trained artificial neural networks
Adaptive Neural Network Controller for Modeling Link Quality in WSANs
... off-line trained artificial neural networks (ANNs) for an adaptive controller to compensate the packet loss and delay which almost occur in the channels of wireless sensor/actuator ...
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Parkinson’s Disease Voice Diagnosis System (PDVDS) Through Pso Trained Neural Networks
... Optimization trained Artificial Neural Networks) voice analysis system is one of the techniques to the detection of PD with high ...the Artificial Neural ...
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Deep learning accurately predicts white shark locomotor activity from depth data
... We trained artificial neural networks (ANNs) to predict ODBA from univariate depth (pressure) data from two free‑swimming white sharks (Carcharodon ...
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Title: CLASSIFICATION ON BREAST CANCER USING GENETIC ALGORITHM TRAINED NEURAL NETWORK
... The neural system is arranged into hidden layers, input and output of the Artificial Neural Networks (ANNs). The neurons are joined together by a series of synaptic weights. An ANN is a ...
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Implementation of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms for the Multi-Objective Property Prediction and Optimization of Emulsion Polymers
... single neural network while separate classification networks were trained for each of the categorical properties resulting in a total of six neural ...the neural networks were ...
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Cotton genotypes selection through artificial neural networks.
... The artificial neural networks presented a high capacity of correct classification of the 20 selected genotypes based on the fiber quality index, so that when using fiber length associated with the ...
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Experimental Investigation of Classification Algorithms for Predicting Lesion Type on Breast DCE MR Images
... as artificial neural networks, support vector machines and artificial bee colony optimization algorithm trained neural ...The neural network trained by a bee colony ...
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Class Identification of Aircrafts by Means of Artificial Neural Networks Trained with Simulated Radar Signatures.
... Figure 10 shows the processed simulated profiles used for training, validating and testing the networks, and the differences among classes can be appreciated. This process is done offline and should not be taken ...
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Improving Post-Filtering of Artificial Speech Using Pre-Trained LSTM Neural Networks
... using networks such as DBNs, and also RBM has been previously studied ...Recurrent Neural Networks (RNNs) was presented in [32], in contrast to standard feedforward networks or models like ...
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Creep modelling of polypropylenes using artificial neural networks trained with Bee algorithms
... of artificial neural networks trained by one of very recently developed metaheuristic algorithms, namely hybrid Bee algorithm, to achieve high precision in the estimation of expected ...is ...
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Prediction of Compressive Strength of Concrete using Artificial Neural Network
... any. Artificial Neural Network (ANN) is used to predict the compressive strength of ...the networks. Networks are trained and tested at various learning rate and momentum factor and ...
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Correlation analysis and prediction of personality traits using graphic data collections
... social networks are ...of artificial neural ...investigated, artificial neural networks are used as a ...convolutional neural network, trained on the images and ...
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Accelerated optimizations of an electromagnetic acoustic transducer with artificial neural networks as metamodels
... the artificial neural networks as the meta- models of an omnidirectional EMAT, including the multilayer feedforward networks trained with the basic and improved back propagation ...
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Deep Neural Models for Medical Concept Normalization in User Generated Texts
... Table 2 shows our results for the concept normal- ization task on the Random and Custom KFolds of the CADEC, PsyTAR, and SMM4H 2017 corpora. To answer RQ2, we compare the performance of examined neural models with ...
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A COMPARATIVE ANALYSIS OF THE RESULTS OF CONVENTIONAL AND COMBINED METHODS OF TRAINING DIRECT PROPAGATION NEURAL NETWORK IN HEALTHY PERSONS TO DETECTING THE DEGREE OF ACTIVITY OF AN AUTONOMOUS NERVOUS SYSTEMFelix A. Pyatakovich*, Lubov V. Khlivnenko, Tatyana I. Yakunchenko, Kristina F. Makkonen, Olga V. MevshaDOWNLOAD/VIEW
... an artificial neural network trained with the help of back error propagation, and of that trained by combining a back error propagation algorithm and a variant of Cauchy stochastic training, ...
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Prediction of Stock Prices Using Artificial N...
... defines artificial neural networks, section III describes the applications of neural networks, section IV shows the characteristics of artificial neural networks, ...
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EEG Signal Classification into Seizure and Non-Seizure Class using Discrete Wavelet Transform and Artificial Neural Network
... Epilepsy is a common chronic neurological disorder. Epilepsy seizures are result of transient and unexpected electrical disturbance of brain. About 50 million people worldwide have epilepsy, and nearly two out of every ...
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A Study on Effective Algorithm for Medical Decision Making System
... or artificial neural networks, which are part of artificial ...A neural network has the ability to mimic this type of decision-making process, and use a knowledge base of information, ...
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On the application and design of artificial neural networks for motor fault detection. II.
... P of the use of artificial neural networks in motor fault detection applications. In Part I1 of this paper, we will discuss how to design an artificial neural network for[r] ...
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The Influence of Composite Laminate Stacking Sequence on Failure Load of Bonding Joints Using Experimental and Artificial Neural Networks Methods
... Failure load prediction of single lap adhesive joints using artificial neural networks. Aydın, An artificial neural network model for predicting compression strength of heat [r] ...
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