Showing papers on "Fuzzy logic published in 1970"

Dynamic Programming and Fuzzy Allocation Processes.

Abstract: : The modeling and computational aspects of certain allocation processes are studied through a new concept in systems theory -- fuzzy decision making. The use of these concepts will generally provide models of better proximity to the systems modelled than the traditional deterministic and stochastic approaches. Some concepts of fuzzy systems theory are first introduced. Fuzzy dynamic programming models with their corresponding flow charts are then provided for an allocation problem arising in R and D systems. The computational problems in fuzzy algorithms are discussed. An extensive bibliography on fuzzy decision theory is included. (Author)

Intelligent Classification Of Rock Masses

Abstract: In this work classifying methods are examined from the view of Artificial Intelligence. Special reference is made to a pre-existing method of classifying rock masses (Bieniawski's classification method) and two typical attempts to use Artificial Intelligence tools are referred: a) Transference of the methodology procedure in an expert system's shell , and b) Training of a neural network with sets of inputs results in order to map the outer performance of the methodology. For an extension, machine learning is proposed as a tool for derivation of new classification methods taylored to specific systems. Fuzzy logic, self adjustable neural networks and dynamic interaction among the input parameters of a system (instead of using net values) are among the new techniques. Key-Words: Classification, Clustering, Artificial Intelligence, Expert Systems, Neural Networks, Fuzzy Logic.

Behaviour based mobile robot navigation technique using ai system: experimental investigation on active media pioneer robot

Abstract: A key issue in the research of an autonomous robot is the design and development of the navigation technique that enables the robot to navigate in a real world environment. In this research, the issues investigated and methodologies established include (a) Designing of the individual behavior and behavior rule selection using Alpha level fuzzy logic system (b) Designing of the controller, which maps the sensors input to the motor output through model based Fuzzy Logic Inference System and (c) Formulation of the decision-making process by using Alpha-level fuzzy logic system. The proposed method is applied to Active Media Pioneer Robot and the results are discussed and compared with most accepted methods. This approach provides a formal methodology for representing and implementing the human expert heuristic knowledge and perception-based action in mobile robot navigation. In this approach, the operational strategies of the human expert driver are transferred via fuzzy logic to the robot navigation in the form of a set of simple conditional statements composed of linguistic variables. Keywards: Mobile robot, behavior based control, fuzzy logic, alpha level fuzzy logic, obstacle avoidance behavior and goal seek behavior

Modelling and Simulation of Stepper Motor For Position Control Using LabVIEW

Abstract: This paper presents hybrid stepper motor (is a type of stepping motor) modelling and simulation which is widely used a kind of motor in industrial applications. In this study, the stepper motor was modelled using bond graph technique and simulation for desired position was executed on LabVIEWgraphical interface. Then, firstly a convenient PID controller was designed for position, speed and current and PID close loopresponse was obtained for position control. Then, PID parameters for each controller were arranged separately to obtain good response Secondly, Fuzzy Logic controller applied to the system and its response was obtained. Finally, both responses are compared. According to comparison, it was observed that Fuzzy Logic controller’s response is better than PID’s. (In this paper, all shown responses were observed for 120 degree desired position)

An Intelligent System For Automated MathematicalModelling And Simulation Of DynamicalEngineering Systems

Abstract: We describe in this paper a computer program for Automated Mathematical Modelling (AMM) and Automated Simulation (AS) of dynamical engineering systems using Artificial Intelligence (AI) techniques. This computer program is an implementation of a new method for AMM using Fuzzy Logic techniques and Fractal Theory, and a new method for AS using Expert Systems technology. Our new method for AMM consists of three main parts: Time Series Analysis, Developing a set of Admissible Models and Selecting the "Best" model. Our method for Time Series Analysis consists in the use of the fractal dimension of a set of points as a measure of the geometrical complexity of the time series. Our method for developing a set of admissible models is based on the use of Fuzzy Logic techniques to simulate the reasoning process of the human experts in mathematical modelling of engineering systems. The selection of the "best" model for the engineering systems is done using heuristics from the experts and statistical calculations. The simulation of the "best" model can be done by using a new method developed by the authors, that enables automated computer exploration of all the dynamical behaviors of the engineering system. Given a mathematical model for a specific Dynamical Engineering System (DES), this method will automatically select (using a rule base) the "best" set of parameter values to perform numerical simulations of the system. This simulations will in turn enable the identification of all the dynamical behaviors of the engineering system.

Qualitative Interval (or Fuzzy) Simulation OfComplex Continuous Processes: FittingInformation Availability And AccuracyRequirements

Abstract: The objective of this paper is to show through a real-world application why qualitative methods based on interval (or fuzzy) arithmetic with strong properties concerning soundness and completeness are adequate for modeling complex continuous processes. Firstly, we discuss modeling characteristics of such an important class of physical systems that includes chemical, nuclear, siderurgical and other industrial processes. On one hand, although it is almost always impossible to define numerical models for complex processes, it is usually possible to define boundaries (intervals) for the system parameters. On the other hand, some precision for the simulations is always required. We show that only interval (or fuzzy) based methods (and not pure numerical or qualitative methods) are adequate. Besides, for the effective use of such methods, soundness and completeness properties are of great importance. Secondly, in order to justify our claims, we present the successful application of QFSIM, a particular fuzzy-based qualitative method, to model a complex siderurgical process at CST "Companhia Siderurgica de Tubarao", a Brazilian-Japanese company located in Vitoria-Brazil. Transactions on Information and Communications Technologies vol 1, © 1993 WIT Press, www.witpress.com, ISSN 1743-3517 280 Artificial Intelligence in Engineering

Energy optimization of wireless sensor network using neuro-fuzzy algorithms

Abstract: Wireless sensor network (WSN) is one of the recent technologies in communication and engineering world to assist various civilian and military applications. It is deployed remotely in severe environment that doesn’t have an infrastructure. Energy is a limited resource that needs efficient management to work without any failure. Energy efficient clustering of WSN is the ultimate mechanism to conserve energy for long time. The major objective of this research was to efficiently consume energy based on the Neuro-Fuzzy approach particularly adaptive Neuro fuzzy inference system (ANFIS). The significance of this study was to examine the challenges of energy efficient algorithms and the network lifetime on WSN so that it could assist several applications. Clustering is one of the hierarchical based routing protocols, which manage the communication between sensor nodes and sink via Cluster Head (CH); CH is responsible for sending and receiving information from multiple sensor nodes and multiple sink nodes. There are various algorithms that can efficiently select appropriate CH and localize the membership of cluster with fuzzy logic classification parameters to minimize periodic clustering which consumes more energy and we have applied neural network learning algorithm to learn various patterns based on the fuzzy rules and measured how much energy was saved from random clustering. Finally, we compared it to our Neuro-Fuzzy logic and consequently demonstrated that our Neuro-Fuzzy model outperformed by saving more than 32% of energy than the random model with 50 and 100-sensor node deployment. We confirmed that by increasing the number of sensor nodes, it was possible to increase the energy utilization but not the energy saved from the network.

Teoria szarych systemów a prognozowanie w naukach społecznych. przyczynek do dyskusji

Abstract: The aim of the publication is to present selected forecasting problems in social sciences. The article focuses on the method of Grey Theory System. Particular attention is paid to the complexity of such systems as the political system, and consequently the shortcomings of traditional research methods such as system analysis. The purpose of the publication is to signal the use of such an exotic “methodology” for political science as grey-scale methodology and fuzzy modeling for forecasting political phenomena.

Fuzzy Fault Evaluation In Causal DiagnosticReasoning

Abstract: Causal reasoning is a practical support for model-based diagnosis [1]. The paper addresses the issues of extending diagnostic reasoning based on abductive analysis of causal structures. The extension is aimed at admitting fuzzy characterization of faults; binary evaluation (i.e. faulty/correct) is no longer necessary. The degree of faultyness is expressed with use of basic fuzzy notions. This extension uses a uniform model for representing causal behaviour of diagnosed systems: it has the form of an AND/OR/NOT causal graph allowing for specification of causality types reflecting the basic logical operations [3]. The graph can be used to search for potential (possible) diagnoses. Validation of generated diagnoses is performed by propagation of fuzzy faults upwards the graph [3]. A motivational discussion introducing the presented ideas at an intuitionistic level is presented in brief. Finally, possibilities for further extensions and related work are pointed out.

Mathematical Modelling And Simulation Of RoboticDynamic Systems Using An Intelligent TutoringSystem Based On Fuzzy Logic And Fractal Theory

Abstract: We describe in this paper a computer program for Mathematical Modelling and Simulation of Robotic Dynamic Systems using Fuzzy Logic Techniques and Fractal Theory. The computer program combines Artificial Intelligence (AI) techniques with mathematical methods and can be considered an Intelligent Tutoring System (ITS) for the domain of modelling and simulation of robotic systems. This domain is quite complex because robotic systems can be viewed as non-linear dynamical systems, and it is a well known fact that even very simple non-linear dynamical systems can exhibit "chaotic" behavior. The computer program simulates the reasoning of a human expert in the process of teaching how to develop mathematical models of Robotic Dynamic Systems (RDS). The program contains the knowledge of the human experts expressed as fuzzy rules (in the knowledge base) for Mathematical Modelling and Simulation (MMS) of robotic systems. The ITS also contains knowledge about teaching methodologies for this domain (in the knowledge base). The ITS uses efficiently AI techniques to teach MMS of RDS, and also to monitor the learning process of students of this domain. Mathematical Modelling and Simulation of Robotic Systems is very important because it can help in the control of an actual system or in the design of a new system using the results of the simulations.

A theory of continuously valued logic.

Abstract: : Motivated by the recognized inadequacy of conventional logic for the representation and manipulation of variables in areas related to artificial intelligence, this paper addresses itself to the investigation of the formal systems obtained by extending well-known operators to continuous arguments. The studied systems, called 'soft algebras,' are generalizations of boolean algebras in that they satisfy all the axioms of the latter ones except the laws of complementarity, i.e., x + x bar = 1 and x(x bar) = 0. It is shown that every soft algebra is a bounded, distributive and symmetric lattice. A specific soft algebra, namely, the family of all expressions of variables valued over the closed interval (0,1), is analyzed in great detail. This particular algebra is a formal unification of many recent results concerning 'fuzzy' logic. It is shown that every 'soft' function can be canonically represented by a pair of normal expressions, i.e., each soft function is representable by a double-array of tables (a generalization of the truth-table representation of boolean functions.) Also, a synthesis and a two-level minimization procedure, which is a generalization of the Quine-McCluskey method, are given. (Author)

Machine Learning Approach To Design OfComplex Distillation Columns

Abstract: Automation of the knowledge acquisition process in building knowledgebased systems for process design is addressed through Machine Learning techniques. A hybrid Machine Learning algorithm developed at the University of South Florida is presented as a knowledge acquisition tool for developing knowledge-based systems. The learning algorithm addresses the knowledge acquisition problem by developing and maintaining the knowledge base through inductive learning from the examples. The learning algorithm named as Symbolic-Connectionist net (SCnet), overcomes the problems associated with neural and symbolic learning systems by integrating the symbolic information into a neural network representation. The learning system allows for knowledge extraction and background knowledge encoding in the form of rules. Fuzzy logic has been made use of in dealing with uncertainty in the learning domain. The description language for the learning system consists of continuous and discrete variables along with relational and fuzzy comparators. The applicability of the learning system for process design is illustrated through a complex column sequencing example. The performance of the learning system is discussed in terms of the knowledge extracted from example cases and its classification accuracy on the test cases. Transactions on Information and Communications Technologies vol 1, © 1993 WIT Press, www.witpress.com, ISSN 1743-3517

Fuzzy logic based controller for maintaining human comfort within intelligent building system

Abstract: This paper presents an intelligent control approach for air handling unit (AHU) which is an integral part of heat, ventilation, and air conditioning (HVAC) system. In the past years various control design for HVAC have been proposed as this system remarkably consumes very high energy. But most of the proposed designs were focused on the control flow of heat-transfer medium such as chilled or heated water while the importance of the efficient mixture of outdoor and indoor enthalpies is sometimes ignored. These enthalpies invariably determine the best strategy to overcome thermal load in a controlled environment to satisfy human comfort, hence a control design strategy must be able to efficiently regulate the flow and mixture of outdoor and indoor enthalpies by a proper control of AHU dampers and fans. This approach requires sensors to measure temperature and relative humidity of both outdoor and indoor environments. However, unpredictable level of disturbances coming from many sources including heat generated by occupants, electrical items and air leaking and the continuous changes of outdoor enthalpy makes it difficult to model the process. Consequently, conventional controllers are not suitable, hence the use of fuzzy logic controller (FLC) is proposed in this paper. This proposed controller operates in a master and slave control loop so as to control the AHU dampers and fans with adjustable output membership function whilst at the same time a scaling-factor method is used to drive the master operation. To implement the proposed system, a small scale prototype has been designed and fabricated. This prototype is an AHU model which consists of ductwork, temperature and humidity sensors, dampers, air cooling and heating systems. A small box is used as a conditioning space in which a room temperature is measured. The control algorithm is programmed using National Instrument (NI) LabVIEW and executed using NI FieldPoint. Experimental results reveal that proper control of AHU dampers and fans is an effective and practical means to satisfy human comfort with minimum energy consumption. Keywords: Human comfort, Intelligent control, Air handling unit, Adaptive fuzzy logic control

A C++ Library For Fuzzy Sensitivity AnalysisAnd Multiple Fuzzy Linear Regression

Abstract: Fuzzy sets theory has proven over the years to be a valuable tool for modeling uncertainty in engineering. It is used extensively in control, in expert systems and in rule-based models. However, applications to sensitivity analysis and regression are still few, mainly because there is no appropriate software available. A C++ library of objects has been developed to easily and efficiently introduce fuzzy sensitivity analysis into new or existing C/C++ code, and to perform multiple fuzzy linear regression. An outline of the library is given, together with examples of applications in hydrologic engineering. For problems involving only fuzzy regression, a more user-friendly interface is currently being developed.

A Fuzzy Recurrent Neural Network Of BinaryNeurons For Content Addressable Memory

Abstract: This paper is concerned with a proposal for a recurrent neural network of fuzzy neurons which may be used as a content addressable memory. The behavior of the fuzzy unit in the network is based on fuzzy logic in that each component of the binary input vector to the fuzzy neuron is compared to a number which represents the membership value for a 0 in that position. The results of the comparisons are then combined using a generalized mean function to produce a single number which is compared to a threshold. A training algorithm is developed based on an algorithm for linear inequalities described by Ho and Kashyap in a paper titled “ An Algorithm for Linear Inequalities and its Applications”. The results obtained by simulation of this content addressable memory look promising enough to warrant further investigation.

Spatial Data Mining With Fuzzy DecisionTrees

Abstract: In this paper, an approach is presented to search for useful patterns and discover hidden information in Spatial Object-Oriented Databases (SOODB). Although many approaches of knowledge discovery for relational spatial databases exist, there is a growing interest in mining SOODB. Indeed, objectoriented databases are well-suited to represent complex spatial information. Moreover, a very large number of existing spatial databases are ready to be mined. We propose an algorithm to mine a SOODB. After a spatial object query and a mathematical and fuzzy preprocessing, we apply decision tree based techniques and fuzzy set theory to discover knowledge. An experiment on a region of France to discover classification rules related to houses and urban area is conducted with this algorithm to validate the interest of the approach.

The Knowledge Engineering Approach ToReliability Analysis

Abstract: This paper presents the FTAES system, a knowledge based environment designed for the assessment and measurement of reliability, availability, maintainability and safety of industrial systems using fault tree representation. Object oriented structures are used to represent the problem's domain, production rules, algorithms and database structures are the basic elements of the system. FTAES was structured into an open blackboard architecture in order to allow further inclusion of real time diagnostic modules and automatic fault tree generation. Uncertainty, vagueness and fuzzyness are represented and solved with fuzzy logic approaches.

An alternative tool to reservoir eutrophication index: fuzzy synthetic evaluation

Abstract: A general methodology for fuzzy synthetic evaluation is developed and illustrated with a case study of trophic status assessment for Fei-Tsui Reservoir in Taiwan. The historical data base was collected from the management agency of Fei-Tsui Reservoir from 1987 to 1995. The results of this investigation show that the long-term change of water quality and the overturn phenomena cannot be observed with the Carlson index from 1987 to 1992 but is expressed by fuzzy synthetic evaluation. Fuzzy synthetic evaluation is better suited than the Carlson index to rating the trophic status of self-sustaining lakes.

A Fuzzy Methodology To Solve TheGroundwater Flow Problem In The PresenceOf Uncertain Hydrogeological Data

Abstract: Several technical and regulatory uncertainties affect the evaluation of the future performance of a High-Level radioactive Waste (HLW) repository. For example, quantitative scientific data obtained during periods of site characterization, repository operation and performance confirmation is often incomplete and imprecise. However, such data can be complemented with a significant amount of qualitative data. To date, there is no proven technique that is flexible enough to incorporate uncertain or qualitative data within the constitutive equations for some of the processes responsible for the long term evolution of the repository system. This prompted the development of an innovative methodology based on fuzzy logic to solve the groundwater flow equation in the presence of qualitative data. In this paper, we describe a fuzzy logic based flow model, followed by some verification results.

Integrated Building Design Decision SupportWith Fuzzy Logic

Abstract: In building design, design decision is an essential part to be accomplished for the execution of the actual design process. Therefore in recent times, design decision support systems have received much attention for the importance of need for optimal decisions. A conceptual integrated decision support system for building design considering technical and functional aspects of building design was described earlier. Considering that systematic approach, this work further describes novel fuzzy logic approach for the implementation of an integrated building design decision support system in a unified form considering multidisciplinary building design information including also their interrelationship in design.

An Incorporated Use of Fuzzy Logic Toolbox and Modelica Library to Design SSSC Damping Controller

Abstract: ObjectStab is a general purpose simulation tool for power system stability studies developed by Modelica which is an object-oriented modeling language. It provides enough modeling flexibility to allow addition or modification of new power system components. This paper describes an incorporated use of fuzzy logic toolbox in Matlab/Simulink and Object-Stab library to enhance the application of this library into fuzzy control design environment. The example provided here is the modeling of the static synchronous series compensator (SSSC) which is the new device developed in the ObjectStab. In addition, the interface of ObjectStab with Matlab/Simulink for an SSSC damping controller design by fuzzy logic toolbox is explained step by step. Simulation studies in a multi-machine power system confirm the effectiveness of the designed fuzzy controller.

Reservoir Operation For Flood Control By BothFuzzy And Neural Networks System

Abstract: Recently, fuzzy set theory and neural networks system are advanced in many engineering field. The automatic reservoir operation for flood control is generally designed to lighten a troublesome workload for the administrator of dam in Japan. Therefore, in this study, the author’s apply two systems of fuzzy and neural networks to the reservoir operation for flood control. The author’s consider to put the control rules of reservoir operation, the information obtained by inquires to actual reservoir operator and the hydrological characteristics in the basin, into the reservoir operation of the dam supporting system. This system of reservoir operation for flood control is that neural networks is applied to the decision of the operational line and fuzzy set theory is applied to the decision of operational volume, that is, release discharge from reservoir of dam. It is obvious that application of reservoir operation gate for flood control by the use of both fuzzy set theory and neural networks system is effective. Transactions on Information and Communications Technologies vol 19, © 1997 WIT Press, www.witpress.com, ISSN 1743-3517

The Direct Adaptive Trajectory Control OfRobot Manipulators Via Fuzzy LogicAlgorithm

Abstract: The paper is addressed to study the trajectory control of robotic mechanisms, particularly, to the transient performance of path tracking control systems. The coefficients and terms of the robot dynamic equations may be partly unknown and some dynamic effects may be unmodeled. One general scheme, which is a combination of the generalization of the computed torque method and the velocity gradient technique, provides a simple way to obtain some known and unknown parameter adaptive control laws, in particular, the exponential path tracking adaptive control algorithms with the important properties of robustness to bounded disturbances and unmodeled dynamics. The algorithms require the on-line measurements of the tracking error and its first derivative. The persistent excitation assumptions for desired paths are not required. The fuzzy logic control strategy is applied to obtain implement able algorithms on the base of the developed adaptive laws. The approach includes the attractive features of both strategies: robustness from direct adaptive control, and simple implementability from fuzzy logic control.

Application Of Fuzzy Inferencing Principles InReservoir Operation Analysis

Abstract: Successful application of fuzzy control to an optimum control problem relies on the ability to make appropriate inferences from fuzzy information. In the reservoir operation problem, the operational rule adopted for simulation of the performance of a reservoir under historical or generated inflows, demands, etc. usually relates to the concept of an optimum release for the 'current' period. The main source of uncertainty in this process arises from the prediction of the value of the inflow during the current period. The value of this inflow is generally known in terms of its distribution. Since the storage volume at the end of each period is highly dependent on this inflow, it also is influenced by this uncertainty. Most stochastic simulation techniques for reservoir operation, however, operate on the basis of strict compliance to, or interpolation of, the operating policies and use as input stochastically generated inflows to account for the inflow uncertainty. Little attention, if any, is given to accounting for uncertainty in the decision itself. Since the optimum release decision obtained from a 3-state variable (storage volume at the beginning of the current period, the inflow in the previous time period, and the reservoir release during the current period) stochastic dynamic program is based on evaluation of the expected value of the return to the system, such a release decision should only be considered as a 'guide' such that, in certain circumstances, deviation of the release decision from the operating rule might be necessary. In this paper, a rational approach for selecting a release decision different from that envisaged in the operation rule is derived from application of the principles of fuzzy inference. The approach is demonstrated by application to the Wadaslintang Reservoir in Prembun, Central Java, Indonesia. Transactions on Ecology and the Environment vol 12, © 1996 WIT Press, www.witpress.com, ISSN 1743-3541

Acquisition Of Fuzzy Rules Using TurboAlgorithm

Abstract: A new method of acquiring fuzzy rules using observational data is proposed. The Turbo Algorithm, which has attracted much attention as an efficient procedure for deriving the nonparametric functions from data, plays a major role in this algorithm. Since the algorithm selects the best combination of rules from various candidates automatically, reliable estimation can be achieved through only a small number of rules. To confirm its validity, this method is applied to the analysis of environmental effects on rice production.