This paper introduces dynamic kernel convolutional neural networks (DK-CNNs), an enhanced type of CNN, by performing line-by-line scanning regular convolution to generate a latent dimension weights. The proposed DK-CNN applies the DK weights, which rely on variable, and discretizes space variable extend new dimension; this process is named “DK convolution”. increases expressive capacity operati...

This paper describes an application of Artificial Neural Networks (ANN) to Load Frequency Control (LFC) of nonlinear power systems. Power systems, such as other industrial processes, have parametric uncertainties that for controller design had to take the uncertainties in to account. For this reason, in the design of LFC controller the idea of robust control theories are being used. To improve ...

Analog electronic and optical computing exhibit tremendous advantages over digital for accelerating deep learning when operations are executed at low precision. Although architectures support programmable precision to increase efficiency, analog today only a single, static In this work, we characterize the relationship between effective number of bits (ENOB) processors, which is limited by nois...

In this paper, experimental responses of the clamped mild steel, copper, and aluminium circular plates are presented subjected to blast loading. The GMDH-type neural networks (Group Method of Data Handling) are then used for the modelling of the mid-point deflection thickness ratio of the circular plates using those experimental results. The aim of such modelling is to show how the mid-point de...

Over the last decade several advance< havc been made in the pddigm of artificial neurd networks with specific emphasis on architectures ad learning algorithms. However, most of the work is fixu.stxi on static ( f d o n m d ) neural networks. These neural networks respond instantaneously to the inputs, for they do not posses any time delay units. The use of time delays in neural networks is neur...

Neural networks are applicable in identification systems from input-output data. In this report, we analyze theHammerstein-Wiener models and identify them. TheHammerstein-Wiener systems are the simplest type of block orientednonlinear systems where the linear dynamic block issandwiched in between two static nonlinear blocks, whichappear in many engineering applications; the aim of nonlinearsyst...

The stability of learning rate in neural network identifiers and controllers is one of the challenging issues which attracts great interest from researchers of neural networks. This paper suggests adaptive gradient descent algorithm with stable learning laws for modified dynamic neural network (MDNN) and studies the stability of this algorithm. Also, stable learning algorithm for parameters of ...