Using a probabilistic neural network and Lagrangian observations from the Global Drifter Program, we model single particle transition probability density function (pdf) of ocean surface drifters. The pdf is represented by Gaussian mixture whose parameters (weights, means, covariances) are continuous functions latitude longitude determined to maximize likelihood observed drifter trajectories. Th...

Neural networks are very often applied to the pattern recognition problem. In 1990 D. Specht introduced a special class of Probabilistic Neural Networks which were unnoticed in the computational practice due to their extremely large computer memory requirement. In this note we present and discuss results of experiments assessing the usability of Probabilistic Neural Networks to the multifont re...

Fairness is crucial for neural networks which are used in applications with important societal implication. Recently, there have been multiple attempts on improving fairness of networks, a focus testing (e.g., generating individual discriminatory instances) and training enhancing through augmented training). In this work, we propose an approach to formally verify against fairness, independence-...

This paper focuses on the statistical based Probabilistic Neural Network (PNN) for pattern classification problems with Expectation – Maximization (EM) chosen as the training algorithm. This brings about the problem of random initialization, which means, the user has to predefine the number of clusters through trial and error. Global k-means is used to solve this and to provide a deterministic ...

Perineuronal nets (PNNs) are mesh-like structures, composed of a hierarchical assembly of extracellular matrix molecules in the central nervous system (CNS), ensheathing neurons and regulating plasticity. The mechanism of interactions between PNNs and neurons remain uncharacterized. In this review, we pose the question: how do PNNs regulate communication to and from neurons? We provide an overv...

-By replacing the sigmoid activation function often used in neural networks with an exponential function, a probabilistic neural network ( PNN) that can compute nonlinear decision boundaries which approach the Bayes optimal is formed. Alternate activation functions having similar properties are also discussed. A fourlayer neural network of the type proposed can map any input pattern to any numb...

Network structure determination is an important issue in pattern classification based on a probabilistic neural network. In this study, a supervised network structure determination algorithm is proposed. The proposed algorithm consists of two parts and runs in an iterative way. The first part identifies an appropriate smoothing parameter using a genetic algorithm, while the second part determin...

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...

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...