Fuzzy systems based on component software

This paper describes hierarchical modeling of fuzzy logic concepts that has been used within the recently developed model of intelligent systems, called OBOA. The model is based on a multilevel, hierarchical, general object-oriented approach. Current methods and software design and development tools for intelligent systems are usually difficult to extend, and it is not easy to reuse their components in developing intelligent systems. The OBOA model tries to reduce these deficiencies. The model starts with a well-founded software engineering principle, making clear distinction between generic, low-level intelligent software components, and domain-dependent, high-level components of an intelligent system. This paper concentrates on modeling and implementation of fuzzy logic concepts within the hierarchical levels of the OBOA model. The fuzzy components described are extensible and adjustable. As an illustration of how these components are used in practice, a practical design example from the domain of medical diagnosis is shown. The paper also suggests some steps towards future design of fuzzy components and tools for intelligent systems.     

T–S fuzzy-model-based robust stabilization for a class of nonlinear discrete-time networked control systems

In this paper, the robust stabilization problem is investigated for a class of nonlinear discrete-time networked control systems (NCSs). To study the system stability and facilitate the design of fuzzy controller, Takagi–Sugeno (T–S) fuzzy models are employed to represent the system dynamics of the nonlinear discrete-time NCSs with effects of the approximation errors taken into account, and a unified model of NCSs in the T–S fuzzy model is proposed by modeling the approximation errors as norm-bounded uncertainties in system metrics, where non-ideal network Quality of Services (QoS), such as data dropout and network-induced delay, are coupled in a unified framework. Then, based on the Lyapunov–Krasovskii functional, sufficient conditions are derived for the existence of a fuzzy controller. By these criteria, two approaches to design a fuzzy controller are developed in terms of linear matrix inequalities (LMIs). Finally, illustrative examples are provided to show the effectiveness of the proposed methods.     

Online identification of Takagi–Sugeno fuzzy models based on self-adaptive hierarchical particle swarm optimization algorithm

This paper presents an approach for online learning of Takagi–Sugeno (T-S) fuzzy models. A novel learning algorithm based on a Hierarchical Particle Swarm Optimization (HPSO) is introduced to automatically extract all fuzzy logic system (FLS)’s parameters of a T–S fuzzy model. During online operation, both the consequent parameters of the T–S fuzzy model and the PSO inertia weight are continually updated when new data becomes available. By applying this concept to the learning algorithm, a new type T–S fuzzy modeling approach is constructed where the proposed HPSO algorithm includes an adaptive procedure and becomes a self-adaptive HPSO (S-AHPSO) algorithm usable in real-time processes. To improve the computational time of the proposed HPSO, particles positions are initialized by using an efficient unsupervised fuzzy clustering algorithm (UFCA). The UFCA combines the K-nearest neighbour and fuzzy C-means methods into a fuzzy modeling method for partitioning of the input–output data and identifying the antecedent parameters of the fuzzy system, enhancing the HPSO’s tuning. The approach is applied to identify the dynamical behavior of the dissolved oxygen concentration in an activated sludge reactor within a wastewater treatment plant. The results show that the proposed approach can identify nonlinear systems satisfactorily, and reveal superior performance of the proposed methods when compared with other state of the art methods. Moreover, the methodologies proposed in this paper can be involved in wider applications in a number of fields such as model predictive control, direct controller design, unsupervised clustering, motion detection, and robotics.     

Modeling and evaluation of cyber-physical systems in civil engineering

A cyber-physical system (CPS) is a coupled system integrating computing, networking, and physical processes. Through actuation, cyber-physical systems control the physical processes, usually with feedback loops, where the physical processes affect computing and networking processes, and vice versa. In civil engineering, the most common fields of CPS applications are structural health monitoring (SHM) and structural control. A typical CPS task is the assessment of a structure based on (i) collected measurement data and (ii) a corresponding model. However, for an accurate and precise assessment of a structure, the CPS itself must be modeled and evaluated. In this paper, a conceptual modeling and evaluation approach is proposed, in which each part of a CPS is evaluated individually. In this study, the conceptual approach is presented for modeling and evaluation of CPS in civil engineering. The evaluation is based on an abstract approach allowing a discussion of a principle (i.e. general) model structure of a CPS, identifying critical issues to be studied in more detail in future research. (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Multi-objective modeling for engineering applications:DIDASN++ system

Modeling and simulation of various physical, technical, environmental or socioeconomic processes is often a preliminary step for using the resulting models in computer-aided design or decision support. In engineering computer-aided design, the decisions of the designer might be supported by multicriteria optimization - which in this case should not be considered as a tool for supporting the final choice of the design, but much more as a tool for helping in a flexible analysis of various design options or even various modeling and simulation options. The paper shows how multicriteria optimization techniques can be used for multi-objective analysis of a model from the beginning stages of model construction. With the advancement of computing technology and the methodology of decision support, it is now possible to revise this way basic approaches to modeling and simulation. Various formats of defining nonlinear and time-discrete models are discussed together with related problems of inverse and softly constrained multi-objective simulation. Algebraic differentiation and sensitivity analysis, fuzzy set representation of modeler preferences are also useful techniques of multi-objective modeling. Such techniques are illustrated by engineering applications of a software package DIDASN++ in mechanics and automatic control.     

An integrated fuzzy simulation-fuzzy data envelopment analysis algorithm for job-shop layout optimization: The case of injection process with ambiguous data

This paper puts forward an integrated fuzzy simulation-fuzzy data envelopment analysis (FSFDEA) algorithm to cope with a special case of single-row facility layout problem (SRFLP). Discrete-event-simulation, a powerful tool for analyzing complex and stochastic systems, is employed for modeling different layout formations. Afterwards, a range-adjusted measure (RAM) is used as a data envelopment analysis (DEA) model for ranking the simulation results and finding the optimal layout design. Due to ambiguousness associated with the processing times, fuzzy sets theory is incorporated into the simulation model. Since the results of simulation are in the form of possibility distributions, the DEA model is treated on a fuzzy basis; therefore, a recent possibilistic programming approach is used to convert the fuzzy DEA model to an equivalent crisp one. The proposed FSFDEA algorithm is capable of modeling and optimizing small-sized SRFLP’s in stochastic, uncertain, and non-linear environments. The solution quality is inspected through a real case study in a refrigerator manufacturing company.     

A modeling approach to logistics in concurrent engineering

This paper advocates logistics involvement in the early phases of product design and development in a concurrent engineering environment. A concurrent engineering environment and the benefits of such involvement are explained in detail. The paper focuses on facilitating an interface and a collaboration between the designer and the logistician. A conceptual interface for design for logistics is presented. Four areas of interface are considered: (a) logistics engineering, (b) manufacturing logistics, (c) design for packaging, and (d) design for transportability. A set of detailed design factors pertaining to each area are presented. A modeling approach, Bond Energy Algorithm, is used to accomplish the design for logistics concerns developed throughout the paper. An example is provided to test and validate the algorithm. The results are analyzed and appropriate perspectives for managerial implication of the methodology are provided. Finally, some conclusions and assessments are presented.     

Modelling class hierarchies in the fuzzy object-oriented data model

In this paper we describe a fuzzy logic based approach to modelling uncertainty in class hierarchies. It is shown that the traditional view of class hierarchies is subsumed in this model as a special case. The problem of multiple inheritance in class hierarchies is discussed and analyzed. The membership value derivations in the inheritance hierarchy reflects the degree of fuzziness existing in the data values and the semantics of the situation being modelled. Thus a more realistic modelling of the universe of discourse is possible through this approach. This model is compatible with existing object-oriented data models.