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.
上传时间： 2013-10-09
上传用户：evil
研究一种基于TMS320F28335 DSP（Digital Signal Processor）的全数字飞行器控制系统的硬件设计，分析了其结构组成：主控制器电路、舵面位置检测电路和通讯等硬件电路设计。经过多次试验调试，所设计的硬件系统可以满足飞行器性能要求。
上传时间： 2013-10-10
上传用户：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.
上传时间： 2013-10-28
上传用户：wujijunshi
为了提高直接转矩控制（DTC）系统定子磁链估计精度，降低电流、电压测量的随机误差，提出了一种基于扩展卡尔曼滤波（EKF）实现异步电机转子位置和速度估计的方法。扩展卡尔曼滤波器是建立在基于旋转坐标系下由定子电流、电压、转子转速和其它电机参量所构成的电机模型上，将定子电流、定子磁链、转速和转子角位置作为状态变量，定子电压为输入变量，定子电流为输出变量，通过对磁链和转速的闭环控制提高定子磁链的估计精度，实现了异步电机的无速度传感器直接转矩控制策略，仿真结果验证了该方法的可行性,提高了直接转矩的控制性能。 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.
上传时间： 2015-01-02
上传用户：qingdou
C++作业，实现vector
标签：
上传时间： 2015-01-21
上传用户：亚亚娟娟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.
上传时间： 2013-12-27
上传用户：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.
标签： classification different document performs
上传时间： 2015-03-03
上传用户：希酱大魔王
最新的支持向量机工具箱，有了它会很方便 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.
上传时间： 2013-12-16
上传用户：亚亚娟娟123
java语言中的系统类，包括String类、 StringBuffer类、 Vector类、 Data类、 Random类
上传时间： 2013-12-20
上传用户：dsgkjgkjg
数据挖掘算法，support vector machine算法源代码，用于分类
标签： 数据挖掘算法
上传时间： 2015-04-11
上传用户：561596
