219 resultados para RBF


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In this paper, a new type of resonant Brewster filters (RBF) with surface relief structure for the multiple channels is first presented by using the rigorous coupled-wave analysis and the S-matrix method. By tuning the depth of homogeneous layer which is under the surface relief structure, the multiple channels phenomenon is obtained. Long range, extremely low sidebands and multiple channels are found when the RBF with surface relief structure is illuminated with Transverse Magnetic incident polarization light near the Brewster angle calculated with the effective media theory of sub wavelength grating. Moreover, the wavelengths of RBF with surface relief structure can be easily shifted by changing the depth of homogeneous layer while its optical properties such as low sideband reflection and narrow band are not spoiled when the depth is changed. Furthermore, the variation of the grating thickness does not effectively change the resonant wavelength of RBF, but have a remarkable effect on its line width, which is very useful for designing such filters with different line widths at desired wavelength.

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Biomimetic pattern recogntion (BPR), which is based on "cognition" instead of "classification", is much closer to the function of human being. The basis of BPR is the Principle of homology-continuity (PHC), which means the difference between two samples of the same class must be gradually changed. The aim of BPR is to find an optimal covering in the feature space, which emphasizes the "similarity" among homologous group members, rather than "division" in traditional pattern recognition. Some applications of BPR are surveyed, in which the results of BPR are much better than the results of Support Vector Machine. A novel neuron model, Hyper sausage neuron (HSN), is shown as a kind of covering units in BPR. The mathematical description of HSN is given and the 2-dimensional discriminant boundary of HSN is shown. In two special cases, in which samples are distributed in a line segment and a circle, both the HSN networks and RBF networks are used for covering. The results show that HSN networks act better than RBF networks in generalization, especially for small sample set, which are consonant with the results of the applications of BPR. And a brief explanation of the HSN networks' advantages in covering general distributed samples is also given.

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In this paper, a novel mathematical model of neuron-Double Synaptic Weight Neuron (DSWN)(l) is presented. The DSWN can simulate many kinds of neuron architectures, including Radial-Basis-Function (RBF), Hyper Sausage and Hyper Ellipsoid models, etc. Moreover, this new model has been implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. The flexibility of the DSWN has also been described in constructing neural networks. Based on the theory of Biomimetic Pattern Recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-II neurocomputer. In these two special cases, the result showed DSWN neural network had great potential in pattern recognition.

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Based on the introduction of the traditional mathematical models of neurons in general-purpose neurocomputer, a novel all-purpose mathematical model-Double synaptic weight neuron (DSWN) is presented, which can simulate all kinds of neuron architectures, including Radial-Basis-Function (RBF) and Back-propagation (BP) models, etc. At the same time, this new model is realized using hardware and implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. In this paper, the flexibility of the new model has also been described in constructing neural networks and based on the theory of Biomimetic pattern recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-H neurocomputer. The result showed DSWN neural network has great potential in pattern recognition.

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A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

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Double weighted neural network; is a kind of new general used neural network, which, compared with BP and RBF network, may approximate the training samples with a move complicated geometric figure and possesses a even greater approximation. capability. we study structure approximate based on double weighted neural network and prove its rationality.

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Dynamic Power Management (DPM) is a technique to reduce power consumption of electronic system by selectively shutting down idle components. In this article we try to introduce back propagation network and radial basis network into the research of the system-level power management policies. We proposed two PM policies-Back propagation Power Management (BPPM) and Radial Basis Function Power Management (RBFPM) which are based on Artificial Neural Networks (ANN). Our experiments show that the two power management policies greatly lowered the system-level power consumption and have higher performance than traditional Power Management(PM) techniques-BPPM is 1.09-competitive and RBFPM is 1.08-competitive vs. 1.79 . 1.45 . 1.18-competitive separately for traditional timeout PM . adaptive predictive PM and stochastic PM.

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One novel neuron with variable nonlinear transfer function is firstly proposed, It could also be called as subsection transfer function neuron. With different transfer function components, by virtue of multi-thresholded, the variable transfer function neuron switch on among different nonlinear excitated state. And the comparison of output's transfer characteristics between it and single-thresholded neuron will be illustrated, with some practical application experiments on Bi-level logic operation, at last the simple comparison with conventional BP, RBF, and even DBF NN is taken to expect the development foreground on the variable neuron.. The novel nonlinear transfer function neuron could implement the random nonlinear mapping relationship between input layer and output layer, which could make variable transfer function neuron have one much wider applications on lots of reseach realm such as function approximation pattern recognition data compress and so on.

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分析了BP、RBF和ARTMAP等人工神经网络在实现非线性映射方面的共同之处,基于RBF等网络对于人脑功能方面的模拟和仿生模式识别的思想,总结出一种处理这类问题的基本框架。该框架的特点是将问题分解为样本覆盖问题和基于模型的映射拟合问题。在利用该框架研究某个函数集在连续函数空间中的稠密性的基础上,提出了一种新的人工神经网络模型——主方向神经网络(PDNN)。通过与BP网络和RBF网络在函数拟合和混沌时间序列预测方面的对比实验,发现PDNN具有非常良好的逼近性能和鲁棒性能。

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通过对高维空间中超弦角(Hyper—Chord Angle)的定义,引出一种高维最小球覆盖的几何算法.结合RBF神经元和优先度排序网络,高维最小球覆盖算法可以有效解决模式识别中若干类样本的分类问题.超弦角的定义也为其他高维空间几何问题的研究提供新思路.

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在本文中提出了一种针对新型双权值神经元网络的数据拟合算法.采用这种新型网络结构和算法,可以克服传统的通用前馈网络中BP算法易陷入局部极小的问题.通过实验比较证明在相同的网络规模下,采用这种新型网络结构和算法可以取得比径向基(RBF)网络更高的拟合精度和更少的迭代次数.

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A new theoretical model of Pattern Recognition principles was proposed, which is based on "matter cognition" instead of "matter classification" in traditional statistical Pattern Recognition. This new model is closer to the function of human being, rather than traditional statistical Pattern Recognition using "optimal separating" as its main principle. So the new model of Pattern Recognition is called the Biomimetic Pattern Recognition (BPR)(1). Its mathematical basis is placed on topological analysis of the sample set in the high dimensional feature space. Therefore, it is also called the Topological Pattern Recognition (TPR). The fundamental idea of this model is based on the fact of the continuity in the feature space of any one of the certain kinds of samples. We experimented with the Biomimetic Pattern Recognition (BPR) by using artificial neural networks, which act through covering the high dimensional geometrical distribution of the sample set in the feature space. Onmidirectionally cognitive tests were done on various kinds of animal and vehicle models of rather similar shapes. For the total 8800 tests, the correct recognition rate is 99.87%. The rejection rate is 0.13% and on the condition of zero error rates, the correct rate of BPR was much better than that of RBF-SVM.

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A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.

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为宜人化双臂操作型服务机器人建立了动力学模型,该模型的特点是独立的机理建模技术结合黑箱技术共同描述出完整的模型;结合建立的模型,提出一种基于NN的自适应鲁棒控制器,并证明了其渐近稳定性.最后,在分析宜人化双臂操作型服务机器人运动特征的基础上,提出一种基于事件的在线协调的策略.

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拟人机器人的动力学具有高度非线性、高度耦合的特点,分析清楚各组成部分之间的交互作用力是实施高级控制方法的基础。文章在以往分析移动机械手的基础上,从整体建模的角度入手,对拟人机器人的交互作用力提出了一个新的模型,即神经网络模型。利用该模型对一个特殊的单一手臂运动的例子进行了拟合,其结果是收敛的。这说明提出的模型是有效的,此后,我们将陆续给出研究成果。