论文标题
部分可观测时空混沌系统的无模型预测
The Deep Learning model of Higher-Lower-Order Cognition, Memory, and Affection- More General Than KAN
论文作者
论文摘要
We firstly simulated disease dynamics by KAN (Kolmogorov-Arnold Networks) nearly 4 years ago, but the kernel functions in the edge include the exponential number of infected and discharged people and is also in line with the Kolmogorov-Arnold representation theorem, and the shared weights in the edge are the infection rate and cure rate, and used activation function by tanh at the node of edge.考虑到由MSE损失的残留或梯度计算出的不变的粗粒颗粒，该ARXIV预印版版本是2022年3月1日的升级版本。改进的KAN是PNN（可塑性神经网络）或Elkan（边缘学习KNN），除了边缘学习外，它还考虑了边缘的修剪。我们不受Kolmogorov-Arnold代表定理的启发，而是受到脑科学的启发。 Elkan解释大脑，变量对应于不同类型的神经元，学习边缘可以通过突触强度的重新平衡和神经胶质细胞吞噬突触来解释，而核功能是指神经元和突触的排出，不同的神经元和边缘的均值。通过余弦测试，Elkan或ORPNN（优化范围PNN）优于KAN或CRPNN（恒定范围PNN）。Elkan更一般地探索大脑，例如意识机理，脑部区域中固有频率的相互作用，突触和神经元排放频率和数据信号信号频率；阿尔茨海默氏病的机制，阿尔茨海默氏症患者在上游大脑区域的频率更高。长期的短期相对较好和劣等记忆，这意味着建筑和建筑的梯度；在不同大脑区域的湍流，需要满足湍流临界条件；量子纠缠的心脑可能会发生在心跳的情绪与大脑电位的突触强度之间。
We firstly simulated disease dynamics by KAN (Kolmogorov-Arnold Networks) nearly 4 years ago, but the kernel functions in the edge include the exponential number of infected and discharged people and is also in line with the Kolmogorov-Arnold representation theorem, and the shared weights in the edge are the infection rate and cure rate, and used activation function by tanh at the node of edge. And this Arxiv preprint version 1 of March 2022 is an upgraded version of KAN, considering the invariant coarse-grained which calculated by residual or gradient of MSE loss. The improved KAN is PNN (Plasticity Neural Networks) or ELKAN (Edge Learning KNN), in addition to edge learning, it also considered the trimming of the edge. We not inspired by the Kolmogorov-Arnold representation theorem but inspired by the brain science. The ELKAN to explain brain, the variables correspond to different types of neurons, the learning edge can be explained by rebalance of synaptic strength and glial cells phagocytose synapses, and the kernel function means the discharge of neurons and synapses, different neurons and edges mean brain regions. Through testing by cosine, the ELKAN or ORPNN (Optimized Range PNN) is better than the KAN or CRPNN (Constant Range PNN).The ELKAN is more general to explore brain, such as mechanism of consciousness, interactions of natural frequencies in brain regions, synaptic and neuronal discharge frequencies, and data signal frequencies; mechanism of Alzheimer's disease, the Alzheimer's patients has more high frequencies in the upstream brain regions; long short-term relatively good and inferior memory which means gradient of architecture and architecture; turbulent energy flow in different brain regions, turbulence critical conditions need to be met; heart-brain of the quantum entanglement may occur between the emotions of heartbeat and the synaptic strength of brain potentials.
