the horizontal bar gymnastic robot is a typical sample of underactuated mimo ( multi input-multi output ) complex systems 單杠上的體操機(jī)器人是欠驅(qū)動(dòng)多輸入多輸出復(fù)雜系統(tǒng)的典型例子。
then a low pass filter is designed to filter high-frequency noise in data . and smooth data again by means of central interpolation algorithm; helicopter model with multi input single output are identified respectively in ls method and ml method (2)剔除并補(bǔ)正了飛行實(shí)驗(yàn)數(shù)據(jù)中的野值;設(shè)計(jì)了一個(gè)低通濾波器來(lái)濾除數(shù)據(jù)中的高頻噪聲;用中心插值算法對(duì)數(shù)據(jù)二次平滑。
adoption of up-to-day non-jumper technique makes the input port to gain universal signal input function, and users can realize light switching between multi input signal thermocouple, thermo resistance, remote pressure, mv, standard voltage current signal only need by change internal parameters 采用最新無(wú)跳線技術(shù),使輸入端口具備萬(wàn)能信號(hào)輸入功能,只需通過(guò)改變內(nèi)部參數(shù),即可實(shí)現(xiàn)多種輸入信號(hào)各種熱電偶熱電阻遠(yuǎn)傳壓力mv標(biāo)準(zhǔn)電壓標(biāo)準(zhǔn)電流信號(hào)之間的輕松切換。
adopt up-to-day non-jumper technique makes the input port to gain universal signal input function, users can realize light switching between multi input signal various thermocouple, thermo-resistance, remote pressure, mv, standard voltage current signal only need by change internal parameters, so has improved the universality and reliability of instrument 采用最新無(wú)跳線技術(shù),使輸入端口具備萬(wàn)能信號(hào)輸入功能,只需通過(guò)改變內(nèi)部參數(shù),即可實(shí)現(xiàn)多種輸入信號(hào)各種熱電偶熱電阻遠(yuǎn)傳壓力mv標(biāo)準(zhǔn)電壓標(biāo)準(zhǔn)電流信號(hào)之間的輕松切換,提高了儀表的通用性和可靠性。
two miso ( multi input single output ) four-layer recipe hybrid fuzzy neural networks were trained to learning the short-term reversible fouling growing trend and long-term irreversible fouling growing trend respectively . the combination of two network outputs provides the prediction for overall fouling . the results of experiments show the method has a better performance on a wort evaporator fouling prediction than the experiential formula 把間歇換熱設(shè)備的周期性結(jié)垢分解為可逆垢和不可逆垢,通過(guò)兩個(gè)多入單出配方混合模糊神經(jīng)網(wǎng)絡(luò)分別學(xué)習(xí)結(jié)垢的短周期可逆垢增長(zhǎng)趨勢(shì)和長(zhǎng)周期不可逆垢增長(zhǎng)趨勢(shì),并由兩者的組合得到更為精確的污垢熱阻預(yù)測(cè)值。