Locating services have come under the spotlight in recent years various applications. However, locating methods that use received signal strength low accuracy due to fluctuations. For this purpose, we present a Wi-Fi based system using artificial neural network enhance positioning process performances. We optimized Levenberg Marquardt algorithm propose better configuration of multi-layer time-d...

In business, industry and government agencies, anticipating future behavior that involves many critical variables for nation wealth creation is vitally important, thus the necessity to make precise decision by the policy makers is really essential. Consequently, an accurate and reliable forecast system is needed to compose such predictions. Accordingly, the aim of this research is to develop a ...

Artificial neural network (ANN) method was applied for modeling and prediction of mean solar radiation in given atmospheric parameters (temperature, pressure, humidity, precipitable water and month) in Turkey (26–45oE and 36–42oN) during the period of 2004–2006. Levenberg-Marquardt (LM) learning algorithms and logistic sigmoid transfer function were used in the network. In order to train the ne...

The aim of this paper is to present the development and validation of a neural network model for on-line prediction of coagulant dosage from raw water characteristics. The main parameters influencing the coagulant dosage are firstly determined via a PCA. A brief description of the methodology used for the synthesis of neural models is given and experimental results are included. The training of...

142 Design and Optimization of Levenberg-Marquardt based Neural Network Classifier for EMG Signals to Identify Hand Motions Muhammad Ibn Ibrahimy, Md. Rezwanul Ahsan, Othman Omran Khalifa Dept. of ECE, Faculty of Engineering, International Islamic University Malaysia, 53100 Kuala Lumpur, Malaysia Dept. of Electrical Electronic and System Engineering, Faculty of Engineering and Built Environment...

Artificial neural networks are applied to model the manoeuvrability characteristics of a ship based on empirical information acquired from experiments with scaled model. This work aims evaluate performance proposed method training artificial network even very small quantity noisy data. The data used for consisted zig-zag and circle manoeuvres carried out in agreement IMO standards. wind effect ...

Brain-Computer Interfaces (BCIs) measure brain signals activity, intentionally and unintentionally induced by users, and provides a communication channel without depending on the brain’s normal peripheral nerves and muscles output pathway. Feature Selection (FS) is a global optimization machine learning problem that reduces features, removes irrelevant and noisy data resulting in acceptable rec...

Multilayer perceptron neural networks continue to be very useful in many fields. Unfortunately training these networks is still a major problem due to lack of convergence. This converge problem stems from the fact that the error curve has vast flat regions, making even higher-order algorithms move the network weights very slowly. In an ongoing series of papers including this one we propose a ne...

The potential of the product unit neural networks built by using different memetic evolutionary algorithms for the simultaneous determination of mixtures of analytes based on dynamic responses was investigated. For this purpose, three methodologies for obtaining the structure and weights of neural networks were proposed, based on the combination of the evolutionary programming algorithm, a clus...

The designing of advanced driver assistance systems and autonomous vehicles needs measurement of dynamical variations of vehicle, such as acceleration, velocity and yaw rate. Designed adaptive controllers to control lateral and longitudinal vehicle dynamics are based on the measured variables. Inertial MEMS-based sensors have some benefits including low price and low consumption that make them ...