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一种组合神经网络非线性判决反馈均衡器 总被引:2,自引:0,他引:2
1 引言数字通信系统的典型模型如图1所示,发送序列s(n)经信道传输后因发生失真及噪声v(n)的影响而成为畸变信号x(n),为此需用均衡器对其进行均衡以恢复发送序列。目前,自适应均衡已成为数字通信中一种非常重要的技术,自适应均衡器的构成也是多种多样,其中最简单的是线性横向均衡器(LTE)和判决反馈均衡器(DFE),它们都比较适用于线性信道。如果信道呈现非线性特性,两者的性能特别是LTE的均衡能力会大大下降,而利用径向基函数网络(RBFN)等构 相似文献
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《Intelligent Data Analysis》1999,3(4):307-313
In this work the kernel Adaline algorithm is presented. The new algorithm is a generalisation of Widrow's and Hoff's linear Adaline, and allows to approximate non-linear functional relationships. Similar to the linear adaline, the proposed neural network algorithm minimises the least-mean-squared (LMS) cost function. It can be guaranteed that the kernel Adaline's cost function is always convex, therefore the method does not suffer from local optima as known in conventional neural networks. The algorithm uses potential function operators due to Aizerman and colleagues to map the training points in a first stage into a very high dimensional non-linear “feature” space. In the second stage the LMS solution in this space is determined by the algorithm. Weight decay regularisation allows to avoid overfitting effects, and can be performed efficiently. The kernel Adaline algorithm works in a sequential fashion, is conceptually simple, and numerically robust. The method shows a high performace in tasks like one dimensional curve fitting, system identification, and speech processing. 相似文献
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Harmonic estimation is the main process in active filters for harmonic reduction. A hybrid Adaptive Neural Network–Particle Swarm Optimization (ANN–PSO) algorithm is being proposed for harmonic isolation. Originally Fourier Transformation is used to analyze a distorted wave. In order to improve the convergence rate and processing speed an Adaptive Neural Network Algorithm called Adaline has then been used. A further improvement has been provided to reduce the error and increase the fineness of harmonic isolation by combining PSO algorithm with Adaline algorithm. The inertia weight factor of PSO is combined along with the weight factor of Adaline and trained in Neural Network environment for better results. ANN–PSO provides uniform convergence with the convergence rate comparable that of Adaline algorithm. The proposed ANN–PSO algorithm is implemented on an FPGA. To validate the performance of ANN–PSO; results are compared with Adaline algorithm and presented herein. 相似文献
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In this paper, a novel fuzzy Generalized Predictive Control (GPC) is proposed for discrete-time nonlinear systems via Takagi-Sugeno system based Kernel Ridge Regression (TS-KRR). The TS-KRR strategy approximates the unknown nonlinear systems by learning the Takagi-Sugeno (TS) fuzzy parameters from the input-output data. Two main steps are required to construct the TS-KRR: the first step is to use a clustering algorithm such as the clustering based Particle Swarm Optimization (PSO) algorithm that separates the input data into clusters and obtains the antecedent TS fuzzy model parameters. In the second step, the consequent TS fuzzy parameters are obtained using a Kernel ridge regression algorithm. Furthermore, the TS based predictive control is created by integrating the TS-KRR into the Generalized Predictive Controller. Next, an adaptive, online, version of TS-KRR is proposed and integrated with the GPC controller resulting an efficient adaptive fuzzy generalized predictive control methodology that can deal with most of the industrial plants and has the ability to deal with disturbances and variations of the model parameters. In the adaptive TS-KRR algorithm, the antecedent parameters are initialized with a simple K-means algorithm and updated using a simple gradient algorithm. Then, the consequent parameters are obtained using the sliding-window Kernel Recursive Least squares (KRLS) algorithm. Finally, two nonlinear systems: A surge tank and Continuous Stirred Tank Reactor (CSTR) systems were used to investigate the performance of the new adaptive TS-KRR GPC controller. Furthermore, the results obtained by the adaptive TS-KRR GPC controller were compared with two other controllers. The numerical results demonstrate the reliability of the proposed adaptive TS-KRR GPC method for discrete-time nonlinear systems. 相似文献
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This paper proposes a novel adaptive multiple modelling algorithm for non-linear and non-stationary systems. This simple modelling paradigm comprises K candidate sub-models which are all linear. With data available in an online fashion, the performance of all candidate sub-models are monitored based on the most recent data window, and M best sub-models are selected from the K candidates. The weight coefficients of the selected sub-model are adapted via the recursive least square (RLS) algorithm, while the coefficients of the remaining sub-models are unchanged. These M model predictions are then optimally combined to produce the multi-model output. We propose to minimise the mean square error based on a recent data window, and apply the sum to one constraint to the combination parameters, leading to a closed-form solution, so that maximal computational efficiency can be achieved. In addition, at each time step, the model prediction is chosen from either the resultant multiple model or the best sub-model, whichever is the best. Simulation results are given in comparison with some typical alternatives, including the linear RLS algorithm and a number of online non-linear approaches, in terms of modelling performance and time consumption. 相似文献
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基于判决反馈结构的自适应均衡算法仿真研究 总被引:3,自引:0,他引:3
在数字通信中,接收信号通常会受到码间干扰的影响,尤其是在多径衰落无线信道环境中,这种现象更为严重。采用自适应均衡技术可以对信道响应进行补偿。由于在数字通信系统中,信道往往为非最小相位系统,此时线性均衡器性能不佳,因此该文对比研究了非线性结构的自适应波特间隔判决反馈均衡器和自适应分数间隔判决反馈均衡器,并对其性能进行了计算机仿真。仿真结果表明,对于非最小相位信道,自适应分数间隔判决反馈均衡器的性能优于波特间隔判决反馈均衡器。 相似文献
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Xiaoqin Zeng Jing Shao Yingfeng Wang Shuiming Zhong 《Neural computing & applications》2009,18(8):957-965
Architecture design is a very important issue in neural network research. One popular way to find proper size of a network is to prune an oversize trained network to a smaller one while keeping established performance. This paper presents a sensitivity-based approach to prune hidden Adalines from a Madaline with causing as little as possible performance loss and thus easy compensating for the loss. The approach is novel in setting up a relevance measure, by means of an Adalines’ sensitivity measure, to locate the least relevant Adaline in a Madaline. The sensitivity measure is the probability of an Adaline’s output inversions due to input variation with respect to overall input patterns, and the relevance measure is defined as the multiplication of the Adaline’s sensitivity value by the summation of the absolute value of the Adaline’s outgoing weights. Based on the relevance measure, a pruning algorithm can be simply programmed, which iteratively prunes an Adaline with the least relevance value from hidden layer of a given Madaline and then conducts some compensations until no more Adalines can be removed under a given performance requirement. The effectiveness of the pruning approach is verified by some experimental results. 相似文献
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Robust adaptive algorithms for underwater acoustic channel estimation and their performance analysis
We introduce a novel family of adaptive robust channel estimators for highly challenging underwater acoustic (UWA) channels. Since the underwater environment is highly non-stationary and subjected to impulsive noise, we use adaptive filtering techniques based on minimization of a logarithmic cost function, which results in a better trade-off between the convergence rate and the steady state performance of the algorithm. To improve the convergence performance of the conventional first and second order linear estimation methods while mitigating the stability issues related to impulsive noise, we intrinsically combine different norms of the error in the cost function using a logarithmic term. Hence, we achieve a comparable convergence rate to the faster algorithms, while significantly enhancing the stability against impulsive noise in such an adverse communication medium. Furthermore, we provide a thorough analysis for the tracking and steady-state performances of our proposed methods in the presence of impulsive noise. In our analysis, we not only consider the impulsive noise, but also take into account the frequency and phase offsets commonly experienced in real life experiments. We demonstrate the performance of our algorithms through highly realistic experiments performed on accurately simulated underwater acoustic channels. 相似文献
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《Advances in Engineering Software》2005,36(7):442-447
In this paper, we propose a new recursive classifier based on a recurrent neural network. A supervised algorithm is employed to estimate the classifier parameters. The proposed classifier is used to form a non-linear Decision Feedback Equalizer (DFE) for communication channels. A new procedure allowing the estimation of the decision delay is also presented so that the classifier parameters and the decision delay are estimated at the same time. This new DFE leads to suitable equalization performances even in presence of non-linear and non–minimum phase channels. 相似文献
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A. Zeiler R. Faltermeier A. M. Tomé C. Puntonet A. Brawanski E. W. Lang 《Neural Processing Letters》2013,37(1):21-32
Biomedical signals are in general non-linear and non-stationary. empirical mode decomposition in conjunction with a Hilbert-Huang Transform provides a fully adaptive and data-driven technique to extract intrinsic mode functions. The latter represent a complete set of locally orthogonal basis functions to represent non-linear and non-stationary time series. Large scale biomedical time series necessitate an online analysis, which is presented in this contribution. It shortly reviews the technique of EMD and related algorithms, discusses the recently proposed weighted sliding EMD algorithm (wSEMD) and, additionally, proposes a more sophisticated implementation of the weighting process. As an application to biomedical signals we will show that wSEMD in combination with mutual information could be used to detect temporal correlations of arterial blood pressure and intracranial pressure monitored at a neurosurgical intensive care unit. We will demonstrate that the wSEMD technique renders itself much more flexible than the Fourier based method used in Faltermeier et al. (Acta Neurochir Suppl, 114, 35–38, 2012). 相似文献
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大规模并行处理机系统(MPP)中路由算法对互联网络通信性能和系统性能起着重要作用。自适应路由算法具有灵活性好、网络的通道利用率高和网络容错能力强等优点,但其实现难度较大,因而目前仅在少数MPP系统中得以实现。文中在mesh结构上提出了一个低代价无死锁的安全自适应最短虫孔路由算法LCFAA,该算法所需虚通道数少,具有代价低、自适应性强的特点。文中证明了算法的无死锁、无活锁性和完全自适应性,并模拟验证 相似文献
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Ning Sun Hai-xian Wang Zhen-hai Ji Cai-rong Zou Li Zhao 《Neural computing & applications》2008,17(1):59-64
Recently, a new approach called two-dimensional principal component analysis (2DPCA) has been proposed for face representation
and recognition. The essence of 2DPCA is that it computes the eigenvectors of the so-called image covariance matrix without
matrix-to-vector conversion. Kernel principal component analysis (KPCA) is a non-linear generation of the popular principal
component analysis via the Kernel trick. Similarly, the Kernelization of 2DPCA can be benefit to develop the non-linear structures
in the input data. However, the standard K2DPCA always suffers from the computational problem for using the image matrix directly.
In this paper, we propose an efficient algorithm to speed up the training procedure of K2DPCA. The results of experiments
on face recognition show that the proposed algorithm can achieve much more computational efficiency and remarkably save the
memory-consuming compared to the standard K2DPCA. 相似文献
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利用局部线性嵌入算法进行图像去噪时,如果局部近邻样本呈现非线性关系,图像去噪效果会受到影响。针对该问题,提出基于核局部线性嵌入算法的图像去噪方法。通过非线性核函数将样本映射到高维线性空间,在高维空间运用局部线性嵌入算法进行图像去噪。实验结果表明,该方法能有效地对高维非线性图像进行去噪,性能优于中值滤波算法和局部线性嵌入算法。 相似文献
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Linear and non-linear adaptive algorithms are investigated for Space Division Multiple Access (SDMA). SDMA is one of the emerging
techniques for multiple access of users in mobile radio, which uses spatial distribution of users for their differentiation.
The performance of the linear Square Root Kalman (SRK) algorithm for SDMA is compared to that of the non-linear Recurrent
Neural Network (RNN) technique. The proposed SDMA-RNN technique is evaluated over Rician fading channels, and it shows improved
Bit Error Rate (BER) performance in comparison with the linear SRK-based technique. The performance of SDMA-RNN is also compared
with that of Code Division Multiple Access (CDMA) systems, showing that it could be used as a viable alternative scheme for
multiple access of users. Finally, a Hybrid CDMA-SDMA system is proposed combining CDMA and SDMA-RNN systems. Hybrid CDMA-SDMA
exhibits a very good potential for increase in the capacity and the performance of mobile communications systems. 相似文献
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提出一种基于不完整自然梯度的变步长约束算法,用来处理非平稳环境下的瞬时盲源分离问题.该算法利用系统上的扰动对代价函数进行约束,对算法中的约束因子采用自适应形式,根据分离情况对约束因子进行自适应调整,以加快收敛速度.同时,引入基于代价函数梯度的变步长,使其具有更好的跟踪性能.仿真结果表明,在非平稳环境下,所提出的算法在提高收敛速度的同时可以有效分离源信号而不产生严重的稳态误差. 相似文献