An adaptive fuzzy integral sliding mode controller for mismatched Time-Varying linear systems is presented in this paper. The proposed fuzzy integral sliding mode controller is designed to have zero steady state system error under step inputs and alleviate the undesired chattering around the sliding surface
標簽： Time-Varying controller mismatched adaptive
上傳時間： 2014-12-04
上傳用戶：luopoguixiong
Wireless communications has become a field of enormous scientific and economic interest. Recent success stories include 2G and 3G cellular voice and data services (e.g., GSM and UMTS), wireless local area networks (WiFi/IEEE 802.11x), wireless broadband access (WiMAX/IEEE 802.16x), and digital broadcast systems (DVB, DAB, DRM). On the physical layer side, traditional designs typically assume that the radio channel remains constant for the duration of a data block. However, researchers and system designers are increasingly shifting their attention to channels that may vary within a block. In addition to time dispersion caused by multipath propagation, these rapidly Time-Varying channels feature frequency dispersion resulting from the Doppler effect. They are, thus, often referred to as being “doubly dispersive.”
標簽： Time-Varying Channels
上傳時間： 2020-06-01
上傳用戶：shancjb
Knowledge of the process noise covariance matrix is essential for the application of Kalman filtering. However, it is usually a difficult task to obtain an explicit expression of for large time varying systems. This paper looks at an adaptive Kalman filter method for dynamic harmonic state estimation and harmonic injection tracking.
標簽： application covariance Knowledge essential
上傳時間： 2014-01-19
上傳用戶：litianchu
To estimate the input-output mapping with inputs x ％ and outputs y generated by the following nonlinear, ％ nonstationary state space model: ％ x(t+1) = 0.5x(t) + [25x(t)]/[(1+x(t))^(2)] ％ + 8cos(1.2t) + process noise ％ y(t) = x(t)^(2) / 20 + 6 squareWave(0.05(t-1)) + 3 ％ + time varying measurement noise ％ using a multi-layer perceptron (MLP) and both the EKF and ％ the hybrid importance-samping resampling (SIR) algorithm.
標簽： input-output the generated following
上傳時間： 2014-01-05
上傳用戶：royzhangsz
This a very simple baseband simulator for SC-FDMA system. This simulator is part of the upcoming book “Single Carrier FDMA: A New Air Interface for Long Term Evolution” (Wiley, Nov. 2008) which I co-authored with professor David J. Goodman at Polytechnic University. The purpose of this simulator is to give some concrete idea of how SC-FDMA system works. It does lack many realistic and sophisticated features such as channel coding, Time-Varying fading channel model, soft decision decoding, etc. Regardless, I am hoping that it will help you understand SC-FDMA which is a fairly new development in 3GPP LTE.
標簽： simulator This baseband upcoming
上傳時間： 2016-08-26
上傳用戶：小草123
a new method for identification of fast fading mobile channels. Estimates both the channel statistics and the time varying channel impulse respone on -line.
標簽： identification Estimates statistic channels
上傳時間： 2014-12-22
上傳用戶：lz4v4
This book is about the systematic application of the switching function technique for theanalysisofpowerelectroniccircuits. Theswitchingfunctionmethodofanalysisis basedonthederivationofthevoltage–currentexpressionsofaswitchedcircuitcover- ing all modes into a single expression, a ‘unified expression’. A ‘unified expression’ istheresultofapplyingthesuperpositiontheoreminordertocombinetheexpressions of all modes of the circuit into one expression with time varying parameters.
上傳時間： 2020-06-07
上傳用戶：shancjb
從時域分析比較了ofdm和stbc與ofdm結(jié)合的性能差別-from time-domain analysis and comparison of OFDM stbc OFDM combined wi
上傳時間： 2013-06-05
上傳用戶：caozhizhi
HT47R20A-1時基(Time Base)使用介紹 HT47 系列單片機的時基可提供一個周期性超時時間周期以產(chǎn)生規(guī)則性的內(nèi)部中斷。時基的時鐘來源可由掩膜選擇設(shè)定為WDT 時鐘、RTC 時鐘或指令時鐘（系統(tǒng)時鐘/4）；其超時時間范圍可由掩膜選擇設(shè)定為“時鐘來源”/212~“時鐘來源”/215。如果時基發(fā)生超時現(xiàn)象，則其對應的中斷請求標志（TBF）會被置位，如果中斷允許，則產(chǎn)生一個中斷服務(wù)到08H 的地址。
上傳時間： 2013-11-15
上傳用戶：13925096126
Backpropagation Network Time-Series Forecasting 源碼, 經(jīng)典的BPN人工神經(jīng)網(wǎng)絡(luò)例子源碼
標簽： Backpropagation Forecasting Time-Series Network
上傳時間： 2015-01-05
上傳用戶：tb_6877751
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