Aiming at the problem that Artificial Neural Network (ANN) has limited memory capability for knowledge reasoning oriented to Knowledge Graph (KG) and the KG cannot deal with uncertain knowledge, a reasoning method named DNC-BN was propsed based on Differentiable Neural Computer (DNC) and Bayesian Network. Firstly, using Long Short-Term Memory (LSTM) network as the controller, the output vector and the interface vector of network were obtained by processing the input vector and the read vector obtained from the memory at each moment. Then, the read and write heads were used to realize the interaction between the controller with the memory, the read weights were used to calculate the weighted average of data to obtain the read vector, and the write operation was performed by combining the erase vector and write vector with the write weights, so as to modify the memory matrix. Finally, based on the probabilistic inference mechanism, the BN was used to judge the inference relationship between the nodes, and the KG was completed. In the experiments, on the WN18RR dataset, DNC-BN has the Mean Rank of 2 615 and the Hits@10 of 0.528; on the FB15k-237 dataset, DNC-BN has the Mean Rank of 202, and the Hits@10 of 0.519. Experimental results show that the proposed method has good application effect on knowledge reasoning oriented to KG.

To solve the problem of nonlinear time-varying signal classification, a Denoising AutoEncoder Deep Convolution Process Neural Network (DAE-DCPNN) was proposed, which combines the information processing mechanism of Process Neural Network (PNN) with convolution operation. The model consists of a time-varying signal input layer, a Convolution Process Neuron (CPN) hidden layer, a deep Denoising AutoEncoder (DAE) network structure and a softmax classifier. The inputs of CPN were time-series signals, and the convolution kernel was taken as a five-order array with gradient property. And convolution operation was carried out based on sliding window to realize the spatio-temporal aggregation of time-series signals and the extraction of process features. After the CPN hidden layer, the DAE deep network and the softmax classifier were stacked to realize the high-level extraction and classification of features of time-varying signals. The properties of DAE-DCPNN were analyzed, and the comprehensive training algorithm of the initial value assignment training based on each information unit and the overall optimization of model parameters was given. Taking 7 kinds of cardiovascular disease classification diagnosis based on 12-lead ElectroCardioGram （ECG） signals as an example, the experimental results verify the effectiveness of the proposed model and algorithm.