The main goal of this paper is to compare the performance which can be achieved by two different hybrid approaches analyzing their applications’ potentiality on real world paradigms (speech recognition and medical diagnosis). We compare the performance obtained with (1) Multinetwork RBF/LVQ structure, we use involves Learning Vector Quantization (LVQ) as a competitive decision processor and Rad...

Oral premalignant lesion (OPL) patients have a high risk of developing oral cancer. In this study we investigate using machine learning techniques with gene expression profiling to predict the possibility of oral cancer development in OPL patients. Four classification techniques were used: support vector machine (SVM), Regularized Least Squares (RLS), multi-layer perceptron (MLP) with back prop...

This paper proposes a new hybrid approach which combines simulated annealing and standard backpropagation for optimizing Multi Layer Perceptron Neural Networks (MLP) for time series prediction. Experimental tests were carried out on four simulated series with known features and on the Sunspot series. The results have shown that this approach selects the appropriate time series lags and builds a...

introduction: in most bci articles which aim to separate movement imaginations, µ and β frequency bands have been used. in this paper, the effect of presence and absence of γ band on performance improvement is discussed since movement imaginations affect γ frequency band as well. methods: in this study we used data set 2a from bci competition iv. in this data set, 9 healthy subjects have perfor...

The Q-Credit Assignment (QCA) is a method, based on Q-learning, for allocating credit to rules in Classiier Systems with internal state. It is more powerful than other proposed methods, because it correctly evaluates shared rules, but it has a large computational cost, due to the Multi-Layer Perceptron (MLP) that stores the evaluation function. We present a method for reducing this cost by redu...

The combined use of multi layer perceptron (MLP) and perceptual linear prediction (PLP) features has been reported to improve the performance of automatic speech recognition systems for many different languages and domains. However, MLP features have not yet been used on unsupervised acoustic model training. This approach is introduced in this paper with encouraging results. In addition, unsupe...

This paper proposes the application to the liver fibrosis stadialization of a novel training technique of feed-forward neural networks based on the Bayesian paradigm. Using the Pearson’s r correlation coefficient instead of the standard backpropagation algorithm to update the synaptic weights of a multi-layer perceptron, the proposed model is compared with traditional machine learning algorithm...

This paper is focused on the incorporation of recent techniques for multi-layer perceptron (MLP) based feature extraction in Temporal Pattern (TRAP) and Hidden Activation TRAP (HATS) feature extraction scheme. The TRAP scheme has been origin of various MLP-based features some of which are now indivisible part of state-of-the-art LVCSR systems. The modifications which brought most improvement – ...

RÉSUMÉ. Les perceptrons multi-couches (ou perceptrons) sont largement utilisés pour l’approximation de fonctions, mais sont très demandeursen temps de calcul. Malheureusement, l’implantation parallèle des perceptrons est un problème complexe ; dans cet article, nous proposons une nouvelle méthode efficace pour leur parallélisation. Nous présentons l’architecture OWE (Orthogonal Weight Estimator...

energy management is one of the main ways of the efficient use of energy resources. the prediction of crop yields based on energy inputs can help farmers and policymakers to estimate the level of production. required data for study were randomly collected from 70 broiler farms in north west of iran. the input energies were included human labour, machinery, fuel, feed and electricity and the out...