ÐThis is an elementary research for assigning color values to voxels of multichannel Magnetic Resonance Imaging (MRI) volume data. The MRI volume data sets obtained under different scanning conditions are transformed to the components by independent component analysis (ICA), which enhances physical characteristics of the tissue. The transfer functions for generating color values from independen...

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...

C o m p a ra t iv e S tu d y of th e C a s c a d e-C o r re la t io n A rc h ite c tu re in P a t te r n R e c o g n itio n A p p lic a tio n s Abstract In this work, an experimental evaluation of the cascade-correlation architecture is carried out in different bench-marking pattern recognition problems. An extensive experimental framework is developed to establish a comparison between the casc...

This paper presents a set of optimizations in learning algorithms commonly used for training radial basis function neural networks. These optimizations are applied to a RBF neural network used in identifying helicopter types processing their rotor sounds. The first method uses an optimum learning rate in each iteration of train process. This method increases the speed of learning process and al...