Fuzzy system identification by generating and evolutionary optimizing fuzzy rule bases consisting of relevant fuzzy rules

One approach forsystem identification among many othersis the fuzzy identification approach. The advantage of this approach compared to other analytical approaches is, that it is not necessary to make an assumption for the model to be used for the identification. In addition, the fuzzy approach can handle nonlinearities easier than analytical approaches. The Fuzzy-ROSA method is a method for data-based generation of fuzzy rules. This is the first step of a two step identification process. The second step is the optimization of the remaining free parameters, i.e. the composition of the rule base and the linguistic terms, to further improve the quality of the model and obtain small interpretable rule bases. In this paper, a new evolutionary strategy for the optimization of the linguistic terms of the output variable is presented. The effectiveness of the two step fuzzy identification is demonstrated on the benchmark problem 'kin dataset' of the Delve dataset repository and the results are compared to analytical and neural network approaches

New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

This paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T-S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T-S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use

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

Development of a vehicle robotic driver with intelligent control system modelling for automated standard driving-cycle tests

New road vehicles are required to undergo several specific tests to meet the requirement set by governing bodies in various markets. These tests are often carried out over specific driving-cycles. To carry out lab-based driving-cycle tests, a typical vehicle manufacturer will employ a trained driver to follow driving profiles on a chassis dynamometer. This project involves development of a robotic driver controller for the automation of dynamometer-based vehicle testing according to industry standard driving cycle tests and produce repeatable results by replacing the traditional method of employing a human driver with a robot driver. The throttle and brake pedals control systems modelling and design for automatic transmission vehicle are implemented, with Fuzzy model reference adaptive control (Fuzzy MRAC) as the main controller. The vehicle model was developed using black-box modelling approach where simulations are performed based on real-time data and processed using Matlab System Identification tool. The Fuzzy MRAC was then designed within the simulations to attain the driving performance. The vehicle model response was sent as feedback to the robotic DC linear actuator motor which was modelled based on DC linear actuator motor design specification. The results obtained from simulation and modelling experiment were discussed and compared. The performed work concludes that system identification modelling with best fit accuracy of 79.93% can be applied in Fuzzy MRAC to ensure smooth and accurate vehicle driving pattern behavior even when the leading vehicle exhibits highly dynamic speed behavior during driving-cycle test. The performance of the vehicle model has shown an average 0.07 MSE for the throttle system and 0.008 MSE for the brake system of the vehicle model

Recursive neuro fuzzy techniques for online identification and control

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de ComputadoresThe main goal of this thesis will be focused on developing an adaptative closed loop control solution, using fuzzy methodologies. A positive theoretical and experimental contribution, regarding modelling and control of fuzzy and neuro fuzzy systems, is expected to be achieved. Proposed non-linear identification solution will use for modelling and control, a recurrent neuro fuzzy architecture. Regarding model solution, a state space approach will be considered during fuzzy consequent local models design. Developed controller will be based on model parameters, being expected not only a stable closed loop solution, but also a static error with convergence towards zero. Model and controller fuzzy subspaces, will be partitioned throughout process dynamical universe, allowing fuzzy local models and controllers commutation and aggregation. With the aim of capturing process under control dynamics using a real time approach, the use of recursive optimization techniques are to be adopted. Such methods will be applied during parameter and state estimation, using a dual decoupled Kalman filter extended with unscented transformation. Two distinct processes one single-input (SISO) other multi-input (MIMO), will be used during experimentation. It is expected from experiments, a practical validation of proposed solution capabilities for control and identification. Presented work will not be completed, without first presenting a global analysis of adopted concepts and methods, describing new perspectives for future investigations

Improving the accuracy while preserving the interpretability of fuzzy function approximators by means of multi-objective evolutionary algorithms

AbstractThe identification of a model is one of the key issues in the field of fuzzy system modeling and function approximation theory. An important characteristic that distinguishes fuzzy systems from other techniques in this area is their transparency and interpretability. Especially in the construction of a fuzzy system from a set of given training examples, little attention has been paid to the analysis of the trade-off between complexity and accuracy maintaining the interpretability of the final fuzzy system. In this paper a multi-objective evolutionary approach is proposed to determine a Pareto-optimum set of fuzzy systems with different compromises between their accuracy and complexity. In particular, two fundamental and competing objectives concerning fuzzy system modeling are addressed: fuzzy rule parameter optimization and the identification of system structure (i.e. the number of membership functions and fuzzy rules), taking always in mind the transparency of the obtained system. Another key aspect of the algorithm presented in this work is the use of some new expert evolutionary operators, specifically designed for the problem of fuzzy function approximation, that try to avoid the generation of worse solutions in order to accelerate the convergence of the algorithm

A new adaptive Mamdani-type fuzzy modeling strategy for industrial gas turbines

The paper presents a new system identification methodology for industrial systems. Using the original Mamdani fuzzy rule based system (FRBS), an adaptive Mamdani fuzzy modeling (AMFM) is introduced in this paper. It differs from the original Mamdani FRBS in that it applies different membership functions and a defuzzification mechanism that is ‘differentiable’ with respect to the membership function parameters. The proposed system also includes a back error propagation (BEP) algorithm that is used to refine the fuzzy model. The efficacy of the proposed AMFM approach is demonstrated through the experimental trails from a compressor in an industrial gas turbine system

Three essays on regression discontinuity design and partial identification

This dissertation consists of three chapters on regression discontinuity (RD) design and partial identification, which are widely used techniques in program evaluation. The first and the second chapters discuss statistic inference for the treatment effect estimator in fuzzy RD designs. Fuzzy RD design and instrumental variables (IV) regression share similar identification strategies and numerically yield the same results under certain conditions. While the weak identification problem is widely recognized in IV regressions, it has drawn much less attention in fuzzy RD designs, where the standard t-test can also suffer from asymptotic size distortions and the confidence interval obtained by inverting such a test becomes invalid. I explicitly model fuzzy RD designs in parallel with IV regressions, and based on the extensive literature of the latter, develop tests which are robust to weak identification in fuzzy RD designs, including the Anderson-Rubin (AR) test, the Lagrange multiplier (LM) test, and the conditional likelihood ratio (CLR) test. These tests have correct size regardless of the strength of identification and their power properties are similar to those in IV regressions. Due to the similarities between a fuzzy RD design and an IV regression, one can choose either method for estimation and inference. However, it is shown that adopting a fuzzy RD design with newly proposed tests has the potential to achieve more power without introducing size distortions in hypothesis testing and is thus recommended. An extension to testing for quantile treatment effects in fuzzy RD designs is also discussed. RD estimators are usually estimated with nonparametric methods and have bias. A new wild bootstrap procedure is proposed to correct bias and construct valid confidence intervals in fuzzy regression discontinuity designs. This procedure uses a wild bootstrap based on second order local polynomials to estimate and remove the bias from linear models. The bias-corrected estimator is then bootstrapped itself to generate valid confidence intervals. While the conventional confidence intervals generated by adopting MSE-optimal bandwidth is asymptotically not valid, the confidence intervals generated by this procedure have correct coverage under conditions similar to Calonico, Cattaneo and Titiunik\u27s(2014, Econometrica) analytical correction. Simulation studies provide evidence that this new method is as accurate as the analytical corrections when applied to a variety of data generating processes featuring heteroskedasticity, endogeneity and clustering. As an example, its usage is demonstrated through a reanalysis of the scholastic achievement data used by Angrist and Lavy (1999). In the third chapter, a novel numerical approach is proposed to partially identify treatment effects. Endogenous treatment and measurement error are very common in survey data and pose threats to reliable estimation of treatment effects. The new approach considers these two issues simultaneously and provides bounds for treatment effects. Conceptually, treatment effects and model assumptions are formulated as linear restrictions on a large set of probability mass. One can then check if any given treatment effect is consistent with model assumptions and observed data. Compared with previous methods, the newly proposed numerical approach is general enough to be applied to various different problems and guarantees sharp bounds. An example is provided to show that how the distribution of a treatment effect and how the averages of multiple treatment effects can be partially identified through this approach

Empirical insights on understanding stakeholder influence

The paper deals with the stakeholder management especially giving focus on the organization's and stakeholder relationships issues. The purpose of the paper is to construct a new methodological approach by developing fuzzy logic model based on experts’ knowledge for conceptual insights on possible solutions for measuring stakeholders’ influence. The objective of the research includes identification of possible organization stakeholder interactions considering stakeholders’ influence according to such attributes/ factors as interest, power, benevolence, and reliability. The results reveal that fuzzy logic technique is a reliable and valid tool for modelling and visualizing knowledge about stakeholders’ influence on the organization. Finally, the results were tested on the real business data concerning stakeholders’ influence. A contribution of this paper is the application of fuzzy logic model to evaluate and/or predict stakeholders’ influence to the issues the organization seeks to solve and to provide relevant information for the stakeholder relationships management. First published online: 02 Oct 201