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1998 | Buch

Fuzzy Hardware Research From Historical Point of View Kapitel lesen Erstes Kapitel lesen

Fuzzy Hardware

Architectures and Applications

herausgegeben von: Abraham Kandel, Gideon Langholz

Verlag: Springer US

Enthalten in: Professional Book Archive

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Über dieses Buch

Fuzzy hardware developments have been a major force driving the applications of fuzzy set theory and fuzzy logic in both science and engineering. This volume provides the reader with a comprehensive up-to-date look at recent works describing new innovative developments of fuzzy hardware.
An important research trend is the design of improved fuzzy hardware. There is an increasing interest in both analog and digital implementations of fuzzy controllers in particular and fuzzy systems in general. Specialized analog and digital VLSI implementations of fuzzy systems, in the form of dedicated architectures, aim at the highest implementation efficiency. This particular efficiency is asserted in terms of processing speed and silicon utilization. Processing speed in particular has caught the attention of developers of fuzzy hardware and researchers in the field.
The volume includes detailed material on a variety of fuzzy hardware related topics such as: Historical review of fuzzy hardware research Fuzzy hardware based on encoded trapezoids Pulse stream techniques for fuzzy hardware Hardware realization of fuzzy neural networks Design of analog neuro-fuzzy systems in CMOS digital technologies Fuzzy controller synthesis method Automatic design of digital and analog neuro-fuzzy controllers Electronic implementation of complex controllers Silicon compilation of fuzzy hardware systems Digital fuzzy hardware processing Parallel processor architecture for real-time fuzzy applications Fuzzy cellular systems Fuzzy Hardware: Architectures and Applications is a technical reference book for researchers, engineers and scientists interested in fuzzy systems in general and in building fuzzy systems in particular.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Fuzzy Hardware Research From Historical Point of View
Abstract
In a paper written in 1961, Lofti A. Zadeh [1] mentioned that a new technique was needed, a fuzzy kind of mathematics. At the time, though, he had no clear idea how this would work. He published his first paper about Fuzzy Logic (FL) only in 1965 [2].
Marco Russo
Chapter 2. Three Generations of Fuzzy Hardware
Abstract
“Fuzzy set theory is a generalization of traditional set theory in the sense that the domain of the characteristic function is extended from the discrete set(0,1) to the closed interval [0,1]” [2]. This extension from discrete values to continuous intervals supported our decision to develop the complete fuzzy inference process in analog techniques as it suits the theory of fuzzy sets best.
Liliane Peters, Shuwei Gou
Chapter 3. Hardware Realization of Fuzzy Neural Networks
Abstract
It is needless to say that a panoply of real-world applications of fuzzy sets call for a variety of systems realizing fuzzy computation. Concurrently, it is highly desirable to develop some universal computing modules that may be easily customized to meet required hardware — software specifications. This concept is not that new, as in two-valued logic we can easily encounter various categories of configurable and programmable devices (PLDs), ranging from PAL’s to FPGA’s. These devices are standardized general purpose logic units, which may be configured to perform specific functions. To follow a similar avenue, it is indispensable to identify a few generic processing modules that are complete enough, when considered from a functional point of view, for general computations with fuzzy linguistic variables. A family of logic-based neurons [1][2], emerges as a collection of processing operations whose role is to model logic-oriented processing dominant in the theory of fuzzy sets. These configurable architectures arising within this framework can directly cope with the topology of the problem at hand.
C. Hart Poskar, Peter J. Czezowski, Witold Pedrycz
Chapter 4. AFAN — A Tool for the Automatic Design of Digital and Analog Neuro-Fuzzy Controllers
Abstract
Since the appearance of VHDL, the design of large blocks of digital circuits is possible at a reasonable cost. The advantages of these high level languages are obvious; among them, they increase the chances of design automation. This paper present a tool aimed at automating the design of digital neural and fuzzy controllers. Note that, in spite of the different neural and fuzzy structures proposed in the literature, only a few of them are used in real applications. Hence, the architecture of these controllers is repeated quite frequently, so that automation is possible. Automation significatively shortens the design time, and reduces design cost. Different tools have recently been proposed to automate the hardware design of neural and fuzzy controllers. Some of them use a digital approach [1], [2], an analog approach [3] or a mixed-signal approach [4].
R. G. Carvajal, M. A. Aguirre Echanova, A. J. Torralba Silgado, L. G. Franquelo
Chapter 5. Silicon Compilation of Fuzzy Hardware Systems Based on Generic LR Fuzzy Cells
Abstract
In recent years, various analog fuzzy hardware systems have been proposed with the idea of realizing high performance implementations [1]-[5]. The fuzzy systems proposed in each of these articles features high performance, small area and low power consumption in their respective application domains. However, accompanying the successful application of fuzzy set theory to many fields, we have seen the development of a wide variety of fuzzy system styles as well as a fusion of fuzzy set theory and neural networks. Therefore, a generic and systematic design methodology for fuzzy hardware is demanded. Moreover, to relieve the annoyance of second order effects in analog hardware systems, generic cells for high level synthesis are also required. To achieve these goals, the proposed silicon compiler adopts a set of general-purpose LR fuzzy cells[21] so as to simplify both the organization of a standard cell library and the synthesis of fuzzy hardware systems from high-level fuzzy linguistic descriptions. Other silicon compilation approaches [6]-[7] for fuzzy hardware have also been proposed to achieve high level synthesis. However, only a single fuzzy system style can be found in these articles. They support only one kind of inference method associated with singleton fuzzy rules and single type membership functions.
Yau-Hwang Kuo, Chao-Lieh Chen
Chapter 6. Serial Architectures for Efficient Digital Fuzzy Hardware Processing
Abstract
The scope of this paper is to show some implementations of serial algorithms for fuzzy processing. Previously, we will review some of basic guidelines that it is convenient to follow in the development of fuzzy processor and brief analysis of the fuzzy processing problem. Later, we will depict some solutions in the implementation of inference units, modifier processors and defuzzifiers. All of them from the serial architecture point of view.
Luis de Salvador Carrasco, Julio Gutierrez-Rios
Chapter 7. Automatic Implementation of Piecewise-Linear Fuzzy Systems Addressing Memory-Performance Trade-Off
Abstract
Many applications of fuzzy controllers are related to complex and fast systems, and require many inputs and rules as well as specific hardware for real-time processing. Hence, many recent investigations and products (e.g. [l]-[5]) aim at coupling speed with complexity.
Riccardo Rovatti, Alberto Ferrari, Michele Borgatti
Chapter 8. A Parallel Processor Architecture for Real-Time Fuzzy Applications
Abstract
Systems in a great number of fields, such as process control [I], Hw-Sw Codesign [2], database [3], decision making [4] and image processing [5]-[6], are modelled using Fuzzy Logic [7]. The increase of the fuzzy applications is due to its inherent capacity to formalize algorithms which can tolerate imprecision and uncertainty, emulating the cognitive processes that human beings use every day [8].
Giuseppe Ascia, Vincenzo Catania
Chapter 9. Short Time Decision VLSI Fuzzy Processor
Abstract
In the recent years numerous examples of industrial and research applications have been done by utilizing Fuzzy Logic. In more details fuzzy processors find most of their applications in control logic fields [1]. Besides that it can be said that once an user has written down some fuzzy rules for describing a particular problem, a related fuzzy algorithm can be created. In general this fuzzy algorithm can be implemented either in SW or HW platforms. Nevertheless, even if Fuzzy Logic is also developing in order to decrease the processing time, the HW/SW implementation of a fuzzy algorithm on commercial fuzzy processors may not give good results in term of speed. Consequently, mainly for high speed applications, dedicated fuzzy processors are required. As far as VLSI implementations [2], many researchers have improved the performances of the HW processors by analog [3], [4], digital [5]-[9] or mixed solutions. The different approaches to the design of the VLSI fuzzy processor architectures are in order to find a trade-off between speed, flexibility and layout silicon area. These are exactly the features we have investigated while designing the architecture of the chip here presented. Particularly, chip dimensions, high speed and membership function shapes have been taken into account during the design feature approach. As far as application fields, the Fuzzy Processor has been designed for future applications in High Energy Physics Experiments (HEPE) fields where high processing rates are a part of the global constraints. In this field a very fast 2 input 1 output fuzzy processor may find many applications as co-processor for problems of particle trajectory recognition.
Alessandro Gabrielli, Enzo Gandolfi, Massimo Masetti
Chapter 10. Designing a Simple System to Greatly Accelerate the Learning Speed of a Large Class of Fuzzy Learning Methods
Abstract
In the last few years the world of computers has evolved in two directions: conventional machines with an ever greater degree of parallelism and machines with increasingly higher Machine Intelligence Quotients (MIQs) [1]. The element which separates these two categories is accuracy. The former, in fact, rely on Hard Computing (HC), i.e. computation based on mathematical accuracy, while the latter are based on Soft Computing (SC), which relies on the lower computational cost inherent in imprecision.
Marco Russo
Chapter 11. Fuzzy Controller Synthesis Method
Abstract
This paper is concerned with the implementation of fuzzy controllers by means of specific components, namely application specific integrated circuits (ASIC) or field programmable gate arrays (FPGA). This kind of solution can be desirable for several reasons. In some cases the required technical specifications cannot be achieved by using standard c.p.u. boards or standard components (microprocessors, microcontrollers). In other cases, specially for large volume production, a specifically developed integrated circuit might be the less expensive choice. Sometimes also there exist strategic reasons that recommend the use of specific components; the most common one is the protection against the illegal reproduction.
Jean-Pierre Deschamps
Chapter 12. Fuzzy Hardware Based on Encoded Trapezoids
Abstract
The application of Fuzzy Logic has evolved in last decades in such a way that areas with specific name have been created: the fuzzy technologies [1] [2] [3].
Antonio Ruiz, Julio Gutierrez, J. A. Felipe Fernandez
Chapter 13. Pulse Stream Techniques for Fuzzy Hardware
Abstract
Two different strategies have been proposed for specific fuzzy hardware: analog and digital. Analog implementations [l]-[7], are very efficient in area, need no A/D conversion to interface the physical world, and achieve good speed performances exploiting parallelism. Nevertheless, analog fuzzy controllers are restricted to small knowledge bases with a few inputs, outputs and rules. Besides, the accuracy of analog computation is limited by technological issues such as mismatching and process variation. Furthermore, analog circuits are prune to noise and interference.
F. Colodro Ruiz, A. J. Torralba Silgado, J. Tombs, L. G. Franquelo
Chapter 14. Fuzzy Cellular System: Characteristics and Architecture
Abstract
In recent years, there has been renewed interest in using large scale homogeneous cellular arrays of simple circuits to perform image processing tasks and to demonstrate interesting pattern forming phenomena.
Riccardo Caponetto, Mario Lavorgna, Luigi Occhipinti, GianGuido Rizzotto
Chapter 15. Fuzzy Wavelets for Feature Extraction and Failure Classification
Abstract
Traditionally, model-based techniques have been used for feature extraction [1]. These techniques rely solely on an accurate model of the system. Failure sensitive filters and multiple hypotheses filter detectors aim at classifying abnormal system behavior using system models. Model-based techniques perform satisfactorily as long as the model characteristics are close to the actual system. However, performance degrades rapidly if the model does not closely represent the actual system. Unfortunately, accurate models are not available for most systems. There is a growing potential for knowledge-based models instead of analytic ones. Knowledge systems have the capability of including a wider range of information sources such as input-output data, heuristics, etc.
George Vachtsevanos, Vipin K. Ramani, Muid Mufti
Chapter 16. A Building Block Approach to the Design of Analog Neuro-Fuzzy Systems in CMOS Digital Technologies
Abstract
There are many practical applications of fuzzy inference systems where the inputs (represented by a multidimensional vector x= {x 1, x 2,…x m} T and the output †1 (represented by a scalar signal y) are analog signals. For instance, this is the case in control, where the inputs are measured using sensors, and the output is used to set the value of some physical variable through a transducer, an actuator, or the like [1]. There are two basic approaches to realize the hardware required for these applications. One employs analog circuitry only at the interfaces, while the processing itself is realized in digital domain by either using general-purpose digital processing ICs or dedicated ASICs [2]. The other uses analog circuitry for the fuzzy processing itself, while the digital circuitry is basically used for programmability [3].
Fernando Vidal-Verdú, Manuel Delgado-Restituto, Rafael Navas-González, Angel Rodríguez-Vázquez
Chapter 17. Electronic Implementation of Complex Controllers
Abstract
In the design of a complex control system it is necessary to use a hierarchical approach. This hierarchical approach is based on a hierarchical structure of objects and on a high level description language oriented to these applications.
Alfredo Sanz, Jorge Falco
Backmatter
Metadaten
Titel
Fuzzy Hardware
herausgegeben von
Abraham Kandel
Gideon Langholz
Copyright-Jahr
1998
Verlag
Springer US
Electronic ISBN
978-1-4615-4090-8
Print ISBN
978-1-4613-6831-1
DOI
https://doi.org/10.1007/978-1-4615-4090-8