Supervised learning neural network regression--of
2016-08-23
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Neural network learning rule, also known as neural networks training algorithm to calculate the update neural network's weights and thresholds. Learning rule has two main categories: supervised learning and unsupervised learning. In supervised learning, you need to learn the rules to provide a series of incorrect network input/output (that is, samples), when the network input, the output is compared with the expected value that corresponds to the network, and then apply the learning rule adjusting the weights and thresholds, so that the output is close to expectations. In unsupervised learning, the weights and threshold adjustment only with the input of the network of relationships, there is no expectation, most of these algorithms with cluster analysis, the input mode is categorized to a limited number of categories. Will be analyzed in detail in this chapter two the widest application of supervised learning neural networks (RBF Neural network and BP neural network) the principle and
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神经网络
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预测
拟合
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