neural network learning process
Machine Learning and Artificial Neural Network Process – Viability and Implications in Stock Market Prediction
... Abstract-The modern world happenings are presented and stored in the form of different types of information which might not mean anything unless probed with a purpose. Data Mining is one of the methods wherein this ...
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A Learning Algorithm For Neural Network Ensembles
... NeuralBAG, since the training process terminates by validation like in the early-stopping method. 6. Conclusions We proposed a new method for selecting diverse members of ANN ensembles. At every stage, the ...
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Learning of N-layers neural network
... the learning factor by suitable method. The stability of the learning process and the rate of convergence depend on the selected ...the learning process management by the relative great ...
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Hybrid Neural Network Architecture for On Line Learning
... surface learning agent and BP as the deep learning agent for the hybrid ...deep learning agent BP in the re- learning process based on whose error is lower for the previously predicted ...
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Convolutional neural network as an architecture for deep learning
... Machine learning has become important for solving problems in many areas: computational finance, image processing and computer vision, face recognition, motion detection, object detection, tumour detection, drug ...
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Deep Learning Binary Neural Network on an FPGA
... the design is found to be faulty, then the design can be corrected just by changing the HDL code and downloading new bitstream onto FPGAs. Being re-configurable, FPGAs can always keep pace with future modification. ...
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‘Neural Network’ a Supervised Machine Learning Algorithm
... IJEDR1502119 International Journal of Engineering Development and Research (www.ijedr.org) 663 Fig.2. Human Neurons A human Neuron is composed of a cell body or soma and two types of out reaching tree like branches: the ...
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Improved spikeprop algorithm for neural network learning
... Alhamdulillah, it is with Allah S.W.T will that I get to finish this thesis in the time given. Here, I would like to express my heartfelt gratitude to my supervisor Professor Dr. Siti Mariyam Shamsuddin and without her ...
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Artificial Neural Network Learning Techniques: A Survey
... REINFORCEMENT LEARNING The system is obliged with trial and error ...reinforcement learning is possible when the inputs given are with high ...decision process is the framework used in reinforcement ...
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Application of Artificial Neural Network in Process Safety Assessment
... the network processes the input from each node according to the specified parameters ...settings. Learning algorithm is required for training ...a network in forward direction and then the output is ...
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Network Intrusion Detection Using an Improved Competitive Learning Neural Network
... competitive learning is illustrated in Figure 3. The network initialized a number of neurons ...This process shows that the SCLN has the ability of performing ...
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Model detecting learning styles with artificial neural network
... Artificial Neural Network (ANN) is an artificial intelligence method that uses the concept of biological neural ...Input process that has the weights will be processed on the hidden layer and ...
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Learning backward induction: a neural network agent approach
... of the time, whereas the ownmax heuristic that simply chooses the cell with the maximum payoff for that agent fares worse explaining behavior roughly 38%, albeit still significantly better than chance. These two ...
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Particle swarm optimization for neural network learning enhancement
... NN learning is called Backpropogation (BP) ...BP learning is basically a hill climbing technique, it runs the risk of being trapped in local minima where every small change in synaptic weight increases the ...
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Particle swarm optimization for neural network learning enhancement
... NN learning is called Backpropogation (BP) ...BP learning is basically a hill climbing technique, it runs the risk of being trapped in local minima where every small change in synaptic weight increases the ...
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Recurrent Neural Network Identification: Comparative Study on Nonlinear Process
... to neural networks as universal approximator yielding the possibility of modeling nonlinear autoregressive models with the exogenous ...variable. Neural networks utilized in modeling and identification of ...
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Analysis of Turning Process By Using Artificial Neural Network Technique
... feed-forward network, is an effective learning technique used to exploit the regularities and exceptions in the training ...of neural networks is their ability to provide flexible mapping between ...
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BACTERIAL FORAGING OPTIMIZATION ALGORITHM FOR NEURAL NETWORK LEARNING ENHANCEMENT
... feedforward neural network to optimize the learning process in terms of convergence rate and classification accuracy but this process needs longer training ...feedforward neural ...
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Modal Learning in a Neural Network
... 5. Conclusions and Future Work In conclusion, the snap-drift algorithm has shown potential in phrase recognition. The results show the learning of the SDNN is fast, stable and reliable in recognizing the input ...
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Neural Network Learning Theoretical Foundations
... It is natural to ask whether there is a Combinatorial' measure analogous to the VC-dimension that can be used to bound the cover- ing numbers of a class of real-valued functions, and hen[r] ...
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