Image Enhancement is for Improving Visibility and can also be used for different task, It is also important to provide a better representation for further automated image processing such as image analysis, detection, segmentation, recognition and data hiding which can be gain by image enhancement technique there are various method are available for the enhancement like histogram equalization et...

We extend XCS with computed prediction by replacing the usual linear prediction used in XCSF with a feedforward multilayer neural network. In XCSF with neural prediction, XCSFNN, classifier exploits a neural network to approximate the payoff surface associated to the target problem while the genetic algorithm adapts both classifier conditions, classifier actions, and the network structure. We c...

LIAO, YI. Neural Networks for Pattern Classification and Universal Approximation (Under the direction of Dr. Shu-Cherng Fang and Dr. Henry L. W. Nuttle). This dissertation studies neural networks for pattern classification and universal approximation. The objective is to develop a new neural network model for pattern classification, and relax the conditions for Radial-Basis Function networks to...

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...

Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic component...

A learning approach for optimal feedback gains nonlinear continuous time control systems is proposed and analysed. The goal to establish a rigorous framework computing approximating using neural networks. rests on two main ingredients. First, an formulation involving ensemble of trajectories with ‘control’ variables given by the gain functions. Second, approximation functions via realizations B...

Abstract In recent work it has been established that deep neural networks (DNNs) are capable of approximating solutions to a large class parabolic partial differential equations without incurring the curse dimension. However, all this restricted problems formulated on whole Euclidean domain. On other hand, most in engineering and sciences finite domains subjected boundary conditions. The presen...