This paper investigates the application of artificial neural network structures to the problem of nonlinear channel equalization. The difficulties caused by channel distortions such us inter symbol interference (ISI) and nonlinearities can be overcome by equalizers employing neural networks. We present a structure of a nonlinear equalizer with decision feedback using multilayer perceptron (MLP ...

Phrase-based statistical machine translation (PBSMT) decoders translate source sentences one phrase at a time using strong independence assumptions over the source phrases. Translation table scores are typically independent of context, language model scores depend on a few words surrounding the target phrase and distortion models do not influence directly the choice of target phrases. In this w...

In this paper, a closed-loop, smart transducer design is proposed, based on artificial neural network (ANN) techniques. The design aims to improve the performance of open-loop, off-the-shelf capacitive acceleration sensors and increase their robustness to manufacturing tolerances. A Òmodel referenceÓ control strategy was adopted for the design of the smart transducer. Multilayer perceptron (MLP...

In this work, a Modified Functional Link Artificial Neural Network (M-FLANN) is proposed which is simpler than a Multilayer Perceptron (MLP) and improves upon the universal approximation capability of Functional Link Artificial Neural Network (FLANN). MLP and its variants: Direct Linear Feedthrough Artificial Neural Network (DLFANN), FLANN and M-FLANN have been implemented to model a simulated ...

This paper is focused on the learning algorithms for dynamic multilayer perceptron neural networks where each neuron synapsis is modelled by an infinite impulse response (IIR) filter (IIR MLP). In particular, the Backpropagation Through Time (BPTT) algorithm and its less demanding approximated on-line versions are considered. In fact it is known that the BPTT algorithm is not causal and therefo...

In this work, a Modified Functional Link Artificial Neural Network (M-FLANN) is proposed which is simpler than a Multilayer Perceptron (MLP) and improves upon the universal approximation capability of Functional Link Artificial Neural Network (FLANN). MLP and its variants: Direct Linear Feedthrough Artificial Neural Network (DLFANN), FLANN and M-FLANN have been implemented to model a simulated ...

A novel classifier architecture is introduced which belongs to both hyperplane and hypersphere families. The basic computational unit in the architecture is a perceptron whose input is augmented by its squared length. Traditional methods of training hyperplane classifiers (perceptron training algorithm, backpropagation, etc.) function in the augmented input space, and induce hyperspherical deci...

In this paper, a palmprint recognition system using bit-plane extraction with MLP neural network is presented. It is an approach where the palmprint feature is extracted by slicing the image into 8 bit-planes. The extracted bit-planes are then serves as input data into the neural network. MLP neural network is applied to train and test images for recognition. Networks are simulated for a few co...

Neural networks with internal temporal dynamic can be applied to non-linear DSP problems. The classical fully connected recurrent architectures, can be replaced by less complex neural networks, based on the well known MultiLayer Perceptron (MLP) where the temporal dynamic is modelled by replacing each synapses either with a FIR filter or with an IIR filter. A general learning algorithm (Back-Pr...

This paper is focused on the learning algorithms for dynamic multilayer perceptron neural networks where each neuron synapsis is modelled by an infinite impulse response (IIR) filter (IIR MLP). In particular, the Backpropagation Through Time (BPTT) algorithm and its less demanding approximated on-line versions are considered. In fact it is known that the BPTT algorithm is not causal and therefo...