This The process of estimating the geographical location of sensor nodes, called localization is an important research area in WSN. Accurate localization or tracking of wireless device is a crucial requirement for many emerging location aware systems. Fields of application include search & research, medical care, intelligent transportation, location based billing, security, home automation, ind...

An extensive analysis of the strong ground motion Mexican data base was conducted using Soft Computing (SC) techniques. A Neural Network NN is used to estimate both orthogonal components of the horizontal (PGA h ) and vertical (PGA v ) peak ground accelerations measured at rock sites during Mexican subduction zone earthquakes. The work discusses the development, training, and testing of this ne...

This paper proposes a novel adaptive decision feedback equalizer (DFE) based on self-constructing recurrent fuzzy neural network (SRFNN) for quadrature amplitude modulation systems. Without the prior knowledge of channel characteristics, a novel training scheme containing both selfconstructing learning and back-propagation algorithms is derived for the SRFNN. The proposed DFE is compared with s...

⇑ Corresponding author. Tel.: +886 928722593; fax E-mail addresses: [email protected] (Y.-J. Chen (H.-C. Huang), [email protected] (R.-C. Hwang). This paper presents the estimations of ammonia concentration by using neural network (NN) models. The shear horizontal surface acoustic wave (SH-SAW) devices coated with L-glutamic acid hydrochloride and polyaniline (PANI) film, respectively, were ap...

It is well known that computed torque robot control is subjected to performance degradation due to uncertainties in robot model, and application of neural network(NN) compensation techniques are promising. In this paper we examine the eeectiveness of NN as a compensator for the complex problem of Cartesian space control. In particular we examine the diierences in system performance when the sam...

This article deals with hybrid expert system that has knowledge base realized through a hierarchical structure of artificial neural networks (NN). The decision tree is built by C4.5 algorithm at first. In the next step the nods of the tree are replaced by NN. They are trained to split the data in the same way as the nods. So the problem is separated into partial sub-problems that are solved by ...

This paper proposes an extension of neural network identification capabilities for on-line identification of a nonlinear closed-loop control system. The neural network (NN) is trained on-line using the backpropagation optimization algorithm with an adaptive learning rate. The optimization algorithm is performed at each sample time to compute the optimal control input. The results confirm the ef...

This paper proposes an improved version of particle swarm optimization (PSO) algorithm for the training of a neural network (NN). An architecture for the NN trained by PSO (standard PSO, improved PSO) is also introduced. This architecture has a data preprocessing mechanism which consists of a normalization module and a data-shuffling module. Experimental results showed that the NN trained by im...

Forecasting accuracy drives the performance of inventory management. This study is to investigate and compare different forecasting methods like Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) with Neural Networks (NN) models as Feed-forward NN and Nonlinear Autoregressive network with eXogenous inputs (NARX). Data used to forecast is acquired from inventory database of...