Sentence examples for both the neural network from inspiring English sources

Generally speaking both the neural network and the fuzzy systems are either insufficient or overcomplicated.

Both the neural network model and the controller were tested for each patient under simulation, and the results obtained show a good performance during food intake and variable exercise conditions.

Although Hillas algorithms may be implemented in software, it is impossible to implement both the neural network and the preprocessing step in the same manner.

Signal peptide predictions were performed by SignalP version 3.0 [44], [44], using both the neural network method and the hidden markov model methods at default cutoffs.

Therefore, we identified signal sequences in the proteins with signalP, using both the neural network and HMM methods [ 74, 75].

In this study we ran all the 93 protein sequences through the web-based SignalP 3.0 program, using both the neural network (NN) and hidden Markov model (HMM) algorithms to predict putative cleavage sites [ 31].

The two regions included in our multiple linear regression model -- the left OFC/In and the right PCG/ACC -- are both part of the neural network associated with response inhibition and selective attention [ 40- 43].

Satisfactory results are obtained in both cases, proving that the neural network based modeling is an appropriate technique and the DE algorithm is able to lead to the near-optimal neural network topology.

On both desktop and mobile, the neural network tries to guess what your pitiful scribbles are like a visual search engine, suggesting several options with a wide margin of error by professional artists from seven different design studios.

Sequences were identified as putatively secretory, predicted with signal peptides if both D-score in the neural network model and prediction probability in the hidden Markov model were significant.

"Our models must be able to perform the task — to fail and succeed — similarly," he says, "and predict the brain-activity patterns we measure". He shows the same images and movies to both the human participants and the neural network models and compares their internal representations of those scenes.