The progress of research in probabilistic neural network (PNN) and related issues is straight related to directly compare the performance of different PNN algorithm versions. In most cases, the PNN application in real life issues involves the classical activation function (Parzen-Cacoulos estimator) only. Although this estimator has been used in most experimental works so far, it is not the onl...

The Probabilistic Neural Network (PNN) algorithm represents the likelihood function of a given class as the sum of identical, isotropic Gaussians. In practice, PNN is often an excellent pattern classifier, outperforming other classifiers including backpropagation. However, it is not robust with respect to affine transformations of feature space, and this can lead to poor performance on certain ...

A self adaptive probabilistic neural network model is proposed. The model incorporates the Particle Swarm Optimization algorithm to optimize the spread parameter of the probabilistic neural network, enhancing thus its performance. The proposed approach is tested on two data sets from the field of bioinformatics, with promising results. The performance of the proposed model is compared to probab...

The problem of distinguishing and classifying the responses of analog integrated circuits containing catastrophic faults has aroused recent interest. The problem is made more difficult when parametric variations are taken into account. Hence, statistical methods and techniques such as neural networks have been employed to automate classification. The major drawback to such techniques has been t...