scholarly journals ON THE TRANSITION FUNCTION

2021 ◽  
pp. 1089-1091
Keyword(s):  
1988 ◽  
Vol 104 (3) ◽  
pp. 363-372 ◽  
Author(s):  
Nicholas C. Barrett ◽  
Denis J. Glencross

2021 ◽  
Vol 11 (15) ◽  
pp. 7161
Author(s):  
Igor Azkarate ◽  
Mikel Ayani ◽  
Juan Carlos Mugarza ◽  
Luka Eciolaza

Industrial discrete event dynamic systems (DEDSs) are commonly modeled by means of Petri nets (PNs). PNs have the capability to model behaviors such as concurrency, synchronization, and resource sharing, compared to a step transition function chart or GRAphe Fonctionnel de Commande Etape Transition (GRAFCET) which is a particular case of a PN. However, there is not an effective systematic way to implement a PN in a programmable logic controller (PLC), and so the implementation of such a controller outside a PLC in some external software that will communicate with the PLC is very common. There have been some attempts to implement PNs within a PLC, but they are dependent on how the logic of places and transitions is programmed for each application. This work proposes a novel application-independent and platform-independent PN implementation methodology. This methodology is a systematic way to implement a PN controller within industrial PLCs. A great portion of the code will be validated automatically prior to PLC implementation. Net structure and marking evolution will be checked on the basis of PN model structural analysis, and only net interpretation will be manually coded and error-prone. Thus, this methodology represents a systematic and semi-compiled PN implementation method. A use case supported by a digital twin (DT) is shown where the automated solution required by a manufacturing system is carried out and executed in two different devices for portability testing, and the scan cycle periods are compared for both approaches.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lindokuhle Talent Zungu ◽  
Lorraine Greyling ◽  
Nkanyiso Mbatha

PurposeThe authors investigate the growth–inequality relationship, using panel data from 13 Southern African Development Community (SADC) countries over the period 1990–2015, to test the validity of the Kuznets and Tribble theories. Furthermore, the authors seek to determine the threshold level at which excessive growth hampers inequality.Design/methodology/approachThe panel smooth transition regression (PSTR) model has several stages. The authors applied the Lagrange multiplier (LM) test to find the appropriate transition variable amongst all candidate variables, to assess the linearity between economic growth and income inequality and to find the sequence for selecting the order m of the transition function. The authors then estimated the PSTR model, but before facilitating the results, the authors first used the wild cluster bootstrap (WCB)–LM-type test to assess the appropriateness of the selected transition.FindingsThe authors found that at lower growth, income inequality tends to be lower, while if growth increases above US$8,969, inequality tends to increase in the SADC region. The findings combine into a U-shaped relationship, contradicting the Kuznets and Tribble theories.Originality/valueThe contribution of this paper is that it becomes the first to provide the threshold level at which excessive growth increases inequality in the selected countries. This study proposes that policymakers should focus on activities aimed at stimulating growth, in other words, activities such as spending more on infrastructure, drawing up a suitable investment portfolio and spending on technological investment for countries that are below US$8,969. An improvement in these activities will create job opportunities, which in turn will add to economic growth and thus lead to lower income inequality and better social cohesion.


2021 ◽  
Author(s):  
Martin Sieberer ◽  
Torsten Clemens

Abstract Hydrocarbon field (re-)development requires that a multitude of decisions are made under uncertainty. These decisions include the type and size of surface facilities, location, configuration and number of wells but also which data to acquire. Both types of decisions, which development to choose and which data to acquire, are strongly coupled. The aim of appraisal is to maximize value while minimizing data acquisition costs. These decisions have to be done under uncertainty owing to the inherent uncertainty of the subsurface but also of other costs and economic parameters. Conventional Value Of Information (VOI) evaluations can be used to determine how much can be spend to acquire data. However, VOI is very challenging to calculate for complex sequences of decisions with various costs and including the risk attitude of the decision maker. We are using a fully observable Markov-Decision-Process (MDP) to determine the policy for the sequence and type of measurements and decisions to do. A fully observable MDP is characterised by the states (here: description of the system at a certain point in time), actions (here: measurements and development scenario), transition function (probabilities of transitioning from one state to the next), and rewards (costs for measurements, Expected Monetary Value (EMV) of development options). Solving the MDP gives the optimum policy, sequence of the decisions, the Probability Of Maturation (POM) of a project, the Expected Monetary Value (EMV), the expected loss, the expected appraisal costs, and the Probability of Economic Success (PES). These key performance indicators can then be used to select in a portfolio of projects the ones generating the highest expected reward for the company. Combining the production forecasts from numerical model ensembles with probabilistic capital and operating expenditures and economic parameters allows for quantitative decision making under uncertainty.


2003 ◽  
Vol 15 (8) ◽  
pp. 1897-1929 ◽  
Author(s):  
Barbara Hammer ◽  
Peter Tiňo

Recent experimental studies indicate that recurrent neural networks initialized with “small” weights are inherently biased toward definite memory machines (Tiňno, Čerňanský, & Beňušková, 2002a, 2002b). This article establishes a theoretical counterpart: transition function of recurrent network with small weights and squashing activation function is a contraction. We prove that recurrent networks with contractive transition function can be approximated arbitrarily well on input sequences of unbounded length by a definite memory machine. Conversely, every definite memory machine can be simulated by a recurrent network with contractive transition function. Hence, initialization with small weights induces an architectural bias into learning with recurrent neural networks. This bias might have benefits from the point of view of statistical learning theory: it emphasizes one possible region of the weight space where generalization ability can be formally proved. It is well known that standard recurrent neural networks are not distribution independent learnable in the probably approximately correct (PAC) sense if arbitrary precision and inputs are considered. We prove that recurrent networks with contractive transition function with a fixed contraction parameter fulfill the so-called distribution independent uniform convergence of empirical distances property and hence, unlike general recurrent networks, are distribution independent PAC learnable.


2000 ◽  
Vol 10 (1) ◽  
pp. 123-162 ◽  
Author(s):  
A. D. Barbour ◽  
S. N. Ethier ◽  
R. C. Griffiths

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