In this paper, a decentralized method for Load Balancing in IEEE 802.11 wireless LANs is proposed. In this proposed method, users autonomously select the most appropriate Access Point (AP) by sensing available APs, and selecting the best one. The Hopfield Neural Networks are used which is an autonomous and decentralized optimization technique. The Hopfield Neural Networks can be used in optimiz...

This paper investigates the scaling properties of neural networks for solving job-shop scheduling problems. Specifically, the Tank-Hopfield linear programming network is modified to solve mixed integer linear programming with the addition of step-function amplifiers. Using a linear energy function, our approach avoids the traditional problems associated with most Hopfield networks using quadrat...

Abstract. As a mathematical model of associative memories, the Hopfield model was now well-established and a lot of studies to reveal the pattern-recalling process have been done from various different approaches. As well-known, a single neuron is itself an uncertain, noisy unit with a finite unnegligible error in the input-output relation. To model the situation artificially, a kind of ‘heat b...

A method to recognize planar objects undergoing affine transformation is proposed in this paper. The method is based upon wavelet multiscale features and Hopfield neural networks. The feature vector consists of the multiscale wavelet transformed extremal evolution. The evolution contains the information of the contour primitives in a multiscale manner, which can be used to discriminate dominant...

This work summarizes a tutorial on the main aspects of the application of Hopfield networks to optimization. The main formulations of the dynamics are studied, and the particular problems that arise in their application to optimization are brought to light. As a particular engineering problem, systems identification is formulated as an optimization problem and the Hopfield methodology is adapte...

This paper reports on results of an empirical simulation study for adaptation of weights through gradient descent for a Hopfield neural network configured as a static optimizer and tested on the traveling salesman problem. Adaptation through gradient descent within the context of recurrent and non-recurrent back-propagation training was attempted in the weight space, which is highdimensional, i...

This contribution discusses the thermodynamic phases and storage capacity of an extension of the Hopfield-Little model of associative memory via kernel functions. The analysis is presented for the case of polynomial and Gaussian kernels in a replica symmetry ansatz. As a general result we found for both kernels that the storage capacity increases considerably compared to the Hopfield-Little model.

Manoel F. Tenorio Dept of Electrical Eng. Purdue University W. Lafayette, IN. 47907 A nonlinear neural framework, called the Generalized Hopfield network, is proposed, which is able to solve in a parallel distributed manner systems of nonlinear equations. The method is applied to the general nonlinear optimization problem. We demonstrate GHNs implementing the three most important optimization a...