Introduction to artificial neural systems
Introduction to artificial neural systems
Forecasting time series with a new architecture for polynomial artificial neural network
Applied Soft Computing
Application of neural networks and fuzzy logic models to long-shore sediment transport
Applied Soft Computing
Comparison of artificial neural network architecture in solving ordinary differential equations
Advances in Artificial Neural Systems
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In this paper, a regression based artificial neural network model for multi input single output (MISO) systems has been developed. The time devoted in training this model is considerably less in comparison with the traditional ANN model and the number of neurons in the hidden layer can be fixed by choosing a regression polynomial of desired degree. The proposed model has been used and simulated for an example problem of transverse vibrations of plates viz. vibration of circular and elliptic annular plates. There exist nine different boundary conditions for the present example problem which are all simulated using the model. The training and testing with unseen patterns show the efficacy and reliability of the proposed technique for the MISO systems. Comparison of the proposed model with the traditional ANN model shows the former to be better and much efficient.