Elman Recurrent Neural Network (ERNN)
Novel Ensemble Neural Network Models for better Prediction using Variable Input Approach
... Artificial Neural Network (ANN) models, namely, Multilayer Perceptron Network (MLPN), Elman Recurrent Neural Network (ERNN), Radial Basis Function Network ...
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A NEURAL FUZZY APPROACH TO MODELING THE THERMAL BEHAVIOR OF POWER TRANSFORMERS
... Feedforward Neural Network (MFNN) and Elman Recurrent Neural Network (ERNN) are chosen to compare with the conventional Institute of Electrical and Electronics Engineers ...
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Role of Hidden Neurons in an Elman Recurrent Neural Network in Classification of Cavitation Signals
... of network has been carried out zone ...proposed network and their efficiency was determined for MSE error ...and network was trained with 15 signals which are chosen from all zones and contains ...
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Enhancement of quantum particle swarm optimization in elman recurrent network with bounded VMAX function
... BP network, such as trap into local minima and may get stuck at regions of a search ...of Elman Recurrent Neural Network (ERNN) with a new enhance method of Particle Swarm ...
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Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks
... an Elman or Jordan neural ...the neural network will converge to local ...of Elman and Jordan neural networks were set to ...the neural network on the remaining ...
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Stability of Fuzzy Elman Neural Network Using Joint Spectral Radius Spectral Radius of Matrix Dynamic Systems
... simple recurrent neural network model. Elman networks are two-layer back propagation networks, with the addition of a feedback connection from the output of the hidden layer to its ...allows ...
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An Investigation of Recurrent Neural Architectures for Drug Name Recognition
... the Elman and Jordan networks can learn long-term dependencies, their exponential decay biases them toward their most recent in- puts (Bengio et ...the network computes both a left, ! h (t), and a right, h ...
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Traffic Signal Prediction Using Elman Neural Network and Particle Swarm Optimization
... artificial neural networks specific patterns are searched within input data, input data for neural network is generated by applying proper delays to the input ...of recurrent Elman ...
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Evolutionary Load Forecasting using Artificial Neural Network
... ABSTRACT: The anticipated effort targets to predict the load by utilizing Artificial Neural Networks (ANN). Short term load forecasting acts an important character for the economic, planning and reliable action of ...
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Knowledge Extraction and Recurrent Neural Networks: An Analysis of an Elman Network trained on a Natural Language Learning Task
... Knowledge Extraction and Recurrent Neural Networks: A n Analysis of an Elman Network trained on a Natural Language Learning.. We present results of experiments with Elman recurrent neura[r] ...
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International Journal of Emerging Technology and Advanced Engineering
... Perceptron Neural Network is proposed as an intelligent tool for predicting Rainfall Time ...other network such as Jordon Elmann Neural Network, SOFM (Self organized feature map), RNN ...
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Image Description Using Deep Neural Network
... deep neural networks and recurrent neural networks ...Long-Term Recurrent Convolutional Network ...the Neural Image Captioning (NIC) algorithm proposed in [4] as well as ...
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Blind Phoneme Segmentation With Temporal Prediction Errors
... In our case, the network itself consists of two LSTM layers (Hochreiter and Schmidhuber, 1997) stacked on one another followed by a linear layer and a softmax. The input and output units have both dimension 8, ...
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Comparison of Artificial Intelligence Methods on the Example of Tea Classification Based on Signals from E nose Sensors
... Abstract The data collected from electronic nose systems are multidimensional and usually contain a lot of redundant information. In order to extract only the relevant data, different computational techniques are ...
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Recurrent Neural Network Grammars
... Formally, an RNNG is a triple (N, Σ, Θ) consisting of a finite set of nonterminal symbols (N ), a finite set of terminal symbols (Σ) such that N ∩ Σ = ∅, and a collection of neural network parameters Θ. It ...
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Tree Copula Theory Based Fusion And Compressive Sensing For Activity Detection using Multi Modal Data
... Convolutional Neural Network (TC-GO based DCNN) activity detection technique undergoes three important steps, namely compressive sensing, fusion and ...
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Enhancing recurrent neural network-based language models by word tokenization
... modified recurrent neural network-based language model for language ...the network input into three ...basic recurrent neural network ...
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Improving Machine Translation Quality Estimation with Neural Network Features
... of neural network language models (Bengio et al. 2003) and neural machine translation encoder-decoder frameworks (Bahdanau et ...forward neural networks to estimate the word con- ...
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Opinion Mining of M Learning Reviews using Soft Computing Techniques
... Each output unit takes a weighted sum of the outputs from units from the previous layer. A nonlinear function is applied to the weighted input. Multilayer feed-forward neural networks can also model the class ...
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Application of Artificial Intelligence for Epilepsy Disease
... Deep learning is a precise structure of the group of machine learning methods. Deep learning is a precise structure of depiction- based learning, where a system assimilates and forms fundamental aspects from each ...
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