小波神经网络在两足步行机器人爬斜坡中的应用

AN APPLICATION OF WAVELET NEURAL NETWORKS IN THE BIPED ROBOT'S SLOPE CLIMBING

  • 摘要: 针对传统的神经网络中神经元模型在结构和信息存储能力上存在的不足,本文提出了一种基于广义小波基函数网络的神经元集聚模型.这种小波神经网络不仅收敛速度快,非线性逼近能力更好,而且具有内部结构变尺度、自适应调整和广义信息存储等智能化特点,更符合生物原型的实际情况.静态学习和准动态学习仿真实验证明这种神经网络结构的有效性.

     

    Abstract: We found that neuron model is inadequate owing to its defects such as those inherent in its structure and in its capability of information storage. So we propose an intelligent neurons assemblage model with generalized wavelet basis function network as its excited function. Not only the wavelet neural networks' convergence rate is much faster and its nonlinear approach capability is much better but also its intelligent characteristics, such as the variable-scale adaptive adjustment of structure and the generalized information storage, make it reflect much more faithfully the biological original. Static learning and pseudo dynamic learning are demonstrated to prove that the proposed mechanism is valid.

     

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