Rotating machinery is the most common machinery in industry. The root of the faults in rotating machinery is often faulty rolling element bearings. This paper presents a technique using optimized artificial neural network by the Bees Algorithm for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (maximum likelihood estima...

this paper intends to offer a new iterative method based on articial neural networks for finding solution of a fuzzy equations system. our proposed fuzzied neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. this architecture of articial neural networks, can get a real input vector and calculates its corresponding fu...

• “An Efficient Training Technique for a Neural-Network Controller for Seismically Excited Structures,” (reference [54]) by Liut et al. • “An overview of Some Non-Traditional Neural-Network Training Strategies for Seismic Response Suppression of Building Structures,” (reference [56]) by Liut et al. • “A Modified Gradient-Search Training Technique for Neural-Network Structural Control,” (referen...

the application of neural networks to model a laboratory scale inverse fluidized bed reactor has been studied. a radial basis function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. in the proposed model, the trained neural network represents the kinetics of biological decomposition of organic matters in the reactor. the neural network has b...

in this paper, a novel hybrid model based on neural network and game theory is proposed to support the analyzers in oil market. in this model, first the neural network is utilized to learn the oil prices associated with opec production level and usa imports level. then the learned neural network is applied by a game model. finally the nash equilibrium points of the game present the optimum deci...