Dynamic Neural Network for Prediction and Identification of Nonlinear Dynamic Systems

An attempt has been made to establish a nonlinear dynamic discrete-time neuron model, the so called Dynamic Elementary Processor (DEP). This dynamic neuron disposes of local memory, in that it has dynamic states. Based on the DEP neuron, a Dynamic Multi Layer Perceptron Neural Network is proposed to predict a time series of nonlinear chaotic system. As an another application of the proposed Dynamic Neural Network (DNN), the identification of a dynamic discrete-time nonlinear system whose measurement data are spoiled with noise is performed. To accelerate the convergence of proposed extended dynamic error back propagation learning algorithm, the momentum method is applied. The learning results are presented in terms that are insensitive to the learning data range and allow easy comparison with other learning algorithms, independent of machine architecture or simulator implementation

Mobile Robot Path Planning in 2D Using Network of Equidistant Path

This article proposes mobile robot path planning in the presence of static obstacles by arbitrary shapes. The realized path is optimal in a global sense (shortest). The path searching process is divided in three steps: network of equidistant path design, optimal path selection and simulation along the optimal path. The obtained freeways model is small, therefore the solution time is short. During the motion, the robot is absolutely safe from obstacles and never makes sudden rotations. The presented algorithm has been successfully tested on many examples in different situations and difficulties. It is able to solve even maze-type problems. Solution times are comparable with human reactions faced with the same problem. The main contribution of this paper is increasing the efficiently of equidistant path searching and introduction the widest path algorithm

Hamiltonian of multipotential field in nanorobotics

As it is well known, nanorobotics is the field that deals with the controlled manipulation with atomic and molecular-sized objects. In order to control nanorobots in the regions of mechanics, electronics, electromagnetism, photonics and biomaterials we have to have the ability to construct of the related artificial control potential fields. At the nanoscale the control dynamics is very complex because there are very strong interactions between nanorobots, manipulated objects and nanoenvironment. The problem is to design the control dynamics that will compensate or/and control the mentioned interactions. The first step in designing of the control dynamics for nanorobots is the development of the relativistic Hamiltonian (Hamilton functions) that will include external artificial control potential fields. Thus, derivation of the first and second form of the relativistic Hamiltonians for nanorobots control is presented in this paper.nbs