The effect of noise degradation on the Hopfield neural netwerk is s t u d i e d The notion of a hysteresis nefwork is defined. A noisy HephsSi =urd network is subsequently proven to be a hysteresis network. The effect of the hysteresis phentmenon on the robustness of the HoppeM neural network to noise degradation is then investigated. An eptiraal Heefield neural network is defined as the Hopfie...

The model of patterns recognition or attractor network model of associative memory, offered by J.Hopfield 1982, is the most known model in theoretical neuroscience. This paper aims to show, that such well-known laws of art perception as the Wundt curve, perception of visual ambiguity in art, and also the model perception of musical tonalities are nothing else than special cases of the Hopfield’...

Outlining of boundaries of organs and tumors in CT and MRI images are prerequisite in medical applications. In this paper, a single layer Hopfield neural network called Contextual Hopfield Neural Network (CHNN) is presented for finding the edges of CT and MRI images. Different from the conventional 2-D Hopfield neural networks, the CHNN maps the two-dimensional Hopfield network at the original ...

In this paper, we propose a semiautomatic road extraction scheme that is based on template matching and optimization by Hopfield neural network. In the semiautomatic way, a road is extracted automatically after a series seed points have been given coarsely by the operator through a convenient interactive image-graphics interface. Attending to accuracy, robustness, speed and interactivity, we us...

Image restoration is a very important task in image processing. The Artificial Neural Network (ANN) approach was used to solve this problem, especially the Discrete Hopfield Network (DHN). This approach suffers from the fluctuation problem due to the use of the hard limit function as activation function. To overcome this shortcoming, we use in this work the Continuous Hopfield Network (CHN) tha...

of Thesis presentecl to COPPE as partia1 fulfillinent of tlie requireinents for tlie degree of Master of Science ( R 4 Sc.) A Learning Method for Neural Aiialog Hopfield Networlís with application in Image Coinpression Jaiie Tavares Alvarez Deceinber, 1992 Thesis Supervisors: Luis Alfreclo Viclal de Casvallio Valinir C. Ba.rbosa. Department: Programa de Engeilhasia de Sistemas e Coinputação. 11...

This paper presents results from lesion experiments on a modular attractor neural network model of semantic access. Real picture data forms the basis of perceptual input to the model. An ultrametric attractor space is used to represent semantic memory and is implemented using a biologically plausible variant of the Hopfield model. Lesioned performance is observed to be in agreement with neurops...

A method for storing analog vectors in Hopfield's continuous feedback model is proposed. By analog vectors we mean vectors whose components are real-valued. The vectors to be stored are set as equilibria of the network. The network model consists of one layer of visible neurons and one layer of hidden neurons. We propose a learning algorithm, which results in adjusting the positions of the equi...

of the Correlation Matrix Memory (CMM), which is useful to work with binary memories. A novel Boolean Orthonormalization Process (BOP) is presented to convert a non-orthonormal Boolean basis, i.e., a set of non-orthonormal binary vectors (in a Boolean sense) to an orthonormal Boolean basis, i.e., a set of orthonormal binary vectors (in a Boolean sense). This work shows that it is possible to im...