A neural network model incorporating radial basis functions is used in a speech-pattern classification problem. The method is compared with a back-propagation neural network model and with a vector-quantised hidden Markov model of the same problem. Training times are over an order of magnitude faster, with similar classification result
Radial Basis Function networks with linear outputs are often used in regression problems because the...
This paper presents the work regarding the implementation of neural network using radial basis funct...
Centre for Intelligent Systems and their ApplicationsThis thesis concerns the automatic generation o...
Neural networks have recently been applied to real-world speech recognition problems with a great de...
In the context of pattern classification, the success of a classification scheme often depends on th...
This thesis presents the application of a minimal radial basis function (RBF) neural network, referr...
Automatic speech recognition (ASR) has been a subject of active research in the last few decades. In...
The optimisation and adaptation of single hidden layer feed-forward neural networks employing radial...
Artificial neural networks are powerfultools for analysing information expressed as data sets, which...
In this paper, we use Radial Basis Function Networks (RBFN) for solving the problem of environmental...
Radial basis function (RBF) neural networks provide attractive possibilities for solving signal proc...
Abstract-In recent years there has been a significant amount of work, both theoretical and experimen...
An important aspect of non-destructive testing is the interpretation and classification of signal ob...
The most important factor in configuring an optimum radial basis function (RBF) network is the train...
Radial basis function neural networks (RBF neural networks), as an alternative to multilayer percept...
Radial Basis Function networks with linear outputs are often used in regression problems because the...
This paper presents the work regarding the implementation of neural network using radial basis funct...
Centre for Intelligent Systems and their ApplicationsThis thesis concerns the automatic generation o...
Neural networks have recently been applied to real-world speech recognition problems with a great de...
In the context of pattern classification, the success of a classification scheme often depends on th...
This thesis presents the application of a minimal radial basis function (RBF) neural network, referr...
Automatic speech recognition (ASR) has been a subject of active research in the last few decades. In...
The optimisation and adaptation of single hidden layer feed-forward neural networks employing radial...
Artificial neural networks are powerfultools for analysing information expressed as data sets, which...
In this paper, we use Radial Basis Function Networks (RBFN) for solving the problem of environmental...
Radial basis function (RBF) neural networks provide attractive possibilities for solving signal proc...
Abstract-In recent years there has been a significant amount of work, both theoretical and experimen...
An important aspect of non-destructive testing is the interpretation and classification of signal ob...
The most important factor in configuring an optimum radial basis function (RBF) network is the train...
Radial basis function neural networks (RBF neural networks), as an alternative to multilayer percept...
Radial Basis Function networks with linear outputs are often used in regression problems because the...
This paper presents the work regarding the implementation of neural network using radial basis funct...
Centre for Intelligent Systems and their ApplicationsThis thesis concerns the automatic generation o...
Add to ORKG