hopfield neural networkcombinatorial optimizationthermal unitmaintenance schedulingMost of the operation planning and system planning tasks of power systems may be reduced to solutions of combinatorial optimization problems. Due to combinatorial explosions, both the dynamic programming (DP) and the branch-and...
Stabilization role of inhibitory self-connections in a delayed neural network In a delayed Hopfield neural network that is strongly connected with non-inhibitory interconnections, fast and inhibitory self-connections lead to global c... P. van den Driessche a,Jianhong Wu b,Xingfu Zou c - 《Phys...
A method of image edge detection using the Hopfield neural network (HNN) is proposed in this paper. The image edge parameters are introduced in detail, and the energy function of HNN is given based on the edge parameters. Tests on the image edge detection show that images detected by the ...
Hopfield Network (HN) This form of recurrent artificial neural network is an associative memory system with binary threshold nodes. Designed to converge to a local minimum, HNs provide a model for understanding human memory. Kohonen Network (KN) A KN organizes a problem space into a two-dimensio...
The first step in analyzing such systems is to learn the dynamics of the system, i.e., system identification. A time-domain approach using a Hopfield network and a frequency-domain approach using spectral decomposition for identification of dynamical systems are presented. Simulation results are ...
The general topics addressed include: low-level vision applications, image processing, target tracking, image compression, manufacturing and control, hardware implementations, 1D signal processing, fusion with AI techniques, applications of Hopfield networks, novel applications, neural network development ...
The purpose of the present chapter is two-fold. It provides an overview of those neural network architectures that are pertinent to the problem of structural analysis and design, including the back-propagation network, the counterpropagation network, the ART network, and the Hopfield network. It ...
Hopfield Network A fully interconnected network of neurons in which each neuron is connected to every other neuron. The network is trained with input patterns by setting a value of neurons to the desired pattern. Then, its weights are computed. The weights are not changed. Once trained for one...
Hopfield and Tank (1985) applied the continuous-time, continuous-output Hopfield neural network (CTCO-HNN) to TSP, thereby initiating a new approach to optimization problems. But the Hopfield neural network is often trapped in local minima because of its gradient descent property. A number of ...
network theory (Chapter 4~13), which mainly includes Perceptron, BP neural network, RBF neural network, ADALINE neural network, HOPFIELD neural network, the deep convolutional neural network, the generative adversarial network, ADABOOST neural network, ELMAN neural network and SOFM neural network. In...