neural network adaptive learning
Deep Learning in an Adaptive Function Neural Network
... Artificial neural network learning is typically accomplished via adaptation between ...the network can learn to respond differentially to classes of incoming ...internal neural ...
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Neural Network and Adaptive Feature Extraction Technique for Pattern Recognition
... We implement this step by using Frequency Sensitive Competitive Learning [1] because they can achieve the goal as long as the number of seed points is not less than the exact number K of clusters. Here, we suppose ...
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Facial Expression Recognition System using Neural Network based on LBP Features
... a neural network with only 70neurons in the hidden layer and trained with Gradient descent with momentum and adaptive learning rate back propagation algorithm and the total number of ...
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DETECTION AND CLASSIFICATION OF DIABETIC RETINOPATHY USING ADAPTIVE BOOSTING AND ARTIFICIAL NEURAL NETWORK
... Probabilistic Neural Network (PNN) and Support Vector Machine were used to diagnosis ...convolutional neural networks in order to segment the blood ...deep learning based computer-aided system ...
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Reinforcement Learning Adaptive PID Controller for an Under-actuated Robot Arm
... Actor-critic learning is one of the variants of reinforcement learning which provides a systematic method for simultaneously obtaining optimal action and expected value in real time ...critic ...
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ACCPndn: Adaptive Congestion Control Protocol in Named Data Networking by learning capacities using optimized Time Lagged Feedforward Neural Network
... We use simulations to quantify e ff ect of conges- tion and its countermeasure. In this work, we used the open-source ndnSIM [70] package, which imple- ments NDN protocol stack for NS-3 network simu- lator ...
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Enhancement in Channel Equalization Using Artificial Neural Network
... Digital communication systems transmi the high speed and efficient data over the communication channels. During this process the transmitting data it is distorted, due to the effect of distortions. Distortion includes ...
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Adaptive Frequency Neural Networks for Dynamic Pulse and Metre Perception.
... The network con- sists of a number of canonical oscillators distributed across a frequency ...derivative-based learning methods in Machine Learn- ...the network adds rhythm-harmonic frequency ...
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Research of adaptive control algorithm research based on rough set and implementation
... an adaptive neural network algorithm has strong compatibility, some noise data, not including related function and reduce the input dimension, a fast learning process, uncertainty processing ...
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An Adaptive Controller using Radial Basis Function Neural Network with Reinforcement Learning
... controllers’ adaptive property is of great importance that is missing in conventional PID controllers, so the common design idea for adaptive PID controllers has received a wide attention in control ...
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Identification and Adaptive Control of Dynamic Nonlinear Systems Using Sigmoid Diagonal Recurrent Neural Network
... new neural network architecture called based on the adaptation of the shape of the sig- moid weight of the hidden layer neurons and have intro- duced its corresponding dynamic back propagation ...
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Model of Electric Power Load by Adaptive Neural Network
... artificial neural network (ANN) is given in ...an adaptive learning algorithm which was proven to guarantee the convergence of training process by updating the learning factor at ...
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GROUP OF RECURRENT NEURAL NETWORKS
... an adaptive method to separate FECG from composite ECG that consists of both maternal and FECG by using Adaline ...momentum, learning rate and initial weights used in the ...high learning rate, low ...
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The Optimal Design of Weighted Order Statistics Filters by Using Support Vector Machines
... or neural network. Although the adaptive neural fil- ter can e ff ectively remove noise of various kinds, including Gaussian noise and impulsive noise, its learning process in- volves a ...
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Neuro-Fuzzy Scheduler for the Control of Real Time Spherical Tank Process
... general neural- network model for fuzzy logic control and decision ...and neural network structure and learning abilities into an integrated neural-network based fuzzy ...
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Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis
... and neural networks, a better understanding of the heuristics underlying the motor fault de- tection/diagnosis process and successful fault detection/diagnosis schemes can be ...two neural fuzzy (NN/FZ) ...
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Time Series Forecasting with Long Short-Term Memory Neural Networks on the Stock Market
... Machine learning is changing the ...Supervised learning is done with a data set which is ...supervised learning are predicting the weather forecast, the stock market and housing ...Unsupervised ...
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Stable Adaptive Neural Control of a Robot Arm
... It is well known that FFN and RNN could be used as components in feedback systems [13]. The control sys- tem must satisfy three main conditions: boundedness of the NN weights, boundedness of the tracking error and ...
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Neural Network Design - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... of neural networks in literature re- search by Aston ...“the network can be taught to recognize individual writing styles, and the researchers used it to compare works attributed to Shakespeare and his ...
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Different Attack Patterns For Deep Brain Implants By Using Cnn
... deep learning methodology to predict different attack stimulations in ...Convolutional Neural Network (CNN) which is a class of deep neural network commonly applied for visual imaginary ...
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