The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The paper presents adaptive neural network based controller of dishwasher. It shows how to prepare input data for training networks and presents the simulation of network performance.
In order to overcome the defect of conventional neural networks, computational algorithm is used to train RBF network to model the Ni-MH battery. First, RBF network centre is identified by the artificial immune data clustering method. A new immune algorithm, adaptive parallel immune evolutionary strategy, PIES is used to train RBF network and RBF neural network training steps are designed. Finally,...
Artificial neural network (ANN) and space mapping are recognized as two major recent advances in microwave CAD. ANNs can be trained to learn EM and physics behaviour from component data, and trained ANNs can be used in high-level circuit design. Space mapping has proved to be a breakthrough in engineering optimization allowing expensive EM optimization to be performed effectively with the help of...
One of most perspective techniques for sensing in ubiquitous computing systems is neural networks. In this paper we describe features of usage of neural networks in ubiquitous computing and its implementation for solving of some tasks in middleware ubiquitous computing system for smart environment.
Based on the analysis of the standard Particle Swarm Optimization and the characteristic of typical multi-intersection for urban trunk road, a traffic flow forecasting model using dynamic recursion neural network is presented. The feature of this network is that the output of the hidden layer connects to the input of itself through the delay and storage of the context layer. The method of self-connection...
This paper focus on adaptive output feedback control of a class of underactuated systems using neural networks. Through Lyapunov-like stability analysis, adaptive laws are obtained to drive neural networkpsilas free parameter adjustment and at the same time assure uniformly ultimate boundedness of the error signals. The approach permits to enhance the performance of an available linear controller...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.