Fuzzy Input Research Articles

Design Fuzzy Input-Based Adaptive Sliding Mode Control for Vessel Lift-Feedback Fin Stabilizers with Shock and Vibration of Waves

An adaptive sliding mode controller based on fuzzy input design is presented, in order to reduce the roll motion of surface vessel fin stabilizers with shock and vibration of waves. The nonlinearities and uncertainties of the system including feedback errors and disturbance induced by waves are analyzed. And the lift-feedback system is proposed, which improves the shortage of conventional fin angle-feedback. Then the fuzzy input-based adaptive sliding mode control is designed for the system. In the controller design, the Lyapunov function is adopted to guarantee the system stability. Finally, experimental results demonstrate the superior performance of the controller designed using fuzzy input, when compared to the PID controller used in practical engineering.

Comparative analysis of unity power factor grid-connected PV system with PI and fuzzy-based controllers

Herein, we report the comparative analysis of a proposed fuzzy logic controller with conventional incremental conductance algorithm for maximum power point tracking (MPPT) with proportional integral (PI) controller of a unity power factor grid-connected photovoltaic (PV) inverter. In the conventional controller, incremental conductance MPPT algorithm with PI controller has been implemented. In the fuzzy logic controller, inputs and outputs are chosen for this study, rules relating them are written and the simulation is performed. Simulation model of a 40 W solar panel using single diode model is developed and results are obtained for PI controller and fuzzy logic controller for different irradiation and temperature conditions. Results show the ability of fuzzy logic controller to give a performance nearly similar to that of incremental conductance algorithm with increased robustness. Also it is inferred that when the conditions become low, PI controller shows poor performance when compared to fuzzy logic controller.

Comparative analysis of unity power factor grid-connected PV system with PI and fuzzy-based controllers

Herein, we report the comparative analysis of a proposed fuzzy logic controller with conventional incremental conductance algorithm for maximum power point tracking (MPPT) with proportional integral (PI) controller of a unity power factor grid-connected photovoltaic (PV) inverter. In the conventional controller, incremental conductance MPPT algorithm with PI controller has been implemented. In the fuzzy logic controller, inputs and outputs are chosen for this study, rules relating them are written and the simulation is performed. Simulation model of a 40 W solar panel using single diode model is developed and results are obtained for PI controller and fuzzy logic controller for different irradiation and temperature conditions. Results show the ability of fuzzy logic controller to give a performance nearly similar to that of incremental conductance algorithm with increased robustness. Also it is inferred that when the conditions become low, PI controller shows poor performance when compared to fuzzy logic controller.

Models of Neuro-fuzzy Agents in Intelligent Environments

The set of agent-oriented information systems that interact in a changing structure and operation conditions, is considered as a distributed intellectual environment. Such a representation is corresponding in geographically distributed systems (region, city), corporate information systems of enterprises and organizations, social groups within the various territorial structures, distributed heterogeneous systems and networks territorial entities, enterprises and organizations. On the basis of known models of neuro-fuzzy agents are proposed a model of the fundamental intelligent agent (FIA), operating in a dynamic heterogeneous information environment and carrying out reception of input messages in a usual source language and fuzzy input language, the creation of messages in the inner language to perform reasoning and reflection which can be either conventional or fuzzy, and transmitting to the external environment accurate and fuzzy messages in appropriate languages. A classification of possible FIA models are constructed in distributed intelligent systems and environments. A behaviour model of the FIA, defined Post calculus axiom schemes, combining precise and fuzzy representation of variables and manipulate the internal states, base attributes and logical formulas in fuzzy logic and first-order predicate calculus are constructed.

Symbolic Approximate Reasoning with Fuzzy and Multi-valued Knowledge

Knowledge-based systems have nearly become omnipresent in various sectors to facilitate decision-making. Their aim is to get close to human induction. For that, dealing imprecise knowledge is essential since human thinks imprecisely. The principal logics that allow manipulating this kind of knowledge in intelligent systems are fuzzy logic and multi-valued logic. Up to now, according to our knowledge, knowledge-based systems manage separately either fuzzy knowledge or multi-valued knowledge. However, modeling heterogeneous knowledge (fuzzy and multi-valued) in the same inference engine should ensure more flexibility and freedom to the user. In that context, our aim is to allow the use of fuzzy and multi-valued knowledge at once. We propose a new approach to convert fuzzy knowledge into symbolic knowledge by projecting fuzzy inputs over the x-axis that corresponds to the universe of discourse of fuzzy variable. In order to demonstrate its applicability, our proposal is tested within a rule-based system. A numerical example is then provided.

Intelligent Systems for Monitoring and Recognition of Cyber Attacks on Information and Communication Systems of Transport

To improve the information security of transportation systems, it is necessary that co research is aimed at the further development of models for the recognition of cyber threats in information and communication environment of transport (ICET) and decision making with vaguely specified inputted information. It was offered a new approach of decision making for cyber security of critically important information systems and automated control systems of ground transportation. It was reviewed the ICET case of cyber defense based on fuzzy regression of logical inference mechanisms for decision support with fuzzy input data.

Fuzzy semi-parametric partially linear model with fuzzy inputs and fuzzy outputs

A large number of accounting studies have focused on parametric or non-parametric forms of fuzzy regression relationships between dependent and independent variables. Notably, semi-parametric partially linear model as a powerful tool to incorporate statistical parametric and non-parametric regression analyses has gained attentions in many real-life applications recently. However, fuzzy data find application in many real studies. This study is an investigation of semi-parametric partially linear model for such cases to improve the conventional fuzzy linear regression models with fuzzy inputs, fuzzy outputs, fuzzy smooth function and non-fuzzy coefficients. For this purpose, a hybrid procedure is suggested based on curve fitting methods and least absolutes deviations to estimate the fuzzy smooth function and fuzzy coefficients. The proposed method is also examined to be compared with a common fuzzy linear regression model via a simulation data set and some real fuzzy data sets. It is shown that the proposed fuzzy regression model performs more convenient and efficient results in regard to six goodness-of-fit criteria which concludes that the proposed model could be a rational substituted model of some common fuzzy regression models in many practical studies of fuzzy regression model in expert and intelligent systems.

A linear elastic Fuzzy Finite Element Method with two fuzzy input parameters

AbstractThis work employs a fuzzy finite element method (FFEM) in order to characterize the non‐statistical properties based on the possibility theory [2]. A linear elastic body with fuzzy input material parameters Young's modulus $\tilde{E}$ and Poisson's ratio $\tilde{\nu}$ is studied. To this end, the fuzzy parameters are represented by possibility distributions generated from sparse experimental data. A computational approach involving the α‐level discretization technique [4] is used in order to calculate the possibility of the system response. Finally, our method is applied in a numerical example. (© 2016 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Fuzzy robust fault estimation scheme for a class of nonlinear systems based on an unknown input sliding mode observer

In this study, a novel fuzzy robust fault estimation scheme is developed for a class of nonlinear systems when both fault and disturbance are considered. The proposed scheme includes component fault with a nonlinear distribution matrix; as a result, the Takagi–Sugeno model is used to create multiple models. While the Takagi–Sugeno model is used for only the nonlinear distribution matrix of the fault signal, a larger category of nonlinear systems will be considered. This paper presents the problem of robust fault estimation based on fuzzy nonlinear observers, the first one is a fuzzy unknown input observer and the other one is a fuzzy sliding mode observer. The approach decoupled the faulty subsystem from the rest of the system through a series of transformations. Then, the objective is to design a fuzzy unknown input observer guaranteeing the asymptotic stability of the error dynamic using the Lyapunov method and completely removing disturbances; meanwhile, a fuzzy sliding mode observer is designed for a faulty subsystem to generate an estimation of fault based on a quadratic Lyapunov function and some matrices inequality convexification techniques. The sliding motion affects only the faulty subsystem through a novel reduced order fuzzy sliding mode observer; meanwhile, all disturbances are completely removed by fuzzy unknown input observer. Sufficient conditions are established in order to guarantee the convergence of the state estimation error and the results are formulated in the form of linear matrix inequalities. Thus, an exact fault estimator is determined on the basis of linear matrix inequality conditions while the estimation fault is completely insensitive to the disturbance. Finally, a simulation study on an electromagnetic suspension system is presented to demonstrate the g performance of the results compared with a pure sliding mode observer.

An optimal fuzzy PID control approach for docking maneuver of two spacecraft: Orientational motion

This paper describes a scheme for a Fuzzy-Proportional Integral Derivative (FPID) controller based on genetic algorithm (GA), in a docking maneuver of two spacecraft. The docking maneuver consists of two parts: translation and orientation. Euler’s gyroscopic equation is applied to obtain governing equations of orientational phase. Here, a designed fuzzy-PID controller for stabilization purpose of orientational phase of a docking maneuver is presented based on the Single Input Fuzzy Inference Motor (SIFIMs) dynamically connected Preferrer Fuzzy Inference Motor (PFIM). This fuzzy-PID controller takes the error signal of Euler’s angles and the error of angular velocities of the chaser as its input items, and the driving force as its output. The parameters of the controller are ascertained by using a genetic algorithm. Conflicting objective functions (which their 3D pareto frontiers are obtained by Multi-objective Genetic Algorithm (MOGA)) are distance errors from the set point, angle errors from the set point, and control efforts. Optimization constraint is maximal of the momentum produced by momentum wheels. The result of optimum point demonstrates that the designed controller makes an efficient performance in the orientational phase of the chaser spacecraft. Compared to similar works, some of system parameters like settling time are improved and overshoot (as a critical parameter in docking maneuver) is decreased.

Dissolved oxygen prediction using a possibility theory based fuzzy neural network

Abstract. A new fuzzy neural network method to predict minimum dissolved oxygen (DO) concentration in a highly urbanised riverine environment (in Calgary, Canada) is proposed. The method uses abiotic factors (non-living, physical and chemical attributes) as inputs to the model, since the physical mechanisms governing DO in the river are largely unknown. A new two-step method to construct fuzzy numbers using observations is proposed. Then an existing fuzzy neural network is modified to account for fuzzy number inputs and also uses possibility theory based intervals to train the network. Results demonstrate that the method is particularly well suited to predicting low DO events in the Bow River. Model performance is compared with a fuzzy neural network with crisp inputs, as well as with a traditional neural network. Model output and a defuzzification technique are used to estimate the risk of low DO so that water resource managers can implement strategies to prevent the occurrence of low DO.

A fuzzy-theory-based behavioral model for studying pedestrian evacuation from a single-exit room

Many mass events in recent years have highlighted the importance of research on pedestrian evacuation dynamics. A number of models have been developed to analyze crowd behavior under evacuation situations. However, few focus on pedestrians' decision-making with respect to uncertainty, vagueness and imprecision. In this paper, a discrete evacuation model defined on the cellular space is proposed according to the fuzzy theory which is able to describe imprecise and subjective information. Pedestrians' percept information and various characteristics are regarded as fuzzy input. Then fuzzy inference systems with rule bases, which resemble human reasoning, are established to obtain fuzzy output that decides pedestrians' movement direction. This model is tested in two scenarios, namely in a single-exit room with and without obstacles. Simulation results reproduce some classic dynamics phenomena discovered in real building evacuation situations, and are consistent with those in other models and experiments. It is hoped that this study will enrich movement rules and approaches in traditional cellular automaton models for evacuation dynamics.

Temperature Control for a Polymer Electrolyte Membrane Fuel Cell by Using Fuzzy Rule

Temperature control for a polymer electrolyte membrane fuel cell (PEMFC) is important in improving power efficiency and increasing fuel cell lifetime. If the temperature of the cell is too low, then the electrochemical reaction response becomes slow, thereby preventing evaporations of liquid water in the membrane. As a result, cell performance is decreased. However, too high temperature leads to waste in catalyst and heat because of excessive chemical reactions and to liquid water evaporation, which decreases proton conductivity. This study develops an electrochemical dynamical model and a thermal model of a PEMFC using MATLAB/Simulink for simulation. Fuzzy control rules are also built to regulate the temperature of a PEMFC. The fuzzy inputs include temperature error, its derivative, and external load current. The cooling fan speed is chosen as an output variable to regulate the temperature of a fuzzy control because the fuel cell utilizes the air cooling method. After confirming that the designed fuzzy rules are effective for controlling cell temperature, a real experimental device is built using an H-100 fuel cell and a cooling fan to verify the effectiveness of the proposed method.

A fuzzy dynamic Inoperability Input–output Model for strategic risk management in Global Production Networks

Strategic decision making in Global Production Networks (GPNs) is quite challenging, especially due to the unavailability of precise quantitative knowledge, variety of relevant risk factors that needs to be considered and the interdependencies that can exist between multiple partners across the globe. In this paper, a risk evaluation method for GPNs based on a novel Fuzzy Dynamic Inoperability Input Output Model (Fuzzy DIIM) is proposed. A fuzzy multi-criteria approach is developed to determine interdependencies between nodes in a GPN using experts’ knowledge. An efficient and accurate method based on fuzzy interval calculus in the Fuzzy DIIM is proposed. The risk evaluation method takes into account various risk scenarios relevant to the GPN and likelihoods of their occurrences. A case of beverage production from the food industry is used to showcase the application of the proposed risk evaluation method. It is demonstrated how it can be used for GPN strategic decision making. The impact of risk on inoperability of alternative GPN configurations considering different risk scenarios is analysed.

Multimodal Vibration Control of Photo-Electric Laminated Thin Cylindrical Shells Via Self-Organizing Fuzzy Sliding Mode Control

In this paper, a novel multipiece actuator configuration is first proposed. This configuration exhibits several advantages over the existing ones, such as: (1) the ability to overcome the deficiency of one-way actuation of PbLaZrTi (PLZT) actuators and (2) all of the actuators in this configuration being placed on the inner surfaces of a thin cylindrical shell and the removal of extra electrical wires between the end surfaces of the actuators. A new index of modal control factors is defined, and an optimization method for allocating actuator is proposed. By using the proposed method, the PLZT actuators can be located in an optimum position. Moreover, in view of the nonlinear and time-variant characteristics of photostrictive actuators, a self-organizing fuzzy sliding mode control (SOFSMC) method is established to attenuate multimodal vibration of photo-electric laminated thin cylindrical shells. A multilevel sliding mode surface is defined as fuzzy input and the SOFSMC method is used to infer the applied light intensity. Its control rule bank can be developed and adjusted continuously via online learning. In addition, using fuzzy sliding mode, the chatter inherent in conventional sliding mode control is therefore managed effectively while ensuring sliding mode behavior. Case studies demonstrate that the proposed approach can efficiently suppress multimodal vibration of photo-electric laminated thin cylindrical shells. It is also founded that SOFSMC can achieve better control effect than fuzzy neural network control (FNNC).

Data envelopment analysis with non-L-R type fuzzy data

This work considers efficiency measures in data envelopment analysis with non-L-R type fuzzy data. It shows that the relative efficiencies of decision-making units with non-L-R type fuzzy inputs and outputs can be measured by solving an optimization problem on a mixed domain. The necessary and sufficient conditions for solving the resulting optimization problems are then investigated. This is the first attempt to measure fuzzy efficiency in data envelopment analysis in view of optimization problems on a mixed domain.

Application of the project management methodology synthesis method with fuzzy input data

The definition of a “project management methodology” is clarified. A method of the project management methodology synthesis for a particular project with fuzzy input data is proposed. The method involves the development of a generalized project management methodology. Based on the information presented in the generalized methodology, an expert or a group of experts selects policies, rules, procedures , practices, life cycle and organizational structure of a particular project, assign roles and responsibilities in the project. The experts have the opportunity to set several combinations of the generalized methodology components, which are the most relevant to the project. The problem of selecting the best methodology for a particular project is solved according to the criteria of complexity , cost of management operations and associated risks.Optimization is carried out with the fuzzy input data. The operation of the given method is shown on the example of the project on the “PTCQR ProjectScopeOptimization” software development for project scope optimization.

Influence of Allocating One New Fuzzy Source on DMUs Efficiency

Many studies have been conducted on sensitivity analysis in DEA which focus on changing the value of data or reallocation of inputs. In this article, some models have been presented which can be used for allocating one or more new inputs between the DMUs in a way that some of the inefficient units have been modified to efficient units. These models have been offered for both crisp inputs and fuzzy inputs. The presented model allocates the new inputs between the DMUs in a way that the distance of new inputs from the fair allocation is minimized by applying the Chebyshev's norm. The mentioned models are utilized in four numerical examples and the related results are reported. To the present, the related studies have been focused on changing the value of the old inputs, however, this article paid particular attention to allocating the new input(s) between the DMUs.

Irrigation Water Allocation Using an Inexact Two‐Stage Quadratic Programming with Fuzzy Input under Climate Change

AbstractAgricultural irrigation accounts for nearly 70% of the total water use around the world. Uncertainties and climate change together exacerbate the complexity of optimal allocation of water resources for irrigation. An interval‐fuzzy two‐stage stochastic quadratic programming model is developed for determining the plans for water allocation for irrigation with maximum benefits. The model is shown to be applicable when inputs are expressed as discrete, fuzzy or random. In order to reflect the effect of marginal utility on benefit and cost, the model can also deal with nonlinearities in the objective function. Results from applying the model to a case study in the middle reaches of the Heihe River basin, China, show schemes for water allocation for irrigation of different crops in every month of the crop growth period under various flow levels are effective for achieving high economic benefits. Different climate change scenarios are used to analyze the impact of changing water requirement and water availability on irrigation water allocation. The proposed model can aid the decision maker in formulating desired irrigation water management policies in the wake of uncertainties and changing environment.

Biobjective Optimization for Efficient Irrigation under Fuzzy Uncertainty

AbstractA biobjective programming model is developed with fuzzy inputs in response to the complexity of conflicting objectives and uncertainties in irrigation water allocation systems. The model is capable of increasing agricultural water productivity and meanwhile reducing irrigation water shortage, compromising the concerns of both agricultural decision makers and farmers. Moreover, the developed model adequately considers the fuzzy uncertainties in parameters, constraints, and objective functions. The potential of the developed model is shown by applying to a real case study in northwest China. Results show alternative decisions for irrigation water allocation under different flow levels, and indicate that the effectiveness of the trading program is explicitly affected by uncertainties expressed as fuzziness. Comparison with a commonly used model for irrigation water allocation demonstrates the feasibility and applicability of the developed model, which is helpful for the development of water-saving hi...