Neurons: The Hopfield network has a finite set of neurons x(i),1≤i≤N, which serve as processing units. Each neuron has a value (or state) at time t described by xt(i). A neuron in the Hopfield net has one of the two states, either -1 or +1; that is, xt(i)∈{-1,+1}...
Artificial Immune Systems (AIS) have recently been proposed as an additional soft computing paradigm. This paper proposes a new multi-layered unsupervised... T Knight,J Timmis - Springer Berlin Heidelberg 被引量: 19发表: 2004年 Metastable states in the hierarchical Dyson model drive parallel proces...
Short term load forecasting models in Czech Republic using soft computing techniques This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid ...
The simulation results on four RNA sequence show that the proposed algorithm performs better than others and has the ability to search the more stable RNA secondary structure for a RNA sequence.YanQiu CheQiping CaoZheng TangInternational Journal of Soft Computing...
An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-independent, identically distributed way. Further, synaptic plasticity in the b
energyfunctionisthusgiveninasimplerformula,fewerparametersandhighercomputingeficiency.Meanwhile,themutation operatorofgeneticalgorithmisappliedinthisHNN,whichmaketheHNNcanserf-adjustundersomeconditions.Moreover, greedyalgorithmanddamtransformationtechniquealeintroducedinthiskindHNN,whichcanmakeHNNescapesfromthe ...
SB Yaakob,J Watada - 《Advances in Intelligent & Soft Computing》 被引量: 6发表: 2010年 Solution method using neural networks for the generator commitment problem This paper studies the feasibility of applying the Hopfield-type neural network to unit commitment problems in a large power system....
Image reconstruction by a Hopfield neural network The reconstruction of cross-sectional images from projections involves the solution of a large system of simultaneous equations in which the unknowns are a... V Srinivasan,YK Han,SH Ong - 《Image & Vision Computing》 被引量: 47发表: 1993年 Mes...
Applied Soft Computing, 2008,8(4):1712-1718. [2] Wu J, Zhang D H. Dynamic target route planning based on Kalman filter and D* algorithm[J]. Electro-optic and control, 2014,21(8):50-53. [3] Alipour M, Hafezi R, Amer M, et al. A new hybrid fuzzy cognitive map-based ...
The sum of output probabilities from the Fully Connected Layer is 1. This is ensured by using theSoftmaxas the activation function in the output layer of the Fully Connected Layer. The Softmax function takes a vector of arbitrary real-valued scores and squashes it to a vector of values bet...