论文标题
深度学习神经皮肤神经网络的培训
Training of Deep Learning Neuro-Skin Neural Network
论文作者
论文摘要
在这篇简短的论文中，为深度学习神经皮神经网络开发了一种学习算法，以提高其学习属性。神经皮是作者最近提出的一种新型神经网络。它由一个细胞膜组成，该细胞膜附着于每个细胞。神经元是细胞核。使用有限元素对神经皮肤进行建模。有限元的每个元素代表一个单元格。每个细胞神经元都有树突状纤维，可将其连接到细胞的淋巴结。另一方面，其轴突连接到许多不同神经元的节点。在收到输入后，对神经皮肤进行了培训。学习是在使用灵敏度分析更新迭代期间进行的。结果表明，尽管神经皮肤无法呈现理想的响应，但它逐渐提高到所需的水平。
In this brief paper, a learning algorithm is developed for Deep Learning Neuro-Skin Neural Network to improve their learning properties. Neuroskin is a new type of neural network presented recently by the authors. It is comprised of a cellular membrane which has a neuron attached to each cell. The neuron is the cells nucleus. A neuroskin is modelled using finite elements. Each element of the finite element represents a cell. Each cells neuron has dendritic fibers which connects it to the nodes of the cell. On the other hand, its axon is connected to the nodes of a number of different neurons. The neuroskin is trained to contract upon receiving an input. The learning takes place during updating iterations using sensitivity analysis. It is shown that while the neuroskin can not present the desirable response, it improves gradually to the desired level.
