Abstract Uncontrolled population increase has a direct impact on the development of human resource quality, and rate economic growth is becoming increasingly challenging. It was purpose study to apply artificial intelligence tools examine forecasts active family planning participants. Between 2019 2020, Pematangsiantar’s participants will be counted as part dataset. The dataset gathered through...

Image compression technique is used to reduce the number of bits required in representing image, which helps to reduce the storage space and transmission cost. In the present research work back propagation neural network training algorithm has been used. Back propagation neural network algorithm helps to increase the performance of the system and to decrease the convergence time for the trainin...

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...

We describe a new method for recognizing humans by their gait using back propagation neural network(BPNN), BPNN algorithm is used to recognize humans by their gait patterns. Automatic gait recognition using Fourier descriptors and independent component analysis (ICA) for the purpose of human identification at a distance. Firstly, a simple background generation algorithm is introduced to subtrac...

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 ...

Image deblurring is the process of obtaining the original image by using the knowledge of the degrading factors. Degradation comes in many forms such as blur, noise, and camera misfocus. A major drawback of existing restoration methods for images is that they suffer from poor convergence properties; the algorithms converge to local minima, that they are impractical for real imaging applications...

A new learning strategy is proposed for training of radial basis functions (RBF) network. We apply two different local optimization methods to update the output weights in training process, the gradient method and a combination of the gradient and Newton methods. Numerical results obtained in solving nonlinear integral equations show the excellent performance of the combined gradient method in ...

Many recent studies have used artificial neural network algorithms to model how the brain might process information. However, back-propagation learning, the method that is generally used to train these networks, is distinctly "unbiological." We describe here a more biologically plausible learning rule, using reinforcement learning, which we have applied to the problem of how area 7a in the post...