Breast cancer diagnosis has been approached by various machine learning techniques for many years. This paper presents a study on classification of Breast cancer using Feed Forward Artificial Neural Networks. Back propagation algorithm is used to train this network. The performance of the network is evaluated using Wisconsin breast cancer data set for various training algorithms. The highest ac...

In this paper, we describe a genetic algorithm (GA) based approach for learning connection weights for an artificial neural network (ANN). We use simulated data sets to compare the GA based approach for learning connection weights against the traditional back-propagation algorithm. Our results indicate that GA based training of ANN has a higher reliability (in terms of over-fitting the training...

a back propagation artificial neural network (bpann) is a well-known learning algorithmpredicated on a gradient descent method that minimizes the square error involving the networkoutput and the goal of output values. in this study, 261 gps/leveling and 8869 gravity intensityvalues of iran were selected, then the geoid with three methods “ellipsoidal stokes integral”,“bpann”, and “collocation” ...

In the previous study, we have proposed a template design method of cellular neural networks with back propagation algorithm. In that method, template learns by using the average error which corresponds to the difference between the output image and the desired image. In this study, we modify the back propagation algorithm for cellular neural networks template design. We inspect the performance...

An adaptive back-propagation algorithm is studied and compared with gradient descent (standard back-propagation) for on-line learning in two-layer neural networks with an arbitrary number of hidden units. Within a statistical mechanics framework , both numerical studies and a rigorous analysis show that the adaptive back-propagation method results in faster training by breaking the symmetry bet...

We investigate a digital back-propagation simplification method to enable computationally-efficient digital nonlinearity compensation for a coherently-detected 112 Gb/s polarization multiplexed quadrature phase shifted keying transmission over a 1,600 km link (20 x 80 km) with no inline compensation. Through numerical simulation, we report up to 80% reduction in required back-propagation steps ...

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...

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...

Metaheuristic algorithm such as BAT algorithm is becoming a popular method in solving many hard optimization problems. This paper investigates the use of Bat algorithm in combination with Back-propagation neural network (BPNN) algorithm to solve the local minima problem in gradient descent trajectory and to increase the convergence rate. The performance of the proposed Bat based Back-Propagatio...