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检索条件"主题词=Backpropagation algorithms"

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Using particle swarm optimization in training neural network for indoor field strength prediction

Using particle swarm optimization in training neural network...

引用

International Symposium on Electronics in Marine (ELMAR)

作者： Ivan Vilović Nikša Burum Đorđe Milić University of Dubrovnik Croatia

This paper presents a comparison of results obtained from neural network training by backpropagation and particle swarm optimization (PSO) algorithms. The neural network model has been developed for field strength prediction in indoor environments. It has been already shown for neural networks as powerful tool in RF propagation prediction. It is very important to choose proper algorithm for training a neural network, so we compared BP training algorithms: gradient descent method and Levenberg-Marquardt algorithm with PSO algorithm. PSO algorithm has been shown as powerful method for global optimization in several applications. A floor of university building in Dubrovnik has been used as case for simulation and measurement of signal strength. The results show that the neural network weights converge faster with PSO than with standard BP algorithms.

关键词： Particle swarm optimization Neural networks backpropagation algorithms Floors Predictive models Artificial neural networks Indoor environments Optimization methods Multilayer perceptrons Testing

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On the data needed for the accurate reconstruction in 3D diffraction tomography

On the data needed for the accurate reconstruction in 3D dif...

引用

IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： A.T. Vouldis C.N. Kechribaris T.A. Maniatis K.S. Nikita N.K. Uzunoglu Department of Electrical and Computer Engineering National and Technical University of Athens Athens Greece

In this paper, the three dimensional (3D) extensions of the two well-known diffraction tomography algorithms, namely direct Fourier interpolation and filtered backpropagation (FBP) are presented. These algorithms solve the linearized version of the inverse scattering problem using either the Born or the Rytov approximations which are known to be valid for weak scatterers. We investigate the problem of the amount of data needed for a full 3D reconstruction. Previous attempts for 3D reconstruction using several inverse scattering algorithms were based on scattered field measurements with incident pulse directions restricted exclusively on the xy-plane. This results in ignoring the contribution of some spatial frequencies which are near the z-axis. This effect is studied here by comparing the results of reconstruction with and without data obtained from other incident directions which fill the spatial frequency domain. We conclude that for a class of objects presenting smooth variation along the z-axis, the use of data obtained for incident direction only in the xy-plane is sufficient to obtain a satisfactory quality of reconstruction while quick variations along the z-axis cannot be imagined.

关键词： Diffraction Tomography Image reconstruction Scattering backpropagation algorithms Interpolation Inverse problems Frequency domain analysis Fourier transforms Pulse measurements

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Genetic programming approach for fault modeling of electronic hardware

Genetic programming approach for fault modeling of electroni...

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Congress on Evolutionary Computation

作者： A. Abraham C. Grosan School of Computer Science and Engineering Chung-Ang University Seoul South Korea Department of Computer Science Babes Bolyai University Cluj-Napoca Romania

This paper presents two variants of genetic programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the stressor - susceptibility interaction model. A circuit or a system is deemed to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after preprocessing and standardization are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.

关键词： Genetic programming Hardware Circuit faults Condition monitoring Electronic circuits Stress measurement Standardization Predictive models Artificial neural networks backpropagation algorithms

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First Order Online Optimisation Using Forward Gradients in Over-Parameterised Systems

First Order Online Optimisation Using Forward Gradients in O...

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European Control Conference (ECC)

作者： Behnam Mafakheri Jonathan H. Manton Iman Shames Department of Electrical and Electronic Engineering University of Melbourne Australia The CIICADA Lab School of Engineering The Australian National University Canberra Australia

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

The success of deep learning over the past decade mainly relies on gradient-based optimisation and backpropagation. This paper focuses on analysing the performance of first-order gradient-based optimisation algorithms with time-varying non-convex cost function under Polyak-Lojasiewicz condition. Specifically, we focus on using the forward mode of automatic differentiation to compute directional derivatives of the cost function in fast-changing problems where calculating gradients using the backpropagation algorithm is either impossible or inefficient. Upper bounds for tracking and asymptotic errors are derived for various cases, showing the linear convergence to a solution or a neighbourhood of an optimal solution, where the convergence rate decreases with the increase in the dimension of the problem. We present numerical results demonstrating the method's correctness and performance.

关键词： Deep learning backpropagation Upper bound Focusing Europe backpropagation algorithms Cost function

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Real-time supervised structure/parameter learning for fuzzy neural network

Real-time supervised structure/parameter learning for fuzzy ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： C.T. Lin C.S.G. Lee School of Electrical Engineering Purdue University West Lafayette IN USA

The authors propose a real-time supervised structure and parameter learning algorithm for constructing fuzzy neural networks (FNNs) automatically and dynamically. This algorithm combines the backpropagation learning scheme for the parameter learning and a novel fuzzy similarity measure for the structure learning. The fuzzy similarity measure is a new tool to determine the degree to which two fuzzy sets are equal. The FNN is a feedforward multilayered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. The structure learning decides the proper connection types and the number of hidden units which represent fuzzy logic rules and the number of fuzzy partitions. The parameter learning adjusts the node and link parameters which represent the membership functions. The proposed supervised learning algorithm provides an efficient way of constructing a FNN in real time. Simulation results are presented to illustrate the performance and applicability of the proposed learning algorithm.< >

关键词： Fuzzy neural networks Fuzzy logic backpropagation algorithms Fuzzy sets Partitioning algorithms Heuristic algorithms Fuzzy control Supervised learning

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GA-Based Learning algorithms to Identify Fuzzy Rules for Fuzzy Neural Networks

GA-Based Learning Algorithms to Identify Fuzzy Rules for Fuz...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： K. Almejalli K. Dahal A. Hossain School of Informatics University of Bradford Bradford UK

Identification of fuzzy rules is an important issue in designing of a fuzzy neural network (FNN). However, there is no systematic design procedure at present. In this paper we present a genetic algorithm (GA) based learning algorithm to make use of the known membership function to identify the fuzzy rules form a large set of all possible rules. The proposed learning algorithm initially considers all possible rules then uses the training data and the fitness function to perform rule- selection. The proposed GA based learning algorithm has been tested with two different sets of training data. The results obtained from the experiments are promising and demonstrate that the proposed GA based learning algorithm can provide a reliable mechanism for fuzzy rule selection.

关键词： Fuzzy neural networks Fuzzy systems Genetic algorithms Fuzzy control Fuzzy sets Neural networks Training data Testing backpropagation algorithms Intelligent systems

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Forecasting time series using logical combinations of neural-based networks

Forecasting time series using logical combinations of neural...

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IEEE International Conference on Systems, Man and Cybernetics

作者： A. Thammano Advanced Computer Application and Design Research Group Faculty of Information Technology King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

Forecasts are the basis for planning and decision making. The more accurate the organization's forecasts, the better prepared it will be to take advantage of future opportunities and to reduce potential risks. Thus, it should come as no surprise that there is a tremendous number of statistical prediction algorithms already in existence. However, most of them are useful only when the time series exhibit little trend or seasonal variations but a great deal of irregular or random variation. Therefore, the objective of this paper is to propose a new intelligent forecasting technique which is constructed by combining n-trained neural-based network together. The experimental results based on simulated data show a significant improvement in forecasting accuracy.

关键词： Neural networks Predictive models Fuzzy systems backpropagation algorithms Economic forecasting Feedforward neural networks Intelligent networks Decision making Econometrics Genetic algorithms

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A fuzzy neural network for fuzzy classification

A fuzzy neural network for fuzzy classification

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IEEE International Conference on Systems, Man and Cybernetics

作者： C. Ramirez-Rodriguez T. Vladimirova Department of Electronic and Electrical Engineering University of Surrey Guildford Surrey UK

A fuzzy neural network (FNN) for fuzzy classification of patterns is presented. An extension of the backpropagation algorithm which uses interval arithmetic is employed to accept fuzzy numbers as inputs to the network using /spl alpha/-level set representation. The learning phase of the neural network is supervised by two fuzzy systems which control the learning rate and the slope of the activation functions. A method for fuzzification of the training data set is proposed. The FNN is compared with a traditional backpropagation network in terms of generalisation capabilities. The training and testing are carried out using data with high degree of ambiguity and overlapping among the classes. The results suggest that the FNN provides a robust classification response in the presence of uncertainty and ambiguity in the training data. Its application as a first stage classification procedure in a hybrid system is discussed.

关键词： Fuzzy neural networks Fuzzy control Training data backpropagation algorithms Arithmetic Fuzzy sets Neural networks Fuzzy systems Control systems Testing

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A high performance digital processor for implementing large artificial neural networks

A high performance digital processor for implementing large ...

引用

Custom Integrated Circuits Conference (CICC)

作者： D.A. Orrey D.J. Myers J.M. Vincent British Telecommunications Research Laboratories UK

A CMOS integrated circuit is described which is capable of implementing very large digital neural networks of the multilayer perceptron (MLP) form. It incorporates on-chip training using the backpropagation algorithm, and the use of pseudorandom noise allows training with coarsely quantized weight values. Dynamic range and precision of the connection weights are automatically adjusted during training, thus allowing the circuit to adapt to different network sizes and topologies. The addition of a small, pseudorandom noise element allows the weights memory to be used more efficiently for large networks. Extensive simulation using a hardware description model over a range of problems has given a high degree of confidence in the design.< >

关键词： CMOS integrated circuits Neural networks Multi-layer neural network Multilayer perceptrons backpropagation algorithms Integrated circuit noise Dynamic range Network topology Circuit topology Circuit noise

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Estimation of artificial neural network parameters for nonlinear system identification

Estimation of artificial neural network parameters for nonli...

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IEEE Conference on Decision and Control

作者： T.L. Ruchti R.H. Brown J.J. Garside Department of Electrical and Computer Engineering Marquette University Milwaukee WI USA Dept. of Electr. & Comput. Eng. Marquette Univ. Milwaukee WI USA

A unified framework for representing ANN (artificial neural network) training algorithms is developed by considering weight selection as a parameter estimation problem. Three existing ANN training strategies are reviewed within this framework, i.e., gradient-descent backpropagation, the extended Kalman algorithm, and the recursive least squares method. A strikingly different approach to error backpropagation is presented, resulting in the development of a novel method of backward signal propagation and target state generation for embedded layers. The proposed technique is suitable for implementation with a linear-Kalman based update algorithm and is applied with a time-varying method of covariance modification for the elimination of transients associated with initial conditions. Results from a nonlinear identification experiment demonstrate an increased rate of convergence in comparison with backpropagation. The new algorithm displayed similar rates of parameter convergence and a decreased computational overhead compared to the extended Kalman algorithm.< >

关键词： Artificial neural networks backpropagation algorithms Kalman filters Convergence Parameter estimation Least squares methods Signal generators Computer displays

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