目的根据中文手机文本输入的特点,以拼音输入法为基础,探讨在操作标准手机键盘时,对于两个接连字母的输入,其字母所处按键的相对距离不同,在操作绩效上是否存在差异。方法实验中采用Sony Ericsson W 800C手机作为实验材料,通过完全被试...目的根据中文手机文本输入的特点,以拼音输入法为基础,探讨在操作标准手机键盘时,对于两个接连字母的输入,其字母所处按键的相对距离不同,在操作绩效上是否存在差异。方法实验中采用Sony Ericsson W 800C手机作为实验材料,通过完全被试内设计,从被试的反应时和正确率两方面对文本输入绩效加以研究。结果当两个字母处于同一按键、相邻按键以及远离的按键时,反应时之间存在显著差异;各按键组合类别之间的正确率差异显著。结论(1)对于连接输入的字母,各字母所处的按键的相对距离对输入绩效有较大影响;(2)被试操作的反应时和正确率均存在男女差异,且差异显著。展开更多
An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the no...An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the nonlinear system and a recurrent neural network to minimize the difference between the linear model and the real nonlinear system. Because the current control input is not included in the input vector of recurrent neural network (RNN), the inverse control law can be calculated directly. This scheme can be used in real-time nonlinear single-input single-output (SISO) and multi-input multi-output (MIMO) system control with less computation work. Simulation studies have shown that this scheme is simple and affects good control accuracy and robustness.展开更多
文摘目的根据中文手机文本输入的特点,以拼音输入法为基础,探讨在操作标准手机键盘时,对于两个接连字母的输入,其字母所处按键的相对距离不同,在操作绩效上是否存在差异。方法实验中采用Sony Ericsson W 800C手机作为实验材料,通过完全被试内设计,从被试的反应时和正确率两方面对文本输入绩效加以研究。结果当两个字母处于同一按键、相邻按键以及远离的按键时,反应时之间存在显著差异;各按键组合类别之间的正确率差异显著。结论(1)对于连接输入的字母,各字母所处的按键的相对距离对输入绩效有较大影响;(2)被试操作的反应时和正确率均存在男女差异,且差异显著。
基金Supported by the National Natural Science Foundation of China (60575009, 60574036)
文摘An adaptive inverse controller for nonliear discrete-time system is proposed in this paper. A compound neural network is constructed to identify the nonlinear system, which includes a linear part to approximate the nonlinear system and a recurrent neural network to minimize the difference between the linear model and the real nonlinear system. Because the current control input is not included in the input vector of recurrent neural network (RNN), the inverse control law can be calculated directly. This scheme can be used in real-time nonlinear single-input single-output (SISO) and multi-input multi-output (MIMO) system control with less computation work. Simulation studies have shown that this scheme is simple and affects good control accuracy and robustness.
