Inspired by biological neuronal systems, artificial neural networks have demonstrated superior characteristics of learning, adaptation, classification and function-approximation. They have been successfully applied to many areas. However, conventional neural networks are feed-forward networks that move the information in only one direction, forward, from the inputs to the outputs. There are no ...

Dynamic security analysis is performed to ensure that a power system will survive any recognized contingency and will transfer to a new but acceptable steady-state condition. This article deals with one of the very important dynamic security indices – the critical clearing time. Some of the popular methods of calculating the critical clearing time (CCT) have been reviewed. A more recent method ...

Most artificial neural networks have nodes that apply a simple static transfer function, such as a sigmoid or gaussian, to their accumulated inputs. This contrasts with biological neurons, whose transfer functions are dynamic and driven by a rich internal structure. Our artificial neural network approach, which we call state-enhanced neural networks, uses nodes with dynamic transfer functions b...

Computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting models propos...

Artificial neural networks have the advantages such as learning, adaptation, fault-tolerance, parallelism and generalization. This paper mainly intends to offer a novel method for finding a solution of a fuzzy equation that supposedly has a real solution. For this scope, we applied an architecture of fuzzy neural networks such that the corresponding connection weights are real numbers. The ...

Modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. Recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. Thisstudy made us of adaptive neuro-fuzzy ...

For seismic resistant design of critical structures, a dynamic analysis, based on either response spectrum or time history is frequently required. Due to the lack of recorded data and randomness of earthquake ground motion that might be experienced by the structure under probable future earthquakes, it is usually difficult to obtain recorded data which fit the necessary parameters (e.g. soil ty...

the main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrectionstrategy supported by a recurrent neural network for finding a near optimal solution of a givenobjective function. recently there have been attempts for using artificial neural networks (anns) in optimizationproblems and some types of anns such as hopfield network and boltzm...

Artificial neural networks involve a network of simple processing elements (artificial neurons) which can exhibit complex global behavior. This behvavior is determined by the connections between the processing elements and element parameters. Artificial neural networks can be easily trained to perform a particular function by adjusting the values of the connections (weights) between elements. I...