ORTHOGONAL HYBRID-FUZZY CONTROLLERS

The main idea of this paper is to present a possibility of application of hybrid-fuzzy controllers in control systems theory. In this paper, we have described a new method оf using orthogonal functions in control of dynamical systems. These functions generate genarilzed quasi-orthogonal filter, which are used in the concluding phase of the fuzzy controllers. Proposed hybrid-fuzzy controllers of Takagi-Sugeno type has been applied to a DC servo drive system and performed experiments have verified efficiency and improvements of a new control method

Advance of Systematic Design Methods on Fuzzy Control

The heating, ventilation and air-conditioning (HVAC) system possesses some characteristics such as multi-parameters, nonlinear, and coupled parameters. Aimed at control problems, the author targets real-time fuzzy control and research systematically via the fuzzification method, fuzzy inference method, fuzzy control rules online obtaining and optimizing method, self-organizing fuzzy control method, and fuzzy predictive control of a time-delayed process. This paper will briefly introduce previous research results

Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling

This paper aims at providing an in-depth overview of designing interpretable fuzzy inference models from data within a unified framework. The objective of complex system modelling is to develop reliable and understandable models for human being to get insights into complex real-world systems whose first-principle models are unknown. Because system behaviour can be described naturally as a series of linguistic rules, data-driven fuzzy modelling becomes an attractive and widely used paradigm for this purpose. However, fuzzy models constructed from data by adaptive learning algorithms usually suffer from the loss of model interpretability. Model accuracy and interpretability are two conflicting objectives, so interpretation preservation during adaptation in data-driven fuzzy system modelling is a challenging task, which has received much attention in fuzzy system modelling community. In order to clearly discriminate the different roles of fuzzy sets, input variables, and other components in achieving an interpretable fuzzy model, a taxonomy of fuzzy model interpretability is first proposed in terms of low-level interpretability and high-level interpretability in this paper. The low-level interpretability of fuzzy models refers to fuzzy model interpretability achieved by optimizing the membership functions in terms of semantic criteria on fuzzy set level, while the high-level interpretability refers to fuzzy model interpretability obtained by dealing with the coverage, completeness, and consistency of the rules in terms of the criteria on fuzzy rule level. Some criteria for low-level interpretability and high-level interpretability are identified, respectively. Different data-driven fuzzy modelling techniques in the literature focusing on the interpretability issues are reviewed and discussed from the perspective of low-level interpretability and high-level interpretability. Furthermore, some open problems about interpretable fuzzy models are identified and some potential new research directions on fuzzy model interpretability are also suggested. Crown Copyright © 2008

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

Identification of discrete-time dynamic systems using modified genetic algorithm

The purpose of this study is to investigate the application of genetic algorithm (GA) in modelling linear and non-linear dynamic systems and develop an alternative model structure selection algorithm based on GA. Orthogonal least square (OLS), a gradient descent method was used as the benchmark for the proposed algorithm. A model structure selection based on modified genetic algorithm (MGA) has been proposed in this study to reduce problems of premature convergence in simple GA (SGA). The effect of different combinations of MGA operators on the performance of the developed model was studied and the effectiveness and shortcomings of MGA were highlighted. Results were compared between SGA, MGA and benchmark OLS method. It was discovered that with similar number of dynamic terms, in most cases, MGA performs better than SGA in terms of exploring potential solution and outperformed the OLS algorithm in terms of selected number of terms and predictive accuracy. In addition, the use of local search with MGA for fine-tuning the algorithm was also proposed and investigated, named as memetic algorithm (MA). Simulation results demonstrated that in most cases, MA is able to produce an adequate and parsimonious model that can satisfy the model validation tests with significant advantages over OLS, SGA and MGA methods. Furthermore, the case studies on identification of multivariable systems based on real experimental data from two systems namely a turbo alternator and a continuous stirred tank reactor showed that the proposed algorithm could be used as an alternative to adequately identify adequate and parsimonious models for those systems

Identification of discrete-time dynamic systems using modified genetic algorithm

The purpose of this study is to investigate the application of genetic algorithm (GA) in modelling linear and non-linear dynamic systems and develop an alternative model structure selection algorithm based on GA. Orthogonal least square (OLS), a gradient descent method was used as the benchmark for the proposed algorithm. A model structure selection based on modified genetic algorithm (MGA) has been proposed in this study to reduce problems of premature convergence in simple GA (SGA). The effect of different combinations of MGA operators on the performance of the developed model was studied and the effectiveness and shortcomings of MGA were highlighted. Results were compared between SGA, MGA and benchmark OLS method. It was discovered that with similar number of dynamic terms, in most cases, MGA performs better than SGA in terms of exploring potential solution and outperformed the OLS algorithm in terms of selected number of terms and predictive accuracy. In addition, the use of local search with MGA for fine-tuning the algorithm was also proposed and investigated, named as memetic algorithm (MA). Simulation results demonstrated that in most cases, MA is able to produce an adequate and parsimonious model that can satisfy the model validation tests with significant advantages over OLS, SGA and MGA methods. Furthermore, the case studies on identification of multivariable systems based on real experimental data from two systems namely a turbo alternator and a continuous stirred tank reactor showed that the proposed algorithm could be used as an alternative to adequately identify adequate and parsimonious models for those systems

Novel implementation technique for a wavelet-based broadband signal detection system

This thesis reports on the design, simulation and implementation of a novel Implementation for a Wavelet-based Broadband Signal Detection System. There is a strong interest in methods of increasing the resolution of sonar systems for the detection of targets at sea. A novel implementation of a wideband active sonar signal detection system is proposed in this project. In the system the Continuous Wavelet Transform is used for target motion estimation and an Adaptive-Network-based Fuzzy inference System (ANFIS) is adopted to minimize the noise effect on target detection. A local optimum search algorithm is introduced in this project to reduce the computation load of the Continuous Wavelet Transform and make it suitable for practical applications. The proposed system is realized on a Xilinx University Program Virtex-II Pro Development System which contains a Virtex II pro XC2VP30 FPGA chip with 2 powerPC 405 cores. Testing for single target detection and multiple target detection shows the proposed system is able to accurately locate targets under reverberation-limited underwater environment with a Signal-Noise-Ratio of up to -30db, with location error less than 10 meters and velocity estimation error less than 1 knot. In the proposed system the combination of CWT and local optimum search algorithm significantly saves the computation time for CWT and make it more practical to real applications. Also the implementation of ANFIS on the FPGA board indicates in the future a real-time ANFIS operation with VLSI implementation would be possible