A new approach to fuzzy modeling

This paper proposes a new approach to fuzzy modeling. The suggested fuzzy model can express a given unknown system with a few fuzzy rules as well as Takagi and Sugeno's model (1985), because it has the same structure as that of Takagi and Sugeno's model. It is also as easy to implement as Sugeno and Yasukawa's model (1993) because its identification mimics the simple identification procedure of Sugeno and Yasukawa's model. The suggested algorithm is composed of two steps: coarse tuning and fine tuning. In coarse tuning, fuzzy C-regression model (FCRM) clustering is used, which is a modified version of fuzzy C-means (FCM). In fine tuning, gradient descent algorithm is used to precisely adjust parameters of the fuzzy model instead of nonlinear optimization methods used in other models. Finally, some examples are given to demonstrate the validity of this algorithm     

A new approach to fuzzy-neural system modeling

We develop simple but effective fuzzy-rule based models of complex systems from input-output data. We introduce a simple fuzzy-neural network for modeling systems, and we prove that it can represent any continuous function over a compact set. We introduce “fuzzy curves” and use them to: 1) identify significant input variables, 2) determine model structure, and 3) set the initial weights in the fuzzy-neural network model. Our method for input identification is computationally simple and, since we determine the proper network structure and initial weights in advance, we can train the network rapidly. Viewing the network as a fuzzy model gives insight into the real system, and it provides a method to simplify the neural network     

A new simulation approach to the freezing transition

The task of accurately locating fluid-crystal phase boundaries by computer simulation is hampered by problems associated with traversing mixed-phase states. We describe a recently introduced Monte Carlo (MC) method that circumvents this problem by implementing a global coordinate transformation (“phase switch”) which takes the system from one pure phase to the other in a single MC step. The method is potentially quite general. We illustrate it by application to the freezing of hard spheres.     

A new approach to mathematical modeling of host-parasite systems

We propose a new approach to mathematical modeling of host-parasite systems by using partial differential equations where the degree of parasitism in a host is represented by a continuous variable p. This contrasts with the standard approach found in the literature of using a countable number of ordinary differential equations, one for each nonnegative integer, corresponding to the class of hosts having exactly that number of parasites. The new model bears some similarity with size-structured models of population dynamics. We specialize the model to a specific pair of helminth macroparasites infecting sea bass. We show that the model is well posed and we study its asymptotic behavior. Finally, we present results from some simulations.     

A new approach to the modeling of recovery block structures

A reliability model is proposed for recovery block structures based on error events which can be observed and distinguished during testing. Strategies are then described for the collection of failure histories needed to estimate the model parameters and obtain dependability predictions. Given that the software goes through different testing stages, the model can be employed at different points of the development cycle to assess or forecast the quality of project choices and the resulting product     

A new approach to the simulation of static converter drives

In electrical drives using an actuator supplied by a static converter, it is possible to work out a model of the converter-motor system which is easily implemented on a hybrid or digital computer. This model can be elaborated to improve the efficiency of the digital simulation. Computation time is reduced especially for the investigation of the steady state behaviour and dynamic around a prefixed operating point.     

A new approach to fuzzy modeling and control of discrete-time systems

We present a new approach to fuzzy modeling and control of discrete-time systems which is based on the formulation of a novel state-space representation using the hyperbolic tangent function. The new representation, designated the hyperbolic model, combines the advantages of fuzzy system theory and classical control theory. On the one hand, the hyperbolic model is easily derived from a set of Mamdani-type fuzzy rules. On the other hand, classical control theory can be applied to design controllers for the hyperbolic model that not only guarantee stability and robustness but are themselves equivalent to a set of Mamdani-type fuzzy rules. Thus, this new approach combines the best of two worlds. It enables linguistic interpretability of both the model and the controller, and guarantees closed-loop stability and robustness.     

A new approach toward modeling causality in commonsense reasoning

This article examines the issue of causality in commonsense reasoning and proposes a connectionist approach for modeling commonsense causal reasoning. Based on an analysis of the advantages and limitations of existing accounts, especially Shoham's logic, a generalized rule-based model FEL is proposed that can take into account the inexactness and the cumulative evidentiality of commonsense reasoning; this model corresponds naturally to a connectionist architecture. Detailed analyses are performed to show how the model handles commonsense causal reasoning. This work shows that a logic-based account of causality can be viewed as an (over)idealization of a more realistic model, which is simpler in form but deals with causality better. This work directly maps a (causal) rule-encoding scheme into a connectionist model; thus, it serves to link rule-based reasoning to connectionist models, notably with direct one-to-one correspondence between the basic structures of the two formalisms. © 1995 John Wiley & Sons, Inc.     

A state-driven modeling approach to human interactions for knowledge intensive services

Knowledge intensive service activities have become to play a fundamentally important role in various industrial fields. Human workers generally undertake complex operations relying heavily on professional knowledge in service processes to develop and deliver the knowledge intensive services. That means the ability of humans to create, disseminate, or utilize the knowledge is the dominant factor in the processes. Therefore, the processes should be managed in a human-oriented way. In order to help humans work together, a strong representation of processes should be provided to facilitate them to clearly understand who they should interact with, which resources are exchanged, and what activities need to be performed. Human Interaction Management (HIM) has been suggested to comprehensively support the human-oriented processes, but it cannot provide a way to structure and visualize the interaction works although the interaction is the most basic nature of human works. Therefore, this paper presents a state-driven approach to modeling human interactions which clearly visualizes the interactions so that human workers can be guided through it. However, it cannot be expected for human workers to follow the guidelines completely. They continuously and dynamically redefine their processes towards the way that they want throughout the life of the processes. To support the dynamic human work behavior, this paper also presents a hybrid modeling methodology that consists of the top-down specification of interaction models for guideline modeling and the bottom-up evolution of the models for flexible enactment. The suggested methodology for human interactions based on the state-driven modeling approach provides a way to effectively manage the complex interactions in a human-oriented way.     

A decentralized approach to integrated flight control synthesis

A new approach to decoupled control of large-scale non-linear systems is applied to a dynamic flight control. Control synthesis is performed in two steps. First the nominal, programmed control is synthesized using the complete model of flight dynamics. This nominal control should realize the nominal trajectory under ideal conditions with no perturbations. In the second step the tracking of the nominal trajectory is realized. The system is viewed as a set of decoupled subsystems and for each subsystem local control is synthesized. Then, the stability of the overall system is analyzed and the global control is introduced to compensate coupling among some of the subsystems. A particular choice of subsystems in a case of flight control and of local and global control synthesis is proposed. A simulation of flight control with the proposed control law is also presented.     

Singular systems: A new approach in the time domain

A new approach in the time domain is developed for the study of singular linear systems of the formEdot{x} = Ax + Bu, y = CxwithEsingular. Central to the approach is the fundamental triple((alpha E - A)^{-1}E, (alphaE - A)^{-1}B, C)with α a real number satisfying det(alpha E - A) neq 0. Controllability and observability matrices are expressed in terms of the fundamental triple. New tests for impulse controllability and impulse observability are also established. Feedback control problems including pole placement, decoupling, and disturbance localization are studied by use of a modified proportional and derivative feedback of the state in the form ofu = F(alpha x - dot{x})+ v.     

Hierarchical simulation approach to accurate fault modeling forsystem dependability evaluation

This paper presents a hierarchical simulation methodology that enables accurate system evaluation under realistic faults and conditions. In this methodology, effects of low-level (i.e., transistor or circuit level) faults are propagated to higher levels (i.e., system level) using fault dictionaries. The primary fault models are obtained via simulation of the transistor-level effect of a radiation particle penetrating a device. The resulting current bursts constitute the first-level fault dictionary and are used in the circuit-level simulation to determine the impact on circuit latches and flip-flops. The latched outputs constitute the next level fault dictionary in the hierarchy and are applied in conducting fault injection simulation at the chip-level under selected workloads or application programs. Faults injected at the chip-level result in memory corruptions, which are used to form the next level fault dictionary for the system-level simulation of an application running on simulated hardware. When an application terminates, either normally or abnormally, the overall fault impact on the software behavior is quantified and analyzed. The system in this sense can be a single workstation or a network. The simulation method is demonstrated and validated in the case study of Myrinet (a commercial, high-speed network) based network system     

A feature-based approach for individualized human head modeling

Published online: 18 September 2001     

A systemic approach to process modeling

This article presents a system's view of a common sense management model for systems (COSMOS) [1]. Salient features of COSMOS are introduced through the unfolding story of process development of a hypothetical corporation called IM Co. This systemic view models the dynamic complexity of a system or organization so that inerrelationships, rather than things, patterns of changes, rather than snapshots, are captured. COSMOS views changes as an ongoing opportunity and provides guidance for system changes to be performed in small steps. However, these small steps can build a long lever that is capable of producing dramatic effects. When performing changes, essential trade-offs have to be considered. COSMOS provides three perspectives—activity, communication, and infrastructure—of a process to assist managers in dealing with these trade-offs. The model also includes a generic two-level hierarchy—control and execution levels—to keep balance among the three perspectives.Small Is Beautiful — Ernst Fredrich Schumacher Give me a lever long enough ... and single-handed I can move the world — Archimedes     

A new approach to time domain analysis of perturbed PWM push-pull DC-DC converter

In this paper,an analytical technique is presented for time domain analysis(transient and steady-state response) of perturbed PWM push-pull DC-DC converter using interesting corollary on Kharitonov’s theorem.The main advantage of the proposed analysis is that even though the transfer function model of a PWM push-pull DC-DC converter is perturbed,the complete analysis has been done on a linear transfer function model of a PWM push-pull DC-DC converter. The proposed analysis is verified using MATLAB simulation.This analysis will be very much useful to power electronics engineers,since the technique is very simple and computationally efficient and easily applicable in precise applications such as aerospace applications.     

A formal approach to fuzzy modeling

A formalism for coding fuzzy models of dynamical systems is presented. It is shown that the formalism is rich enough to capture the performance of arbitrary conventional discrete time dynamical systems whose transition maps are polynomials with rational coefficients. The proof of this fact provides a constructive algorithm for generating fuzzy models to arbitrarily closely approximate an arbitrary map on a compact set. Our modeling formalism highlights the similarities between fuzzy systems and hybrid control systems. We hope to be able to exploit these similarities by extending results from the area of hybrid systems to the fuzzy domain and vice versa     

A knowledge-based approach to CIM modeling

This paper discusses issues in building a conceptual modeling approach that has sufficient semantic power to represent the complexities of decision making in CIM systems. To express the information in a conceptual model we make use of an amalgam of powerful paradigms: a hybrid methodology, that integrates the concepts of object-oriented programming, messagepassing semantics, and temporal logic. We further illustrate an example incorporating some of these concepts.     

A fact-oriented ontological approach to human process modeling for knowledge-intensive business services

Modern business environment emphasizes the role of knowledge-intensive business services (KIBS). As well as the enterprises in any other kinds of industries, business process management (BPM) can be a source of core competency for the enterprises in KIBS industry. However, most of the business processes in KIBS are human processes which are collaborative, innovative, and dynamic, which cannot be supported well by current BPM technologies. Human interaction management (HIM) has emerged as an alternative theory to deal with human processes in KIBS. But, the current ways of process modeling and management in HIM have a problem of complexity, especially in terms of realizing its principle of ‘supportive activity management’. This research adopts fact-oriented ontological approach to address this problem. Based on fact-oriented approach, human-friendly condition modeling and state management during supportive activity management can be achieved.     

A simple approach to system modeling

This paper describes an approach to system modeling based on heuristic mean value analysis. The virtues of the approach are conceptual simplicity and computational efficiency. The approach can be applied to a large variety of systems, and can handle features such as resource constraints, tightly and loosely coupled multiprocessors, distributed processing, and certain types of CPU priorities. Extensive validation results are presented, including truly predictive situations. The paper is intended primarily as a tutorial on the method and its applications, rather than as an exposition of research results.