[PDF] Top 20 Deep Learning in an Adaptive Function Neural Network
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Deep Learning in an Adaptive Function Neural Network
... We generated 254 input patterns using the pre-tagged corpus from the Lancaster Parsed Corpus (LPC). A total of 49 bits are used to encode all possible input symbols. The terminal symbol groups are: punctuation (Pu), ... See full document
7
Designinga Neuro-Sliding Mode Controller for Networked Control Systems with Packet Dropout
... switching function used in the sliding mode control design, an improved genetic algorithm is ...an adaptive neural sliding mode control based on radial-basis function neural ... See full document
10
Prediction of prostate cancer by deep learning with multilayer artificial neural network
... activation function of hidden layers was the ReLu function, loss function was cross entropy error function, back propagation algorithm was gradient descent, and output function was ... See full document
13
Automatic Plastic Waste Segregation And Sorting Using Deep Learning Model
... weakly-supervised deep learning approach (DCNN-GPC) for detection and recognition of nuclear waste ...on deep learning and also able to detect and categorize unknown waste ...of Deep ... See full document
5
Multi resolution auditory cepstral coefficient and adaptive mask for speech enhancement with deep neural network
... of deep learning for speech enhancement and proposed some speech enhancement ...on deep neural network-support vector machine (DNN-SVM) ...using deep neural networks for ... See full document
16
Automated detection of atrial fibrillation using long short term memory network with RR interval signals
... a deep learning system to detect AF beats in Heart Rate (HR) ...a deep Recurrent Neural Network (RNN) with Long Short-Term Memory ...the deep learning system, which gets ... See full document
18
A Survey on Deep Learning Based IoT Approach for Precision Crop Suggestion
... the Deep Learning based Artificial Neural Networks is found to be more effective for prediction and ...the Deep Learning technique such as Deep Neural Network (DNN) ... See full document
7
A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images
... on Deep Learning (DL) strategies have been introduced for the classification and segmentation of ...details, deep architectures, such as Deep Neural Networks (DNNs) or Convolutional ... See full document
12
Model of Electric Power Load by Adaptive Neural Network
... a learning law for updating the weights of interconnection between two ...Lyapunov function [6, 7] approach was used to provide stability analysis of Backpropagation training algorithm of such ...and ... See full document
6
Completeness Problem of the Deep Neural Networks
... Hornik, Stinchcombe, & White [9] establish that the standard multilayer feedforward networks with hidden layers using arbitrary squashing functions are capable of approximating any measurable function from one ... See full document
13
An Adaptive Controller using Radial Basis Function Neural Network with Reinforcement Learning
... The shortcoming of this technique is said to be its long execution time. This can be solved by reducing the number of the particles that hardly shows any effect in the given results and speeds up the algorithm. In this ... See full document
7
Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs
... as neural networks and regression-based techniques have also made great progresses ...models, adaptive fuzzy logistic and multilayer perceptrons are the most popular kinds of methods ...the deep ... See full document
18
Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis
... using adaptive networks to fine tune membership functions of a fuzzy rule base has received more attention ...into adaptive fuzzy systems ...the learning ability of NN’s to implement and automate the ... See full document
11
Research on image classification model based on deep convolution neural network
... played an important role in medical diagnosis from the beginning to now. Especially, image classification tech- nology, from the initial theoretical research to clinical diagnosis, has provided effective assistance for ... See full document
11
Deep Learning Based Visual Tracking: A Review
... first neural-network tracker that combines convolutional and recurrent networks with RL algorithm in ...reinforcement learning (RL) agent making target location ... See full document
5
Emotion Recognition And Classification Using Eeg: A Review
... machine learning to get automatic artifact removal. SEED or DEEP database are commonly used by 70% researchers, which have used 64 or 32 ...Machine learning techniques like LDA, kNN are simple to ... See full document
10
Face recognition with Bayesian convolutional networks for robust surveillance systems
... [9], neural networks [10] and ensemble classi- fiers [11]. Recently, deep learning (DL)-based tech- niques, especially deep convolutional neural networks (DCNNs) have shown excellent ... See full document
10
Facial Recognition Using Deep Learning Neural Network
... of deep learning and deep belief network, a facial recognition system algorithm is ...of neural network and greedy layer wise training of the same network will ensure ... See full document
7
Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE
... as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the ...Various deep learning architectures such as deep neural ... See full document
5
DETECTION AND CLASSIFICATION OF DIABETIC RETINOPATHY USING ADAPTIVE BOOSTING AND ARTIFICIAL NEURAL NETWORK
... AdaBoost learning procedure consists of 'M' iterations. In each iteration, a weak classifier is learned using appropriate machine learning algorithm. Training samples that are classified correctly get a ... See full document
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