Richard P. Lippmann Lincoln Laboratory, MIT Room B-349 Lexington, MA 02173-9108 Eight neural net and conventional pattern classifiers (Bayesianunimodal Gaussian, k-nearest neighbor, standard back-propagation, adaptive-stepsize back-propagation, hypersphere, feature-map, learning vector quantizer, and binary decision tree) were implemented on a serial computer and compared using two speech recog...

Mutual information neuro-evolutionary system (MINES) presents a novel self-governing approach to determine the optimal quantity and connectivity of the hidden layer of a three layer feed-forward neural network founded on theoretical and practical basis. The system is a combination of a feed-forward neural network, back-propagation algorithm, genetic algorithm, mutual information and clustering....

| This paper extends back-propagation to continuous-time feed-forward networks with internal, adaptable time delays. The new technique is suitable for parallel hardware implementation, with continuous multidimensional training signals. The resulting networks can be used for signal prediction, signal production, and spatiotemporal pattern recognition tasks. Unlike conventional back-propagation n...

In this contribution we address the problem of robust video transmission in error prone environments. The approach is compatible with the ITU-T video coding standard H.263 and has been adopted in the ITU-T standard H.324 (\Terminal for Low Bitrate Multimedia Communication"). Fading situations in mobile networks are tolerated and the image quality degradation due to spatio-temporal error propaga...

| In this paper we address the problem of robust video transmission in error prone environments. The approach is compatible with the ITU-T video coding standard H.263. Fading situations in mobile networks are tolerated and the image quality degradation due to spatio-temporal error propagation is minimized utilizing a feedback channel between transmitter and receiver carrying acknowledgment info...

the aim of this study was to estimate suspended sediment by the ann model, dt with cart algorithm and different types of src, in ten stations from the lorestan province of iran. the results showed that the accuracy of ann with levenberg-marquardt back propagation algorithm is more than the two other models, especially in high discharges. comparison of different intervals in models showed that r...

Back-propagation modeling of viscosity and shear stress Ionic-MXene nanofluid is carried out in this work. The data for 0.05, 0.1, 0.2 mass concentration (mass%) are collected from the experimental analysis. Shear as a function rate mass% MXene nanoparticles used input. Additionally, temperature % separately. Based on possible combinations, five back-propagation algorithms developed. In each al...

The conjugate gradient optimization algorithm usually used for nonlinear least squares is presented and is combined with the modified back propagation algorithm yielding a new fast training multilayer perceptron (MLP) algorithm (CGFR/AG). The approaches presented in the paper consist of three steps: (1) Modification on standard back propagation algorithm by introducing gain variation term of th...

Financial forecasting is an example of signal processing problems. A number of ways to train/learn the network are available. We have used Levenberg-Marquardt algorithm for error back-propagation for weight adjustment. Pre-processing of data has reduced much of the variation at large scale to small scale, reducing the variation of training data. Keywords— Gradient descent method, jacobian matri...