Our previous study showed that the possibility of using an optical three-layer feedforward neural network employing the gradient descent learning algorithm for automated assessment of normality of the electrogastrogra...
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Our previous study showed that the possibility of using an optical three-layer feedforward neural network employing the gradient descent learning algorithm for automated assessment of normality of the electrogastrogram. However, problems with this algorithm are slow convergence rate and critical user-dependent parameters. In the present study, two conjugate gradient learning algorithms (quasi-Newton and scaled conjugate algorithm) were introduced and compared with the gradient descent learning algorithm for the classification of the normal and abnormal electrogastrogram. Three indexes, the convergence rate, complexity per iteration and parameter robustness, were used to evaluate the performance of each algorithm. The results showed that the scaled conjugate gradient algorithm performed the best, which was robust and provided a super linear convergence rate.
This observe examined the impact of different system learning algorithms on textual content class results. Guide Vector Machines, Logistic Regression, Naïve Bayes, k nearest acquaintances and choice timber have b...
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In this paper, a new neuron model with different output-feedback factor and a neural network model that is composed of output feedback neural model are proposed. And its learning algorithm is derived and proofed in th...
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ISBN:
(纸本)9781424463343
In this paper, a new neuron model with different output-feedback factor and a neural network model that is composed of output feedback neural model are proposed. And its learning algorithm is derived and proofed in theory. This neural network can learn not only the static knowledge, but can learn dynamic knowledge;not only can remember static information, but can remember dynamic information;so that it is a truly dynamic neural network. Based on the minimum variance theory, the learning algorithm of the neural network with output feedback is proofed in theory. And the algorithm is summed up in the form of the theorem. Theoretical studies have shown that the static weights indicate the performance of the static mapping. Neuron output feedback coefficient implies the dynamic evolution performance of the neural network. And different feedback coefficients express the dynamic performance of different neurons. Therefore, the Study for dynamic characteristics and learning strategies of output feedback neural network is of great theoretical significance and application value.
Backpropagation (BP) learning algorithm is the most widely supervised learning technique which is extensively applied in the training of multi-layer feed-forward neural networks. Many modifications of BP have been pro...
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This article proposes a design method for an intelligent management system for university archives based on learning algorithms. This system utilizes advanced learning algorithms and artificial intelligence technology...
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We introduce a novel class of accelerated data-driven concurrent learning algorithms. These algorithms are suitable for the solution of high-performance system identification and parameter estimation problems with con...
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An important task in machine learning is determining which learning algorithm works best for a given data set. When the amount of data is small the same data needs to be used repeatedly in order to get a reasonable es...
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As an important component of China's marine construction and development strategy, the development and utilization of marine resources can provide strong impetus for economic development, and the rational developm...
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With the advent of high-throughput technologies, 1 regularized learning algorithms have attracted much attention recently. Dozens of algorithms have been proposed for fast implementation, using various advanced optimi...
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A hidden layer is introduced into nonlinear principal component type learning algorithms. The algorithms are derived from nonlinear optimization criteria. Both subspace type and hierarchical versions are considered. T...
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