The severely distorting channels limit the use of linear equalizers and the use of the nonlinear equalizers then becomes justifiable. Neural-network-based equalizers, especially the multilayer perceptron (MLP)-based equalizers, are computationally efficient alternative to currently used nonlinear filter realizations, e.g., the Volterra type. The drawback of the MLP-based equalizers is, however,...

We present a training method which adjusts the weights of the MLP (Multilayer Perceptron) to preserve the distance invariance in a low dimensional space. We apply visualization techniques to display the detailed representations of the trained neurons.

An implementation of an optical perceptron with a soft optical threshold trained with an adapted BP algorithm is described as a precursor to an optical multilayer perceptron (MLP). It has 64 inputs and ten outputs. The soft threshold is implemented by a liquid crystal light valve. Experimental results on perceptron recall are also reported. The eeect of a modiied grey-scale to weight mapping fo...

In the present study, prediction of Alumina recovery efficiency (A.R.E), the amount of produced red mud (A.P.R), red mud settling rate (R.S.R) and bound-soda losses (B.S.L) in Bayer process red mud has been carried out for the first time in the field. These predictions are based on Lime to bauxite ratio and chemical analyses of bauxite and lime as Bayer process feed materials. Radial basis func...

In this paper, an empirical comparison of two multilayer perceptron (MLP)-based techniques for keyword speech recognition (wordspotting) is described. The techniques are the predictive neural model (PNM)-based wordspotting, in which the MLP is applied as a speech pattern predictor to compute a local distance between the acoustic vector and the phone model, and the hybrid HMM/MLP-based wordspott...

abstract: this paper represents a novel use of artificial neural networks in medical science. the proposed technique involves training a multi layer perceptron (mlp) (a kind of artificial neural network) with a bp learning algorithm to recognize a pattern for the diagnosing and prediction of five blood disorders, through the results of blood tests from h1 machine. the blood test parameters and ...

This paper presents DMP3 (Dynamic Multilayer Perceptron 3), a multilayer perceptron (MLP) constructive training method that constructs MLPs by incrementally adding network elements of varying complexity to the network. DMP3 differs from other MLP construction techniques in several important ways, and the motivation for these differences are given. Information gain rather than error minimization...

Dynamic neural network (DNN) models provide an excellent means for forecasting and prediction of nonstationary time series. A neural network architecture, known as locally recurrent neural network ((LRNN) [71], is preferred to the traditional multilayer perceptron (MLP) because the time varying nature of a stock time series can be better represented using LRNN. The use of LRNN has demonstrated ...

In this paper, it is shown that Multilayer perceptron Neural Network can elegantly perform nonlinear regression of transfer characteristic of electronic devices. After rigorous computer simulations authors develop the optimal MLP NN models, which elegantly perform such a nonlinear regression. Results show that the proposed optimal MLP NN models have optimal values of MSE (mean square error), r ...

In our previous work we proposed a Multilayer Perceptron Neural Networks (MLP NN) consisting of fuzzy flipflops (F3) based on various operations. We showed that such kind of fuzzy-neural network had good learning properties. In this paper we propose an evolutionary approach for optimizing fuzzy flip-flop networks (FNN). Various popular fuzzy operation and three different fuzzy flip-flop types w...