FFNNFeed Forward Neural Network
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ANFIS, FFNN, FITNET, CFNN, GRNN, CART, SVR and MLR models were trained and tested with the dataset of atomic coordinates for CNTs.
In this paper, we propose to use the integration of FFNN, linear discriminate analysis (LDA), SVM, and deep neural network (DNN) algorithms for scalable and accurate positioning, as proposed in our previous work [26].
Then, to identify the wormhole attack, we propose the Wormhole Resistant Hybrid Technique (WRHT) with fuzzification method along FFNN. The proposed WRHT allows the source node in the sensor network to calculate the wormhole presence probability (WPP) for a path in addition to HC (Hop Count) information.
A multilayer FFNN is used in this research for type identification and localization of fault within network.
As in the FFNN, the backpropagation algorithm adjusts the weights, but the architecture includes a connection from the inputs and every layer to following layers.
In this paper we have proposed a simple competitive neural network which has been trained using different unsupervised learning techniques that looks more natural than the previously proposed feed forward neural network (FFNN) based techniques.
From a detailed discussion on the results obtained by applying FFNN RBF network it is clear that the performance of the RBF networks is better than the conventional type PSSs.
To obtain more insight into the modeling capability of k-NN, another widely used nonlinear modeling technique the feed-forward neural network (FFNN) (Araghinejad et al., 2011; Deka et al., 2012; Kuo-Lin, 2011; Vafakhah, 2012; Wu and Chau, 2010) was employed.
The classification accuracy of the proposed Normalized Cubic Spline Feed Forward Neural Network (NCS- FFNN) is compared with RBF, CART and MLP.
Como es comun en lenguas con ausencia de una marca distinta para acusativo y dativo (vease Blansitt, 1984; Bossong, 1991; Malchukov, 2008), en p'orhepecha algunas FFNN con funcion de paciente/tema pueden prescindir de la marca de caso, es decir, la lengua presenta el fenomeno de marcacion diferencial de objeto (MDO)(Comrie, 1989: 128; Aissen, 2003; Croft, 2003: 132, 166, 167).
Discrete-Time Recurrent Neural Networks based controllers were tested, with two variants: plastic neural networks (PNN) and standard feed-forward (FFNN) networks.