (RBF) Networks greatly depends on how the center points of the basis functions are selected. In this work we investigate the use of instance reduction techniques, originally developed to reduce the storage requirements of instance based learners, for this purpose. Five Instance-Based Reduction Techniques were used to determine the set of center points, and ...

To assess the roles of feeding behavior (eating and rumination) and systemic arterial pressure (SAP) on determination of the circadian rhythm of renal blood flow (RBF), 20 sheep fitted with ultrasonic flow-metering probes around both renal arteries and a submandibular balloon to monitor jaw movements (6 of them with a telemetry measurement system into the carotid artery for SAP recording), were...

The response surface methodology (RSM), which typically uses quadratic polynomials, is predominantly used for metamodeling in crashworthiness optimization because of the high computational cost of vehicle crash simulations. Research shows, however, that RSM may not be suitable for modeling highly nonlinear responses that can often be found in impact related problems, especially when using limit...

Sensor systems are not always equipped with the ability to track targets. Sudden maneuvers of a target can have a great impact on the sensor system, which will increase the miss rate and rate of false target detection. The use of the generic particle filter (PF) algorithm is well known for target tracking, but it can not overcome the degeneracy of particles and cumulation of estimation errors. ...

A method is described for using Radial Basis Function (RBF) neural networks to generate a certainty factor reliability measure along with the network's normal output. The certainty factor approach is then compared with another technique for measuring RBF reliability, Parzen windows. Both methods are implemented into RBF networks, and the results of using each approach are compared. Advantages a...

In this paper we investigate the use of compactly supported RBF kernels for nonlinear function estimation with LS-SVMs. The choice of compact kernels recently proposed by Genton may lead to computational improvements and memory reduction. Examples however illustrate that compactly supported RBF kernels may lead to severe loss in generalization performance for some applications, e.g. in chaotic ...

The paper presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level. Nonlinear time series modeling and predicti...

Air pollution is a challenging issue in some of the large cities in developing countries. Air quality monitoring and interpretation of data are two important factors for air quality management in urban areas. Several methods exist to analyze air quality. Among them, we applied the dynamic neural network (TDNN) and Radial Basis Function (RBF) methods to predict the concentrations of ground-level...

This paper presents a new sequential multi-category classifier using radial basis function (SMC-RBF) network for real-world classification problems. The classification algorithm processes the training data one by one and builds the RBF network starting with zero hidden neuron. The growth criterion uses the misclassification error, the approximation error to the true decision boundary and a dist...