a sigmoid function is necessary for creation a chaotic neural network (cnn). in this paper, a new function for cnn is proposed that it can increase the speed of convergence. in the proposed method, we use a novel signal for controlling chaos. both the theory analysis and computer simulation results show that the performance of cnn can be improved remarkably by using our method. by means of this...

A sigmoid function is necessary for creation a chaotic neural network (CNN). In this paper, a new function for CNN is proposed that it can increase the speed of convergence. In the proposed method, we use a novel signal for controlling chaos. Both the theory analysis and computer simulation results show that the performance of CNN can be improved remarkably by using our method. By means of this...

The classification of various document images is considered an important step towards building a modern digital library or office automation system. Convolutional Neural Network (CNN) classifiers trained with backpropagation are considered to be the current state of the art model for this task. However, there are two major drawbacks for these classifiers: the huge computational power demand for...

In this paper, we study the dynamic range of multi-layer cellular neural networks (CNN’s) by using Lyapunov functions. A theorem is presented to guarantee the existence of equilibrium point of multi-layer CNN. A theorem on globally stable equilibrium point of multi-layer CNN is given. Multi-layer CNN’s are used to model some functions of nitric oxide (NO) in nervous systems. In a 2-layer CNN mo...

Groenhoff, MSN, MBA, RN, CNN (South Florida Flamingo Chapter), Sue McManus, MSN, CFNP, CNN (Acadiana Chapter); and Janie Martinez, BSN, RN, CCRN, CNN (Alamo Chapter); ANNA members Emily Hodgin, BSN, RN, CNN, CPHQ (Cardinal Chapter); Carmen Mejias, BSN, RN, CNN (Heart of Texas Chapter); Betsy King, MSN, RN, CNN; (Northeast TriState Chapter); Darene Hall, MSN, RN, CNN (Chumash Chapter); and Glady...

A systematic design methodology for finding CNN parameters with prescribed functions is proposed. A given function (task) is translated into several local operations, and they are realized as stable states of the CNN system. Many CNN parameters (CNN genes) with the same functions can be easily derived by using this design methodology. A genetic algorithm based CNN gene design methodology is als...

Deep Convolutional Neural Networks (CNN) have exhibited superior performance in many visual recognition tasks including image classification, object detection, and scene labeling, due to their large learning capacity and resistance to overfit. For the image classification task, most of the current deep CNNbased approaches take the whole size-normalized image as input and have achieved quite pro...

This study investigated the combined prognostic value of pretreatment anemia and cervical node necrosis (CNN) in patients with nasopharyngeal carcinoma (NPC). Retrospective review of 1302 patients with newly diagnosed nonmetastatic NPC treated with intensity-modulated radiotherapy (IMRT) ± chemotherapy. Patients were classified into four groups according to anemia and CNN status. Survival was c...