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Description: Title: MMSE Receiver for DS-SS in AWGN Channel
Author: Panson Tantikovit
Summary: An adaptive receiver for DS-SS systems
MATLAB Release: R12.1
Required Products: Communications Toolbox,Signal Processing Blockset
Description: This is an adaptive receiver for a direct-sequence spread spectrum (DS-SS) system over an AWGN channel. The adaptive receiver block is modified from the LMS adaptive filter block in DSP Blockset. For DS-SS signal reception, the adaptive filter needs to have multi-rate operation. The input sample rate is equal to chip rate and the output is at symbol rate. Two rates are related by PG, processing gain.
-Title: MMSE Receiver for DS-SS in AWGN Channel Author: Panson Tantikovit Summary: An adaptive receiver for DS-SS systems MATLAB Release: R12.1 Required Products: Communications Toolbox,Signal Processing Blockset Description: This is an adaptive receiver for a direct-sequence spread spectrum (DS-SS) system over an AWGN channel. The adaptive receiver block is modified from the LMS adaptive filter block in DSP Blockset. For DS-SS signal reception, the adaptive filter needs to have multi-rate operation. The input sample rate is equal to chip rate and the output is at symbol rate. Two rates are related by PG, processing gain.
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Size: 20127 |
Author: zzp |
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Description: 基于PD增益自适应调节的模型参考自适应控制-based on PD Gain adaptive regulation of MRAC
Platform: |
Size: 1199 |
Author: 满延杰 |
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Description: 迭代自适应Simpson，Lobatto积分
In almost every standard book on numerics quadrature algorithms like the adaptive Simpson or the adaptive Lobatto algorithm are presented in a recursive way. The benefit of the recursive programming is the compact and clear representation. However, recursive quadrature algorithms might be transformed into iterative quadrature algorithms without major modifications in the structure of the algorithm.
We present iterative adaptive quadrature algorithm (adaptiveSimpson and adaptiveLobatto), which preserves the compactness and the clarity of the recursive algorithms (e.g. quad, quadv, and quadl). Our iterative algorithm provides a parallel calculation of the integration function, which leads to tremendous gain in run-time, in general. Our results suggest a general iterative and not a recursive implementation of adaptive quadrature formulas, once the programming language permits parallel access to the integration function. For details the attached PDF file Conrad_08.pdf.
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Size: 232144 |
Author: zzn |
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Description: This an adaptive receiver for a direct-sequence spread spectrum (DS-SS) system over an AWGN channel. The adaptive receiver block is modified from the LMS adaptive filter block in DSP Blockset. For DS-SS signal reception, the adaptive filter needs to have multi-rate operation. The input sample rate is equal to chip rate and the output is at symbol rate. Two rates are related by PG, processing gain
Platform: |
Size: 8182 |
Author: 张非 |
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Description: This an adaptive receiver for a direct-sequence spread spectrum (DS-SS) system over an AWGN channel. The adaptive receiver block is modified from the LMS adaptive filter block in DSP Blockset. For DS-SS signal reception, the adaptive filter needs to have multi-rate operation. The input sample rate is equal to chip rate and the output is at symbol rate. Two rates are related by PG, processing gain
Platform: |
Size: 8192 |
Author: 张非 |
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Description: Title: MMSE Receiver for DS-SS in AWGN Channel
Author: Panson Tantikovit
Summary: An adaptive receiver for DS-SS systems
MATLAB Release: R12.1
Required Products: Communications Toolbox,Signal Processing Blockset
Description: This is an adaptive receiver for a direct-sequence spread spectrum (DS-SS) system over an AWGN channel. The adaptive receiver block is modified from the LMS adaptive filter block in DSP Blockset. For DS-SS signal reception, the adaptive filter needs to have multi-rate operation. The input sample rate is equal to chip rate and the output is at symbol rate. Two rates are related by PG, processing gain.
-Title: MMSE Receiver for DS-SS in AWGN Channel Author: Panson Tantikovit Summary: An adaptive receiver for DS-SS systems MATLAB Release: R12.1 Required Products: Communications Toolbox,Signal Processing Blockset Description: This is an adaptive receiver for a direct-sequence spread spectrum (DS-SS) system over an AWGN channel. The adaptive receiver block is modified from the LMS adaptive filter block in DSP Blockset. For DS-SS signal reception, the adaptive filter needs to have multi-rate operation. The input sample rate is equal to chip rate and the output is at symbol rate. Two rates are related by PG, processing gain.
Platform: |
Size: 19456 |
Author: zzp |
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Description: 基于PD增益自适应调节的模型参考自适应控制-based on PD Gain adaptive regulation of MRAC
Platform: |
Size: 1024 |
Author: 满延杰 |
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Description: 自适应量化。读取wav文件，使用2到12比特量化。抽样频率8khz-Adaptive quantization. Read wav files, using 2-12 bit quantify. Sampling frequency of 8kHz
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Size: 1024 |
Author: kenny |
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Description: 应用自适应的干扰对消法去除高斯白噪声，程序中给出两种相关噪声产生的方法，第一种只有一个噪声是随机产生的，第二种两个噪声都是随机产生的。程序中给出了去噪后信噪比和均方差的增益。-Application of adaptive interference cancellation method to remove Gaussian white noise, the procedure given in the relevant noise generated by the two methods, first there is only one noise is randomly generated, the second two are randomly generated noise. Denoising process is given after the signal to noise ratio and mean square deviation of the gain.
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Size: 1024 |
Author: 李冰 |
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Description: 迭代自适应Simpson，Lobatto积分
In almost every standard book on numerics quadrature algorithms like the adaptive Simpson or the adaptive Lobatto algorithm are presented in a recursive way. The benefit of the recursive programming is the compact and clear representation. However, recursive quadrature algorithms might be transformed into iterative quadrature algorithms without major modifications in the structure of the algorithm.
We present iterative adaptive quadrature algorithm (adaptiveSimpson and adaptiveLobatto), which preserves the compactness and the clarity of the recursive algorithms (e.g. quad, quadv, and quadl). Our iterative algorithm provides a parallel calculation of the integration function, which leads to tremendous gain in run-time, in general. Our results suggest a general iterative and not a recursive implementation of adaptive quadrature formulas, once the programming language permits parallel access to the integration function. For details the attached PDF file Conrad_08.pdf. -Iterative Adaptive Simpson, Lobatto Points In almost every standard book on numerics quadrature algorithms like the adaptive Simpson or the adaptive Lobatto algorithm are presented in a recursive way. The benefit of the recursive programming is the compact and clear representation. However, recursive quadrature algorithms might be transformed into iterative quadrature algorithms without major modifications in the structure of the algorithm.We present iterative adaptive quadrature algorithm (adaptiveSimpson and adaptiveLobatto), which preserves the compactness and the clarity of the recursive algorithms (eg quad, quadv, and quadl) . Our iterative algorithm provides a parallel calculation of the integration function, which leads to tremendous gain in run-time, in general. Our results suggest a general iterative and not a recursive implementation of adaptive quadrature formulas, once the programming language permits parallel access to the integration function. For details the attached PDF file Conrad_08.pdf.
Platform: |
Size: 231424 |
Author: zzn |
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Description: Experiments with Kalman Gain for a simple noisy measurements - Adaptive Kalman filter technique
Platform: |
Size: 2048 |
Author: srivardhan |
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Description: 用局部参数最优化方法设计一个模型参考自适应系统,可调增益的初值Kc(0)=0.2，给定值r(t)为单位阶跃信号，即r(t)=A×1(t)。
要求：
1把连续系统离散化(采样时间可取0.1)。
2编制并运行这个系统的计算机程序(注意调整B值，使系统获得较好的自适应特性)。
3记录ym、yp的曲线 记录kp×kc的曲线 记录广义输出误差e的变化曲线。
4在参数收敛后，让Kp=2变为Kp=1，重新观察Kp×Kc及e的变化曲线。
5找出在确定的B值下，使系统不稳定的A值(阶跃信号的幅值)，并与用劳斯稳定判据计算的结果比较。
-With local parameter optimization method to design a model reference adaptive system, adjustable gain initial Kc (0) = 0.2, for a given value of r (t) for the unit step signal that r (t) = A × 1 ( t). Requirements: a continuous system discretization (sampling time of 0.1 preferred). 2 compiled and run the system computer program (Note B to adjust the value of the system to obtain better adaptive characteristics). 3 records ym, yp curve record kp × kc curve records generalized output error e curves. 4 parameter convergence, let Kp = 2 into the Kp = 1, re-observed Kp × Kc and e curves. B-5 to find value in determining the next, making the system unstable A value (step signal of amplitude), and using Routh stability criterion and the comparison of the results calculated.
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Size: 1024 |
Author: yk |
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Description: a gain-adjusted fuzzy PI/PD (GFPIPD) adaptive controller is proposed. The proposed controller first
constructs fuzzy rules for fuzzy PD/PI controller with the fixed weighting. Then the fuzzy rules, which self-learning their parameters for a desired condition, are learned through the accumulated GA. Finally, fuzzy gain-adjusted mechanism is further learned through the accumulated GA for the variations
of a measured system dynamics. The proposed GFPIPD controller is tested by the high order systems with time delay and variable system dynamics. The experimental results illustrate the performance of the proposed method.
Platform: |
Size: 166912 |
Author: dp |
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Description: 用李雅普诺夫第二法推出自适应算法来保证自适应系统的全局稳定性。-With the the Lyapunov second law introduced adaptive algorithm to ensure global stability of the adaptive system.
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Size: 10240 |
Author: 罗丹 |
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Description: This paper proposes an adaptive fuzzy sliding mode controller for robotic manipulators. An adaptive single-input single-output (SISO) fuzzy system is applied to calculate each element of the control gain vector in a sliding mode controller. The adaptive law is designed based on the Lyapunov method. Mathematical proof for the stability and the convergence of the system is presented. Various operation situations such as the set point control and the trajectory control are simulated. The simulation results demonstrate that the chattering and the steady state errors, which usually occur in the classical sliding mode control, are eliminated and satisfactory trajectory tracking is achieved.
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Size: 1337344 |
Author: Diamant |
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Description: MIT归一化算法，为了克服MIT自适应律的缺陷需要对其做些修正，使得自适应增益与输入信号幅值无关-MIT normalization algorithm, MIT adaptive law in order to overcome its shortcomings need to do to fix such adaptive gain is independent of the input signal amplitude
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Size: 1024 |
Author: 胡双 |
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Description: MIT的模型参考自适应控制算法：其中：num：实际模型分子多项式系数； den：实际模型分母多项式系数； numm：辨识模型分子多项式系数； denm：辨识模型分母多项式系数； Kp：实际模型静态增益； Km：辨识模型静态增益； n：模型阶次； r：自适应增益； H：参考输入幅值。-MIT model reference adaptive control algorithm: Where: num: actual model numerator polynomial coefficients den: actual model denominator polynomial coefficients numm: identification model numerator polynomial coefficients denm: identification model denominator polynomial coefficients Kp: static gain practical model Km: identification model static gain n: model order r: adaptive gain H: reference input amplitude.
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Size: 1024 |
Author: li |
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Description: 语音增强，采用自适应增益平均和低延时卷积的谱减，函数可直接调用，简单易行-Speech enhancement, the use of adaptive gain average and low delay convolution spectrum subtraction, the function can be called directly, simple and easy to use
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Size: 2048 |
Author: |
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Description: 利用MATLAB编写的自适应控制算法，自适应增益为0.1，参考输入幅值为0.6时参考模型输出与实际输出。-Prepared using MATLAB adaptive control algorithm, adaptive gain of 0.1, the reference input amplitude of 0.6 reference model output and the actual output.
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Size: 1024 |
Author: 谢冬冬 |
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Description: 基于模糊自适应增益调整的机器人滑模控制 二自由度机器人 使用 s-function(An s-function is used to control the robot sliding mode control based on fuzzy adaptive gain adjustment.)
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Size: 4096 |
Author: 风落烂柯寺 |
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