neural network research papers-23

Landslide risk analysis using artificial neural network model focusing on different training sites
S Lee ,International Journal of Physical , 2009 ,

This paper presents landslide hazard and risk analysis using remote sensing data, GIS tools
and artificial neural network model. Landslide locations were identified in the study area
from interpretation of aerial photographs and from field surveys. Topographical and 

A neural network method for identification of RNA-interacting residues in protein
E Jeong, IF Chung, S Miyano ,GENOME INFORMATICS SERIES, 2004 ,

Abstract Identification of the most putative RNA-interacting residues in protein is an
important and challenging problem in a field of molecular recognition. Structural analysis of
protein-RNA complexes reveals a strong correlation between interaction residues and 

A mixed-mode analog neural network using current-steering synapses
J Schemmel, S Hohmann, Analog Integrated Circuits , 2004 ,Springer

Abstract A hardware neural network is presented that combines digital signalling with
analog computing. This allows a high amount of parallelism in the synapse operation while
maintaining signal integrity and high transmission speed throughout the system. The 

Artificial neural network ensembles and their application in pooled flood frequency analysis
C Shu ,Water Resources Research, 2004 ,

[2] An artificial neural network (ANN), as a relatively new approach to modeling both
regression and classification problems, has numerous applications in many scientific fields.
ANNs have been widely used for solving a range of hydrological problems such as rainfall

Cryptanalysis of a chaotic neural network based multimedia encryption scheme
D Zhang ,Advances in Multimedia Information , 2005 ,Springer

Recently, Yen and Guo proposed a chaotic neural network (CNN) for signal encryption,
which was suggested as a solution for protection of digital images and videos. The present
paper evaluates the security of this CNN-based encryption scheme, and points out that it 

Artificial neural network modelling of driver handling behaviour in a driver-vehicle-environment system
Y Lin, P Tang, WJ Zhang ,International Journal of Vehicle , 2005 ,Inderscience

Modelling driver handling behaviour in a driver-vehicle-environment (DVE) system is
essentially useful for the design of vehicle systems and transport systems in the light of the
safety and efficiency of human mobility. Driver handling behaviour is reflected in two 

Stable predictive control of chaotic systems using self-recurrent wavelet neural network
SJ Yoo, JB Park ,Int. J. Control Autom. Syst, 2005 ,

Abstract: In this paper, a predictive control method using self-recurrent wavelet neural
network (SRWNN) is proposed for chaotic systems. Since the SRWNN has a self-recurrent
mother wavelet layer, it can well attract the complex nonlinear system though the SRWNN 

Groundwater level forecasting in a shallow aquifer using artificial neural network approach
YRS Rao ,Water Resources Management, 2006 ,Springer

Abstract Forecasting the ground water level fluctuations is an important requirement for
planning conjunctive use in any basin. This paper reports a research study that investigates
the potential of artificial neural network technique in forecasting the groundwater level 

entrapment in alginate beads for stability improvement and site-specific delivery: Physicochemical characterization and factorial optimization using neural network
MG Sankalia, RC Mashru, JM Sankalia ,Aaps Pharmscitech, 2005 ,Springer

Abstract This work examines the influence of various process parameters (like sodium
alginate concentration, calcium chloride concentration, and hardening time) on papain
entrapped in ionotropically cross-linked alginate beads for stability improvement and site-

Optimal groundwater remediation design using an adaptive neural network genetic algorithm
S Yan ,Water Resources Research, 2006 ,

[2] Finding optimal solutions to real-world water resource problems, such as optimal
groundwater remediation designs, can be challenging, because the process often requires
coupling an optimization algorithm with complex simulation models to evaluate potential 

Neural network and genetic algorithm based global path planning in a static environment
D Xin, C Hua-hua ,Journal of Zhejiang University-Science , 2005 ,Springer

Abstract Mobile robot global path planning in a static environment is an important problem.
The paper proposes a method of global path planning based on neural network and genetic
algorithm. We constructed the neural network model of environemntal information in the 

Intrinsic bursting enhances the robustness of a neural network model of sequence generation by avian brain area HVC
DZ Jin, FM Ramazanoglu ,Journal of computational , 2007 ,Springer

Abstract Avian brain area HVC is known to be important for the production of birdsong. In
zebra finches, each RA-projecting neuron in HVC emits a single burst of spikes during a
song motif. The population of neurons is activated in a precisely timed, stereotyped 

Credit risk analysis using a reliability-based neural network ensemble model
K Lai, L Yu, S Wang ,Artificial Neural Networks–ICANN 2006, 2006 ,Springer

Credit risk analysis is an important topic in the financial risk management. Due to recent
financial crises and regulatory concern of Basel II, credit risk analysis has been the major
focus of financial and banking industry. An accurate estimation of credit risk could be