A general problem in model selection is to obtain the right parameters that make a model t observed data. If the model selected is a Multilayer Perceptron (MLP) trained with Backpropagation (BP), it is necessary to nd appropriate initial weights and learning parameters. This paper proposes a method that combines Simulated Annealing (SimAnn) and BP to train MLPs with a single hidden layer, terme...

In this paper, an automated approach for electrocardiogram (ECG) signal noise removing using artificial neural network is investigated. First, 150 of noisy heart signal are collected form MIT-BIH database. Then signals are transformed to frequency domain and cutoff frequency is calculated. Since heart signals are lowpass frequency, a Finite Impulse Response (FIR) filter is adequate to remove th...

Multilayer perceptron networks (MLP) are investigated with respect to the problem of the detection of spurious patterns. A novel mechanism is proposed based on the ideas of boot-strapping that when incorporated into the standard MLP provides the network with the ability to continuously modifying its responses across the input space. The mechanism makes use of the outputs given by the network it...

This paper presents the Optimally-Pruned Extreme Learning Machine (OP-ELM) toolbox. This novel, fast and accurate methodology is applied to several regression and classification problems. The results are compared with widely known Multilayer Perceptron (MLP) and Least-Squares Support Vector Machine (LS-SVM) methods. As the experiments (regression and classification) demonstrate, the OP-ELM meth...

Noise cancellation in the field of Adaptive filtering has become the essential requirement of the signal processing .The standard multilayer perceptron (MLP) model of Neural Networks is now popular in Adaptive filtering This paper presents the noise-cancellation technique based on Generalized-mean neuron network (GMN). This network consists of an aggregation function, which is based on the gene...

Wireless capsule endoscopy (WCE) is an effective means of diagnosis of gastrointestinal disorders. Detection of informative scenes by WCE could reduce the length of transmitted videos and can help with the diagnosis. In this paper we propose a simple and efficient method for segmentation of the bleeding regions in WCE captured images. Suitable color channels are selected and classified by a mul...

We present the hardware implementation of partially connected neural network that is defined as an extended of the MultiLayer Perceptron (MLP) model. We demonstrate that partially connected neural networks lead to a higher performance in terms of computing speed (requiring less memory and computing resources). This work addresses a complete study that compares the hardware implementation of MLP...

In this paper we present our latest investigation on multilingual bottle-neck (BN) features and its application to rapid language adaptation to new languages. We show that the overall performance of a Multilayer Perceptron (MLP) network improves significantly by initializing it with a multilingual MLP. Furthermore, ASR performance increases on both, on those languages which were used for multil...

The process of amygdalin extraction from plum seeds was optimized using central composite design (CCD) and multilayer perceptron (MLP). The effect of time, ethanol concentration, solid-to-liquid ratio, and temperature on the amygdalin content in the extracts was estimated using both mathematical models. The MLP 4-3-1 with exponential function in hidden layer and linear function in output layer ...

In this paper two nonlinear modelling approaches are employed to derive single nonlinear models for a Rolls Royce aircraft gas turbine. The first approach is based on the estimation of a NARMAX model using conventional structure selection and parameter estimation techniques, and the second approach is based on the use of feedforward Multilayer-Perceptron (MLP) neural networks. The performances ...