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检索条件"主题词=Backpropagation Algorithms"

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Use of artificial neural network to help the valuation of football referees' physical fitness

Use of artificial neural network to help the valuation of fo...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： E.S. Fernandes A.M.do V. Filho M.A.A. Scremin L.C. Bastos S. Pezzi R.C. Bastos J.B.da M. Alves Centro Tecnológico-Departamento de Informatica e Estatistica Campus Trindade-Florinaopolis Universidade Federal de Santa Catarina Santa Catarina Brazil

This paper describes the use of backpropagation neural network to help the valuation of football referees' physical fitness, performing referees classification in an efficient and fast way. They are classified into two types: capable or incapable to referee. This neural network was developed under Matlab for windows, and its friendly user interface was developed under Visual Basic 4.0.

关键词： Artificial neural networks Cost accounting Neural networks backpropagation algorithms Kinetic theory Instruments Abdomen User interfaces Visual BASIC Statistics

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Feature selection in codebook based methods provides high accuracy

Feature selection in codebook based methods provides high ac...

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International Joint Conference on Neural Networks (IJCNN)

作者： M. Mar Abad Grau L.D.H. Molinero Department Lenguajes y Sist. Information Universidad de Granada Spain DepartmentInformática Intelig. Artif. y Electr Universidad de Murcia Spain

Despite the higher efficiency obtained by some algorithms (quasi-Newton methods, cascade correlation, etc.) in feedforward neural networks, faster learning methods such as those based on codebook vectors are still needed. We propose to perform feature selection in codebook based methods to improve their accuracy. However, we define a neural network with an exact and fast parallel implementation of the nearest network rule which allows previous feature selection by means of a pruning method. Moreover, we apply this feature selection algorithm upon another codebook based classifier - the Kohonen's linear vector quantization.

关键词： Neural networks Databases Sampling methods Vector quantization Multi-layer neural network Feedforward neural networks Nearest neighbor searches backpropagation algorithms Intelligent networks Learning systems

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Activation functions with learnable amplitude

Activation functions with learnable amplitude

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International Joint Conference on Neural Networks (IJCNN)

作者： E. Trentin ITC-irst Povo Trento and Università di Firenze Italy

Network training algorithms have heavily concentrated on the learning of connection weights. Little effort has been made to learn the amplitude of the activation functions, which defines the range of values that the function can take. This paper introduces novel algorithms to learn the amplitudes of nonlinear activations in layered networks, without any assumption on their analytical form. Three instances of the algorithms are developed: (i) a common amplitude is shared among all the nonlinear units; (ii) each layer has its own amplitude; (iii) neuron-specific amplitudes are allowed. Experimental results validate the approach to a large extent, showing a dramatic improvement in performance over the nets with fixed amplitudes.

关键词： Shape control Shape measurement Kernel Cost function Training data backpropagation algorithms

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An improved learning algorithm for rule refinement in neuro-fuzzy modeling

An improved learning algorithm for rule refinement in neuro-...

引用

International Conference on Knowledge-Based Intelligent Information Engineering Systems (KES)

作者： Chen-Sen Ouyang Shie-Jue Lee Department of Electrical Engineering National Sun Yat-Sen University Kaohsiung Taiwan

We propose an improved learning algorithm for rule refinement in neuro-fuzzy modeling. This algorithm is mainly based on a well-known technique, i.e., singular value decomposition (SVD). By using the method of SVD, the learning algorithm can converge quickly. Besides, the reasoning operator adopted in our algorithm is a compensatory fuzzy operator which has the advantage of being more adaptive and effective. Experimental results show that the proposed algorithm converges quickly and the obtained fuzzy rules are more precise.

关键词： Data mining Neural networks Fuzzy sets Fuzzy neural networks Singular value decomposition Fuzzy systems Convergence backpropagation algorithms Fuzzy reasoning Fuzzy control

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Extended Kalman filter learning algorithm for hyper-complex multilayer neural networks

Extended Kalman filter learning algorithm for hyper-complex ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： H.C.S. Rughooputh S.D.D.V. Rughooputh Univ. of Mauritius Reduit Mauritius

A new type of multilayer perceptron (MLP), developed in quaternion algebra, has been used to perform hyper-complex nonlinear mappings and time series prediction employing a reduced network complexity with respect to conventional MLPs. The extended Kalman filter (EKF) technique has been used as an online algorithm to train MLP neural networks. This training technique significantly reduces the convergence and memory requirement compared with the standard backpropagation method. In this paper, a new learning algorithm, the hyper-complex EKF is derived for the hyper-complex MLP neural network. As an application, a short term prediction of a time-series generated from the hyperchaotic Saito circuit is considered.

关键词： Neural networks Multi-layer neural network Quaternions Signal processing algorithms Algebra Multilayer perceptrons Convergence Artificial neural networks backpropagation algorithms Circuits

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EA crossover schemes for a MLP channel equaliser

EA crossover schemes for a MLP channel equaliser

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： P. Power F. Sweeney C.F.N. Cowan Communications Signal Processing Electrical and Electronic Engineering Department Queen''s University Belfast UK Queen's University Belfast Belfast Belfast GB

This paper presents an evolutionary algorithm (EA) in a form similar to the LMS algorithm, which is applied to MLP learning. The gradient-based update term of the LMS is replaced with the EA non-gradient-based random distancing matrix. This matrix is created to share solution gene information between selected parent chromosomes. The channel equalisation problem is used to compare this algorithm against an EA averaging style operator, which has previously been examined in this area. It is shown that there is an improved learning capability in the MLP filter when the EA updating operators are unrestrained in the range of the gene exchange.

关键词： Finite impulse response filter Evolutionary computation backpropagation algorithms Signal to noise ratio Least squares approximation Iterative algorithms Noise level Filtering Neural networks Nonlinear filters

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FSR-ARTMAP: a modified fuzzy ARTMAP with fuzzy signals and rules

FSR-ARTMAP: a modified fuzzy ARTMAP with fuzzy signals and r...

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International Conference on Neural Information Processing

作者： D.T. Pham Y.M.A. Shankir Systems Engineering Division Cardiff School of Engineering Cardiff University Cardiff UK

Introduces a neural architecture termed the "Fuzzy Signals and Rules ARTMAP" (FSR-ARTMAP). It is a modified fuzzy ARTMAP for incremental supervised learning and multidimensional mapping using fuzzy signals. This neural architecture is able to process and store linguistic information in the form of fuzzy logic rules and membership functions for fuzzy inference. The FSR-ARTMAP architecture is composed of two similar "Fuzzy Signals and Rules ART" (FSR-ART) modules and a mapping field. FSR-ART is a modified version of fuzzy ART. The mapping field between the input and the output FSR-ART modules can be one-to-one, many-to-one or one-to-many.

关键词： Fuzzy neural networks Fuzzy logic backpropagation algorithms Subspace constraints Signal mapping Artificial neural networks Neural networks Systems engineering and theory Supervised learning Multidimensional systems

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Channel equalization by feedforward neural networks

Channel equalization by feedforward neural networks

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： B. Lu B.L. Evans Embedded Signal Processing Laboratory Department of Electrical and Computer Engineering University of Technology Austin TX USA

A signal suffers from nonlinear, linear, and additive distortion when transmitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear channel distortion. As an alternative, nonlinear equalizers have the potential to compensate for all three sources of channel distortion. Previous authors have shown that nonlinear feedforward equalizers based on either multilayer perceptron (MLP) or radial basis function (RBF) neural networks can outperform linear equalizers. In this paper, we compare the performance of MLP vs. RBF equalizers in terms of symbol error rate vs. SNR. We design a reduced complexity neural network equalizer by cascading an MLP and a RBF network. In simulation, the new MLP-RBF equalizer outperforms MLP equalizers and RBF equalizers.

关键词： Neural networks Feedforward neural networks Equalizers Neurons backpropagation algorithms Least squares approximation Convergence Simulated annealing Bayesian methods Finite impulse response filter

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Design methodology for neural network simulation of sequential circuits using neural storage elements

Design methodology for neural network simulation of sequenti...

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SICE Annual Conference

作者： N. Dagdee N.S. Chaudhari Department of Computer Engineering Shri G.S. Institute of Technology and Science Indore India

Multilayer feedforward networks have been found suitable for applications in which they need to learn binary-to-binary mappings. We propose a design methodology to simulate sequential functions using neural networks. The combinational function is implemented by a perceptron network with single hidden layer trained using an ETL algorithm. Design of neural storage elements similar to flip-flops is also proposed, which are used as memory elements to store the internal states. Use of the ETL algorithm guarantees convergence for any binary-to-binary mapping, and generally leads to faster convergence than the backpropagation algorithm. The resulting network only consists of neural elements, with all the neurons having integer valued weights and activation thresholds making the network more suitable for hardware implementation using digital VLSI technology.

关键词： Design methodology Neural networks Circuit simulation Sequential circuits backpropagation algorithms Biological neural networks Flip-flops Neurons Computational modeling Multi-layer neural network

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A data-driven rule-based neural network model for classification

A data-driven rule-based neural network model for classifica...

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International Conference on Neural Information Processing

作者： K.A. Smith School of Business Systems Monash University Clayton VIC Australia

A novel approach for generating rules from neural networks is proposed. Rather than extracting rules from a trained general neural network, we use a neural network structure which permits rules to be more readily interpreted. This network incorporates logic neurons, with a combination of both fixed and adaptive weights. The backpropagation learning rules is adapted to reflect the new architecture. The proposed model also provides an opportunity for encoding expert rules and combining these rules with data driven decisions.

关键词： Neural networks Neurons Artificial neural networks Logic Multi-layer neural network Feedforward neural networks backpropagation algorithms Australia Encoding Expert systems

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