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中文摘要：
本论文揭示,作为两种并行的神经计算模型,BP和Hopfield类型神经网络都可以有效地对二次型V（x）=x^TPx/2＋q^Tx实现最小化求解。而且,尽管BP和Hopfield类型神经网络在网络设计思想和网络结构上呈现出很大的差异,但是它们在二次型函数最小化问题上都表现出了相同的学习能力,这说明两者具有本质的联系.
英文摘要：
As parallel-computational models, both BP （Back Propagation） and Hopfield-type neural networks can be used in the minimization of quadratic functions. BP neural network is substantially different from Hopfield-type neural network in terms of network architecture and learning pattern. However, both neural networks possess a common nature of learning during online minimization of quadratic functions. Simulation results are also given.
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