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...

A modified Hopfield neural network with a novel cost function was presented for detecting wood defects boundary in the image. Different from traditional methods, the boundary detection problem in this paper was formulated as an optimization process that sought the boundary points to minimize a cost function. An initial boundary was estimated by Canny algorithm first. The pixel gray value was de...

In this paper, a possibilistic Hopfield neural network (PHNN) has been proposed for clustering and subsequently applied to brain hemorrhage image segmentation based on a series of CT images. The neural network structure has been implemented by imbedding the weighting possibilistic c-means algorithm into a Hopfield neural network. The network solved the coincidental cluster problem by using a we...

Autoregressive Moving average (ARMA) is a parametric based method of signal representation. It is suitable for problems in which the signal can be modeled by explicit known source functions with a few adjustable parameters. Various methods have been suggested for the coefficients determination among which are Prony, Pade, Autocorrelation, Covariance and most recently, the use of Artificial Neur...

This paper presents an observation on adaptation of Hopfield neural network dynamics configured as a relaxation-based search algorithm for static optimization. More specifically, two adaptation rules, one heuristically formulated and the second being gradient descent based, for updating constraint weighting coefficients of Hopfield neural network dynamics are discussed. Application of two adapt...

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...