1,640 research outputs found
A collection of fuzzy logic-based tools for the automated design, modelling and test of analog circuits
We have developed a collection of tools for the design, modeling, and test of analog circuits. Sharing a common fuzzy-logic based framework, the tools are part of FASY (Fuzzy-Logic-Based Analog Synthesis), an analog design package developed at the University of Seville. The first tool uses fuzzy logic for topology selection of analog cells. It follows decision rules directly entered by a human expert or automatically generated from its experience with earlier designs. Second, a performance-modeling tool provides a qualitative description of a circuit's behavior. Alternatively, it can use a learning process to accurately model circuit performance. Finally, an analog testing tool uses a fuzzy-neuron classifier to detect and classify faults in analog circuits
Automatic programming methodologies for electronic hardware fault monitoring
This paper presents three variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the Stressor - susceptibility interaction model. A circuit or a system is considered to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after pre-processing and standardization are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm and classification and regression trees. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.This research was supported by the International Joint Research Grant of the IITA (Institute of Information Technology Assessment) foreign professor invitation program of the MIC (Ministry of Information and Communication), Korea
A Trust Based Fuzzy Algorithm for Congestion Control in Wireless Multimedia Sensor Networks (TFCC)
Network congestion has become a critical issue for resource constrained
Wireless Sensor Networks (WSNs), especially for Wireless Multimedia Sensor
Networks (WMSNs)where large volume of multimedia data is transmitted through
the network. If the traffic load is greater than the available capacity of the
sensor network, congestion occurs and it causes buffer overflow, packet drop,
deterioration of network throughput and quality of service (QoS). Again, the
faulty nodes of the network also aggravate congestion by diffusing useless
packets or retransmitting the same packet several times. This results in the
wastage of energy and decrease in network lifetime. To address this challenge,
a new congestion control algorithm is proposed in which the faulty nodes are
identified and blocked from data communication by using the concept of trust.
The trust metric of all the nodes in the WMSN is derived by using a two-stage
Fuzzy inferencing scheme. The traffic flow from source to sink is optimized by
implementing the Link State Routing Protocol. The congestion of the sensor
nodes is controlled by regulating the rate of traffic flow on the basis of the
priority of the traffic. Finally we compare our protocol with other existing
congestion control protocols to show the merit of the work.Comment: 6 pages, 5 figures, conference pape
A genetic algorithm for the design of a fuzzy controller for active queue management
Active queue management (AQM) policies are those
policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the
hosts on the network borders, and the adoption of a suitable control
policy. This paper proposes the adoption of a fuzzy proportional
integral (FPI) controller as an active queue manager for Internet
routers. The analytical design of the proposed FPI controller is
carried out in analogy with a proportional integral (PI) controller,
which recently has been proposed for AQM. A genetic algorithm is
proposed for tuning of the FPI controller parameters with respect
to optimal disturbance rejection. In the paper the FPI controller
design metodology is described and the results of the comparison
with random early detection (RED), tail drop, and PI controller
are presented
CMOS design of a current-mode multiplier/divider circuit with applications to fuzzy controllers
Multiplier and divider circuits are usually required in the fields of analog signal processing and parallel-computing neural or fuzzy systems. In particular, this paper focuses on the hardware implementation of fuzzy controllers, where the divider circuit is usually the bottleneck. Multiplier/divider circuits can be implemented with a combination of A/D-D/A converters. An efficient design based on current-mode data converters is presented herein. Continuous-time algorithmic converters are chosen to reduce the control circuitry and to obtain a modular design based on a cascade of bit cells. Several circuit structures to implement these cells are presented and discussed. The one that is selected enables a better trade-off speed/power than others previously reported in the literature while maintaining a low area occupation. The resulting multiplier/divider circuit offers a low voltage operation, provides the division result in both analog and digital formats, and it is suitable for applications of low or middle resolution (up to 9 bits) like applications to fuzzy controllers. The analysis is illustrated with Hspice simulations and experimental results from a CMOS multiplier/divider prototype with 5-bit resolution. Experimental results from a CMOS current-mode fuzzy controller chip that contains the proposed design are also included
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