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[PDF] Top 20 CONSTRUCTIVE NEURAL NETWORKS: A REVIEW

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CONSTRUCTIVE NEURAL NETWORKS: A REVIEW

CONSTRUCTIVE NEURAL NETWORKS: A REVIEW

... conventional neural networks, we have to define the architecture prior to training but in constructive neural networks the network architecture is constructed during the training ...we ... See full document

9

As redes neurais artificiais e o ensino da medicina.

As redes neurais artificiais e o ensino da medicina.

... artificial neural networks (ANN) – computer systems with a mathematical structure inspired by the human brain – which proved to be useful in the evaluation process and the acquisition of knowledge among ... See full document

9

A RECURRENT ELMAN NEURAL NETWORK - BASED APPROACH TO DETECT THE PRESENCE OF EPILEPTIC ATTACK IN ELECTROENCEPHALOGRAM (EEG) SIGNALS

A RECURRENT ELMAN NEURAL NETWORK - BASED APPROACH TO DETECT THE PRESENCE OF EPILEPTIC ATTACK IN ELECTROENCEPHALOGRAM (EEG) SIGNALS

... to review the EEG data is reduced considerably due to ...using neural networks, namely, Elman neural network using a time-domain feature of the EEG signal called Approximate Entropy (ApEn)[5] ... See full document

4

Ica and neural networks for kannada signature identification

Ica and neural networks for kannada signature identification

... A review of handwritten signature verification systems and methodologies is described by Padmanjali and Aprameya ...A review of research work and methodologies in the field of handwritten signature ... See full document

8

A REVIEW OF INTELLIGENT CONTROL SYSTEMS APPLIED TO THE INVERTED-PENDULUM PROBLEM

A REVIEW OF INTELLIGENT CONTROL SYSTEMS APPLIED TO THE INVERTED-PENDULUM PROBLEM

... To show one of the applications of Neural Networks (NN) in the inverted pendulum problem. Now it was applied to the system under consideration. In first place, one must make some experiments and acquire ... See full document

47

Estimating Bankruptcy Using Neural Networks Trained with Hidden Layer Learning Vector Quantization

Estimating Bankruptcy Using Neural Networks Trained with Hidden Layer Learning Vector Quantization

... a neural network trained with one or two hundred observations, as was reported by some authors previous mentioned in the literature ...the neural network should have a number of connection weights not much ... See full document

24

A Framework for the Merger and Practical Exploitation of Formal Logic and Artificial Neural Networks

A Framework for the Merger and Practical Exploitation of Formal Logic and Artificial Neural Networks

... Artificial Neural Network technology into a formal software development ...artificial neural network model is employed to refine an existing formal software model in order to produce increasingly better ... See full document

6

Identification of the non-linear systems using internal recurrent neural networks

Identification of the non-linear systems using internal recurrent neural networks

... Figure no.1 The block schema of the system The block schemas represented in the figures above suggest two neural networks structures which will be used at the identification of a system. In the following, ... See full document

8

Estimating soybean yields with artificial neural networks

Estimating soybean yields with artificial neural networks

... ABSTRACT. The complexity of the statistical models used to estimate the productivity of many crops, including soybeans, restricts the use of this practice, but an alternative is the use of artificial neural ... See full document

9

Generation of Lyapunov Functions by Neural Networks

Generation of Lyapunov Functions by Neural Networks

... Stability of nonlinear dynamic systems plays an important role in systems theory and engineering. There are several approaches in the literature addressing this problem. The most useful and general approach for studying ... See full document

5

Flood routing modelling with Artificial Neural Networks

Flood routing modelling with Artificial Neural Networks

... This contribution presented a new methodology to com- bine hydrodynamic numerical modelling with artificial intel- ligence. The goal is to overcome both the restricted extrap- olation capabilities of artificial ... See full document

6

Applications of neural networks to control systems

Applications of neural networks to control systems

... artificial neural network, multilayer perceptrons ...the neural PID tuner mimics an experienced plant operator, with the advantage that several iterations are not needed for ... See full document

202

Attribute-value inference using deep neural networks

Attribute-value inference using deep neural networks

... Supervised, semi-supervised and unsupervised machine learning are the meth- ods used to recognize and tag named entities, standing out for the huge adoption of supervised and semi-supervised methods where Hidden Markov ... See full document

78

Spatial predictive mapping using artificial neural networks

Spatial predictive mapping using artificial neural networks

... Artificial neural networks (ANN) allow a multivariate data analysis of complex problems. Their ability to learn from given examples without an explicit programming of problem solution allows a ubiquitous ... See full document

8

Fuzzy nonlinear regression using artificial neural networks

Fuzzy nonlinear regression using artificial neural networks

... artificial neural network (ANN) technique with error- back- propagation algorithm has been successfully used to provide prediction of ISMR on monthly and seasonal time scales (Sahai et al, ... See full document

11

Ribosome binding site recognition using neural networks

Ribosome binding site recognition using neural networks

... the networks, characterizing a non-conventional ...the networks to represent the RBS concept, aiming to reduce drastically the training ...the networks, together with those extreme sequences, al- ... See full document

7

Evaluation of deciduous broadleaf forests mountain using satellite data using neural network method near Caspian Sea in North of Iran

Evaluation of deciduous broadleaf forests mountain using satellite data using neural network method near Caspian Sea in North of Iran

... fuzzy neural networks technique has been conducted to separate classes of forest from non-forest using SPOT satellite neural network method with the accuracy of 64% and kappa coefficient of ... See full document

6

Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density

Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density

... Abstract – The objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation ... See full document

9

Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

... Neural networks [28] are nonparametric nonlinear regression models which can be fit to highly nonlinear data ...a neural network model consists of lay- ers that contains several nodes, conducting the ... See full document

13

Correction: Persistent Activity in Neural Networks with Dynamic Synapses.

Correction: Persistent Activity in Neural Networks with Dynamic Synapses.

... Correction: Persistent Activity in Neural Networks with Dynamic Synapses Omri Barak, Misha Tsodyks.. doi: 10.1371/journal.pcbi.0030035[r] ... See full document

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