multilayer feedforward neural networks
Prediction of Henry's law constants for organic compounds using multilayer feedforward neural networks based on linear solvation energy relationship
... using Multilayer Feedforward Neural Networks combined with linear solvation energy relationship to predict the Henry's law constants of organic compounds is a easier and more precise method ...
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Using artificial neural network to monitor and predict induction motor bearing (IMB) failure
... Backpropagation learning algorithm is one of the earliest and the most common method for training multilayer feedforward neural networks. Development of this learning algorithm was one of the ...
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Research status and applications of nature-inspired algorithms for agri-food production
... the neural network process models were inversed through numerical optimization to design and implement model predictive controllers to handle the nonlinearity and input-output time lags of the process [46-50] ...
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Accelerated optimizations of an electromagnetic acoustic transducer with artificial neural networks as metamodels
... artificial neural networks as the meta- models of an omnidirectional EMAT, including the multilayer feedforward networks trained with the basic and improved back propagation algorithms ...
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A Survey On Backpropagation Algorithms For Feedforward Neural Networks
... a multilayer feed-forward neural network and the desired ...Artificial neural networks(ANN) and comparisons with new methods like genetic algorithms(GA) and showing why it is still effective ...
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Prediction of thermophysical properties of oxygen using linear prediction and multilayer feedforward neural network
... Artificial Neural Networks (ANNs) to predict the thermophysical properties of the chemical ...and Multilayer Feedfoward Neural Network (MLFN) ...
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Date Fruits Classification using MLP and RBF Neural Networks
... Learning weights of the MLP by Backpropagation algorithm has two phases. First, a training input pattern is presented to the network input layer. The network propagates the input pattern from layer to layer until output ...
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Towards comprehensive foundations of computational intelligence
... For feedforward neural networks such framework involves possible architectures, from simplest to more complex, and a taxonomy of different types of transfer functions [57, 58], allowing for ...
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Behavior Emergence in Autonomous Robot Control by Means of Feedforward and Recurrent Neural Networks
... In the following sections we introduce multilayer percep- tron networks (MLP), Elman’s networks (ELM) and ra- dial basis function networks (RBF). Then we take a look at Khepera robots and ...
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Economic classification and regression problems and neural networks
... Multilayer feedforward networks form an important class of neural networks (see for example haykin 1999). The network consists of a set of sensory units (receptors) that constitute the ...
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Vol 9, No 5 (2019)
... Convolutional neural network (CNN). Neural network is a computational model that works in a similar way to the neurons in the ...Convolutional neural network is a type of artificial neural ...
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Towards Neural Network Model for Insulin/Glucose in Diabetics-II
... network. The LM NN, on the other hand, adjusts all the parameters of the network at every training sample and hence, all parameters of the network contribute to the generation of the output concurrently. This would give ...
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Event Related Features in Feedforward Neural Networks Contribute to Identifying Causal Relations in Discourse
... We describe here the architecture of the Feedfor- ward Neural Network with an enriched feature set. The core components of the architecture are a look-up step, a hidden layer and the final lo- gistic regression ...
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Verification of Closed-loop Systems with Neural Network Controllers
... In this case study, we present a six-dimensional control-affine model of a quadrotor controlled by a feed-forward neural network initially introduced in [9], although here we have modified the controller to have ...
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Diagnosing angina using a simple neural network architecture
... simple neural network in diagnosing angina in patients complaining of chest ...Simple neural networks (simple perceptrons) were built and trained using a subset of 470 records with and without ...
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427
... Multilayered feedforward neural networks (ANNs) are in essence non-parametric regression methods, which approximate the underlying functionality in data by mi- nimizing the loss ...the neural ...
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Vol 60, No 2 (2017)
... Artificial Neural Networks using classical generalized delta rule have shown ability to classify the data in previously defined clusters, represent the information internally in a way that extracts de ...
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Pay Per Request Deployment of Neural Network Models Using Serverless Architectures
... In our implementation, the model is initialized outside of the Lambda handler function scope so that if an existing Event Context is available, a previously-loaded model can be reused. If the model has not been ...
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The accuracy of a procedural approach to specifying feedforward neural networks for forecasting
... of feedforward neural networks (NN) when applied to time series forecasting problems remains ...a feedforward neural network models and evaluates its effectiveness by examining its ...
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Blind Navigation System using Artificial Intelligence
... a multilayer perceptron’s to obtain minimal ...artificial neural networks (SIANN), due to their shared-weight architecture and translation invariance ...convolutional neural network can ...
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