A symbolic sensor for an Antilock brake system of a commercial aircraft

The design of a symbolic sensor that identifies thecondition of the runway surface (dry, wet, icy, etc.) during the braking of a commercial aircraft is discussed. The purpose of such a sensor is to generate a qualitative, real-time information about the runway surface to be integrated into a future aircraft Antilock Braking System (ABS). It can be expected that this information can significantly improve the performance of ABS. For the design of the symbolic sensor different classification techniques based upon fuzzy set theory and neural networks are proposed. To develop and to verify theses classification algorithms data recorded from recent braking tests have been used. The results show that the symbolic sensor is able to correctly identify the surface condition. Overall, the application example considered in this paper demonstrates that symbolic information processing using fuzzy logic and neural networks has the potential to provide new functions in control system design. This paper is part of a common research project between E.N.S.I.C.A. and Aerospatiale in France to study the role of the fuzzy set theory for potential applications in future aircraft control systems

Fuzzy logic applications to expert systems and control

A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings

Identification of Evolving Rule-based Models.

An approach to identification of evolving fuzzy rule-based (eR) models is proposed. eR models implement a method for the noniterative update of both the rule-base structure and parameters by incremental unsupervised learning. The rule-base evolves by adding more informative rules than those that previously formed the model. In addition, existing rules can be replaced with new rules based on ranking using the informative potential of the data. In this way, the rule-base structure is inherited and updated when new informative data become available, rather than being completely retrained. The adaptive nature of these evolving rule-based models, in combination with the highly transparent and compact form of fuzzy rules, makes them a promising candidate for modeling and control of complex processes, competitive to neural networks. The approach has been tested on a benchmark problem and on an air-conditioning component modeling application using data from an installation serving a real building. The results illustrate the viability and efficiency of the approach. (c) IEEE Transactions on Fuzzy System

Real time control of nonlinear dynamic systems using neuro-fuzzy controllers

The problem of real time control of a nonlinear dynamic system using intelligent control techniques is considered. The current trend is to incorporate neural networks and fuzzy logic into adaptive control strategies. The focus of this work is to investigate the current neuro-fuzzy approaches from literature and adapt them for a specific application. In order to achieve this objective, an experimental nonlinear dynamic system is considered. The motivation for this comes from the desire to solve practical problems and to create a test-bed which can be used to test various control strategies. The nonlinear dynamic system considered here is an unstable balance beam system that contains two fluid tanks, one at each end, and the balance is achieved by pumping the fluid back and forth from the tanks. A popular approach, called ANFIS (Adaptive Networks-based Fuzzy Inference Systems), which combines the structure of fuzzy logic controllers with the learning aspects from neural networks is considered as a basis for developing novel techniques, because it is considered to be one of the most general framework for developing adaptive controllers. However, in the proposed new method, called Generalized Network-based Fuzzy Inferencing Systems (GeNFIS), more conventional fuzzy schemes for the consequent part are used instead of using what is called the Sugeno type rules. Moreover, in contrast to ANFIS which uses a full set of rules, GeNFIS uses only a limited number of rules based on certain expert knowledge. GeNFIS is tested on the balance beam system, both in a real- time actual experiment and the simulation, and is found to perform better than a comparable ANFIS under supervised learning. Based on these results, several modifications of GeNFIS are considered, for example, synchronous defuzzification through triangular as well as bell shaped membership functions. Another modification involves simultaneous use of Sugeno type as well as conventional fuzzy schemes for the consequent part, in an effort to create a more flexible framework. Results of testing different versions of GeNFIS on the balance beam system are presented

Fuzzy Neural Networks

Práce je věnována fuzzy neuronovým sítím. Protože se jedná o techniku kombinování fuzzy logiky s neuronovými sítěmi pro řešení nejrůznějších úloh z oblasti umělé inteligence, řízení či strojového učení, teoretická část práce se nejprve zabývá neuronovými sítěmi a fuzzy logikou odděleně. Po vysvětlení nezbytných základů je zbytek teoretické části zaměřen převážně na teorii fuzzy neuronových sítí, jejich klasifikaci a použití v praxi. Na základě popsané teorie jsou navrhnuty a implementovány fuzzy asociativní paměti, jejichž cílem je řešit úlohu klasifikace a úlohu z oblasti řízení. Na závěr jsou výsledné implementace a jejich výsledky zhodnoceny a porovnány s jinými přístupy k řešení zvolených úloh.This thesis focuses on fuzzy neural networks. The combination of the fuzzy logic and artificial neural networks leads to the development of more robust systems. These systems are used in various field of the research, such as artificial intelligence, machine learning and control theory. First, we provide a quick overview of underlying neural networks and fuzzy systems to explain fundamental ideas that form the basis of the fields, and follow with the introduction of the fuzzy neural network theory, classification and application. Then we describe a design and a realization of the fuzzy associative memory, as an example of these systems. Finally, we benchmark the realization using the pattern recognition and control tasks. The results are evaluated and compared against existing systems.

Contribution to the Artifical Neural Network Speed Estimator in a Degraded Mode for Sensor-Less Fuzzy Direct Control of Torque Application Using Dual Stars Induction Machine

Recently one of the major topic of research is the involvement of the intelligence artificial in the control system. This paper deals with application of a new combination between two-control strategy known as fuzzy direct control of torque and then an adaptive Neuronal Speed estimator utilizing dual starts induction motor. The research discussed consist to replace the switching table used in the conventional direct control method and adaptive mechanism of the classic MRAS estimator with fuzzy controller and new neural network accordingly, both strategies can manage the degraded and normal modes. The neural networks used are the back-propagation, to reduce the training patterns and increase the execution speed of the training process. As results we achieved can be summarised as follows: 1) high degree of reliability of speed estimation even with using only one start voltages and currents and parameters; 2) Minimization of the torque and flux ripples; and                3) Minimization of the current total harmonic distortion

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS