scholarly journals System Identification Using Multilayer Differential Neural Networks: A New Result

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
J. Humberto Pérez-Cruz ◽  
A. Y. Alanis ◽  
José de Jesús Rubio ◽  
Jaime Pacheco
Keyword(s):  
Neural Networks ◽  
Upper Bound ◽  
Exponential Convergence ◽  
Dead Zone ◽  
A Priori ◽  
A Priori Knowledge ◽  
Unmodeled Dynamics ◽  
A Value ◽  
Tuning Process ◽  
Learning Law

In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.

Incorporating a priori knowledge into neural networks

1995 ◽  
Vol 31 (22) ◽  
pp. 1930-1931 ◽  
Author(s):  
D. Anguita ◽  
S. Rovetta ◽  
S. Ridella ◽  
R. Zunino
Keyword(s):  
Neural Networks ◽  
A Priori ◽  
A Priori Knowledge ◽  
Priori Knowledge

Damage Mechanisms Identification in FRP Using Acoustic Emission and Artificial Neural Networks

2006 ◽  
Vol 514-516 ◽  
pp. 789-793 ◽  
Author(s):  
Rui de Oliveira ◽  
António Torres Marques
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Artificial Neural Networks ◽  
Tensile Test ◽  
A Priori ◽  
The Self ◽  
A Priori Knowledge ◽  
Self Organizing Maps ◽  
Damage Mechanisms ◽  
Artificial Neural

In this study is proposed a procedure for damage discrimination based on acoustic emission signals clustering using artificial neural networks. An unsupervised methodology based on the self-organizing maps of Kohonen is developed considering the lack of a priori knowledge of the different signal classes. The methodology is described and applied to a cross-ply glassfibre/ polyester laminate submitted to a tensile test. In this case, six different AE waveforms were identified. The damage sequence could so be identified from the modal nature of those waves.

A Survey on Neural Networks in Automated Negotiations

2011 ◽  
pp. 1524-1529 ◽  
Author(s):  
Ioannis Papaioannou ◽  
Ioanna Roussaki ◽  
Miltiades Anagnostou
Keyword(s):  
Neural Networks ◽  
A Priori ◽  
Research Field ◽  
A Priori Knowledge ◽  
Automated Negotiation ◽  
Multilateral Negotiation ◽  
The Third ◽  
Automated Negotiations ◽  
Single Instance ◽  
Or Modelling

Automated negotiation is a very challenging research field that is gaining momentum in the e-business domain. There are three main categories of automated negotiations, classified according to the participating agent cardinality and the nature of their interaction (Jennings, Faratin, Lomuscio, Parsons, Sierra, & Wooldridge, 2001): the bilateral, where each agent negotiates with a single opponent, the multi-lateral which involves many providers and clients in an auction-like framework and the argumentation/persuasion-based models where the involving parties use more sophisticated arguments to establish an agreement. In all these automated negotiation domains, several research efforts have focused on predicting the behaviour of negotiating agents. This work can be classified in two main categories. The first is based on techniques that require strong a-priori knowledge concerning the behaviour of the opponent agent in previous negotiation threads. The second uses mechanisms that perform well in single-instance negotiations, where no historical data about the past negotiating behaviour of the opponent agent is available. One quite popular tool that can support the latter case is Neural Networks (NNs) (Haykin, 1999). NNs are often used in various real world applications where the estimation or modelling of a function or system is required. In the automated negotiations domain, their usage aims mainly to enhance the performance of negotiating agents in predicting their opponents’ behaviour and thus, achieve better overall results on their behalf. This paper provides a survey of the most popular automated negotiation approaches that are using NNs to estimate elements of the opponent’s behaviour. The rest of this paper is structure as follows. The second section elaborates on the state of the art bilateral negotiation frameworks that are based on NNs. The third section briefly presents the multilateral negotiation solutions that exploit NNs. Finally, in the last section a brief discussion on the survey is provided.

Self-Stabilizing Weak Leader Election in Anonymous Trees Using Constant Memory per Edge

2017 ◽  
Vol 27 (02) ◽  
pp. 1750002 ◽  
Author(s):  
Ajoy K. Datta ◽  
Stephane Devismes ◽  
Lawrence L. Larmore ◽  
Vincent Villain
Keyword(s):  
Upper Bound ◽  
Message Passing ◽  
A Priori ◽  
Leader Election ◽  
A Priori Knowledge ◽  
Stabilization Time ◽  
Global Parameter ◽  
Priori Knowledge

We propose a deterministic silent self-stabilizing algorithm for the weak leader election problem in anonymous trees. Our algorithm is designed in the message passing model, and requires only O(1) bits of memory per edge. It does not necessitate the a priori knowledge of any global parameter on the network. Finally, its stabilization time is at most [Formula: see text] time units, where 𝒟 is the diameter of the network, X is an upper bound on the time to execute some recurrent code by processes, and Imax is the maximal number of messages initially in a link.

A Survey on Neural Networks in Automated Negotiations

2009 ◽  
pp. 2360-2366
Author(s):  
Ioannis Papaioannou ◽  
Ioanna Roussaki ◽  
Miltiades Anagnostou
Keyword(s):  
Neural Networks ◽  
A Priori ◽  
Research Field ◽  
A Priori Knowledge ◽  
Automated Negotiation ◽  
Multilateral Negotiation ◽  
The Third ◽  
Automated Negotiations ◽  
Single Instance ◽  
Or Modelling

Automated negotiation is a very challenging research field that is gaining momentum in the ebusiness domain. There are three main categories of automated negotiations, classified according to the participating agent cardinality and the nature of their interaction (Jennings, Faratin, Lomuscio, Parsons, Sierra, & Wooldridge, 2001): the bilateral, where each agent negotiates with a single opponent, the multi-lateral which involves many providers and clients in an auction-like framework and the argumentation/persuasion-based models where the involving parties use more sophisticated arguments to establish an agreement. In all these automated negotiation domains, several research efforts have focused on predicting the behaviour of negotiating agents. This work can be classi- fied in two main categories. The first is based on techniques that require strong a-priori knowledge concerning the behaviour of the opponent agent in previous negotiation threads. The second uses mechanisms that perform well in single-instance negotiations, where no historical data about the past negotiating behaviour of the opponent agent is available. One quite popular tool that can support the latter case is Neural Networks (NNs) (Haykin, 1999). NNs are often used in various real world applications where the estimation or modelling of a function or system is required. In the automated negotiations domain, their usage aims mainly to enhance the performance of negotiating agents in predicting their opponents’ behaviour and thus, achieve better overall results on their behalf. This paper provides a survey of the most popular automated negotiation approaches that are using NNs to estimate elements of the opponent’s behaviour. The rest of this paper is structure as follows. The second section elaborates on the state of the art bilateral negotiation frameworks that are based on NNs. The third section briefly presents the multilateral negotiation solutions that exploit NNs. Finally, in the last section a brief discussion on the survey is provided.

Deep Neural Networks in the Diagnosis of Postoperative Complications of Acute Appendicitis

2020 ◽  
pp. 35-65
Author(s):  
Vladimir Gorbachenko
Keyword(s):  
Neural Networks ◽  
Postoperative Complications ◽  
Personalized Medicine ◽  
Acute Appendicitis ◽  
Preoperative Evaluation ◽  
A Priori ◽  
Predictive Medicine ◽  
A Priori Knowledge ◽  
Surgical Interventions ◽  
Treatment And Prevention

Digital models are needed in medicine for diagnosis and prediction. Such models are especially needed in personalized medicine. In this area, it is necessary to evaluate and predict the patient's condition from a priori knowledge obtained from other patients. Therefore, a new direction of predictive medicine appeared. Predictive medicine, or “in silicon medicine” is the use of computer modeling and intelligent technologies in the diagnosis, treatment, and prevention of diseases. Using predictive medicine, the doctor can determine the likelihood of the development of certain diseases and choose the optimal treatment. Predictive medicine begins to be applied in surgery. The prognosis in surgery consists in the preoperative evaluation of various surgical interventions and in the evaluation of possible outcomes of surgical interventions.

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