application of computer networking using neural network

... nervous system of animal Most of the neural network structures used presently are static (feed- Chapter Introduction forward) neural networks [28] These neural networks that have a number of neurons ... concentration of food or toxin Cmax,f Maximum concentration of food Cf Concentration of food Ctx Concentration of toxin Clef t Concentration of food or toxin on the left side Cright Concentration of food ... VC1 Voltage of CPG neuron C1 VC2 Voltage of CPG neuron C2 VC3 Voltage of CPG neuron C3 VC4 Voltage of CPG neuron C4 Llu,i Length of relaxed muscle i on left-up side Lld,i Length of relaxed muscle...

Ngày tải lên: 10/09/2015, 09:12

... node of MANN, see Fig In our implementation uses back-propagation Neural Network which has layers with the transfer function is sigmoid function [4] for SNN and CNN The number of hidden nodes of ... by all SNN(s) Output of CNNj is the jth output of MANN model It gives the probability of image in the jth class It is easy to see that to build MANN model only use Neural Network technology to ... Compare of image classification 0.8 In this paper, we have improved Multi Artificial Neural Network (MANN) with parameters (m,n,L) from MANN (m,L), where m is the number of feature vectors of pattern...

Ngày tải lên: 28/04/2014, 10:06

... downloading data off the Internet to e-commerce, networking has made our world much smaller and changed the way we communicate forever The Computer Network What is a Computer Network net·work: ... transmit or receive information Navigation Links Network Diagram – Basic Layout and map of a traditional computer network Network Types – Computer networks vary in shape and size depending on usage ... Component) Wired Network PC Firewall The Internet Fiber Optic Network Cable Router Switch Server Other LANS Wireless Network The Three Types of Networks Navigation Network Diagram Network Types...

Ngày tải lên: 24/10/2014, 13:22

... Surftest profilometer with a roughness cut-off of 0.8 mm The results of the first set of tests are reported in Tables and 4, while only the results of the 5th repetition of the second set of tests ... introduction to computing with neural nets IEEE ASSP &lag., 1987, April, [16] Simpson, P K., Foundations of neural networks Artificial Neural Network, Paradigms, Application and Hardware Implementations ... in Fig 8, a neural network consists of N neurons, each of which is connected to the neurons of the adjacent layers A threshold value 0j is associated with each neuron The output of each neuron...

Ngày tải lên: 25/11/2016, 21:53

... denition of the number of layers and neural nodes required for particular applications This study proposes the application of the design of experiments (DOE) methodology for the design of neural networks ... the main attractive in the solution of problems using articial neural networks, according to Braga, Carvalho, and Ludermir (2007) It is a main task of a neural network to learn a model from from ... value of mean S D Ratio for roughness average (Ra) Size of training set Algorithm for selection of spread of radial function Algorithm for calculation of centers of radial function Number of radial...

Ngày tải lên: 25/11/2016, 21:54

... Surftest profilometer with a roughness cut-off of 0.8 mm The results of the first set of tests are reported in Tables and 4, while only the results of the 5th repetition of the second set of tests ... introduction to computing with neural nets IEEE ASSP &lag., 1987, April, [16] Simpson, P K., Foundations of neural networks Artificial Neural Network, Paradigms, Application and Hardware Implementations ... in Fig 8, a neural network consists of N neurons, each of which is connected to the neurons of the adjacent layers A threshold value 0j is associated with each neuron The output of each neuron...

Ngày tải lên: 09/12/2016, 23:05

Ngày tải lên: 03/09/2012, 15:54

... for the neural network analysis D Contours classification using neural network We extract the above features which are helpful for classification of text and non-text area The large number of features ... Making use of neural networks will also make the features useful for all types of images because it need not set different thresholds for different type of images To train the neural network, we ... and show advantage of using relative alignment features in classifying our fanciful designed name card images We did some tests of using only the spatial features for the neural network and confirm...

Ngày tải lên: 05/11/2012, 14:54

... database grows Using the proposed fuzzy ART neural network, the system is capable of dealing with the appearance frequency of a specific form feature, while keeping the advantage of adaptive resonance ... based on deep models of features, Journal of Intelligent Manufacturing, 6(4):245-262 Lin, Z.-C and H Chang, (1996) Application of fuzzy set theory and back-propagation neural networks in progressive ... editing, Computers and Industrial Engineering, 32(2):383-397 Smith, S.D.G., et al., (1997) A deployed engineering design retrieval system using neural networks, IEEE Transactions on Neural Networks,...

Ngày tải lên: 17/12/2013, 06:15

... Era Generations of Computers 1.2 Computer Systems Computer Hardware 8 1.3 Data Representation 11 1.4 Computer Software 13 3 CONTENTS System Software 13 Application Software 14 Software Development ... components of computer hardware To be aware of how information is stored in binary form in computer memory To differentiate between system and application software To become aware of the seven phases of ... main memory 1.4 COMPUTER SOFTWARE SYSTEM SOFTWARE is software that manages the computer system and consists primarily of the operating system (e.g., Windows 2000) APPLICATION SOFTWARE are programs...

Ngày tải lên: 17/01/2014, 06:20

... case studies Using Neural Networks in HYSYS Neural Networks What is a Neural Network? A Neural Network (strictly an ‘Artificial Neural Network as opposed to a ‘Biological Neural Network ) is ... box’ approach Process Overview Using Neural Networks in HYSYS Using Neural Networks in HYSYS Steps for using Neural Networks in HYSYS The procedure for using Neural Networks in HYSYS is as follows: ... compared to those from the Neural Network model In this case, the error should be negligible for all of the variables Using Neural Networks in HYSYS Embed the Neural Network into the HYSYS Flowsheet...

Ngày tải lên: 23/03/2014, 02:20

... from a set C of possible POS tags The SNOW Approach The S N O W (Sparse Network Of Linear separators) architecture is a network of threshold gates Nodes in the first layer of the network represent ... testing corpus The Tagger Network The tagger network consists of a collection of linear separators, each corresponds to a distinct part of speech The input nodes of the network correspond to the ... Schmid 1994 Part -of- speech tagging with neural networks In COLING-94 H Schfitze 1995 Distributional part -of- speech tagging In Proceedings of the 7th Conference of the European Chapter of the Association...

Ngày tải lên: 23/03/2014, 19:20

... Taxonomy of Neural Networks Now that we have presented the basic elements of neural networks, we will give an overview of some different types of networks This overview will be organized in terms of ... unfounded With the advent of backpropagation, neural networks have enjoyed a third wave of popularity, and have now found many useful applications 3.2 Fundamentals of Neural Networks In this section ... review the fundamentals of neural networks There are many different types of neural networks, but they all have four basic attributes: • • • • A set of processing units; A set of connections; A computing...

Ngày tải lên: 28/04/2014, 10:18

... nested neural network layer permits an entire neural network to be added to the network being edited Using this component, it is possible to build modular neural networks, constituted of several ... both of the two script types On the left side of the editor there is the list of the available scripts for the neural network For a new network, the list already contains the definitions of all ... end of a round of training, adding noise may ‘jolt’ the netork out of a local minimum so that further training produces a better network 3.5.3 Randomize This reset the weights of a neural network, ...

Ngày tải lên: 29/04/2014, 14:52

... Tour of Brain Theory and Neural Networks 25 How to Use Part II 27 II.1 The Meta-Map 27 II.2 Grounding Models of Neurons and Networks 29 Grounding Models of Neurons 29 Grounding Models of Networks ... models of single neurons; analysis of a wide variety of neurobiological systems; “connectionist” studies; mathematical analyses of abstract neural networks; and technological applications of adaptive, ... activity of the neural network, control the movement of muscles and the secretion of glands In between, an intricate network of neurons (a few hundred neurons in some simple creatures, hundreds of...

Ngày tải lên: 11/06/2014, 16:35

... features (Section 2.1) and the neural network (Section 2.2) Section will define the considered problems: the quantization of the weights of the neural network, and use of approximations for the activation ... the effects of the finite precision over the features without affecting the final performance of the neural network Another important property of the MLP is related to the output of the network Considering ... by comparing the outputs of the neural network and looking for the greatest value, once the network is trained there is no need of determining the complete output of the network (zi ), being enough...

Ngày tải lên: 21/06/2014, 20:20

... both pairwise and standard multiclass neural networks were implemented in Matlab, using neural networks Toolbox The pairwise classifiers and the multiclass networks include hidden and output layers ... performances of both recognition systems trained on the datasets containing different number of samples per subject The numbers of these samples are given 12, 4.5 Impact of the number of classes in case of ... are capable of solving this classification problem However, practically the performance of a multiclass neural network is dependent on the initial weights as well as on the density of data samples...

Ngày tải lên: 22/06/2014, 19:20

... of our proposed neural network ensemble in Figure The ensemble consists of two layers: a set of sequentially trained component networks {hk | ≤ k ≤ N }, and a decision network g The outputs of ... FNET-C (b) Left: structure of FNET-C; right: structure of FNET-D Figure 2: Topology of four types of component networks (2) Decision neural network 2.3 For the decision network g (see Figure 1), ... ensemble consists of all component networks of FNET-D, plus a set of additional component networks that are variants of FNET-D These additional component networks allow overlapping of locally connected...

Ngày tải lên: 22/06/2014, 19:20

... using neural networks for image compression is given in [3] Several neural network architectures, such as multilayer perceptron, functional link neural network, and radial basis function network, ... both schemes, a portion of the ECG signal samples is used for training the MLP until the goal is reached The weights and biases of the trained neural network along with the network setup information ... for identical network setup The first p samples are also sent to the receiving end for prediction, where p is the order of prediction Prediction is done using the trained neural network at the...

Ngày tải lên: 22/06/2014, 20:20