Showing papers on "Soft computing published in 1996"

Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence

Abstract: Included in Prentice Hall's MATLAB Curriculum Series, this text provides a comprehensive treatment of the methodologies underlying neuro-fuzzy and soft computing. The book places equal emphasis on theoretical aspects of covered methodologies, empirical observations, and verifications of various applications in practice.

Fuzzy logic, neural networks, and soft computing

Abstract: The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial intelligence. In the years ahead, this may well become a widely held position.

Soft computing and fuzzy logic

Abstract: Discusses soft computing, a collection of methodologies that aim to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low solution cost. Its principal constituents are fuzzy logic, neurocomputing, and probabilistic reasoning. Soft computing is likely to play an increasingly important role in many application areas, including software engineering. The role model for soft computing is the human mind. >

Soft computing: fuzzy logic, neural networks and distributed artificial intelligence by F. Aminzadeh and M. Jamshidi (Eds.), PTR Prentice Hall, Englewood Cliffs, NJ, pp 301, ISBN 0-13-146234-2.

Abstract: No wonder you activities are, reading will be always needed. It is not only to fulfil the duties that you need to finish in deadline time. Reading will encourage your mind and thoughts. Of course, reading will greatly develop your experiences about everything. Reading soft computing fuzzy logic neural networks and distributed artificial intelligence is also a way as one of the collective books that gives many advantages. The advantages are not only for you, but for the other peoples with those meaningful benefits.

The roles of fuzzy logic and soft computing in the conception, design and deployment of intelligent systems

Abstract: The essence of soft computing is that, unlike the traditional, hard computing, it is aimed at an accommodation with the pervasive imprecision of the real world. Thus, the guiding principle of soft computing is: '...exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better rapport with reality'. In the final analysis, the role model for soft computing is the human mind. Soft computing is not a single methodology. Rather, it is a partnership. The principal partners at this juncture are fuzzy logic, neuro-computing and probabilistic reasoning, with the latter subsuming genetic algorithms, chaotic systems, belief networks and parts of learning theory. In coming years, the ubiquity of intelligent systems is certain to have a profound impact on the ways in which man-made intelligent systems are conceived, designed, manufactured, employed and interacted with. It is within this perspective that the basic issues relating to soft computing and intelligent systems are addressed in this paper.

1996 Biennial Conference of the North American Fuzzy Information Processing Society - NAFIPS

Abstract: From the Publisher: The theme of this year's conference is "New Frontiers in Fuzzy Logic and Soft Computing". The past few years have witnessed a crystallization of soft computing as a distinct body of concepts and techniques which is orientated toward the conception and design of intelligent systems. Soft computing is not a single methodology. Rather it is a partnership of fuzzy logic, neurocomputing and probabilistic reasoning, with the latter subsuming genetic algorithms, chaotic systems, belief networks and parts of learning theory.

Management of uncertainty in sensor validation, sensor fusion, and diagnosis of mechanical systems using soft computing techniques

Abstract: This dissertation provides means to deal with uncertainty in complex sensor driven systems through sensor validation, sensor fusion, and diagnosis. These means include probability theory, neural network theory, and fuzzy logic. In particular, this thesis introduces means for fuzzy sensor validation and fusion which were compared with a probabilistic data association scheme. The fuzzy sensor validation and fusion approach uses non-symmetric validation regions in which sensors readings are assigned confidence values. Each sensor has its own dynamic validation curve which is shaped according to sensor characteristics, taking into account the range, external factors affecting the sensor, reliability of the sensor, etc. The curves have their maximum value at the predicted value which is arrived at using fuzzy exponential weighted moving average time series predictor. Confidence curves attain minima at the boundaries of the validation gate which are determined by the maximum physically possible change a system can undergo in one time sample. Since readings outside the gate are implausible, they are discarded. Fusion is performed using a weighted average of sensor readings and confidence values, the predicted value scaled by the operating condition, and--if available--functionally redundant values calculated from sensors other than the directly redundant ones. Each method performs best in the presence of certain types of noise and recommendations are made as to which approach is more appropriate under various conditions. Several applications from extant systems (intelligent vehicle highway systems, gas turbine power plants, milling machines) show the feasibility of the approaches developed. Another aspect of this dissertation is to provide a tool for diagnosis in the presence of vague symptoms. This is achieved though fuzzy abduction which can diagnose crisp as well as soft faults. This means that faults can be diagnosed if they occur to some degree. The proposed algorithm computes a closeness measure taking into account the distance from an observed symptom set to the modeled symptom set for all failure combinations. It then ranks the failure sets according to maximum closeness measure and minimum cover, i.e., number of faults. As an extension, a framework for fuzzy influence diagrams is provided which uses this closeness measure.

The roles of soft computing and fuzzy logic in the conception, design and deployment of intelligent system

Abstract: Fuzzy information granulation (IG) is central to human reasoning and concept formation. It is this aspect of fuzzy IG that underlies its essential role in the conception and design of intelligent systems. What is conclusive is that there are many tasks which humans can perform with ease and that no machine could perform without the use of fuzzy information granulation. This conclusion has a thought-provoking implication for AI: without the methodology of fuzzy IG in its armamentarium, AI cannot achieve its goals.

Soft computing in intelligent systems and information processing : proceedings of the 1996 Asian Fuzzy Systems Symposium, Kenting, Taiwan, ROC, December 11-14, 1996

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A neurofuzzy approach for the anticipatory control of complex systems

Abstract: Anticipatory control refers to system regulation based on information about anticipated future states. A preview of the future is typically obtained via predictive models and decisions about changes of state are made at the present taking into account the output of such models. Significant improvements in soft computing methodologies support the development of anticipatory control that integrates planning and control sequencing functions with feedback control algorithms. We present a neurofuzzy approach for anticipatory control using radial basis neural models and fuzzy rules and demonstrate it through nuclear reactor regulation. The control method does not require knowledge of plant parameters or structure. It is model-independent, and thus may be applied to other nonlinear time-varying dynamic systems. Simulation results show that the neurofuzzy anticipatory control approach improves tracking performance and smoothness and may be quite insensitive to noise. The results suggest that it is a flexible and powerful approach that can easily be extended to other problems, and it is compatible with other techniques. The relevance of the approach to the control of large complex systems is also discussed.

Soft computing : a tool for building intelligent systems

Abstract: The essence of soft computing is that unlike the traditional, hard computing, soft computing is aimed at an accommodation with the pervasive imprecision of the real world. Thus, the guiding principle of soft computing is: '...exploit the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness, low solution cost and better rapport with reality'. In the final analysis, the role model for soft computing is the human mind. [1] In this paper terms associated with soft computing are defined and its main components are introduced. It is argued, using a number of practical applications, that the hybrid approach of soft computing can provide a methodology for increasing machine intelligence.

Intelligent control for human-machine systems

Abstract: In this article we try to summarize the field of intelligent control (IC) for human-machine systems (HMS) from a subjective point of view. Mechatronic components have become intelligent from integrating mechanical, electrical and software intelligence in minimum space, certainly an important step in building IC systems. Because of this, and of successes using artificial neural networks, fuzzy logic and genetic algorithms, so called soft computing, the field of IC is exciting with a bright future. This article attempts to anticipate this future and discusses directions of research to approach the realization of more intelligent systems. Human inclusion in such IC systems is as important as to increase autonomy, flexibility, and fault tolerance. Iterative improvement over trials and optimal system design are important issues. The authors' research to realize IC of dextrous manipulation is briefly reviewed discussing the key results and lessons.

Rough Set and Fuzzy Set Methods Applied to Acoustical Analyses

Abstract: A novel approach to computer assessment of acoustical quality has been made using soft computing methods. Rough set and fuzzy set theories proved to be especially interesting in applications to acoustical assessments. Objective measurement and listening test results were collected in order to provide data for the rough set computations. Correspondingly, examples of automatic assessment of sound quality were obtained. Moreover, some exemplary data derived from performed subjective tests were processed using a fuzzy set-based method. Conclusions concerning the artificial intelligence approach to processing of acoustic data were included.

Soft Computing: Goal, Tools and Feasibility

Abstract: This paper discusses what soft computing is, the need for soft computing and real world computing (RWC) systems, the essential ingredients necessary to realise this, ie, neural networks, fuzzy logic and probabilistic reasoning and their role in soft computing. The development of hybrid computational paradigms are also explored and they are projected as a frontier research area in the evolution of sixth generation computing systems.

Soft computing paradigms for learning fuzzy controllers with applications to robotics

Abstract: Three soft computing paradigms for automated learning in robotic systems are briefly described. The first employs genetic programming to evolve rules for fuzzy behaviors to be used in mobile robot control. The second paradigm develops a two-level hierarchical fuzzy control structure for flexible manipulators. It incorporates genetic algorithms in a learning scheme to adapt to various environmental conditions. The third paradigm concentrates on a methodology that uses a neural network to adapt a fuzzy logic controller in manipulator control tasks. Simulation results of fuzzy controllers learned with the aid of these soft computing paradigms are presented.

A Simulink environment for simulation and control of flexible manipulator systems

Abstract: This chapter presents the development of SCEFMAS (Simulation and Control Environment for Flexible MAnipulator Systems) software package. This is a user-friendly interactive software environment based on Matlab and associated tool boxes. The environment incorporates a finite difference (FD) simulation algorithm of a constrained planar single-link flexible manipulator system for analysis, simulation, modelling of dynamic behaviour of the system. The package also incorporates a range of control techniques, including open-loop control such as filtered command, Gaussian-shaped and command shaping, collocated and non-collocated closed-loop control methods of fixed and adaptive types, and intelligent soft computing control techniques. The environment allows the user to set-up the system by providing its physical parameters and to select the controller type through an interactive graphical user interface (GUI). Data analyses can be performed in time and frequency domains on the controller and system input and outputs signals. The environment is suitable as an education package as well as for research purposes for investigating and developing various simulations, modelling and controller designs for flexible manipulator systems.

Intelligent systems and soft computing for nuclear science and industry : proceedings of the 2nd International FLINS Workshop, Mol, Belgium, September 25-27, 1996

Abstract: Following FLINS '94, the 1st International workshop on fuzzy logic and intelligent technologies in nuclear science, FLINS '96 aims to introduce the principles of intelligent systems and soft computing, including fuzzy logic, neural network, genetic algorithms, knowledge-based expert systems and complex problem-solving techniques, within nuclear science and industry. The papers in this volume were carefully selected from more than 20 countries and cover theoretical aspects of intelligent systems and soft computing and their application, in nuclear science and industry.

Soft-computing techniques for the control of walking robots

Abstract: An overview is given of some soft computing techniques which have been applied to several walking robots developed at the University of Catania. The first robot described is ROBINSPEC (ROBot for INSPECtion), a robot designed to solve inspection problems in industrial plants with particular application to petrochemical storage tanks. The second project concerns PLIF (Piezo Light Intelligent Flea), a walking micro-robot moved by piezoelectric legs with peculiar features that make it suitable for micro-machining applications and for the study of the collective behaviour of colonies of robots. The two projects have been executed concurrently and independently, adopting two different soft computing approaches.

Intelligent Systems and Soft Computing for Nuclear Science and Industry: Proceedings of the 2nd International FLINS Workshop

Abstract: Following FLINS '94, the 1st International workshop on fuzzy logic and intelligent technologies in nuclear science, FLINS '96 aims to introduce the principles of intelligent systems and soft computing, including fuzzy logic, neural network, genetic algorithms, knowledge-based expert systems and complex problem-solving techniques, within nuclear science and industry. The papers in this volume were carefully selected from more than 20 countries and cover theoretical aspects of intelligent systems and soft computing and their application, in nuclear science and industry.

Computational intelligence and soft computing for space applications

Abstract: Systems using computational intelligence and soft computing have been successfully developed for many industrial and space applications. These systems seek to emulate the type of reasoning that humans perform when solving complex tasks. The field of soft computing, as defined by Zadeh-the inventor of fuzzy logic-encompasses fuzzy logic as well as other methodologies such as neural networks, genetic algorithms, and uncertainty management. It is expected that soft-computing techniques will eventually become as common and prevalent as traditional methods of computer science. This paper presents an overview of applications of fuzzy logic and soft computing to space projects. The role of fuzzy systems that can learn from experience to improve their performance is discussed. We present a report on applications of these adaptive systems to NASA space projects such as the orbital operations of the Space Shuttle, which include attitude control and rendezvous/docking operations. We also provide insights on the future of computational intelligence and soft computing and of their vast potential in industrial applications.

Proceedings of the International Workshop on Soft Computing in Remote Sensing Data Analysis, Milan, Italy, December 4-5, 1995

Abstract: Seidam - an intelligent data fusion system with case-based reasoning, D.G. Goodenough et al soft classification and spatial-spectral mixing, R.A. Schowengerdt information management, image analysis and image compression research in support of NASA's mission to planet Earth, J.C. Tilton et al fuzzy logic and neural techniques integration - a review, P. Blonda and A. Bennardo classification of remotely sensed images by the neural-network approach - an experimental comparison, S.B. Serpico et al numeric and symbolic data fusion - a soft computing approach for remote sensing images analysis, J. Desachy classification algorithms - where next?, G.G. Wilkinson analysis large satellite data sets for global change studies - the case of vegetation monitoring, D. Ehrlich and J.P. Malingreau information fusion in remote sensing image interpretation, A. Pinz et al and other papers. (Part contents).

The concepts of fuzzy sets and genetic algorithms: contrasts and similarities of expectations and development patterns (or would you rather mutate or go fuzzy?)

Abstract: Fuzzy sets and generic algorithms are two important approaches in the area of soft computing. However, despite some similarities in their research development pattern (such as their reliance on approximate, rather than absolutely precise reasoning, and therefore on solutions to problems which are good but not necessarily optimal), there are nevertheless contrasts which adapts the suitability of each approach to a particular problem environment. This paper analyses those contrasts and similarities, with the hope that if they are better understood it will be easier to employ both approaches complementarily and, therefore, to profit more from the synergetic combination of their strengths in a soft computing context.

Fuzzy inheritance in the UFO database model

Abstract: The UFO database model is an extension of an object-oriented database model (OODBM), so that it allows for the processing and recording of fuzzy and uncertain (imprecise) information. The fuzziness part of the UFO DBM enhances the modelling capabilities of database applications, by making it possible to model approximations of reality in a flexible way within the database scheme. This is also referred to as "soft computing", a strategy which, by modelling a simplified version of the actual problem, very quickly leads to the desired, good results. There are several aspects to this fuzzy extension, both at the data level and at the level of the database scheme. This paper discusses one issue at the database scheme level, i.e. how the inheritance relationship can be fuzzified in a meaningful way. A fuzzy inheritance relationship between classes in the UFO DBM allows for a flexible modelling of fuzzy extensions of both crisp and fuzzy notions. Its influence to the inheritance of both structure and behaviour is also discussed.

Encoding of PARCOR coefficients using fuzzy rules

Abstract: We present a simple and efficient new method for encoding LPC parameters based on soft computing (SC). More specifically the authors, using a novel training technique that is able to extract fuzzy knowledge, propose a method of transformation from reflection coefficients into a new set of parameters related to the fuzzy inferential process. Performance evaluations in terms of spectral distortion and comparisons of perceptive quality in listening tests, show that this new approach is appropriate for low bit-rate speech coding as it achieves a good trade-off between quality and bit rate with a very low computational load.