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检索条件"主题词=Backpropagation Algorithms"

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Recursively partitioning neural networks for radar target recognition

Recursively partitioning neural networks for radar target re...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： T. Kerstetter S. Massey J. Roberts XonTech Inc. USA THAAD Project Office PEO Air and Missile Defense USA

Conflicting information in the training data is responsible for most of the problems experienced by the backpropagation algorithm during network training. The self-partitioning neural network (SPNN) approach has been shown to be effective in overcoming the ill effects of learning conflict that exists among the patterns of a given class. Intra-class learning conflict present in the training patterns is reduced by using a divide-and-conquer approach. A cluster seeking algorithm is used to partition training patterns according to a conflict metric. Simulation studies have verified that the SPNN has distinct advantages over the conventional backpropagation network. However for the large training sets mandated by theater missile defense performance constraints, calculation of the conflict metrics used in the SPNN is computationally intractable. Furthermore, cluster seeking algorithms are not guaranteed to converge. The recursively partitioning neural network (RPNN) was created to remove these two shortcomings of the SPNN. Two computationally efficient methods for measuring conflict and an efficient partitioning scheme are developed. Simulation results are presented to demonstrate the RPNN's capabilities.

关键词： Neural networks Radar Target recognition Clustering algorithms Partitioning algorithms backpropagation algorithms Missiles Computer networks Training data Computational modeling

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Support vector machine for multiuser detection in CDMA communications

Support vector machine for multiuser detection in CDMA commu...

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Asilomar Conference on Signals, Systems & Computers

作者： Xiaohong Gong A. Kuh Department of Electrical Engineering University of Hawaii Manoa Honolulu HI USA

We apply support vector machines (SVM) or optimal margin classifiers to multiuser detection problems. SVM are well suited for multiuser detection problems as they are based on principles of statistical learning theory where the goal is to construct a maximum margin classifier. We show that a linear SVM converges to the MMSE receiver in the noiseless case. The SVM are also modified to construct nonlinear receivers by using kernel functions and they approximate optimal nonlinear multiuser detection receivers. Using the sequential minimization optimization (SMO) algorithm, we implement SVM as receivers in CDMA systems and compare SVM with traditional and adaptive receivers. The simulation performance of SVM compares favorably to these receivers.

关键词： Support vector machines Multiuser detection Multiaccess communication Support vector machine classification Additive noise Kernel Detectors Decorrelation Neural networks backpropagation algorithms

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Non-normalised compensatory hybrid fuzzy neural networks

Non-normalised compensatory hybrid fuzzy neural networks

引用

International Joint Conference on Neural Networks (IJCNN)

作者： H. Seker D.H. Evans Division of Medical Physics Faculty of Medicine Leicester University Leicester UK

Fuzzy neural networks have been shown to be superior to conventional multilayered backpropagation neural networks (BPNN). However, it is still an important problem to make fuzzy neural networks learn faster and to optimise membership functions of fuzzy rule based models to converge to a local minimum. Moreover, while learning faster and optimising, it is important to use less memory and to need less CPU time. In this paper, to overcome these problems, we propose non-normalised compensatory hybrid fuzzy neural networks (non-normalised CFBPNN) incorporating fuzzy c-means clustering as a fuzzy inference engine, fuzzy logic and backpropagation learning algorithms. The results have shown that the proposed algorithm overcomes these problems, and yields a very high performance. This algorithm was tested on the XOR problem, nonlinear function learning and pattern classification, and compared with normalised CFBPNN and BPNN to verify the algorithm.

关键词： Fuzzy neural networks Inference algorithms backpropagation algorithms Fuzzy logic Clustering algorithms Neural networks Multi-layer neural network Engines Testing Pattern classification

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Assembling neural networks for modelling of large nonlinear dynamic systems

Assembling neural networks for modelling of large nonlinear ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： C. Cole R. Stonier Central Queensland University Australia

Several feedforward neural networks with one hidden layer were trained to predict a selected coupler force in a large and highly nonlinear freight train dynamic system. Neuron activation functions were hyperbolic tangent and linear for hidden and output layers respectively. Data inputs included train control information and train control history. For more complex responses the first differences of the inputs and input histories were also used. Neural networks were successfully trained to predict coupler forces for selected positions in empty and loaded trains.

关键词： Assembly systems Neural networks Neurons backpropagation algorithms Nonlinear dynamical systems History Equations Feedforward neural networks Parallel processing Convergence

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The two spirals benchmark: lessons from the hidden layers

The two spirals benchmark: lessons from the hidden layers

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International Joint Conference on Neural Networks (IJCNN)

作者： S.B. Garavaglia Integrated Therapeutics Group Schering-Plough Corporation

A classic benchmark of nonlinear discrimination, the two-dimensional pattern of two intertwined spirals, is employed to demonstrate an approach to neural network performance improvement. It is generally recognized that the simple three layer backpropagation network does not succeed in solving the problem. By graphically displaying the hidden unit outputs and their resulting spirals, it becomes clear that this basic network correctly identifies the curved shape dimension but fails to recognize the spatial relationship between the spirals. These results suggest variations on the architecture and transformations of the input that lead to a better solution. The best results are obtained by extending the input vectors with a sum-of-squares value and its reciprocal, and creating peer-level hidden layers. An economic cost-benefit discussion of the networks justifies the model selection.

关键词： Spirals Neural networks Shape Frequency Feedforward systems Feedforward neural networks backpropagation algorithms Statistics Wave functions

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A novel learning algorithm for dynamic fuzzy neural networks

A novel learning algorithm for dynamic fuzzy neural networks

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American Control Conference (ACC)

作者： Shiqian Wu Meng Joo Er Jun Liao Intelligent Machine Research Laboratory School of Electrical and Electronic Engineering Singapore

A learning algorithm for dynamic fuzzy neural networks based on extended radial basis function (RBF) neural networks, which are functionally equivalent to TSK fuzzy systems, is proposed. The algorithm comprises four parts: (1) criteria of neurons generation; (2) allocation of parameters of RBF units; (3) weight adjustment; and (4) pruning technology. The algorithm has fast learning speed as the weights are modified by the linear least square method and no iteration is needed. The synergy of fuzzy and neural systems with dynamic structure shows that a parsimonious structure with high performance can be achieved. Simulation studies and comprehensive comparisons with some other learning algorithms demonstrate that the proposed algorithm is superior. It is also shown that the method is promising for real-time applications.

关键词： Heuristic algorithms Fuzzy neural networks Neural networks Fuzzy systems Neurons backpropagation algorithms Erbium Learning systems Laboratories Least squares methods

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NEFCLASS for Java-new learning algorithms

NEFCLASS for Java-new learning algorithms

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： D. Nauck U. Nauck R. Kruse Faculty of Computer Science University of Magdeburg Magdeburg Germany

Neuro-fuzzy classification approaches aim at creating fuzzy classification rules from data. Our neuro-fuzzy model NEFCLASS is able to learn fuzzy rules and fuzzy sets by simple heuristics. The aim of NEFCLASS is to quickly create interpretable fuzzy classifiers. In this paper, we present NEFCLASS-J-a new version of our approach that was written in Java and contains some additions to the learning algorithms, like the treatment of missing values, the ability to use symbolic data, automatic determination of the size of the rule base, and a new automatic pruning strategy.

关键词： Java Fuzzy sets Training data Computer science Electronic mail World Wide Web Partitioning algorithms backpropagation algorithms

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Pattern recognition using constructive algorithms

Pattern recognition using constructive algorithms

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International Joint Conference on Neural Networks (IJCNN)

作者： H. Costa de Sousa A. de Carvalho Universidade de Saão Paulo Sao Paulo Brazil

This paper aims to analyze the advantages of using constructive algorithms to achieve a functional neural network topology automatically. A constructive neural network simulator, named Kipu, was implemented in order to accomplish the design and analysis of constructive algorithms. Four constructive algorithms were modified to allow the use of real-valued data. Experiments using three pattern recognition tasks were conducted in the Kipu simulator and the results suggest a better functioning of the constructive algorithms compared with the fixed topology MLP network trained with backpropagation algorithm.

关键词： Pattern recognition Network topology Neural networks backpropagation algorithms Neurons Algorithm design and analysis Error analysis Analytical models Artificial neural networks Multilayer perceptrons

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A learning algorithm for multilayer perceptron as classifier

A learning algorithm for multilayer perceptron as classifier

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhen Zhang Weimin Shao Hong Zhang Medical University of South Carolina USA Horus Theraputics Inc. USA

Multilayer perceptron can be trained with empirical data to estimate general real-valued functions or to be used as a pattern classifier to estimate indicator functions. The typical backpropagation learning algorithm and its variations do not distinguish the training of an MLP as a pattern classifier from that of a general function estimator. In this paper, we present a learning algorithm based on an optimization layer by layer (OLL) procedure. Its main difference from previously reported OLL-type learning algorithms is that the weights between the last hidden layer and the output layer are determined through optimization of a piecewise linear objective function subject to constraints designed specifically for training an MLP to be a pattern classifier. The performance of the proposed learning algorithm is compared with that of the backpropagation algorithm, the modified Newton's method and the improved descending epsilon algorithm over multiple training sessions using both simulated and real data classification problems.

关键词： Multilayer perceptrons backpropagation algorithms Piecewise linear techniques Algorithm design and analysis Pattern classification Error correction Constraint optimization Design optimization Training data Risk management

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Real time adaptive fuzzy control of inverted pendulum

Real time adaptive fuzzy control of inverted pendulum

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： P.D. Djurdjevic Nasa Center for Autonomous Control Engineering Engineering Department New Mexico Highlands University Las Vegas NM USA

In this paper, we provide a real-time adaptive fuzzy controller utilized for balancing a self-erecting inverted pendulum with time-varying mechanical parameters. One learning algorithm is based upon truncated temporal backpropagation and weight perturbation suitable for realization in hardware. Main focus was the ability to learn from trials and the possibility to implement the functions in hardware for on-chip learning. The simulations and testing were done using hardware-in-the-loop, the robustness and adaptivity of the controller are shown from simulation traces.

关键词： Programmable control Adaptive control Fuzzy control Hardware backpropagation algorithms Analytical models Delay effects Robust control Feedforward systems Sampling methods

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