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认知心理学认为语言表征是心理表征的符号现实,自我和谐是心理学人格理论中最重要的概念之一,语言表征也呈现多样化。通过相关分析法和顺序后退法在高维特征空间中进行主效应信号筛选,降低人工神经网络的输入维数。针对外显语言表征投射内隐心理状态的非线性特征,运用BP网络的高度非线性映射能力,对自我和谐人格水平进行分类诊断。结果表明:自我和谐人格水平的语言表征主效应信号诊断准确率达到95.8%。
Cognitive psychology that language representation is the symbolic reality of psychological representation, self-harmony is one of the most important concepts in the psychology of personality theory, language representation is also diversified. Through the correlation analysis and the order regression method, the main effect signal is screened in the high dimensional feature space to reduce the input dimension of the artificial neural network. In view of the non-linear features of explicit language characterizing implicit mental state, this paper uses the highly nonlinear mapping ability of BP network to classify the self-harmonious personality. The results show that the diagnostic accuracy rate of main effect signal of speech with self-harmonious personality level reaches 95.8%.