<acronym id="cs0uy"></acronym> The Xilinx Zynq-7000 Extensible Processing Platform (EPP) redefines the possibilities for embedded systems, giving system and software architects and developers a flexible platform to launch their new solutions and traditional ASIC and ASSP users an alternative that aligns with today’s programmable imperative. The new class of product elegantly combines an industrystandard ARMprocessor-based system with Xilinx 28nm programmable logic—in a single device. The processor boots first, prior to configuration of the programmable logic. This, along with a streamlined workflow, saves time and effort and lets software developers and hardware designers start development simultaneously.
上傳時間： 2013-10-09
上傳用戶：evil
研究一種基于TMS320F28335 DSP（Digital Signal Processor）的全數字飛行器控制系統的硬件設計，分析了其結構組成：主控制器電路、舵面位置檢測電路和通訊等硬件電路設計。經過多次試驗調試，所設計的硬件系統可以滿足飛行器性能要求。
上傳時間： 2013-10-10
上傳用戶：z1191176801
The power of programmability gives industrial automation designers a highly efficient， cost-effective alternative to traditional motor control units （MCUs）。 The parallel-processing power， fast computational speeds， and connectivity versatility of Xilinx® FPGAs can accelerate the implementation of advanced motor control algorithms such as Field Oriented Control （FOC）。 Additionally， Xilinx devices lower costs with greater on-chip integration of system components and shorten latencies with high-performance digital signal processing （DSP） that can tackle compute-intensive functions such as PID Controller， Clark/Park transforms， and Space Vector PWM. The Xilinx Spartan®-6 FPGA Motor Control Development Kit gives designers an ideal starting point for evaluating time-saving， proven， motor-control reference designs. The kit also shortens the process of developing custom control capabilities， with integrated peripheral functions （Ethernet， PowerLink， and PCI® Express）， a motor-control FPGA mezzanine card （FMC） with built-in Texas Instruments motor drivers and high-precision Delta-Sigma modulators， and prototyping support for evaluating alternative front-end circuitry.
上傳時間： 2013-10-28
上傳用戶：wujijunshi
為了提高直接轉矩控制（DTC）系統定子磁鏈估計精度，降低電流、電壓測量的隨機誤差，提出了一種基于擴展卡爾曼濾波（EKF）實現異步電機轉子位置和速度估計的方法。擴展卡爾曼濾波器是建立在基于旋轉坐標系下由定子電流、電壓、轉子轉速和其它電機參量所構成的電機模型上，將定子電流、定子磁鏈、轉速和轉子角位置作為狀態變量，定子電壓為輸入變量，定子電流為輸出變量，通過對磁鏈和轉速的閉環控制提高定子磁鏈的估計精度，實現了異步電機的無速度傳感器直接轉矩控制策略，仿真結果驗證了該方法的可行性,提高了直接轉矩的控制性能。 Abstract: In order to improve the Direct Torque Control（DTC） system of stator flux estimation accuracy and reduce the current， voltage measurement of random error， a novel method to estimate the speed and rotor position of asynchronous motor based on extended Kalman filter was introduced. EKF was based on d-p axis motor and other motor parameters （state vector: stator current， stator flux linkage， rotor angular speed and position； input: stator voltage； output: staror current). EKF was designed for stator flux and rotor speed estimation in close-loop control. It can improve the estimated accuracy of stator flux. It is possible to estimate the speed and rotor position and implement asynchronous motor drives without position and speed sensors. The simulation results show it is efficient and improves the control performance.
上傳時間： 2015-01-02
上傳用戶：qingdou
C++作業，實現vector
標簽：
上傳時間： 2015-01-21
上傳用戶：亞亞娟娟123
A windows BMP file is a common image format that Java does not handle. While BMP images are used only on windows machines, they are reasonably common. Reading these shows how to read complex structures in Java and how to alter they byte order from the big endian order used by Java to the little endian order used by the windows and the intel processor.
上傳時間： 2013-12-27
上傳用戶：gaojiao1999
Rainbow is a C program that performs document classification usingone of several different methods, including naive Bayes, TFIDF/Rocchio,K-nearest neighbor, Maximum Entropy, Support Vector Machines, Fuhr sProbabilitistic Indexing, and a simple-minded form a shrinkage withnaive Bayes.
標簽： classification different document performs
上傳時間： 2015-03-03
上傳用戶：希醬大魔王
最新的支持向量機工具箱，有了它會很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999.
上傳時間： 2013-12-16
上傳用戶：亞亞娟娟123
java語言中的系統類，包括String類、 StringBuffer類、 Vector類、 Data類、 Random類
上傳時間： 2013-12-20
上傳用戶：dsgkjgkjg
數據挖掘算法，support vector machine算法源代碼，用于分類
標簽： 數據挖掘算法
上傳時間： 2015-04-11
上傳用戶：561596
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