Systems in neural networks

This paper deals with synchronization analysis problem for a class of fractional-order neural networks with unbounded delays. Using the Lyapunov function method combined with fractional Halanay inequality, we derive a novel sufficient condition for asymptotic stability of the error system resulting in two neural networks are synchronized.

9p viprimi 16-12-2024 0 0   Download

This thesis develops a flexible customer behavior analysis system, including essential head pose estimation or F-formation modules. This system will be evaluated in an actual retail store. Further, after studying the system, realizing the mentioned problems of the head pose problem, we also propose a process to collect the head pose dataset and multi-task deep neural network model, fusing face detection and head pose estimation to yield face position and head pose at the same time.

72p khanhchi0912 12-04-2024 5 2   Download

Research aims: This thesis is concerned with the stability of some classes of nonlinear time-delay systems in neural networks. Investigating the problem of stability of non-autonomous neural networks with heterogeneous time-varying delays in the effect of destablizing impulses. Stabilizing Hopfiled neural networks with proposition delays subject to stabilizing and destablizing impulsive effects simultaneously.

27p tunelove 10-06-2021 22 4   Download

The research works were published at international paper mainly focused on diagnosing epilepsy sleep disorders, coma and brain death, stress, depresssion pathological… in automation field as spelling, eye blink, head movement, mental arithmetic… this were performed by offline, no mainly focus on resolving in realtime and in control automation field.

33p gaocaolon6 30-07-2020 45 3   Download

This paper intends to present the System Dynamics (SD) as a novel method to simulate the thrust force developed during drilling of GFRP composites. Good quality holes are extremely fundamental so as to accomplish equally good joints amid creation of components prepared from composite for better execution.

9p lucastanguyen 01-06-2020 12 3   Download

The thesis aims to propose some control techniques for mobile target Robot-camera. After that, I studied some of the torque control techniques of joints for the Robot-camera system sticking to the mobile target and the Robot-camera system, paying attention to the motivating motor sticking to the mobile target. Finally, the author also proposed some control algorithms for Robotic-camera arm system with irregular model, external noise and preventing system degradation, using nonlinear sliding controller (TSMC) in combination with Artificial neural networks to estimate uncertain numbers.

31p xacxuoc4321 09-07-2019 28 2   Download

In this paper we address the problem of extracting features relevant for predicting protein±protein interaction sites from the three-dimensional structures of protein complexes. Our approach is based on information about evolutionary con-servation and surface disposition. We implement a neural network based system, which uses a cross validation proce-dure and allows the correct detection of 73% of the residues involved in protein interactions in a selected database comprising 226 heterodimers.

6p research12 29-04-2013 31 3   Download

EURASIP Journal on Applied Signal Processing 2003:12, 1229–1237 c 2003 Hindawi Publishing Corporation Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent Mohamed Ibnkahla Electrical and Computer Engineering Department, Queen’s University, Kingston, Ontario, Canada K7L 3N6 Email: mohamed.ibnkahla@ece.queensu.ca Received 13 December 2002 and in revised form 17 May 2003 We use natural gradient (NG) learning neural networks (NNs) for modeling and identifying nonlinear systems with memory.

9p sting12 10-03-2012 44 6   Download

EURASIP Journal on Applied Signal Processing 2003:9, 890–901 c 2003 Hindawi Publishing Corporation An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System Javad Haddadnia Engineering Department, Tarbiat Moallem University of Sabzevar, Sabzevar, Khorasan 397, Iran Email: haddadnia@sttu.ac.ir Majid Ahmadi Electrical and Computer Engineering Department, University of Windsor, Windsor, Ontario, Canada N9B 3P4 Email: ahmadi@uwindsor.

12p sting12 10-03-2012 37 6   Download

Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008, Article ID 468693, 7 pages doi:10.1155/2008/468693 Research Article Comparing Robustness of Pairwise and Multiclass Neural-Network Systems for Face Recognition J. Uglov, L. Jakaite, V. Schetinin, and C. Maple Computing and Information System Department, University of Bedfordshire, Luton LU1 3JU, UK Correspondence should be addressed to V. Schetinin, vitaly.schetinin@beds.ac.uk Received 16 June 2007; Revised 28 August 2007; Accepted 19 November 2007 Recommended by Konstantinos N.

7p dauphong18 24-02-2012 64 4   Download

Convergence of Online Learning Algorithms in Neural Networks An analysis of convergence of real-time algorithms for online learning in recurrent neural networks is presented. For convenience, the analysis is focused on the real-time recurrent learning (RTRL) algorithm for a recurrent perceptron. Using the assumption of contractivity of the activation function of a neuron and relaxing the rigid assumptions of the fixed optimal weights of the system, the analysis presented is general and is applicable to a wide range of existing algorithms....

9p doroxon 12-08-2010 91 9   Download

Stability Issues in RNN Architectures Perspective The focus of this chapter is on stability and convergence of relaxation realised through NARMA recurrent neural networks. Unlike other commonly used approaches, which mostly exploit Lyapunov stability theory, the main mathematical tool employed in this analysis is the contraction mapping theorem (CMT), together with the fixed point iteration (FPI) technique. This enables derivation of the asymptotic stability (AS) and global asymptotic stability (GAS) criteria for neural relaxive systems.

19p doroxon 12-08-2010 105 9   Download

Artificial neural network (ANN) models have been extensively studied with the aim of achieving human-like performance, especially in the field of pattern recognition. These networks are composed of a number of nonlinear computational elements which operate in parallel and are arranged in a manner reminiscent of biological neural interconnections. ANNs are known by many names such as connectionist models, parallel distributed processing models and neuromorphic systems (Lippmann 1987).

8p doroxon 12-08-2010 118 24   Download