Sentence examples for architecture input from inspiring English sources

The software code, its architecture, input and output files, the solution algorithm, flow diagrams and source programs are described in detail.

The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs.

This paper presents a new hybrid evolutionary algorithm for determining both architecture (input variables and neurons of hidden layer) and network parameters (centers, width and weights) of radial basis function neural networks (RBFNNs) simultaneously.

In the complete receiver architecture, input memories of the turbo decoder are duplicated to allow buffering of next input streaming frame from the demapper while executing the iterative turbo decoding on the current received frame.

It is worth noting that in the complete receiver architecture, the input memories of the turbo decoder are duplicated to allow buffering of next input streaming frame from the demapper while executing the iterative turbo decoding on the current received frame.

Its unique architecture allows input and output formats to be defined independent of each other by two separate programs.

The choice of a neural network architecture and input features can pose a problem for a novice user.

In this paper, we propose a deep neural network (DNN) architecture called Input Output Deep Architecture (IODA) for solving the problem of image labeling.

Each of the three constituent QBDSONNs in the proposed network architecture comprises input, hidden or intermediate, and output layers of neurons.

In this study, as a part of determining the optimal network architecture, important input parameters related to the output has been sorted out first.

Under accurate premise, the simple architecture (less input and hidden nodes) of network model is constructed in order to improve networks' adaptation and generalization ability, and to greatly reduce the subjective choice of structural parameters.