Application of neural network to hybrid systems with binary inputs

Boolean input systems are in common used in the electric industry. Power supplies include such systems and the power converter represents these. For instance, in power electronics, the control variable are the switching ON and OFF of components as thyristors or transistors. The purpose of this paper is to use neural network (NN) to control continuous systems with Boolean inputs. This method is based on classification of system variations associated with input configurations. The classical supervised backpropagation algorithm is used to train the networks. The training of the artificial neural network and the control of Boolean input systems are presented. The design procedure of control systems is implemented on a nonlinear system. We apply those results to control an electrical system composed of an induction machine and its power converter.

Control strategy for Boolean input systems

The purpose of this paper is to design a control law for continuous systems with Boolean inputs allowing the output to track a desired trajectory. Such systems are controlled by items of commutation. This type of systems, with Boolean inputs, has found increasing use in the electric industry. Power supplies include such systems and a power converter represents one of theses systems. For instance, in power electronics the control variable is the switching OFF and ON of components such as thyristors or transistors. In this paper, a method is proposed for the designing of a control law in state space for such systems. This approach is implemented in simulation for the control of an electronic circuit.

A comparison between bond graphs switching modelling techniques implemented on a boost dc-dc converter

A Bond Graph is a graphical modelling technique that allows the representation of energy flow between the components of a system. When used to model power electronic systems, it is necessary to incorporate bond graph elements to represent a switch. In this paper, three different methods of modelling switching devices are compared and contrasted: the Modulated Transformer with a binary modulation ratio (MTF), the ideal switch element, and the Switched Power Junction (SPJ) method. These three methods are used to model a dc-dc Boost converter and then run simulations in MATLAB/SIMULINK. To provide a reference to compare results, the converter is also simulated using PSPICE. Both quantitative and qualitative comparisons are made to determine the suitability of each of the three Bond Graph switch models in specific power electronics applications

Emergence dynamics of barnyardgrass and jimsonweed from two depths when switching from conventional to reduced and no-till conditions

A cylinder experiment was conducted in northern Greece during 2005 and 2006 to assess emergence dynamics of barnyardgrass (Echinochloa crus-galli (L.) Beauv.) and jimsonweed (Datura stramonium L.) in the case of a switch from conventional to conservation tillage systems (CT). Emergence was surveyed from two burial depths (5 and 10 cm) and with simulation of reduced tillage (i.e. by soil disturbance) and no-till conditions. Barnyardgrass emergence was significantly affected by burial depth, having greater emergence from 5 cm depth (96%) although even 78% of seedlings emerged from 10 cm depth after the two years of study. Emergence of barnyardgrass was stable across years from the different depths and tillage regimes. Jimsonweed seeds showed lower germination than barnyardgrass during the study period, whereas its emergence was significantly affected by soil disturbance having 41% compared to 28% without disturbance. A burial depth x soil disturbance interaction was also determined, which showed higher emergence from 10 cm depth with soil disturbance. Jimsonweed was found to have significantly higher emergence from 10 cm depth with soil disturbance in Year 2. Seasonal emergence timing of barnyardgrass did not vary between the different burial depth and soil disturbance regimes, as it started in April and lasted until end of May in both years. Jimsonweed showed a bimodal pattern, with first emergence starting end of April until mid-May and the second ranging from mid-June to mid-August from 10 cm burial depth and from mid-July to mid-August from 5 cm depth, irrespective of soil disturbance in both cases.

Effects of dense code-switching on executive control

Bilingualism is reported to re-structure executive control networks, but it remains unknown which aspects of the bilingual experience cause this modulation. This study explores the impact of three code-switching types on executive functions: (1) alternation of languages, (2) insertion of lexicon of one language into grammar of another, (3) dense code-switching with co-activation of lexicon and grammar. Current models hypothesise that they challenge different aspects of the executive system because they vary in the extent and scope of language separation. Two groups of German-English bilinguals differing in dense code-switching frequency participated in a flanker task under conditions varying in degree of trial-mixing and resulting demands to conflict-monitoring. Bilinguals engaging in more dense code-switching showed inhibitory advantages in the condition requiring most conflict-monitoring. Moreover, dense code-switching frequency correlated positively with monitoring skills. This suggests that the management of co-activated languages during dense code-switching engages conflict-monitoring and that the consolidation processes taking place within co-activated linguistic systems involve local inhibition. Code-switching types requiring greater degrees of language separation may involve more global forms of inhibition. This study shows that dense code-switching is a key experience shaping bilinguals’ executive functioning and highlights the importance of controlling for participants’ code-switching habits in bilingualism research.