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检索条件"主题词=learning algorithm"

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Sensitivities of Fuzzy Bidirectional Associative Memories to Perturbations of Training Pattern Pairs

Sensitivities of Fuzzy Bidirectional Associative Memories to...

引用

智能计算及其应用国际会议

作者： XU Weihong1,2, ZENG Fanfu1 , SONG Luanjiao2 (1. College of Mathematics and Computer Science, Jishou University, Jishou 416000,China) (2. College of Computer and Communications Engineering, Changsha University of Science and Technology, Changsha 410077,China)

A few of common cases are listed when training pattern pairs may perturb for fuzzy neural network systems. Next, a new concept is proposed which is the sensitivity of a Fuzzy Bidirectional Associative

关键词： fuzzy neural network fuzzy bidirectional associative memory learning algorithm pattern perturbation sensitivity

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Training and Application of Process Neural Network Based on Quantum Shuffled Frog Leaping algorithm

Training and Application of Process Neural Network Based on ...

引用

International Conference on Computer Science and Network Technology

作者： Lijie LIU Qiang ZHANG Coll. Of Inf. Technol. Heilongjiang Bayi Agric. Univ. Daqing China

ISBN: (纸本)9781479905607

Aiming at the problem that it is difficult for BP algorithm to converge because of more parameters in training of process neural networks based on orthogonal basis expansion, a quantum shuffled frog leaping algorithm is presented which combines the quantum theory and is to train the process neural network. In this algorithm, the individuals are expressed with Bloch spherical coordinates of qubits. The quantum individuals are updated by quantum rotation gates, and the mutation of individuals is achieved with Hadamard gates. For the size and direction of rotation angle of quantum rotation gates, a simple determining method is proposed. Above operations extend the search of the solution space effectively. To predict sunspot as an example to validate the presented algorithm.

关键词： backpropagation neural nets quantum computing BP algorithm Bloch spherical coordinates Hadamard gates determining method orthogonal basis expansion process neural network quantum rotation gates quantum shuffled frog leaping algorithm quantum theory qubits Logic gates Neural networks Optimization Quantum computing Sociology Statistics Training learning algorithm Process neural network Quantum Shuffled frog leaping algorithm Neural network quantum computing quanta quantum theory learning algorithms Functional training Sociology Logic gates back propagation Solution space Training quantum gates

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Nonlinear Control Based on an Improved Neural Predictive Control Scheme

Nonlinear Control Based on an Improved Neural Predictive Con...

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International Conference on Computer Science and Network Technology

作者： Huaiqing Ren Yongliang Zhang Dept. of Comput. Sci. Tonghua Normal Univ. Tonghua China

ISBN: (纸本)9781479905607

Generalized neural predictive control is a kind of receding horizontal control method, in which the minimum cost function is used to optimize the control input, but large calculation is needed in the minimization of the cost function. In this paper, time-delay neural network is imposed in the neural predictive control, and the new updating algorithm can return many derivative in Jacobian or Hessian at different time step, which improves the learning algorithm of the traditional neural predictive control in real-time control speed. The simulation results indicate the advantage of the proposed scheme in real-time control speed.

关键词： Hessian matrices Jacobian matrices delays learning systems neurocontrollers nonlinear control systems predictive control Hessian derivative Jacobian derivative improved neural predictive control scheme learning algorithm minimum cost function nonlinear control real-time control speed receding horizontal control method time-delay neural network updating algorithm Biological neural networks Cost function Delay effects Neurons Predictive control neural predictive control

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Application of Optimized Neural Network In Data Processing

Application of Optimized Neural Network In Data Processing

引用

作者： Jianjun WU Modern Education Technology Center Hunan City University

As the ART2 neural network clustering occurs normalization in the data inputting mode by vector and nonlinear transformation pretreatment process is easy to be filtered as a substrate for an important, but a minor component of the noise, while there are still phenomenon of the drifting mode in the learning process due to the correction of the value of weight, this paper proposes an improved method of ART2 neural network. The improved method stores the amplitude information in the learning process, and it is considering the shortest distance of being inputted into the center of the cluster, increasing a threshold limit value for determining outliers at the same time and eliminating the influence of outliers of the clustering results. Finally, the clustering of data samples experimental results show that: the improved ART2 network can handle negative data, the four quadrants of data can be effectively clustered, the performance is superior to the traditional ART2 network.

关键词： Adaptive resonance learning algorithm Neurons Resonance

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Neural Networks

Neural Networks

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作者： Zheng Rong Yang

Neural networks are a class of intelligent learning machines establishing the relationships between descriptors of real-world objects. As optimisation tools they are also a class of computational algorithms implemented using statistical/numerical techniques for parameter estimate, model selection, and generalisation enhancement. In bioinformatics applications, neural networks have played an important role for classification, function approximation, knowledge discovery, and data visualisation. This chapter will focus on supervised neural networks and discuss their applications to bioinformatics. less

关键词： Neural Network Model Regression Analysis evaluation bioinformatics regression classification Neural networks generalization learning rule learning algorithm cross-validation

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THE RESEARCH OF RAINFALL PREDICTION MODELS BASED ON MATLAB NEURAL NETWORK

THE RESEARCH OF RAINFALL PREDICTION MODELS BASED ON MATLAB N...

引用

2011 IEEE International Conference on Cloud Computing and Intelligence Systems(2011年第一届IEEE云计算与智能系统国际会议 IEEE CCIS2011)

作者： Guang Liu Xianggen Gan Lihong Chen Dongbao Yang Jiangxi Vocational &Technical College of Information Application Nanchang China Nanchang Meteorological Bureau Nanchang China Xinyu Meteorological Bureau Xinyu China

The continuously cloudy or rainy forecast is an important basis that is used to make choice of wheat harvest time but multiple regression weather forecast models hardly content the rate of required accuracy. Matlab neural network toolbox is composed of a series of typical neural network activation functions that make computing network output into calling activation functions. BP artificial neural network that is based on Matlab platform and utilizes error back propagation algorithm to revise network weight has dynamic frame characteristics and is convenient for constructing network and programming. After it has been trained by input forecast samples, network forecast model that has three neural cells possesses very good generalization capability. After we contrast fitting rate and accuracy rate of network model with ones of regression model, network model has a distinct advantage over regression model.

关键词： forecast models BP network learning algorithm network training

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K-Means Clustering algorithm in Kernel Function Space

K-Means Clustering Algorithm in Kernel Function Space

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智能计算及其应用国际会议

作者： LIANG Jiuzhen1, Wang Jiyi1 , XU Xiubin2 (1. School of Information Science and Engineering, Zhejiang Normal University, Jinhua, 321004, China) (2. Department of Information and Computation, Zhejiang Normal University, Jinhua, 321004, China)

K-Means algorithm hardly attains higher accuracy for sparsely distributed samples. While a nonlinear separable problem can be changed to a linear (or approximately linear) separable one, by using kern

关键词： K-Means clustering Kernel functions learning algorithm linear separability Corpus Clustering

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VOLTERRA EQUALIZERS BASED ON MBER AND RESTARTED BFGS algorithm FOR NONLINEAR CHANNELS

VOLTERRA EQUALIZERS BASED ON MBER AND RESTARTED BFGS ALGORIT...

引用

2009 IEEE International Conference on Communications Technology and Applications(2009年IEEE国际通信技术与应用大会 IEEE ICCTA2009)

作者： Renxiang Zhu Lenan Wu Zhengyi Wu School of Electronics and Information Engineering Ningbo University of Technology Ningbo 315016 C School of Information Science and Engineering Southeast University Nanjing 210096 China Department of Physics and Electronics Science Changshu Institute of Technology Changshu 215500 Ch

A Volterra equalizer based on MBER (Minimum Bit Error Rate) and restarted BFGS method is proposed in this paper for equalization of nonlinear channels. Restarted BFGS could quicken convergence speed in MBER equalizer trainings, and the updated matrix of BFGS is restarted conditionally and it follows that the new method becomes much more robust. By canceling line search, it is convenient to implement the new method online. In simulations, Volterra equalizers based on minimum mean square error principle degenerate rapidly in nonlinear channels, but that based on MBER provide very low bit error rate. MBER equalizers are trained online by restarted BFGS algorithm, and the results show that its convergence rate is much faster than that of stochastic gradient algorithm.

关键词： nonlinear equalization minimum biterror rate learning algorithm

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The Viscous Display: a transient adaptive interface for collective play in public space 04

The Viscous Display: a transient adaptive interface for coll...

引用

Proceedings of the 2nd international conference on Computer graphics and interactive techniques in Australasia and South East Asia

作者： Lily Shirvanee Glorianna Davenport MIT Media Lab

ISBN: (纸本)9781581138832

The Viscous Display explores the exchange of social information through transient public interfaces. Shaped by principles of 'underground public art', the Viscous Display is conceived as a novel mobile communication medium, where messages can be shared in public spaces. Inspired by biological learning systems; the Viscous Display learns sensorial information that form along traces of a participant's touch and maps this information onto a flexible display. Because it is made up of inexpensive materials, the Viscous Display is also a disposable artifact that may be collected in public spaces. It combines multi-modal sensing, learning algorithms, and a pliable silicone display.

关键词： interpersonal communication transient tangible user interface learning algorithm 'underground public art' social networks real-time graphics mobile device wireless

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Toward a Theory of Validation of Hybrid MinMax FuzzyNeuro Systems

Toward a Theory of Validation of Hybrid MinMax FuzzyNeuro Sy...

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6th IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA 2008)

作者： Mokhtar BELDJEHEM Department degenie informatique et genie logiciel Ecole Polytechnique de Montreal Montreal QUE Canada

The Validation and Verification (V&V) of Hybrid FuzzyNeuro (HFN) or Hybrid NeuroFuzzy (HNF) systems Becomes of increasing concern as these systems are fielded and embedded in the every day operations of medical diagnosis, pattern recognition, fuzzy control and other industries - particularly so when life-critical and environment-critical aspects are involved. We provide in this paper a V&V perspective on the nature of HFN components, an appropriate life-cycle, and applicable systematic formal testing approaches. We consider why HFN V&V may be both easier and harder than traditional means, and we conclude with a series of practical V&V guidelines. Validation of HFN systems brings us to a systematic study of value approximation performed during the inference phase. It is accepted that generalization capability is proportional to value approximation.

关键词： Validation Hybrid FuzzyNeuro System MinMax systems learning algorithm Proximity Measure Approximately Equal Fuzzy Values MinMax Compositional Rule Value approximation Property of Approximation Generalization

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