In this paper, we propose a methodology for the characterization of the suspended particulate matter along the Algiers’s bay. An approach by multi-layer perceptron (MLP) with training by back propagation of the gradient optimized by the algorithm of Levenberg-Marquardt (LM) is used. The accent was put on the choice of the components of the base of training where a comparative study made for fou...

A simple neural model for electromagnetic field prediction in indoor environment was created based on field strength measurements at 2.4 GHz, conducted at University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (FESB Split). Vertical rod antenna (omnidirectional in the horizontal plane) was placed in a faculty hallway and used as the electromagnetic...

In this paper, a new method of bandwidth estimation using variation of slot length on interdigital band pass filter has been presented using artificial neural networks for desired frequency range between 1.7—2.5 GHz. Interdigital filter is multifinger periodic structure which offers compact filter design space. An ANN model has been developed and tested for estimating the cut off frequency of b...

The ability to model the time varying dynamics of an unmanned rotorcraft is an important aspect in the development of adaptive flight controller. This paper presents a recursive Gauss-Newton based training algorithm to model the attitude dynamics of a small scale rotorcraft based unmanned aerial system using the neural network (NN) modelling approach. It focuses on selection of optimised networ...

This paper presents a new fuzzy time series model combined with ant colony optimization (ACO) and auto-regression. The ACO is adopted to obtain a suitable partition of the universe of discourse to promote the forecasting performance. Furthermore, the auto-regression method is adopted instead of the traditional high-order method to make better use of historical information, which is proved to be...

The paper focuses on the practical implementation of a new robust control method to an automotive active suspension system using skyhook and adaptive active force control (SANAFC) strategy. The overall control system essentially comprises four feedback control loops to cater for a number of specific taks. Neural networks (NN) with modified adaptive Levenberg-Marquardt (LM) learning algorithms w...

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price will maximize investor’s gains. This paper proposes a machine learning model to predict stock market price. The proposed algorithm integrates Particle swarm optimization (PSO) and least squ...

A novel algorithm is proposed for CANDECOMP/PARAFAC tensor decomposition to exploit best rank-1 tensor approximation. Different from the existing algorithms, our algorithm updates rank-1 tensors simultaneously in-parallel. In order to achieve this, we develop new all-at-once algorithms for best rank-1 tensor approximation based on the Levenberg-Marquardt method and the rotational update. We sho...

In this paper, we propose an artificial neural network (ANN) as a design technique for multilayer circular microstrip antennas based on Levenberg Marquardt training algorithm for modelling, simulation and optimization. Levenberg – Marquart (LM) algorithm has been used to train the MultiLayer Perceptron Neural Networks (MLPNNs). In the design procedure, the feed forward network is defined as a s...

In this paper, we present a new method for epilepsy seizure detection based on autoregressive modelling. The method, termed linear prediction coding (LPC), is used to model ictal and seizure-free EEG signals. It is found that the modeling error energy is substantially higher for ictal EEG signals compared to seizure-free EEG signals. Moreover, it is known that ictal EEG signals have higher ener...