Description: 1.级数求和
2.多项式和有理函数
3.切比雪夫逼近
4.积分和导数的切比雪夫逼近
5.有切比雪夫逼近函数的多项式逼近-1. Sum 2. Polynomials and rational functions 3. Chebyshev approximation 4. Integrals and derivatives of Chebyshev approximation 5. There Chebyshev approximating function of the polynomial approximation Platform: |
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Author:wzz |
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Description: 3~5 GHz Cascoded UWB Power Amplifier.PDF
A 1-8 GHz MMIC Down-Conversion Mixer with Input-Output.PDF
A 3.1- to 8.2-GHz Zero-IF Receiver.PDF
A Design Technique for a High-Gain, 10-GHz Class.PDF
A High-Power Low-Distortion GaAs HBT Power Amplifier with 3.3 V Supply for 5-6 GHz Broadband Wireless Applications.PDF
A novel cascode feedback GaAs MMIC LNA with transformer-coupled output using multiple fabrication processes.pdf
A single-bias diode-regulated 60 GHz monolithic LNA.pdf
Design of MMIC LNA for 1.9 GHz CDMA portable communication.pdf
Study on Matching Performance for Lossless.PDF
二项式与切比雪夫多项式宽带匹配的研究.pdf
-3 ~ 5 GHz Cascoded UWB Power Amplifier.PDF A 1-8 GHz MMIC Down-Conversion Mixer with Input-Output.PDF A 3.1-to 8.2-GHz Zero-IF Receiver.PDF A Design Technique for a High-Gain, 10-- GHz Class.PDF A High-Power Low-Distortion GaAs HBT Power Amplifier with 3.3 V Supply for 5-6 GHz Broadband Wireless Applications.PDF A novel cascode feedback GaAs MMIC LNA with transformer-coupled output using multiple fabrication processes.pdf A single- bias diode-regulated 60 GHz monolithic LNA.pdf Design of MMIC LNA for 1.9 GHz CDMA portable communication.pdf Study on Matching Performance for Lossless.PDF binomial and Chebyshev polynomials of the study of broadband matching. pdf Platform: |
Size: 6309888 |
Author:rolenss |
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Description: 使用matlab进行数值分析时重要的参考资料，具体包含内容可以看英文描述-This text is written primarily for students/readers who have a good background of high school algebra, geometry, trigonometry, and the fundamentals of differential and integral calculus. This text includes the following chapters and appendices: . Introduction to MATLAB . Root Approximations . Sinusoids and Complex Numbers . Matrices and Determinants . Review of Differential Equations . Fourier, Taylor, and Maclaurin Series . Finite Differences and Interpolation . Linear and Parabolic Regression . Solution of Differential Equations by Numerical Methods . Integration by Numerical Methods . Difference Equations . Partial Fraction Expansion . The Gamma and Beta Functions . Orthogonal Functions and Matrix Factorizations . Bessel, Legendre, and Chebyshev Polynomials . Optimization Methods . Difference Equations in Discrete Time Systems . Introduction to Simulink . Ill Conditioned Matrices Each chapter contains numerous practical applications supplemented with detailed instructions for using Platform: |
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Author:何亮 |
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Description: 给定一个普通多项式p(x)，用列向量表示，阶次从高到低排列，此函数返回p(x)的切比雪夫多项式展开的系数，返回的的结果也是一个列向量，同样按切比雪夫多项式的阶次从高到低排列
另外该函数用cos(x)项傅里叶级数展开一个多项式-
ChebyshevExpansion.m by David Terr, Raytheon, 5-26-04
Given a polynomial f(x) of degree n expressed as a row vector of coefficients of x^k with
highest power on the left, expand f(x) as a sum of scalar multiples of
Chebyshev polynomials, i.e. return the column vector of coefficients a_k with k
running from n to 0 from top to bottom such that f(x) = sum_{k=0}^n{a_k P_k(x)}. Platform: |
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Author:Xijun Ye |
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Description: 介绍了切比雪夫多项式的实施过程，以及其主要的步骤和方法。-Introduced the Chebyshev polynomials of the implementation process, and its main steps and methods. Platform: |
Size: 126976 |
Author:yjqiao |
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Description: 提出了一种基于自适应 Chebyshev 多项式神经网络(ACNN)的 Logistic 混沌系统控制算法。该算法采用 Chebyshev
正交多项式作为神经网络的激励函数, 构建 Logistic 混沌系统的预测与控制模型。为了保证算法的稳定性, 提出和证明了收敛定
理, 并利用自适应学习率算法提高神经网络的学习效率和收敛速度。通过采用自适应 Chebyshev 神经网络直接学习 Logistic 混
沌系统的动态特性, 并对系统实施目标函数控制。实验仿真结果表明, 该算法在 Logistic 混沌系统有外部干扰的情况下仍能对其
进行有效控制, 网络学习时间为 0.178 s, 训练步长为 10, 均方误差达到 1.15×10
− 4 , 与其他常见算法相比具有计算量小、速度快、
精度高和网络结构简单等优点。 - A novel algorithm for controlling Logistic chaotic system based on adaptive Chebyshev polynomials
neural networks (ACNN) is presented. In the algorithm, the activation function of hidden units is defined by Chebyshev
orthogonal polynomials in the neural networks, and the forecast and control model of Logistic chaotic system is estab-
lished. In order to ensure stability of the algorithm, the convergence theorem of the algorithm is proposed and proved.
Then the adaptive learning rate algorithm is used for improving the learning efficiency and convergence speed. The
adaptive Chebyshev neural networks directly learn dynamic characters of Logistic chaotic system and control it to target
function. The simulation results show that the algorithm is still effective when there are external disturbance in the Lo-
gistic chaotic system, now the learning time is 0.178s, training steps is 10 and mean square error is 1.15×10 − 4 . Com-
pared with other ordinary Platform: |
Size: 1381376 |
Author: |
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Description: 给定一个普通多项式p(x)，用列向量表示，阶次从高到低排列，此函数返回p(x)的切比雪夫多项式展开的系数，返回的的结果也是一个列向量，同样按切比雪夫多项式的阶次从高到低排列-Given a general polynomial p (x), with a column vector representation, the order highest to lowest, the function returns p (x) Chebyshev polynomial expansion coefficients of the returned result is a column vector, the same press cut the order highest to lowest Chebyshev polynomials Platform: |
Size: 1024 |
Author:Ansser |
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Description: 蚁群算法,简称最初是由美国加州大学伯克利大学的和为求解切比雪夫多项式而提出的一种新型无约束直接寻优算法。
-Colony algorithm, referred originally developed by the University of California at Berkeley and the University of solving Chebyshev polynomials and submitted a new unconstrained optimization algorithm directly. Platform: |
Size: 2048 |
Author:田乐 |
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Description: 以优化SVR算法的参数c和g为例，对DE（差分进化）算法MATLAB源码进行了详细中文注解。(Differential Evolution algorithm (DE) is a heuristic random search algorithm based on group differences. This algorithm is proposed by R.S and k.p. rice for solving Chebyshev polynomials. DE algorithm is also an intelligent optimization algorithm, which is similar to the previous heuristic algorithm, such as ABC, PSO, etc., which is a heuristic optimization algorithm. The DE algorithm is an optimization algorithm that I used in solving the case study of box coverage.) Platform: |
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Author:mercy认真的雪
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