5,355 research outputs found
Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification
In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework
Prediction of seismic-induced structural damage using artificial neural networks
Peer reviewedPostprin
Artificial Neural Network-based error compensation procedure for low-cost encoders
An Artificial Neural Network-based error compensation method is proposed for
improving the accuracy of resolver-based 16-bit encoders by compensating for
their respective systematic error profiles. The error compensation procedure,
for a particular encoder, involves obtaining its error profile by calibrating
it on a precision rotary table, training the neural network by using a part of
this data and then determining the corrected encoder angle by subtracting the
ANN-predicted error from the measured value of the encoder angle. Since it is
not guaranteed that all the resolvers will have exactly similar error profiles
because of the inherent differences in their construction on a micro scale, the
ANN has been trained on one error profile at a time and the corresponding
weight file is then used only for compensating the systematic error of this
particular encoder. The systematic nature of the error profile for each of the
encoders has also been validated by repeated calibration of the encoders over a
period of time and it was found that the error profiles of a particular encoder
recorded at different epochs show near reproducible behavior. The ANN-based
error compensation procedure has been implemented for 4 encoders by training
the ANN with their respective error profiles and the results indicate that the
accuracy of encoders can be improved by nearly an order of magnitude from
quoted values of ~6 arc-min to ~0.65 arc-min when their corresponding
ANN-generated weight files are used for determining the corrected encoder
angle.Comment: 16 pages, 4 figures. Accepted for Publication in Measurement Science
and Technology (MST
A Concurrent Fuzzy-Neural Network Approach for Decision Support Systems
Decision-making is a process of choosing among alternative courses of action
for solving complicated problems where multi-criteria objectives are involved.
The past few years have witnessed a growing recognition of Soft Computing
technologies that underlie the conception, design and utilization of
intelligent systems. Several works have been done where engineers and
scientists have applied intelligent techniques and heuristics to obtain optimal
decisions from imprecise information. In this paper, we present a concurrent
fuzzy-neural network approach combining unsupervised and supervised learning
techniques to develop the Tactical Air Combat Decision Support System (TACDSS).
Experiment results clearly demonstrate the efficiency of the proposed
technique
Reactive power minimization of dual active bridge DC/DC converter with triple phase shift control using neural network
Reactive power flow increases dual active bridge (DAB) converter RMS current leading to an increase in conduction losses especially in high power applications. This paper proposes a new optimized triple phase shift (TPS) switching algorithm that minimizes the total reactive power of the converter. The algorithm iteratively searches for TPS control variables that satisfy the desired active power flow while selecting the operating mode with minimum reactive power consumption. This is valid for the whole range of converter operation. The iterative algorithm is run offline for the entire active power range (-1pu to 1pu) and the resulting data is used to train an open loop artificial neural network controller to reduce computational time and memory allocation necessary to store the data generated. To validate the accuracy of the proposed controller, a 500-MW 300kV/100kV DAB model is simulated in Matlab/Simulink, as a potential application for DAB in DC grids
- …