In this work an approach for time series forecasting by simulating stochastic processes through time lagged feed-forward neural network is presented. The learning rule used to adjust the neural network (NN) weights is based on the Levenberg-Marquardt method. In function of the long or short term stochastic dependence of the time series, an on-line heuristic law to set the training process and t...

The discrete-time system of multilayer composite plate is modeled using neural network (NN) to produce a nonlinear exogenous autoregressive moving-average model (NARMAX). The model is implemented by training a NN with input-output experimental data. Each damaged sample can be modeled by a parameter governed by the propagation behaviors of the NN. A residual signal is evaluated from the differen...

In this paper, direct adaptive neural network (NN) control is developed for a class of multi-input and multi-output (MIMO) nonlinear systems in discrete-time. To solve the difficulty of nonaffine appearance of control, implicit function theorem is exploited to assert the existence of an ideal desired feedback control (IDFC). Then, high-order-neural-network (HONN) is employed to approximate the ...

According to recent advances in Digital devices, the problem of image noise reduction becomes more significant than ago. Median filter (MF), as an efficient solution for this problem, has been widely applied in practice. In this paper, to improve the quality of filtered image, using a Neural Network (NN) is proposed. A NN, which is trained in a real time manner, can be estimated the noise densi...

A hybrid system (hidden neural network) based on a hidden Markov model (HMM) and neural networks (NN) was trained to predict the bonding states of cysteines in proteins starting from the residue chains. Training was performed using 4136 cysteine-containing segments extracted from 969 non-homologous proteins of well-resolved 3D structure and without chain-breaks. After a 20-fold cross-validation...

In this paper, the problems faced in the constrained force control is studied (uncertainties in dynamic model and the unknown constraints). A neural network (NN) controller is proposed based on the derived dynamic model of robot in the task space. The feed-forward neural network is used to adaptively compensate for the uncertainties in the robot dynamics. Training signals are proposed for the f...

An algorithm based on the radiance transfer model (RM) and a dynamic learning neural network (NN) for estimating water vapor content from moderate resolution imaging spectrometer (MODIS) 1B data is developed in this paper. The MODTRAN4 is used to simulate the sun-surface-sensor process with different conditions. The dynamic learning neural network is used to estimate water vapor content. Analys...

Creation procedure of associative patterns ensemble in terms of formal logic with using neural network (NN) model is formulated. It is shown that the associative patterns set is created by means of unique procedure of NN work which having individual parameters of entrance stimulus transformation. It is ascertained that the quantity of the selected associative patterns possesses is a constant.

This paper presents a neural network methodology to retrieve wind vectors from ERS1 scatterometer data. First a neural network (NN-INVERSE) computes the most probable wind vectors. Probabilities for the estimated wind direction are given. At least 75 % of the most probable wind directions are consistent with ECMWF winds (at ± 20°). Then the remaining ambiguities are resolved by an adapted PRESC...

Empirical Mode Decomposition (EMD), recently proposed by Huang et al. [12], appears to be a novel data analysis method for nonlinear and non-stationary time series. By decomposing a time series into a small number of independent and concretely implicational intrinsic modes based on scale separation, EMD explains the generation of time series data from a novel perspective. This paper presents an...