Biomedical subjects

A B Rad

Publications and source records attributed to A B Rad.

6 recordsLinked to original sources

PubMed2004

Model-based fault diagnosis in continuous dynamic systems.

Traditional fault detection and isolation methods are based on quantitative models which are sometimes difficult and costly to obtain. In this paper, qualitative bond graph (QBG) reasoning is adopted as the modeling scheme to generate a set of qualitative equations. The QBG method provides a unified approach for modeling engineering systems, in particular, mechatronic systems. An input-output qualitative equation derived from QBG formalism performs continuous system monitoring. Fault diagnosis is activated when a discrepancy is observed between measured abnormal behavior and predicted system behavior. Genetic algorithms (GA's) are then used to search for possible faulty components among a system of qualitative equations. In order to demonstrate the performance of the proposed algorithm, we have tested it on a laboratory scale servo-tank liquid process rig. Results of the proposed model-based fault detection and diagnosis algorithm for the process rig are presented and discussed.

Journal Article↗

PubMed2004

Development of a circuit for functional electrical stimulation.

This paper examines the various design of a multiple-purpose portable functional electrical stimulator which is used in surface stimulation of paralyzed muscle of patients with stroke and results in limb activation. The functionality, circuit performance and reliability of the circuits will be examined. Analysis, design, and experimental results are presented.

Computer Simulation↗

PubMed2003

On-line lower-order modeling via neural networks.

This paper presents a novel method to determine the parameters of a first-order plus dead-time model using neural networks. The outputs of the neural networks are the gain, dominant time constant, and apparent time delay. By combining this algorithm with a conventional PI or PID controller, we also present an adaptive controller which requires very little a priori knowledge about the plant under control. The simplicity of the scheme for real-time control provides a new approach for implementing neural network applications for a variety of on-line industrial control problems. Simulation and experimental results demonstrate the feasibility and adaptive property of the proposed scheme.

Algorithms↗

PubMed2003

Self-tuning control of systems with unknown time delay via extended polynomial identification.

Much of the work reported on self-tuning control addresses the class of systems with known time delay. In this paper, the continuous time self-tuning control algorithm is extended to systems with unknown or varying time delay. The original polynomial identification is further modified in this paper to estimate both poles, zeros, and unknown time delay. An explicit self-tuner is then designed based on the estimated parameters. Experimental studies are used to evaluate the performance of this algorithm.

Journal Article↗

PubMed2002

Fusion of qualitative bond graph and genetic algorithms: a fault diagnosis application.

In this paper, the problem of fault diagnosis via integration of genetic algorithms (GA's) and qualitative bond graphs (QBG's) is addressed. We suggest that GA's can be used to search for possible fault components among a system of qualitative equations. The QBG is adopted as the modeling scheme to generate a set of qualitative equations. The qualitative bond graph provides a unified approach for modeling engineering systems, in particular, mechatronic systems. In order to demonstrate the performance of the proposed algorithm, we have tested the proposed algorithm on an in-house designed and built floating disc experimental setup. Results from fault diagnosis in the floating disc system are presented and discussed. Additional measurements will be required to localize the fault when more than one fault candidate is inferred. Fault diagnosis is activated by a fault detection mechanism when a discrepancy between measured abnormal behavior and predicted system behavior is observed. The fault detection mechanism is not presented here.

Air Movements↗

PubMedDate not supplied

A cascading structure and training method for multilayer neural networks.

A new structure and training method for multilayer neural networks is presented. The proposed method is based on cascade training of subnetworks and optimizing weights layer by layer. The training procedure is completed in two steps. First, a subnetwork, m inputs and n outputs as the style of training samples, is trained using the training samples. Secondly the outputs of the subnetwork is taken as the inputs and the outputs of the training sample as the desired outputs, another subnetwork with n inputs and n outputs is trained. Finally the two trained subnetworks are connected and a trained multilayer neural networks is created. The numerical simulation results based on both linear least squares back-propagation (LSB) and traditional back-propagation (BP) algorithm have demonstrated the efficiency of the proposed method.

Algorithms↗