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A Greedy Learning Approach for MultiLayer Perceptions
An Improved Learning Rate Technique to SpeedUp the Measurement
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activation analysis applications approach architecture artificial neural networks back-propagation BiCMOS binary classification cluster Computer connection convergence defined distribution dynamic encoding equation error estimation evaluation example feature feature extraction fuzzy logic fuzzy rules fuzzy set Gaussian GEMNET global Hamming distance hidden layer hidden nodes Hopfield Hopfield network IEEE IEEE Trans implemented input pattern input variables input vector iteration learning algorithm learning rate linear linguistic mapping matching matrix membership functions method minimize module multilayer perceptron neuron noise nonlinear obtained optimal output layer paper parameters pattern recognition Perceptron performance pixel prediction problem Proc proposed recognition rate region represent representation robot samples schemas segmentation sensor shown in Figure sigmoid function signal simulation results solution step structure supervised learning Table target task technique threshold training algorithm training data training patterns training set unit updated weight space weight vector WOS filter