Fuzzy controllers are a class of knowledge based controllers using artificial intelligence techniques with origins in fuzzy logic. They can be found either as stand-alone control elements or as int ...

An Introduction to Fuzzy Control

In this paper, various aspects of digital fuzzy logic controller (FLC) design and implementation are discussed, Classic and improved models of the single-input single-output (SISO), multiple-input single-output (MISC), and multiple-input multiple-output (MIMO) FLCs are analyzed in terms of hardware cost and performance. A set of universal parameters to characterize any hardware realization of digital FLCs is defined. The comparative study of classic and alternative MIMO FLCs is presented as a generalization of other controller configurations. A processing element for the parallel FLC architecture realizing improved inferencing of MIMO system is designed, characterized, and tested. Finally, as a case feasibility study, a direct data stream architecture for complete digital fuzzy controller is shown as an improved solution for high-speed, cost-effective, real-time control applications.

Digital fuzzy logic controller: design and implementation

https://hal-supelec.archives-ouvertes.fr/hal-00879431/document

Predicting component reliability and level of degradation with complex-valued neural networks

In the modern scenario, reliability has becomes the most challenging and demanding theory. The theory and the methods of reliability analysis have been developed significantly during the last 40 years and have also been acknowledged in a number of publications. So, a reliability engineer is aware about the importance of each reliability measure of the system and its fields. In this research work, a survey of reliability approaches in various fields of engineering and physical sciences is carried out. In this survey, the author tried to provide the major areas i.e. past, current and future trends of reliability methods and applications for the readers.

On system reliability approaches: a brief survey

This paper puts forth a new encoding method for using neural network models to estimate the reliability of telecommunications networks with identical link reliabilities. Neural estimation is computationally speedy, and can be used during network design optimization by an iterative algorithm such as tabu search, or simulated annealing. Two significant drawbacks of previous approaches to using neural networks to model system reliability are the long vector length of the inputs required to represent the network link architecture, and the specificity of the neural network model to a certain system size. Our encoding method overcomes both of these drawbacks with a compact, general set of inputs that adequately describe the likely network reliability. We computationally demonstrate both the precision of the neural network estimate of reliability, and the ability of the neural network model to generalize to a variety of network sizes, including application to three actual large scale communications networks.

A General Neural Network Model for Estimating Telecommunications Network Reliability

Neural network for the reliability analysis of simplex systems

Neural network realization of Markov reliability and fault-tolerance models

Systolic VLSI arrays have been proposed for fast fuzzy inference processes in control systems Due to their complexity. VLSI circuits sometimes fail and the result is an erroneous output. In this paper, faults in fuzzy logic systolic arrays are considered. It will be shown that a single fault in the array may complement the fuzzy output. Faults in the fuzzy logic systolic array can be tolerated by either hardware redundancy or time redundancy. The recomputing with shifted operands (RESO) method, time redundancy, and the triple modular redundancy (TMR) method, hardware redundancy, are applied to fuzzy logic systolic array.

Faults in fuzzy logic systolic arrays

Overcurrent relay on a FPGA chip

A fuzzy replacement algorithm (FRA) for cache memories has been proposed (Hossain et al., 1991). In this paper we present a modified fuzzy replacement algorithm (MFRA) that improves the performance of the cache memory manyfold over the FRA. The following notable changes have been made to the FRA: the number of inputs has been reduced from three to two, the number of fuzzy linguistic variables and the dimensions of the universes of discourse have been increased, and a larger knowledge base that consists of 34 new rules is employed. The performance of the MFRA, as shown in the results, is comparable with the performance of traditional replacement algorithms, such as least recently used (LRU) and first in first out (FIFO), under real work load conditions. The interesting feature of the MFRA is that the hardware (Hossain and Manzoul, 1993) proposed for the FRA can readily be used to implement the MFRA.

Mahmoud A. Manzoul

Papers

Neural network for the reliability analysis of simplex systems

Neural network realization of Markov reliability and fault-tolerance models

Faults in fuzzy logic systolic arrays

Overcurrent relay on a FPGA chip

An improved fuzzy replacement algorithm for cache memories