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检索条件"主题词=Backpropagation Algorithms"

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Non Linear Dynamical Process Control Using Neural Networks

Non Linear Dynamical Process Control Using Neural Networks

引用

Singapore International Conference on Intelligent Control and Instrumentation, SICICI

作者： M. Houkari C. Parey J.M. Martinez LRE Université EVE 22 France CEN/SACLAY Gif-sur-Yvette France

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A new time-evolving neural network architecture and algorithm for nonlinear system identification using adaptive filtering techniques

A new time-evolving neural network architecture and algorith...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： G. Govind P.A. Ramamoorthy Department of Electrical & Computer Engineering University of Cincinnati Cincinnati OH USA

Concepts from adaptive filtering and some heuristics are used to obtain a fast convergent online neural network especially suited for nonlinear system identification. Rather than training a fixed neural network structure, the algorithm presented allocates nodes when required. This provides for an optimal allocation of hidden nodes in this structure. The results obtained show that the neural network model presented is a viable approach for nonlinear system identification and can be applied to a large class of nonlinear systems. Simulations are provided that show the fast convergence of this neural network structure.< >

关键词： Neural networks Nonlinear systems System identification backpropagation algorithms Multi-layer neural network Parameter estimation Computer architecture Filtering algorithms Adaptive filters Convergence

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Decomposed connectionist architecture for fast and robust learning of robot dynamics

Decomposed connectionist architecture for fast and robust le...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： D. Katic M. Vukobratovic Robotics Department Mihajlo Pupin Institut Belgrade Serbia

The application of connectionist architectures for fast and robust online learning of dynamic relations used in robot control at the executive hierarchical level is discussed. The proposed connectionist robot controllers use decomposition of robot dynamics. This method enables the training of neural networks on the simpler input/output relations with sigfnificant reduction of learning time. The other important features of these algorithms are fast and robust convergence properties because the problem of adjusting the weights of internal hidden units is considered as a problem of estimating parameters by the recursive least squares method and the extended Kalman filter approach. From simulation examples of robot trajectory tracking it is shows that when a sufficiently trained network is desired, the learning speed of the proposed algorithm is faster than that of the traditional backpropagation algorithms.< >

关键词： Robustness Robot control backpropagation algorithms Robust control Neural networks Convergence Parameter estimation Recursive estimation Least squares methods Trajectory

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A guaranteed training of binary pattern mappings using Gaussian perceptron networks

A guaranteed training of binary pattern mappings using Gauss...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： T.M. Kwon Department of Computer Engineering University of Minnesota Duluth MN USA

Training algorithms are introduced for single- and multiple-layered networks of Gaussian perceptrons. One characteristic of these algorithms is that they can guarantee that a network structure and the corresponding weights will be found for any arbitrarily given mapping relation of binary patterns. A number of computer simulation results are presented to demonstrate the performance of the proposed algorithms.< >

关键词： backpropagation algorithms Computer simulation Neurons Computer networks Feedforward systems Neural networks Feedforward neural networks Vectors Proposals Supervised learning

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Direct neuro-adaptive control of robot manipulators

Direct neuro-adaptive control of robot manipulators

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： A.Y. Zomaya M.E. Suddaby A.S. Morris Department of Electrical and Electronic Engineering University of Western Australia Perth WA Australia Department of Control Engineering University of Sheffield Sheffield UK

The authors present a method for the adaptive control of a robot arm based on a feedforward neural network. The method is based on the backpropagation algorithm. backpropagation is used within a learning by reinforcement framework instead of learning by teaching. A neural network is used to estimate the adaptive control law based only on an error signal resulting from the deviation between the desired position, velocity, and acceleration inputs to the robot inverse model and those generated by the robot system. The proposed method does not require any explicit parameter estimation of robot parameters. A cylindrical three-degree-of-freedom robot arm was simulated to demonstrate the control algorithm.< >

关键词： Robot control Manipulators Adaptive control Neural networks Feedforward neural networks backpropagation algorithms Education Error correction Acceleration Inverse problems

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Comparison of reinforcement algorithms on discrete functions: learnability, time complexity, and scaling

Comparison of reinforcement algorithms on discrete functions...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： K.L. Markey M.C. Mozer Department of Computer Science & Institute of Cognitive Science University of Colorado Boulder CO USA

The authors compare the performances of a variety of algorithms in a reinforcement learning paradigm, including Ar-p, Ar-i, reinforcement-comparison (plus a new variation), and backpropagation of reinforcement gradient through a forward model. The task domain is discrete multioutput functions. Performance is measured in terms of learnability, training time, and scaling. Ar-p outperforms all others and scales well relative to supervised backpropagation. An ergodic variant of reinforcement-comparison approaches Ar-p performance. For the tasks studied, total training time (including model and controller) for the forward model algorithm is 1 to 2 orders of magnitude more costly than for Ar-p, and the controller's success is sensitive to forward model accuracy. Distortions of the reinforcement gradient predicted by an inaccurate forward model cause the controller's failures.< >

关键词： Predictive models Stochastic processes backpropagation algorithms Time measurement Computer science Cognitive science Learning systems Control systems Signal design Algorithm design and analysis

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Nonlinear system identification using diagonal recurrent neural networks

Nonlinear system identification using diagonal recurrent neu...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： C.-C. Ku K.Y. Lee Department of Electrical and Computer Engineering Pennsylvania State University University Park PA USA

The recurrent neural network is proposed for system identification of nonlinear dynamic systems. When the system identification is coupled with control problems, the real-time feature is very important, and a neuro-identifier must be designed so that it will converge and the training time will not be too long. The neural network should also be simple and implemented easily. A novel neuro-identifier, the diagonal recurrent neural network (DRNN), that fulfils these requirements is proposed. A generalized algorithm, dynamic backpropagation, is developed to train the DRNN. The DRNN was used to identify nonlinear systems, and simulation showed promising results.< >

关键词： Nonlinear systems Recurrent neural networks Neurons Nonlinear dynamical systems backpropagation algorithms System identification Aerodynamics Neurofeedback Neural networks Heuristic algorithms

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A neural network model based on fuzzy classification concept

A neural network model based on fuzzy classification concept

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Cheng-I Kao Yau Hwang Kuo Institute of Information Engineering National Cheng Kung University Tainan Taiwan

A fuzzy-based neural network (FBNN) model, which applies a one-pass algorithm is proposed. The theory of the FBNN model originates from embedding a fuzzy classification concept into a parallel neural network architecture. Conventional neural networks, such as propagation using energy functions as learning principles, suffer from two major drawbacks, that of the local minimum problem and long training time. FBNN has the advantage of fast training, and avoids the local minimum problem. Experiments and comparisons between FBNN and some other neural network models are given. According to these results, FBNN shows stronger reliability on classification with respect to a probabilistic neural network, backpropagation, and a linear matching method.< >

关键词： Neural networks Fuzzy neural networks Pattern recognition backpropagation algorithms Pattern classification Abstracts Polynomials Fuzzy set theory Fuzzy sets Data structures

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Strategies and issues in applications of neural networks

Strategies and issues in applications of neural networks

引用

International Joint Conference on Neural Networks (IJCNN)

作者： L.A. Feldkamp G.V. Puskorius L.I. Davis F. Yuan Ford Motor Company Limited Dearborn MI USA

The authors survey some of the fundamental aspects of neural networks that have been found crucial to their application to practical problems in diagnostics, modeling, and control. The analysis is a loosely connected collection of remarks on difficulties that have been encountered and the approach to dealing with them. Promising approaches now being explored and suggestions for future work are outlined. The issues raised concern the following: neural nets and engineering; training by higher order methods; sparse data and generalization; local representation networks; prestructured networks; scaling nodes; context switching; recurrent networks; neural controller development; and fusion of neural nets and fuzzy logic.< >

关键词： Intelligent networks Neural networks Biological neural networks Least squares approximation backpropagation algorithms Differential equations Neuroscience Biological system modeling Resonance Gradient methods

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Sentence processing with realistic feedback

Sentence processing with realistic feedback

引用

International Joint Conference on Neural Networks (IJCNN)

作者： D. Schulenburg Department of Information & Computer Science University of California Irvine CA USA

The author presents a connectionist natural language processing model, SAIL1, which uses a recurrent network topology to process English sentences. SAIL1 will build the sentence meaning representation incrementally, incorporating into the meaning only the information derived from words prior to the current word. The network is trained only on that part of the sentence meaning representing the data presented up to that time. Thus, the recurrent feedback is said to be realistic. SAIL1 has demonstrated its usefulness by successfully generalizing to create meaning representations for novel sentences.< >

关键词： Network topology Natural language processing Natural languages backpropagation algorithms Computer science Output feedback Instruments

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