Estimation of the nonlinear buckling capacity of thin walled shells is one of the most important aspects of structural mechanics. In this study the axial buckling load of 132 stiffened shells were numerically calculated. The applicability of artificial neural networks (ANN) in predicting the buckling capacity of vertically stiffened shells was studied. To this end feed forward (FF) multi-layer ...

This paper concerns dynamic neural networks for signal processing: architectural issues are considered but the paper focuses on learning algorithms that work on-line. Locally recurrent neural networks, namely MLP with IIR synapses and generalization of Local Feedback MultiLayered Networks (LF MLN), are compared to more traditional neural networks, i.e. static MLP with input and/or output buffer...

This paper concerns dynamic neural networks for signal processing: architectural issues are considered but the paper focuses on learning algorithms that work on-line. Locally recurrent neural networks, namely MLP with IIR synapses and generalization of Local Feedback MultiLayered Networks (LF MLN), are compared to more traditional neural networks, i.e. static MLP with input and/or output buffer...

estimate of sediment load is required in a wide spectrum of water resources engineering problems. the nonlinear nature of suspended sediment load series necessitates the utilization of nonlinear methods to simulate the suspended sediment load. in this study artificial neural networks (anns) are employed to estimate daily suspended sediment load. two different ann algorithms, multi layer percept...

The Multi-Layered Perceptron (MLP) Neural networks have been very successful in a number of signal processing applications. In this work we have studied the possibilities and the met difficulties in the application of the MLP neural networks for the prediction of daily solar radiation data. We have used the PolackRibière algorithm for training the neural networks. A comparison, in term of the s...

Wind speed high-precision prediction is one of the most important technical aspects to protect the safety of wind power utilization. In this study, two new hybrid methods [FEEMD-MEA-MLP/FEEMD-GA-MLP] are proposed for the wind speed accurate multi-step predictions by combining FEEMD (Fast Ensemble Empirical Mode Decomposition), MEA (Mind Evolutionary Algorithm), GA (Genetic Algorithm) and MLP (M...

today, with the advanced statistical techniques and neural networks, predictive models of distribution have been rapidly developed in ecology. purpose of this research is to predict and map the distribution of tetranychus urticae koch (acari: tetranychidae) using mlp neural networks combined with genetic algorithm in surface of farm. population data of pest was obtained in 2016 by sampling in 1...

This paper presents an application of Multi-layer Perceptrons (MLP) neural networks to model the demographic characteristics of antenatal clinic attendees in South Africa. The method of cross-validation is used to examine the betweensample variation of neural networks for HIV prediction. MLP neural networks for classifying both the HIV negative and positive clinic attendees are developed and ev...

in this paper, the gain in ld-celp speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (pso) algorithms to optimize the structure and parameters of neural networks. elman, multi-layer perceptron (mlp) and fuzzy artmap are the candidate neural models. the optimized number of nodes in the first and second hidden layers of el...