In this paper a discrete competitive neural network is introduced to calculate the optimal robot arm movements for processing a considered commitment of tasks, using the branch and bound methodology. A special method based on the branch and bound methodology, modified with a travelling path for adapting in the neural network, is introduced. The main neural network of the system consists of diff...

Compressive strength and concrete slump are the most important required parameters for design, depending on many factors such as concrete mix design, concrete material, experimental cases, tester skills, experimental errors etc. Since many of these factors are unknown, and no specific and relatively accurate formulation can be found for strength and slump, therefore, the concrete properties ca...

abstract prediction of input flow into water resources is regarded as one of the most important issues in optimum planning and management in producing electro-water energy and optimum allocation of water into different consumption sources. different parameters affect on input discharge into dams. climate variables including temperature and rainfall have the most effect on input runoff rate to w...

the aim of this paper is how to predict stock return by using accounting ratios and also by using the procedure of neural network. this paper has considered the prediction of stock return by using accounting ratios with two procedures, the artificial neural network and least square regression. the independent variables in this paper are accounting ratios and dependent variable of stock return, ...

    The article attempts to have neural network and genetic algorithm techniques present a model for classification on dataset. The goal is design model can the subject acted a firewall in network and this model with compound optimized algorithms create reliability and accuracy and reduce error rate couse of this is article use feedback neural network and compared to previous methods increase a...

    The article attempts to have neural network and genetic algorithm techniques present a model for classification on dataset. The goal is design model can the subject acted a firewall in network and this model with compound optimized algorithms create reliability and accuracy and reduce error rate couse of this is article use feedback neural network and compared to previous methods increase a...

We propose a method (TT-GP) for approximate inference in Gaussian Process (GP) models. We build on previous scalable GP research including stochastic variational inference based on inducing inputs, kernel interpolation, and structure exploiting algebra. The key idea of our method is to use Tensor Train decomposition for variational parameters, which allows us to train GPs with billions of induc...