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This paper proposes a hybrid algorithm for training a feed-forward neural network by combining both Particle Swarm Optimization (PSO) and Information Gain with Backpropagation (BP) algorithm. A conventional neural network training algorithm, i.e. BP, has several drawbacks in its slow convergence and local optima. Although PSO can be applied to search for the near optimal set of weights in the neural...
In the area of artificial neural networks, the Back Propagation (BP) learning algorithm has proved to be efficient in many engineering applications especially in pattern recognition, signal processing and system control. Although the BP learning has been a significant research area of neural network, it has also been known as an algorithm with a poor convergence rate. Many attempts have been made...
Personal Credit Scoring is of great significance for commercial banks to circumvent credit consumption, the original BP algorithm's convergence rate is slow, learning precision is low, the training process is easy to fall into local minimum, this paper presents an improved algorithm with variable learning rate based on BP algorithm, and applied to simulate personal credit scoring. After comparing...
A improved gradient-based backpropagation training method is proposed for neural networks in this paper. Based on the Barzilai and Borwein steplength update and some technique of Resilient Propagation method, we adapt the new learning rate to improves the speed and the success rate. Experimental results show that the proposed method has considerably improved convergence speed, and for the chosen test...
Agricultural products information on the Internet is constructed repeatedly, the content is haphazard and sharing resources can not be used, then a classification of improved neural network which is based on the adjustment and optimization of the weight is presented. The adjustment of weight, optimization of network structure and reasonable adjustment of parameters of BP neural network are discussed,...
Neural networks (NNs) have been widely used to predict financial distress because of their excellent performances of treating non-linear data with self-learning capability. However, common neural networks often suffer from long convergent processes and occasionally involve in a local optimal solution that more or less limited their applications in practice. To overcome the drawbacks of neural networks,...
To forecast quickly the operation condition of loom, optimizing operation parameters of loom, and improve the production efficiency of loom. The paper studied operation prediction of loom production based on neural network. Because traditional network method had the defects of slow convergence velocity and low prediction accuracy, BP algorithm was improved by combined algorithms by the merging of...
Back-propagation neural networks with Gaussian function synapses have better convergence property over those with linear-multiplying synapses. In digital simulation, more computing time is spent on Gaussian function evaluation. We present a compact analog synapse cell which is not biased in the subthreshold region for fully-parallel operation. This cell can approximate a Gaussian function with accuracy...
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