We use deep feedforward artificial neural networks to approximate solutions of partial differential equations of advection and diffusion type in complex geometries. We derive analytical expressions of the gradients of the cost function with respect to the network parameters, as well as the gradient of the network itself with respect to the input, for arbitrarily deep networks. The method is bas...

This paper proposes an approach to solve binary classification problems using Duo Output Neural Network (DONN). DONN is a neural network trained to predict a pair of complementary outputs which are the truth and falsity values. In this paper, outputs obtained from two DONNs are aggregated and used to predict the classification result. The first DONN is trained to predict a pair of truth and fal...

This paper makes the detailed analyses of computational complexities and related parameters selection on our proposed constrained learning neural network root-finders including the original feedforward neural network root-finder (FNN-RF) and the recursive partitioning feedforward neural network root-finder (RP-FNN-RF). Specifically, we investigate the case study of the CLA used in neural root-f...

DNA microarray is a multiplex technology used in molecular biology and biomedicine. It consists of an arrayed series of thousands of microscopic spots of DNA oligonucleotides, called features, of which the result should be analyzed by computational methods. Analyzing microarray data using intelligent computing methods has attracted many researchers in recent years. Several approaches have been ...

This paper examines the e ectiveness of using a quasi-Newton based training of a feedforward neural network for forecasting. We have developed a novel quasi-Newton based training algorithm using a generalized logistic function. We have shown that a well designed feed forward structure can lead to a good forecast without the use of the more complicated feedback/feedforward structure of the recur...

The optimum design of solar energy systems strongly depends on the accuracy of  solar radiation data. However, the availability of accurate solar radiation data is undermined by the high cost of measuring equipment or non-functional ones. This study developed a feed-forward backpropagation artificial neural network model for prediction of global solar radiation in Makurdi, Nigeria (7.7322  N lo...

One of the most popular neural networks are multilayered feedforward neural networks, which represent the most standard configuration of biological inspired mathematical models of simplified neural system. These networks represent massive parallel systems with a high number of simple process elements and therefore it is natural to try to implement this kind of systems on parallel computer archi...

This paper proposes an evolutionary design methodology of multilayer feedforward neural networks based on constructive approach. We elaborate an adjustable processing element as primitive neuron model. The neural layer can be constructed by assembling several neurons. The multilayer neural network can be finally constructed through cascading several neural layers. The constructive approach faci...