This paper 1 proposes a method to extract nonlinear discriminant features from given input measurements by using outputs of multilayer Perceptron (MLP). Linear Discriminant Analysis (LDA) is one of the best known methods to construct linear features which are suitable for class discrimination. Otsu showed that LDA can be extended to nonlinear if we can estimate Bayesian a posteriori probabiliti...

A novel improvement in neural network training for pattern classification is presented in this paper. The proposed training algorithm is inspired by the biological metaplasticity property of neurons and Shannon’s information theory. This algorithm is applicable to artificial neural networks (ANNs) in general, although here it is applied to a multilayer perceptron (MLP). During the training phas...

In this paper the neural network model for transient ischemic attacks recognition have been addressed. The proposed approach is based on integration of the NPCA neural network and multilayer perceptron. The dataset from clinic have been used for experiments performing. Combining two different neural networks (NPCA and MLP) it is possible to produce efficient performance in terms of transient is...

This paper deals with an optical character recognition (OCR) system of handwritten digit, with the use of neural networks (MLP multilayer perceptron). And a method of extraction of characteristics based on the digit form, this method is tested on the MNIST handwritten isolated digit database (60000 images in learning and 10000 images in test). This work has achieved approximately 80% of success...

This study introduces the classiication of musical instrument sounds by artiicial neural networks (ANN). The time varying spectral contents of sounds are estimated based on Short-time Fourier Transform (STFT) and are applied to ANN structures for classiication. Recognition results obtained from a multilayer perceptron (MLP), time delay neural network (TDNN) and a hybrid self organizing map radi...

Recurrent Self Organizing Map RSOM is studied in three di erent time series prediction cases RSOM is used to cluster the series into local data sets for which corresponding local linear models are estimated RSOM includes recurrent di erence vector in each unit which allows storing con text from the past input vectors Multilayer perceptron MLP network and autoregressive AR model are used to comp...

In this paper we propose a Multilayer Perceptron Neural Network (MLP NN) consisting of fuzzy flip-flop neurons based on various fuzzy operations applied in order to approximate a real-life application, two input trigonometric functions, and two and six dimensional benchmark problems. The Bacterial Memetic Algorithm with Modified Operator Execution Order algorithm (BMAM) is proposed for Fuzzy Ne...

We report an implementation of a clinical information extraction tool that leverages deep neural network to annotate event spans and their attributes from raw clinical notes and pathology reports. Our approach uses context words and their partof-speech tags and shape information as features. Then we hire temporal (1D) convolutional neural network to learn hidden feature representations. Finally...

We propose a two-stage training for the multilayer perceptron (MLP). The first stage is bottom-up, where we use a class separability measure to conduct hidden layer training and the least squared error criterion to train the output layer. The second stage is top-down, we use a criterion derived from classification error rate to further train the network weights. We demonstrate the effectiveness...

In this paper, learning algorithm for a multiplicative neural network motivated by spiking neuron model (MSN) is proposed and tested for various applications where a multilayer perceptron (MLP) neural network is conventionally used. It is observed that the inclusion of a few more biological phenomena in the formulation of artificial neural network models make them more prevailing. Several bench...