Search results for "Artificial Intelligence"

Classification of Satellite Images with Regularized AdaBoosting of RBF Neural Networks

2008

A Memetic-Neural Approach to Discover Resources in P2P Networks

2008

This chapter proposes a neural network based approach for solving the resource discovery problem in Peer to Peer (P2P) networks and an Adaptive Global Local Memetic Algorithm (AGLMA) for performing in training of the neural network. The neural network, which is a multi-layer perceptron neural network, allows the P2P nodes to efficiently locate resources desired by the user. The necessity of testing the network in various working conditions, aiming to obtain a robust neural network, introduces noise in the objective function. The AGLMA is a memetic algorithm which employs two local search algorithms adaptively activated by an evolutionary framework. These local searchers, having different fe…

Two-level branch prediction using neural networks

2003

Dynamic branch prediction in high-performance processors is a specific instance of a general time series prediction problem that occurs in many areas of science. Most branch prediction research focuses on two-level adaptive branch prediction techniques, a very specific solution to the branch prediction problem. An alternative approach is to look to other application areas and fields for novel solutions to the problem. In this paper, we examine the application of neural networks to dynamic branch prediction. We retain the first level history register of conventional two-level predictors and replace the second level PHT with a neural network. Two neural networks are considered: a learning vec…

Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators

2021

One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…

Understanding social behavior evolutions through agent-based modeling

2012

Agent-based social simulation as a computational approach to social simulation has been largely used to explore social phenomena. The purpose of this paper is to describe a theoretical model of transmission and evolution of social behaviors in a network of artificial societies (artificial world) using agent-based modeling technology. In this model, each agent (society) is subdivided into social behaviors where individual and social learning occur. The agent-agent interactions are carried out by their social behaviors; otherwise the agent-environment interactions through consumption of ecological resources by its social behaviors in repression and satisfaction. We distinguish social behavior…

3D Matrix-Based Visualization System of Association Rules

2017

With the growing number of mining datasets, it becomes increasingly difficult to explore interesting rules because of the large number of resultant and its nature complexity. Studies on human perception and intuition show that graphical representation could be a better illustration of how to seek information from the data using the capabilities of human visual system. In this work, we present and implement a 3D matrix-based approach visualization system of association rules. The main visual representation applies the extended matrix-based approach with rule-to-items mapping to general transaction data set. A novel method merging rules and assigning weight is proposed in order to reduce the …

Predicting hospital associated disability from imbalanced data using supervised learning.

2019

Hospitalization of elderly patients can lead to serious adverse effects on their functional capability. Identifying the underlying factors leading to such adverse effects is an active area of medical research. The purpose of the current paper is to show the potential of artificial intelligence in the form of machine learning to complement the existing medical research. This is accomplished by studying the outcome of hospitalization of elderly patients as a supervised learning task. A rich set of features characterizing the medical and social situation of elderly patients is leveraged and using confusion matrices, association rule mining, and two different classes of supervised learning algo…

Internal Simulation of an Agent’s Intentions

2013

We present the Associative Self-Organizing Map (A-SOM) and propose that it could be used to predict an agent’s intentions by internally simulating the behaviour likely to follow initial movements. The A-SOM is a neural network that develops a representation of its input space without supervision, while simultaneously learning to associate its activity with an arbitrary number of additional (possibly delayed) inputs. We argue that the A-SOM would be suitable for the prediction of the likely continuation of the perceived behaviour of an agent by learning to associate activity patterns over time, and thus a way to read its intentions.

Asynchronous L1 control of delayed switched positive systems with mode-dependent average dwell time

2014

Abstract This paper investigates the stability and asynchronous L 1 control problems for a class of switched positive linear systems (SPLSs) with time-varying delays by using the mode-dependent average dwell time (MDADT) approach. By allowing the co-positive type Lyapunov–Krasovskii functional to increase during the running time of active subsystems, a new stability criterion for the underlying system with MDADT is first derived. Then, the obtained results are extended to study the issue of asynchronous L 1 control, where “asynchronous” means that the switching of the controllers has a lag with respect to that of system modes. Sufficient conditions are provided to guarantee that the resulti…

Stabilization of positive switched systems with time-varying delays under asynchronous switching

2014

Published version of an article in the journal: International Journal of Control, Automation and Systems. Also available from the publisher at: http://dx.doi.org/10.1007/s12555-013-0486-x This paper investigates the state feedback stabilization problem for a class of positive switched systems with time-varying delays under asynchronous switching in the frameworks of continuous-time and discrete-time dynamics. The so-called asynchronous switching means that the switches between the candidate controllers and system modes are asynchronous. By constructing an appropriate co-positive type Lyapunov-Krasovskii functional and further allowing the functional to increase during the running time of ac…