cognitive architectures (cas) are the core of artificial cognitive systems. a ca is supposed to specify the human brain at a level of abstraction suitable for explaining how it achieves the functions of the mind. over the years a number of distinct cas have been proposed by different authors and their limitations and potentials were investigated. these cas are usually classified as symbolic and...

Early-exiting dynamic neural networks (EDNN), as one type of networks, has been widely studied recently. A typical EDNN multiple prediction heads at different layers the network backbone. During inference, model will exit either last head or an intermediate where confidence is higher than a predefined threshold. To optimize model, these together with backbone are trained on every batch training...

in this paper, convex semi-infinite programming is converted to an optimal control model of neural networks and the optimal control model is solved by iterative dynamic programming method. in final, numerical examples are provided for illustration of the purposed method.

An artificial neural network can be used as an intelligent controller to control non-linear, dynamic system through learning. It can easily accommodate non-linearities and time dependencies. Most common multi-layer feed-forward neural networks have the drawbacks of large number of neurons and hidden layers required to deal with complex problems and require large training time. To overcome these...

Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

production of highly viscous tar sand bitumen using steam assisted gravity drainage (sagd) with a pair of horizontal wells has advantages over conventional steam flooding. this paper explores the use of artificial neural networks (anns) as an alternative to the traditional sagd simulation approach. feed forward, multi-layered neural network meta-models are trained through the back-error-propaga...

In this paper, convex semi-infinite programming is converted to an optimal control model of neural networks and the optimal control model is solved by iterative dynamic programming method. In final, numerical examples are provided for illustration of the purposed method.