There was a long training time for the norm BP neural network for GPS Height fitting,and easily converging to local minimum ***,introduced momentum and adaptive learning rate algorithm to improve the norm BP neural ne...
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There was a long training time for the norm BP neural network for GPS Height fitting,and easily converging to local minimum ***,introduced momentum and adaptive learning rate algorithm to improve the norm BP neural network for resolving the problem of the training and *** with the standard neural network,and calculating by a regional elevation control point coordinates,additional momentum adaptive neural network algorithm accuracy of GPS height conversion was much higher and more stable,and the convergence was much faster.
Handwritten images are an important source of information in the human world, and machine learning has always been an important method for dealing with handwritten visual problems. Most of today's machine learning...
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ISBN:
(纸本)9781728136608
Handwritten images are an important source of information in the human world, and machine learning has always been an important method for dealing with handwritten visual problems. Most of today's machine learning uses shallow structures such as SVM (Support Vector Machine) and kernel regression. These algorithms cannot he complex. The image information is more accurately predicted, and the recognition method of machine learning has quite high requirements on the quality of the image. This paper takes the MNIST data set of the National Institute of Standards and Technology as an example to enhance the data of the handwritten image data. The Convolutional Neural Network (CNN) algorithm builds the model and uses the adaptive learning rate algorithm (Adadelta) to enhance the model accuracy. The experimental results on the MNIST dataset show that the proposed method can effectively improve the recognition of handwritten images and quickly and correctly identify handwritten images. Its accuracy of 99.6% is significantly better than neural network and logistic algorithm.
A novel algorithm for training pyramidal pRAM neural networks on an unbalanced training set is proposed. The behaviour of the standard reinforcement learningalgorithm is analysed and an adaptivelearningrate algorit...
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A novel algorithm for training pyramidal pRAM neural networks on an unbalanced training set is proposed. The behaviour of the standard reinforcement learningalgorithm is analysed and an adaptive learning rate algorithm that modifies the reinforcement learningalgorithm based on readily available a priori class probability is developed.
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