auto-associative neural network
Dimension Reduction and Clustering of High Dimensional Data using Auto Associative Neural Networks
... artificial neural networks technique known as Auto-Associative Neural Networks ...bottleneck neural networks (BNN) due to its unique ...
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Enhanced data clustering and classification using auto-associative neural networks and self organizing maps
... Networks (ANN) can be considered as one of the promising tools in this field. Inspired by advances in biomedical research, ANN forms a class of algorithms aiming to simulate the biological neural networks. One of ...
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Facial Expression and Visual Speech based Person Authentication
... a neural network for person authentication. Among several neural network models, auto associative neural network is used due to its features distribution capturing ...
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A non linear neural network technique for updating of river flow forecasts
... and Parent, 1998; Mroczkowski et al., 1997; Ye et al., 1997; Franchini and Galeati, 1997; Yapo et al., 1996; Gan et al., 1997; Lauzon et al., 1997; Lamb, 1997; Sumner et al., 1997; Cooper et al., 1997; Todini, 1996). ...
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A neural network auto regression model to forecast per capita disposable income
... Time series analysis is an important technique for future forecasting of time dependent variables. Keeping future visualization in mind, time series analysis is applicable to a wide variety of applications. In this work, ...
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Associative Neural Network
... of neural network ensemble to correctly represent topology of the ...gating network, which decides whether old or new knowledge should be used in each particular ...gating network can also use ...
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Performance analysis of a recurrently connected auto-associative memory
... In th e case of simple feedforward associative netw orks, th e choice of optim al th re sh o ld can be analytically com puted on th e basis of sta tistic a l expectations. However, w hen th e netw ork is recu rren ...
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A hybrid group method of data handling (GMDH) with the Wavelet Decomposition for Time Series Forecasting: A review
... artificial neural networks since such evolutionary algorithms are particularly useful for dealing with complex problems having large search spaces with many local ...type neural network for each ...
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Deep Auto-Encoder Neural Network for Phishing Website Classification
... Jameel and George presented a feedforward neural network to classify the phishing email by mining features from the email’s slogan and HTML organization. Their proposed algorithm was verified on 18 features ...
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DEVELOPMENT AND APPLICATION OF A STAGE GATE PROCESS TO REDUCE THE UNERLYING RISKS OF IT SERVICE PROJECTS
... Wildes [32] employed the Hough transform to localize the iris and a Laplacian pyramid with four resolution levels to produce the code of the iris. Boles [4] found an iris representation by means of zero crossing of the ...
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Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks
... designed neural net- work models can simulate any Turing machine (Siegelmann and Sontag ...applying neural network models to solve algorithmic tasks such as learning context-sensitive languages (Gers ...
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Appling a Novel Cost Function to Hopfield Neural Network for Defects Boundaries Detection of Wood Image
... Hopfield neural network, a neuron can not only be used for an input neuron, but also an output ...Hopfield neural network has a so-called cost function (or an energy function), which is used ...
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INTELLIGENT SELF TUNING PID CONTROLLER USING HYBRID IMPROVED PARTICLE SWARM OPTIMIZATION FOR ULTRASONIC MOTOR
... In this paper, ANFIS model is utilized to predict the dynamic response of the vibrating pipe cylinder caused by VIV. ANFIS is an adaptive neural network that utilized a fuzzy inference system as an ...
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Short Term Load Forecasting With Feed Forward Neural Network Algorithm
... The network weights are adjusted by training the ...the network learns through examples. The idea is to give the network input signals and desired ...the network produces an output signal, and ...
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Machine Learning based EEG Signal Classification
... uses a back propagation neural network with periodogram and auto regressive (AR) features as inputs for automated detection of epileptic seizures. Ghosh Dastidar et al. discussed a clas[r] ...
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Neural Associative Memory for Dual Sequence Modeling
... cently demonstrated impressive results on various text modeling tasks. LSTM-Networks (LSTMN) select a previous hidden state via attention on a memory tape of past states (intra-attention) op- posed to using the hidden ...
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Comparison between Cascade Forward and Multi-Layer Perceptron Neural Networks for NARX Functional Electrical Stimulation (FES)-Based Muscle Model
... The spinal cord is a collection of nerves that sends commands to muscles to induce movement. Damage to the spinal cord causes paraplegia that results in loss of sensation and voluntary movement. The level of spinal cord ...
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Exponential convergence of Cohen Grossberg neural networks with continuously distributed leakage delays
... Cohen-Grossberg neural networks with continuously distributed leakage delays. By using the Lyapunov functional method and differential inequality techniques, we propose a new approach to establishing some sufficient ...
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Deep Learning Based Visual Tracking: A Review
... different network models, including auto-encoder (SAE), convolutional neural network (CNN), recurrent neural networks (RNN) deep reinforcement learning (DRL) and the fusion of them were ...
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Review of Deep Neural Network Based on Auto encoder
... Convolutional Neural Network Based on Auto-Encoder The convolutional neural network has an efficient generalization ability for image data, but the first few layers cannot be ...
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