Results 181 to 190 of about 258,177 (241)

PROBABILISTIC LOGIC OF FORMAL NEURON NETWORKS

, 1972
K. N. Leibovic
openalex   +2 more sources

Some of the next articles are maybe not open access.

Related searches:

Asymptotical feedback controllability of probabilistic logic control networks

Systems & Control Letters, 2021
Abstract In this paper, we investigate the asymptotical feedback controllability (AFC) of probabilistic logic control networks (PLCNs). First, the concept of asymptotical feedback reachability (AFR) is proposed, allowing us to define AFC. Then, we prove that a state is asymptotically feedback reachable if and only if (iff) it is a control fixed point,
Zhitao Li, Yuqian Guo, Weihua Gui
openaire   +2 more sources

A Human-in-the-Loop Probabilistic CNN-Fuzzy Logic Framework for Accident Prediction in Vehicular Networks

IEEE Sensors Journal, 2021
The vehicle accident prediction methods are designed to improve the vehicular safety and reduce the rescue response time in the case of an accident.
Muhammad Usman, Anil Carie, Bhaskar Marapelli, H. D. Bedru, Kamanashis Biswas   +4 more
semanticscholar   +1 more source

Controllability and stabilizability of probabilistic logical control networks

2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012
Necessary and sufficient conditions of controllability and stabilizability of probabilistic Boolean control networks are first introduced, by using the controllability matrix of switched Boolean control networks. Then, these results are generalized to mix-valued logical control networks, which include the Boolean control networks as a special case. The
Yin Zhao, Daizhan Cheng
openaire   +2 more sources

Reasoning about Large-Scale Social Networks with Probabilistic Logic

, 2011
Network Matching and Link Prediction are relatively unexplored in the area of Social network analysis, but solving those problems in an efficient way is crucial in many real‐world applications. Network Matching is a generalized problem of node identification.
Davor Lauc, Siniša Grgić
openalex   +2 more sources

On memory capacity of the Probabilistic Logic Neuron network

Journal of Computer Science and Technology, 1993
In this paper, the memory capacity of Probabilistic Logic Neuron (PLN) network is discussed. We obtain two main results: (1) the method for constructing a PLN network with a given memory capacity; (2) the relationship between the memory capacity and the size of a PLN network.
Ling Zhang, Bo Zhang
openaire   +2 more sources

Probabilistic Logic Networks in a Nutshell [PDF]

, 2011
We begin with a brief overview of Probabilistic Logic Networks, distinguish PLN from other approaches to reasoning under uncertainty, and describe some of the main conceptual foundations and goals of PLN. We summarize how knowledge is represented within PLN and describe the four basic truth-value types.
openaire   +1 more source

Asymptotic stability of probabilistic logical networks with random impulsive effects

Information Sciences, 2021
Abstract This paper investigates the asymptotic set stability of probabilistic logical networks (PLNs) with random impulsive disturbances. A hybrid index model is applied to describe the impulsive PLN. Both the sequence of switching signals and the sequence of impulsive intervals are assumed to be independent and identically distributed (i.i.d ...
Lianglin Xiong, Bingquan Chen, Jie Zhong, Jinde Cao, Jinde Cao   +4 more
openaire   +2 more sources

Probabilistic logic programming and Bayesian networks

1995
We present a probabilistic logic programming framework that allows the representation of conditional probabilities. While conditional probabilities are the most commonly used method for representing uncertainty in probabilistic expert systems, they have been largely neglected by work in quantitative logic programming.
Liem Ngo, Peter Haddawy
openaire   +2 more sources

Representing logical relations automatically by Probabilistic Logical Dynamical Neural Network

2016 International Joint Conference on Neural Networks (IJCNN), 2016
Most of current ANN represents relations in the way of functional approximation. It is good for representing the numeric relations or ratios of things. However, it is not proper to represent logical relations in the form of ratio. Therefore, aiming for representing logical relations directly, we propose a new ANN model PLDNN (Probabilistic Logical ...
De-Shuang Huang, Gang Wang
openaire   +2 more sources