Evolutionary artificial neural networks (EANNs) can be considered as a combination of artificial neural networks (ANNs) and evolutionary search procedures such as genetic algorithms (GAs). This paper distinguishes among three levels of evolution in EANNs, i.e. the evolution of connection weights, ...
This paper discusses the encoding representations of evolving neural networks,analyses the advantages and disadvantages of these methods. 本文讨论了进化神经网络的编码表示机制,分析了它们的优缺点;提出了遗传算法的一种图文法编码表示机制,给出了相应的算子定义,以及模式、模式长度及其阶的定义;证明了一个基于图...
Evolutionary artificial neural networks (EANNs) can be considered as a combination of artificial neural networks (ANNs) and evolutionary search procedures such as genetic algorithms (GAs). This paper distinguishes among three levels of evolution in EANNs, i.e. the evolution of connection weights, ...
a review of evolutionary artificial neural networks搜索 AReviewofEvolutionaryArti?cialNeuralNetworks??XinYaoCommonwealthScienti?candIndustrialResearchOrganisationDivisionofBuilding?ConstructionandEngineeringPOBox???Highett?Victoria???AUSTRALIA?PublishedinInternationalJournalofIntelligentSystems???Partofthisworkwasdonewhile...
Velsker, T.; Eerme, M.; Majak, J.; Pohlak, M.; Karjust, K. Artificial neural networks and evolutionary algorithms in engineering design. J. Achiev. Mater. Manuf. Eng. 2011, 44, 88-95.Velsker T., Eerme M., Majak J., Pohlak M., and Karjust K., (2011). Artificial neural ...
Closed-loop control using artificial neural networks (ANNs) are developed, which is an intelligent control method. The ANNs are trained with biogeographybased optimization (BBO), which is a recently developed evolutionary algorithm. This research contributes to the field of evolutionary algorithms by ...
Evolutionary neural networks (ENNs) are an adaptive approach that combines the adaptive mechanism of Evolutionary algorithms (EAs) with the learning mechanism of Artificial Neural Network (ANNs). In view of the difficulties in design and development of DNNs, ENNs can optimize and supplement deep lear...
Artificial Neural NetworksOptimizationAlthough a great amount of algorithms have been devised to train the weights of a neural network for a fixed topology, most of them are hillclimbing procedures, which usually fall in a local optimum; that is why results obtained depend to a great extent on ...
Automated design of artificial neural networks by evolutionary algorithms (neuroevolution) has generated much recent researchboth because successful approaches will facilitate wide-spread use of intelligent systems based on neural networks,and because it will shed light on our understanding of how "real" ...
Artificial neural networks have the ability to approximate arbitrary linear or nonlinear mapping by means of learning. Because, of the learning ability, the neural networks have been developed to compensate for the nonlinearities and uncertainties in design of control systems. Particularly, the RBF-NNs...