閔金嬋

摘要：

為提高兒童心理健康分析的精度和效率，結(jié)合《中小學(xué)生心理健康診斷測驗(yàn)》量表，從8個維度分析兒童心理健康狀態(tài)。針對FCM聚類結(jié)果易受其初始聚類中心選擇的影響，將和聲搜索算法應(yīng)用于FCM初始聚類中心的選擇，提出一種基于IHSFCM的兒童心理健康分析方法。與HSFCM和BPNN對比，IHSFCM可以有效提高兒童心理健康聚類分析的精度，為兒童心理健康分析提供了新的方法。

關(guān)鍵詞：

和聲搜索算法; 模糊C均值聚類; 心理健康分析; 神經(jīng)網(wǎng)絡(luò); 反向?qū)W習(xí); 兒童

中圖分類號： R395.6

文獻(xiàn)標(biāo)志碼： A

An Analysis of Children's Mental Health Based on IHSFCM

MIN Jinchan

（School of Physical Education， Shanxi Preschool Teachers College， Xian， Shanxi 710100， China）

Abstract：

In order to improve the accuracy and efficiency of children's mental health analysis， the mental health status of children was analyzed from 8 dimensions by combining with the diagnostic test of mental health of primary and secondary school students. In view of the fact that FCM clustering results are easy to be affected by the selection of its initial clustering center， the harmony search algorithm is applied to the selection of FCM initial clustering center， and a children mental health analysis method based on IHSFCM is proposed. Compared with HSFCM and BPNN， IHSFCM can effectively improve the accuracy of children's mental health cluster analysis and provide a new method for children's mental health analysis.

Key words：

harmony search algorithm; fuzzy cmeans clustering; mental health analysis; neural network; reverse learning; children