| This paper presents the Time Delay Radial Basis Function Network (TDRBF) for recognition of pho-nemes. The TDRBF combines features from Time Delay Neural Networks (TDNN) and Radial Basis Functions (RBF). The ability to detect acoustic features and their temporal relationship independent of position in time is inherited from TDNN. The use of RBFs leads to shorter training times and less parame...

In order to determine the relative or absolute railway track and foundation deformation, ground-based laser scanning technology is utilised in this study to attain a precise 3D track reference. Located at the University of Nottingham’s Innovation Park, the newly built Nottingham Geospatial Building, where the Nottingham Geospatial Institute is based, has a roof laboratory that has unique testin...

Query expansion methods have been extensively studied in information retrieval. This paper proposes a query expansion method. The HQE method employs a combination of ontology-based collaborative filtering and neural networks to improve query expansion. In the HQE method, ontology-based collaborative filtering is used to analyze semantic relationships in order to find the similar users, and the ...

This paper presents an easy to use constructive training algorithm for Probabilis tic Neural Networks a special type of Radial Basis Function Networks In contrast to other algorithms prede nition of the network topology is not required The pro posed algorithm introduces new hidden units whenever necessary and adjusts the shape of already existing units individually to minimize the risk of miscl...

In the context of regression analysis with penalised linear models (such as RBF networks) certain model selection criteria can be diierentiated to yield a re-estimation formula for the regularisation parameter such that an initial guess can be iteratively improved until a local minimum of the criterion is reached. In this paper we discuss some enhancements of this general approach including imp...

This paper proposes a framework for constructing and training a radial basis function (RBF) neural network. For this purpose, a sequential learning algorithm is presented to adapt the structure of the network, in which it is possible to create a new hidden unit and also to detect and remove inactive units. The structure of the Gaussian functions is modi"ed using a pseudoGaussian function (PG) i...

A boosting-based method for centers placement in radial basis function networks (RBFN) is proposed. Also, the influence of several methods for drawing random samples on the accuracy of RBFN is examined. The new method is compared to trivial, linear and non-linear regressors including the multilayer Perceptron and alternative RBFN learning algorithms and its advantages are demonstrated for learn...

Incremental Net Pro IncNet Pro with local learning feature and statistically controlled growing and pruning of the network is intro duced The architecture of the net is based on RBF networks Extended Kalman Filter algorithm and its new fast version is proposed and used as learning algorithm IncNet Pro is similar to the Resource Allocation Network described by Platt in the main idea of the expan...

5 We propose an advanced framework for the automatic configuration of 6 spectral dimensionality reduction methods. This is achieved by introducing, 7 first, the mutual information measure to assess the quality of discovered 8 embedded spaces. Secondly, unsupervised Radial Basis Function network is 9 designated for mapping between spaces where the learning process is derived 10 from graph theory...

An effective technique for identifying nonlinear discrete-time systems using raisedcosine radial basis function (RBF) networks is presented. Raised-cosine RBF networks are bounded-input bounded-output stable systems, and the network output is a continuously differentiable function of the past input and the past output. The evaluation speed of an n-dimensional raised-cosine RBF network is high b...