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Recently, performance of deep neural networks, especially convolutional neural networks (CNNs), has been drastically increased by elaborate network architectures, by new learning methods, and by GPU-based high-performance computation. However, there are still several difficult problems concerning back propagation, which include scheduling of learning rate and controlling locality of search (i.e.,...
The selection for the number of hidden nodes for a neural network is of critical importance. This paper proposes a novel algorithm to determine the number of hidden nodes of a neural network and optimize it. In the method, the number of hidden nodes H is first computed by empirical formulas, and the range of H is determined according to computed result. Then, the "three points search" is...
Although many global optimization search algorithms may be used to train feedforward neural networks, these algorithms have some weaknesses such as dependence of initial solution. This paper proposes a novel hybrid global optimization method for classification problem, called GTA, which combines the advantages of Genetic algorithm and Tabu search. The training process in proposed method is divided...
A Neural Network (NN) models is developed and applied to measure the company board governance capacity in China, incorporating Genetic Algorithm (GA) techniques for detecting the networks' structure. By introducing Genetic Algorithm, NNGA Model can improve the global search capability and robust. Empirical results show that NNGA model improved the networks' performance comparing with traditional NN...
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