detecting faces in cluttered backgrounds and real world has remained as an unsolved problem yet. in this paper, by using composition of some kind of independent features and one of the most common appearance based approaches, and multilayered perceptron (mlp) neural networks, not only some questions have been answered, but also the designed system achieved better performance rather than the pre...

We derive a new sequential learning algorithm for Multilayered Perceptron (MLP) neural network robust to outliers. Presence of outliers in data results in failure of the model especially if data processing is performed on-line or in real time. Extended Kalman filter robust to outliers (EKF-OR) is probabilistic generative model in which measurement noise covariance is modeled as stochastic proce...

nowadays, software cost estimation (sce) with machine learning techniques are more performance than other traditional techniques which were based on algorithmic techniques. in this paper, we present a new hybrid model of multi-layer perceptron (mlp) artificial neural network (ann) and ant colony optimization (aco) algorithm for high accuracy in sce called multilayer perceptron ant colony optimi...

This paper reports on an experimental approach to find a modularized artificial neural network solution for the UCI letters recognition problem. Our experiments have been carried out in two parts. We investigate directed task decomposition using expert knowledge and clustering approaches to find the subtasks for the modules of the network. We next investigate processes to combine the modules ef...

A general framework for minimal distance methods is presented. Radial Basis Functions (RBFs) and Multilayer Perceptrons (MLPs) neural networks are included in this framework as special cases. New versions of minimal distance methods are formulated. A few of them have been tested on a real-world datasets obtaining very encouraging results.

Abstract To reduce the background z -Vertex Track Trigger estimates collision origin in Belle II experiment using neural networks. The main part is a pre-trained multilayer perceptron. task of this perceptron to estimate -vertex suppress from outside interaction point. For this, low latency real-time FPGA implementation needed. We present an overview architecture and neuronal network preprocess...

The current paper presents a robust optimum design of friction stir welding (FSW) lap joint AA1100 aluminum alloy sheets using Monte Carlo simulation, NSGA-II and neural network. First, to find the relation between the inputs and outputs a perceptron neural network model was obtained. In this way, results of thirty friction stir welding tests are used for training and testing the neural network...

This paper features a new nonlinear parameter optimisation technique for a local model network and gives a case based comparison to multilayer perceptron networks in the context of a complex industrial application. The architecture of a local model network as well as an effective training algorithm which allows to build reliable and interpretable models is presented. The performance and suitabi...

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 ...