نتایج جستجو برای: hopfield

تعداد نتایج: 1925  

2009
Xiang Li Xiaoyu Zhang Ning Liu

Radio fuze needs to detect exactly target signal from the echo signal being polluted by noise in real time. Traditional interference cancellation system cannot meet the needs. The Hopfield neural network not only has the ability of nonlinear mapping but also has the ability of selflearning. So it can be used to possess a desired result against the effect of uncertainties and incomplete informat...

2013
M. P. Singh Rinku Sharma Dixit Kevin Takasaki Gang Wei Zheyuan Yu Neil Davey S. P Hunt Rod Adams Frank Emmert Christophe L. Labiouse Albert A. Salah Irina Starikova

In this paper we are studying the tolerance of Hopfield neural network for storage and recalling of fingerprint images. The feature extraction of these images is performed with FFT, DWT and SOM. These feature vectors are stored as associative memory in Hopfield Neural Network with Hebbian learning and Pseudoinverse learning rules. The objective of this study is to determine the optimal weight m...

2004
Yuyao He

Many difficult combinatorial optimization problems arising from science and technology are often difficult to solve exactly. Hence a great number of approximate algorithms for solving combinatorial opthintion problems have been developed [lo], [IS]. Hopfield and Tank applied the continuowtime, continuous-output Hopfield neural network (CTCGH?W) to TSP, thereby initialing a new approach to optim...

2014
Ila Fiete David J. Schwab Ngoc M. Tran

A Hopfield network is an auto-associative, distributive model of neural memory storage and retrieval. A form of error-correcting code, the Hopfield network can learn a set of patterns as stable points of the network dynamic, and retrieve them from noisy inputs – thus Hopfield networks are their own decoders. Unlike in coding theory, where the information rate of a good code (in the Shannon sens...

Journal: :CoRR 2011
C. Ramya G. Kavitha K. S. Shreedhara

In the present paper, an effort has been made for storing and recalling images with Hopfield Neural Network Model of auto-associative memory. Images are stored by calculating a corresponding weight matrix. Thereafter, starting from an arbitrary configuration, the memory will settle on exactly that stored image, which is nearest to the starting configuration in terms of Hamming distance. Thus gi...

2006
Vo Ngoc Dieu Weerakorn Ongsakul

This paper proposes an augmented Lagrangian Hopfield network (ALHN) for combined heat and power economic dispatch (CHPED) problem. The ALHN is the continuous Hopfield neural network based on augmented Lagrangian relaxation as its energy function. In the proposed ALHN, its energy function is augmented by Hopfield terms from Hopfield neural network and penalty factors from augmented Lagrangian re...

2005

In this paper, an identification method is proposed for discrete-time nonlinear systems using a Hopfield neural network (HNN) as a coefficient learning mechanism to obtain optimized coefficients over a set of Gaussian basis functions. The outputs of the HNN, which are coefficients over a set of Gaussian basis functions, are discretized to be a discrete Hopfield learning model and completely app...

Journal: :CoRR 2017
Huiling Zhen Shang-Nan Wang Hai-Jun Zhou

Unsupervised learning in a generalized Hopfield associative-memory network is investigated in this work. First, we prove that the (generalized) Hopfield model is equivalent to a semi-restricted Boltzmann machine with a layer of visible neurons and another layer of hidden binary neurons, so it could serve as the building block for a multilayered deep-learning system. We then demonstrate that the...

Journal: :IEEE transactions on neural networks 1998
Satoshi Matsuda

An "optimal" Hopfield network is presented for many of combinatorial optimization problems with linear cost function. It is proved that a vertex of the network state hypercube is asymptotically stable if and only if it is an optimal solution to the problem. That is, one can always obtain an optimal solution whenever the network converges to a vertex. In this sense, this network can be called th...

2007
N. Moodley S. H. Mneney

This paper explores the use of recurrent neural networks for sub-optimal detection in code division multiple access systems. Research has shown that detectors based on the Hopfield recurrent neural network suffer from localized optimization. The basic Hopfield model is reviewed and we illustrate its use as a multiuser receiver. We investigate the use of stochastic methods to achieve a global mi...

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