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learning, neural network and computer to simulate such a neural network

机译:学习,神经网络和计算机来模拟这样的神经网络

摘要

The invention relates to a learning method implemented within a neural network which operates in accordance with the gradient back-propagation algorithm. In order to determine the new synaptic coefficients with a minimal learning period, the invention introduces parameters which favour corrections based on the sign of the error at the start of learning, and which gradually execute less abrupt corrections. This can be supplemented with other parameters favouring a layer-by-layer strategy, accelerating learning on input layers relative to output layers. It is also possible to append a strategy operating throughout the neural network. Application: layered neural networks. IMAGE
机译:本发明涉及在神经网络内实现的学习方法,该神经网络根据梯度反向传播算法进行操作。为了以最小的学习周期确定新的突触系数,本发明引入了基于学习开始时的误差的符号而有利于校正的参数,并且这些参数逐渐执行较少的突然校正。可以使用其他有利于逐层策略的参数进行补充,从而加快相对于输出层的输入层学习。也可以附加在整个神经网络中运行的策略。应用:分层神经网络。 <图像>

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