in this paper, we present an application of evolved neural networks using a real coded genetic algorithm for simulations of monthly groundwater levels in a coastal aquifer located in the shabestar plain, iran. after initializing the model with groundwater elevations observed at a given time, the developed hybrid genetic algorithm-back propagation (ga-bp) should be able to reproduce groundwater ...

In this paper, back propagation is reinvestigated for an efficient evaluation of the gradient in arbitrary interconnections of recurrent subsystems. It is shown that the error has to be back-propagated through the adjoint model of the system and that the gradient can only be obtained after a delay. A faster version, accelerated back propagation, that eliminates this delay, is also developed. Va...

In this paper, we present an application of evolved neural networks using a real coded genetic algorithm for simulations of monthly groundwater levels in a coastal aquifer located in the Shabestar Plain, Iran. After initializing the model with groundwater elevations observed at a given time, the developed hybrid genetic algorithm-back propagation (GA-BP) should be able to reproduce groundwater ...

In this paper, we study the impact of the many core Graphics Processing Units (GPUs) system on the implementation of parallel algorithm for back-propagation neural network training. We provide a comparison between the running times taken on the GPU and on the conventional CPU to perform the training of a back-propagation neural network. We design and implement a back-propagation neural network ...

Card games are interesting for many reasons besides their connection with gambling. Bridge is being a game of imperfect information, it is a well defined, decision making game. The estimation of the number of tricks to be taken by one pair of bridge players is called Double Dummy Bridge Problem (DDBP). Artificial Neural Networks are Non – Linear mapping structures based on the function of the h...

The paper demonstrates performance enhancement using selective cloning on evolutionary neural network over the conventional genetic algorithm and neural back propagation algorithm for data classification. Introduction of selective cloning improves the convergence rate of the genetic algorithm without compromising on the classification errors. The selective cloning is tested on five data sets. T...

This paper investigates the ability of several alternate models of Artificial Neural Network (ANN) to predict the probability of occurrence of Hypertension (HT) in a mixed patient population. To do this a two-layer feed-forward neural network with 13 inputs and 1 output was created with a single hidden layer . Different types of networks structures, such as NEWFF (feed-forward back propagation ...

—Regarding to the problems of low rate of convergence and fault saturation for neural network classifier based on the algorithm of error back propagation during the signal recognition, bee colony algorithm is applied in this paper so as to extract combined feature module of signal and suggest three different algorithms including algorithm with rapidly support, super self-adaption error back pr...

Back propagation is one of the well known training algorithms for multilayer perceptron. However the rate of convergence in back propagation learning tends to be relatively slow, which in turn makes it computationally excruciating. Over the last years many modifications have been proposed to improve the efficiency and convergence speed of the back propagation algorithm. The main emphasis of thi...