This paper presents two innovative evolutionary-neural systems based on feed-forward and recurrent neural networks used for quantitative analysis. These systems have been applied for approximation of phenol concentration. Their performance was compared against the conventional methods of artificial intelligence (artificial neural networks, fuzzy logic and genetic algorithms). The proposed syste...

The least squares adjustment (LSA) method is studied as an optimisation problem and shown to be equivalent to the undamped Gauss-Newton (GN) optimisation method. Three problem-independent damping modifications of the GN method are presented: the line-search method of Armijo (GNA); the LevenbergMarquardt algorithm (LM); and Levenberg-Marquardt with Powell dogleg (LMP). Furthermore, an additional...

In this paper, the Artificial Neural Network (ANN) approach is applied for forecasting groundwater level fluctuation in Aghili plain,southwest Iran. An optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (GDM), levenberg marquardt (LM), resilient back propagation (RP), and scaled conjugate gradient (SCG). Rain,evaporation, relative...

A related input parameter is used in this case study to forecast solar thermal systems (STS) capabilities and compare which artificial neural network (ANN) algorithms other intelligence (AI) methods have the most reliable predictor for STS performance. In order gauge performance of STS, research aims implement AI predicting by comparing ANN technique with methods. Three different training are L...

Prediction of the residual useful life Lithium-ion batteries is one hotspots presently. In order to further obtain prediction Li-ion battery, degeneration data it obtained from university Maryland are analyzed. Discrete point filtering performed on degraded simplify processing. Due defects slow learning speed and easy fall into local minimum Back Proragation Neural Network (BPNN), fast Levember...

This article presents a new approach based on artificial neural networks (ANNs) to calculate the characteristic parameters of elliptic and circular-shaped microshield lines. Six learning algorithms, bayesian regularization (BR), Levenberg-Marquardt (LM), quasiNewton (QN), scaled conjugate gradient (SCG), resilient propagation (RP), and conjugate gradient of Fletcher-Reeves (CGF), are used to tr...

Abstract: In testing digital waveform recorders, an important part is to fit a sinusoidal model to recorded data, and calculate the parameters that result in the best fit. Methods are already standardized; however, they demand high computational power. In this article a new, quick and accurate sinefitting algorithm will be shown based on Levenberg-Marquardt (LM) method. The constraints of conve...

Traditional learning algorithms with gradient descent based technique, such as back-propagation (BP) and its variant Levenberg-Marquardt (LM) have been widely used in the training of multilayer feedforward neural networks. The gradient descent based algorithm may converge usually slower than required time in training, since many iterative learning step are needed by such learning algorithm, and...

This paper investigates artificial neural networks prediction modeling of foreign currency rates using Levenberg Marquardt (LM) learning algorithms. The models were trained from historical data using US Dollar (USD) currency rates against Indonesian Rupiah (IDR). The forecasting performance of the models was evaluated using a number of statistical measurements and compared. The results show tha...

OBJECTIVE In this study, artificial neural network (ANN) analysis of virotherapy in preclinical breast cancer was investigated. MATERIALS AND METHODS In this research article, a multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated in order to develop a predictive model. The input parameters of the model were virus dose, week and tamoxifen ci...