Adaptive Hopfield Network

Real Power Optimization with Load Flow Using Adaptive Hopfield Neural Network

This paper presents real power optimization with load flow using an adaptive Hopfield neural network. In order to speed up the convergence of the Hopfield neural network system, the two adaptive methods, slope adjustment and bias adjustment, were used with adaptive learning rates. Algorithms of economic load dispatch for piecewise quadratic cost functions using the Hopfield neural network have ...

Framework for Hopfield Network based Adaptive routing - A design level approach for adaptive routing phenomena with Artificial Neural Network

Routing, as a basic phenomena, by itself, has got umpteen scopes to analyse, discuss and arrive at an optimal solution for the technocrats over years. Routing is analysed based on many factors; few key constraints that decide the factors are communication medium, time dependency, information source nature. Parametric routing has become the requirement of the day, with some kind of adaptation to...

TOSSIM simulation of wireless sensor network serving as hardware platform for Hopfield neural net configured for max independent set

Structure and Dynamics of Random Recurrent Neural Networks

In contradiction with Hopfield-like networks, random recurrent neural networks (RRNN), where the couplings are random, exhibit complex dynamics (limit cycles, chaos). It is possible to store information in these networks through hebbian learning. Eventually, learning “destroys” the dynamics and leads to a fixed point attractor. We investigate here the structural change in the networks through l...

Solving a Dynamic Traveling Salesman Problem with an Adaptive Hopfield Network

This paper mathematically solves a dynamic traveling salesman problem (DTSP) with an adaptive Hopfield network (AHN). The DTSP is an extension of the conventional TSP where intercity distances are variable parameters that depend on time; while the AHN, in contrast with current deterministic networks, has the capability to adapt itself to changes in the outside environment. The result is stated ...

Adaptive scaled mean square error filtering by neural networks

An adaptive scaled mean square error (SMSE) filter using a Hopfield neural-network-based algorithm is presented. We show the development of the original SMSE filter from the minimum mean square error (MMSE) filter and the parametric mean square error (PMSE) filter, both of which suffer from the oversmooth phenomena. The SMSE filter is more efficient than the PMSE filter in terms of noise remova...