This paper presents the quantification of uncertain natural frequency for laminated composite plates by using a novel surrogate model. A group method of data handling in conjunction to polynomial neural network (PNN) is employed as surrogate for numerical model and is trained by using Latin hypercube sampling. Subsequently the effect of noise on a PNN based uncertainty quantification algorithm ...

Neural networks (NNs) have been increasingly used in recent years for the solving complex nonlinear problems. NNs are seen as an attractive alternative to process based modeling approaches, as they are able to extract an underlying relationship from the data when knowledge of physical process is lacking. The paper evaluates the predictive power of a model, which emulates an army commander on th...

* This work was partially supported by the Italian MURST. AbstractThe aim of this paper is to present a novel technique for defect identification by neural networks based on the classification of remote field effect eddy current (RFEC) data. We consider a kind of neural network that does not require a long training and is particularly well suited for fast classification, the Probabilistic Neura...

In this paper, the processing of sonar signals has been carried out using Minimal Resource Allocation Network (MRAN) and a Probabilistic Neural Network (PNN) in differentiation of commonly encountered features in indoor environments. The stability-plasticity behaviors of both networks have been investigated. The experimental result shows that MRAN possesses lower network complexity but experien...

Straightforward implementation of the exact pairwise nearest neighbor (PNN) algorithm takes O(N3) time, where N is the number of training vectors. This is rather slow in practical situations. Fortunately, much faster implementation can be obtained with rather simple modifications to the basic algorithm. In this paper, we propose a fast O(tauN2) time implementation of the exact PNN, where tau is...

The condition of joints in steel truss bridges is critical to railway operational safety. available methods for the quantitative assessment different types joint damage are, however, very limited. This paper numerically investigates feasibility using a probabilistic neural network (PNN) and finite element (FE) model updating technique assess bridges. A two-step identification procedure develope...

This article presents a novel technique to distinguish between magnetizing inrush current and internal fault current of power transformer. An algorithm has been developed around the theme of the conventional differential protection method in which parallel combination of Probabilistic Neural Network (PNN) and Power Differential Protection (PDP) methods have been used. Both PNN and PDP method ar...

Perineuronal nets (PNN) ensheath GABAergic and glutamatergic synapses on neuronal cell surface in the central nervous system (CNS), have neuroprotective effect in animal models of Alzheimer disease and regulate synaptic plasticity during development and regeneration. Crucial insights were obtained recently concerning molecular composition and physiological importance of PNN but the microstructu...

This correspondence compares the computational complexity of the pair-wise nearest neighbor (PNN) and Linde-Buzo-Gray (LBG) algorithms by deriving analytical expressions for their computational times. It is shown that for a practical codebook size and training vector sequence, the LBG algorithm is indeed more computationally efficient than the PNN algorithm.

Retinal disease is of different type which can be identified as the diabetic inflicted blindness. The feature extraction methods are required to identify the regions. In this work, a three stage analytical model is presented for disease identification. In the first stage of this model, the feature improvement and effective region extraction process are defined. In the second stage, the feature ...