The paper presents an S-Transform based multilayer perceptron neural network (MLP) classifier for the identification of power quality (PQ) disturbances.The proposed method is used to extract the three input features (Standard deviation, peak value and variances) from the distorted voltage waveforms simulated using parametric equations. The features extracted through S-transform are trained by a...

Systems (DEMS) is proposed. This MLP algorithm uses a gradient metric based on hardware-software cost and performance as the core metric for selection of optimal partitions and consists of three nested levels. The innermost level is a simple binary search that allows quick evaluations of a large number of possible partitions. The middle level iterates over different possible allocations of proc...

The main goal of this paper is to compare the performance which can be achieved by five different approaches analyzing their applications’ potentiality on real world paradigms. We compare the performance obtained with (1) Multi-network RBF/LVQ structure (2) Discrete Hidden Markov Models (HMM) (3) Hybrid HMM/MLP system using a Multi LayerPerceptron (MLP) to estimate the HMM emission probabilitie...

Classification of heart sound signals to normal or their classes of disease are very important in screening and diagnosis system since various applications and devices that fulfilling this purpose are rapidly design and developed these days. This paper states and alternative method in improving classification accuracy of heart sound signals. Standard and improvised Multi-Layer Perceptron (MLP) ...

Conventional MLP classifiers used in phonetic recognition and speech recognition may encounter local minima during training, and they often lack an intuitive and flexible adaptation approach. This paper presents a hybrid MLP-SVM classifier and its associated adaptation strategy, where the last layer of a conventional MLP is learned and adapted in the maximum separation margin sense. This struct...

In humans, cytoskeletal dystrophin and muscle LIM protein (MLP) gene mutations can cause dilated cardiomyopathy, yet these mutations may have different effects in mice, owing to increased accumulation of other, compensatory cytoskeletal proteins. Consequently, we characterized left-ventricular (LV) morphology and function in vivo using high-resolution cine-magnetic resonance imaging (MRI) in 2-...

We use a multi-layer perceptron (MLP) to transform cepstral features into features better suited for speaker recognition. Two types of MLP output targets are considered: phones (Tandem/HATS-MLP) and speakers (Speaker-MLP). In the former case, output activations are used as features in a GMM speaker recognition system, while for the latter, hidden activations are used as features in an SVM syste...

I. Abstract It is well-established that a multi-layer perceptron (MLP) with a single hidden layer of N neurons and an activation function bounded by zero at negative infinity and one at infinity can learn N distinct training sets with zero error. Previous work has shown that the input weights and biases for such a MLP can be chosen in an effectively arbitrary manner; however, this work makes th...

In this study, we propose a Multi-Layer Perceptron (MLP) with pulse glial network having dynamic period of inactivity. We connect glias to neurons in a hidden-layer. The glia is excited by the connecting neuron output. Then, the glia generates a pulse. The pulse is propagated to the connecting neuron and the neighboring glia. In the previous method, we fix a period of inactivity. The period of ...

This paper concerns dynamic neural networks for signal processing: architectural issues are considered but the paper focuses on learning algorithms that work on-line. Locally recurrent neural networks, namely MLP with IIR synapses and generalization of Local Feedback MultiLayered Networks (LF MLN), are compared to more traditional neural networks, i.e. static MLP with input and/or output buffer...