Neural networks (NNs) are the method of choice for building learning algorithms. They now being investigated other numerical tasks such as solving high-dimensional partial differential equations. Their popularity stems from their empirical success on several challenging problems (computer chess/Go, autonomous navigation, face recognition). However, most scholars agree that a convincing theoreti...

to determine the amount of food amino acid and to spend time in the laboratories are expensive & time-consuming due to a chemical analysis. in the current laboratories, digestion nirs method is widely used for this purpose. but this method has technical limitation. therefor is important find appropriate method for estimate amount of amino acids. artificial neural network (ann) can provide a bet...

This paper develops fundamental limits of deep neural network learning by characterizing what is possible if no constraints are imposed on the algorithm and amount training data. Concretely, we consider Kolmogorov-optimal approximation through networks with guiding theme being a relation between complexity function (class) to be approximated approximating in terms connectivity memory requiremen...

Vibration based techniques of structural damage detection using model updating method, are computationally expensive for large-scale structures. In this study, after locating precisely the eventual damage of a structure using modal strain energy based index (MSEBI), To efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, the M...

This paper is concerned with the consensus tracking problem of high order MIMO nonlinear multi-agent systems. The agents must follow a leader node in presence of unknown dynamics and uncertain external disturbances. The communication network topology of agents is assumed to be a fixed undirected graph. A distributed adaptive control method is proposed to solve the consensus problem utilizing re...