Abstract
Aiming at the problem of time-varying signal pattern classification, a sparse auto-encoder deep process neural network (SAE-DPNN) is proposed. The input of SAE-DPNN is time-varying process signal and the output is pattern category. It combines the time-varying signal classification method of process neural network (PNN) and the data feature extraction and hierarchical sparse representation mechanism of sparse automatic encoder (SAE). Based on the feedforward PNN model, SAE-DPNN is constructed by stacking the process neurons, SAE network and softmax classifier. It can maintain the time-sequence and structure of the input signal, express and synthesize the process distribution characteristics of multidimensional time-varying signals and their combinations. SAE-DPNN improves the identification of complex features and distinguishes between different types of signals, realizes the direct classification of time-varying signals. In this paper, the feature extraction and representation mechanism of time-varying signal in SAE-DPNN are analyzed, and a specific learning algorithm is given. The experimental results verify the effectiveness of the model and algorithm.
Highlights
The classification of complex time-varying signals in nonlinear systems has always been an important issue in the field of signal processing and artificial intelligence
Aiming at the classification of nonlinear timevarying signal, this paper proposed a sparse auto encoder deep process neural network model (SAEDPNN)
In this paper we review the challenges of timevarying signal classification, the status of artificial neural networks used for time-series signal processing, and point out the idea and algorithm of SAEDPNN
Summary
The classification of complex time-varying signals in nonlinear systems has always been an important issue in the field of signal processing and artificial intelligence. PNN represents and extracts the process characteristics of the time-varying signal by the connection weight function between the input layer and the hidden layer It is an extension of the traditional artificial neural network in the time domain. Aiming at the classification of nonlinear timevarying signal, this paper proposed a sparse auto encoder deep process neural network model (SAEDPNN). It is constructed by stacking the process neuron hidden layer, McCulloch-Pitts neuron hidden layer, SAE network unit and softmax classifier. The implicit expression of input time-varying signal is realized by using the algorithm strategy based on orthogonal function basis expansion This method simplifies the network structure and information processing flow, and realizes the direct classification of the time-varying signals.
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